Perspectives | SmartTechNXT

The Importance of Building Resilience

Joshua van Aswegen — Wed, 05 Aug 2026 09:18:09 +0000

Perspectives

How organisations can grow, without breaking, in a difficult economic market

Executive Summary

Growth remains important, but it is no longer the only measure of business success. Organisations today face economic uncertainty, workforce shortages, supply chain disruption and rapidly changing technology. This article explores why operational resilience has become a strategic capability, how automation supports long-term scalability, and why resilient operating models are becoming a competitive advantage for modern organisations.

For too long, the dominant business narrative has been straightforward: to grow larger, scale faster, and capture market share before anyone else. Growth in a company has become the proxy for health, and rising revenue has become proof that a business was effective.

However, with shifts in the economic markets, the operating environment underneath that narrative has changed. Companies are now navigating several sources of uncertainty at once: unpredictable demand, economic volatility, supply chain disruption, rising operating costs, workforce shortages, geopolitical instability, and rapid technology shifts. While none of these is particularly new, this era has seen all of them arrive together without warning.

In this kind of environment, growth becomes unreliable if it is the sole indicator of a company’s success. An organisation that adds customers, products, or markets faster than its operations can absorb them is not expanding so much as accumulating exposure. Growth becomes a liability the moment the underlying operation cannot flex with it.

This begs the question: what if the biggest threat to growth is not a lack of ambition, but an inability to operate reliably when conditions change?

Why Growth Alone No Longer Signals a Healthy Business

Revenue growth, customer acquisition, and market expansion describe what a business is achieving. They say too little about how it is achieving it, or what would happen if the conditions behind that achievement shifted overnight. A business can gain customers and revenue, but its capacity to adapt is dependent on more than just its profit. Under difficult market conditions, dependence on a handful of hard-to-replace people and a reliance on manual, person-dependent processes can all quietly move progress in the wrong direction. Profitability, when pursued without an accompanying focus on resilience, may increase short-term shareholder value while doing little to strengthen the organisation’s long-term stability and adaptability.

This difference matters because these progressions can deviate sharply under pressure. A business that looks strong on paper, with rising revenue and an expanding customer base, may in practice be unable to maintain service levels during a disruption, may have operating costs that swing unpredictably from one month to the next, or may find that each new customer requires more coordination and administrative effort than the last. More revenue can mask a build-up of dependencies and workarounds that stays invisible until something goes wrong, at which point it becomes very visible very quickly. Measuring growth without also measuring the organisation’s capacity to adapt overlooks half of what actually determines whether that growth is safe to sustain.

Businesses should give this explicit attention now because the underlying conditions that once made disruption temporary have themselves eroded. Supply chains, capital flows, and information now move through more interconnected and less redundant systems, so a shock in one part of the network reaches the rest of it faster and further than it used to. Technology cycles compress the window before a competitive advantage erodes; new tools, platforms, and standards now displace one another over months rather than years, so an operating model built around a particular technology can find its dominance gone before the investment in it has been recovered.

Labour markets have tightened structurally rather than cyclically. An ageing workforce is retiring faster than younger cohorts are entering the same trades and professions, immigration and mobility patterns have shifted in ways that reduce the pool of available workers in many economies, and the skills a modern operation needs, particularly where technical and digital capability is involved, are scarcer than the roles themselves. The result is that staffing gaps no longer resolve themselves the way they once did with the next hiring cycle; they persist, because the supply of available people has narrowed rather than simply slowed.

None of these shifts is reversible in the way a bad quarter or a difficult year used to be, which is what separates the current environment from ordinary volatility. Instead of asking how to optimise for the single most likely future, leadership teams need to start asking how to build an operation that stays effective across several possible futures at once.

Resilience is a strategic capability as well as a defensive measure. While a strategic capability broadens the range of optimal decisions a business can make, a defensive approach restricts potential risks. Resilience can accomplish both, expanding options and limiting downsides, but its primary role in increasing decision-making opportunities makes it more relevant to growth strategies than just insurance or contingency planning. An organisation that has built in resilience has more room to manoeuvre when circumstances change, which businesses will need in order to navigate this harsh and ever-changing market.

How Automation Supports Operational Resilience and Scalability

Most organisations define scalability as the ability to handle more demand, thereby treating it as a simple relationship: more customers equals more revenue. This definition captures growth capacity, but it misses operational scalability, which asks a more demanding question: can the business take on more customers and more revenue without a proportional increase in operational complexity, cost, or risk?

A company can have an excellent product and strong demand and still struggle to scale, especially if every additional customer draws on more manual intervention, more employees, more administrative work, more exceptions, and more coordination. Revenue climbs, but so does the strain on the people and processes holding the business together, and at some point, that strain becomes the limiting factor rather than demand itself.

If growth requires the organisation to become increasingly dependent on scarce human capacity, can it really be scalable? Workforce shortages are often treated as a human resources matter to be solved separately from strategy, but in practice these shortages can reach into capacity, customer experience, service continuity, productivity, cost predictability, and the ability to expand at all. A functioning workforce is critical to a business’s strategy; when an important process depends on a small number of people, the organisation becomes exposed to absence, turnover, retirement, and the ordinary difficulty of recruiting in a tight labour market. A single resignation can turn into a visible service problem within weeks, not because the business lacked demand or ambition, but because its ability to deliver depended on individuals rather than resilient, repeatable systems.

The necessary response is to look to options outside of just employing more people. Technology can help solve this issue, not to replace people but rather to build operations that depend less on any one individual’s availability and more on repeatable, scalable systems. Thus, human judgement is reserved for the work where it adds the most value. This is the specific point at which automation becomes relevant, and why it needs to be discussed in conjunction with scalability and workforce risk rather than as a separate efficiency initiative.

Business process automation is usually framed around reducing manual work, saving time, and lowering costs, and those benefits can be true, but framing it only that way understates the value it has in making organisations more resilient. Automation functions as continuity infrastructure: it helps organisations maintain critical processes when staffing is constrained, reduces key-person dependencies, standardises how work gets done, removes avoidable variation between process executions, holds service levels steady during demand spikes, and keeps operating costs more predictable.

Automation initiatives should be implemented in the business areas where organisations need to maintain consistent operations regardless of individual staff availability, not only where the greatest cost savings can be achieved. A process that has been standardised and automated to the point where it does not depend on any one person is scalable, because it can absorb more volume without requiring a proportional increase in coordination or risk.

Why Cost Predictability is Important

Nobody wants to pay a lot of money; that much is obvious. But in uncertain conditions, low costs matter less on their own than predictable ones, and most cost conversations still default to minimisation rather than stability. A business that can forecast its operating costs with confidence has more freedom to make decisions, because it is not constantly recalculating its position against a shifting cost base. Resilience and financial flexibility are linked for exactly this reason: automation and scalable operating models reduce the volatility introduced by fluctuating labour conditions. These sources of volatility make planning harder, and each is reduced by the same operational choices that make a business more resilient in the first place.

Predictable costs give a business room to invest, adapt, and grow without repeatedly rebuilding its operating model from scratch each time conditions shift. Organisations that focus exclusively on growth tend to prioritise speed over durability. They add customers, products, markets, employees, and processes in quick succession, and each addition feels justified on its own terms, because each one, viewed in isolation, looks like progress. However, complexity compounds. Each new layer interacts with the ones beneath it and can lead to devastating consequences. Organisations that deliberately invest in resilience often look slower in the short term, next to competitors adding headline growth every quarter, which is why resilience is easy to underinvest in: its costs are visible immediately, in the form of forgone speed, while its benefits only become visible later, when conditions change, and the investment pays off.

Nevertheless, what resilient organisations build instead is harder to see on a quarterly chart but more valuable over time: the ability to grow repeatedly without breaking. This resolves the apparent tension between growth and resilience rather than simply asserting that both matter. And in the end, the goal is not to choose between growth and resilience, as though a business must sacrifice one for the other. The goal is to build resilience so that growth becomes safer, more sustainable, and less expensive to support when it comes. Thus, whether the organisation can maintain critical operations if key people are unavailable, absorb sudden swings in demand, scale without adding complexity in proportion, keep costs predictable as labour markets tighten, and continue delivering value when the original plan no longer applies, becomes a defensive checklist for risk committees to assess. These become the terms on which growth itself is judged, making resilience a board-level growth enabler rather than a hedge against bad outcomes.

Questions Every Executive Team Should Be Asking

Can our critical operations continue if key people become unavailable?
Which manual processes introduce unnecessary operational risk?
Are we scaling sustainably or simply increasing operational complexity?
Which workflows could be standardised or automated?
How resilient is our operating model when business conditions change?

Conclusion

Growth tells us how fast a business is moving. Resilience tells us whether it can keep moving when conditions change. In today’s economic environment, the second may matter more than the first.

The organisations that thrive in uncertain environments will be those that have built operations capable of absorbing disruption without losing momentum, not necessarily those pursuing the most aggressive growth plans. The real strategic advantage lies in the ability to keep operating, keep adapting and keep creating value when the world does not cooperate with the plan.

Building that level of resilience starts with understanding where operational risk exists. By identifying process bottlenecks, reducing workforce dependency and creating scalable, repeatable operations, organisations are better positioned to grow sustainably, adapt confidently and navigate future uncertainty.

If your organisation is evaluating how automation can strengthen operational resilience, SmartTechNXT can help.

Book an Automation Assessment

Frequently Asked Questions

What is operational resilience?

Operational resilience is an organisation’s ability to continue delivering products and services despite operational disruption, economic uncertainty or changing market conditions.

Why is operational resilience important?

It helps organisations maintain service levels, manage operational risk, improve business continuity and support sustainable growth.

How does automation improve operational resilience?

Automation reduces reliance on manual processes, standardises workflows and enables organisations to continue operating efficiently when staffing or market conditions change.

How can organisations improve operational resilience?

By identifying operational bottlenecks, reducing workforce dependency, improving process visibility and implementing scalable automation where appropriate.

The post The Importance of Building Resilience appeared first on SmartTechNXT.

AI Governance Is Becoming a Board-Level Responsibility

Joshua van Aswegen — Thu, 02 Jul 2026 10:43:38 +0000

Perspectives

Executive summary

Artificial intelligence’s development in the past decades has shaken the corporate world, moving it beyond information technology departments and into a wider variety of departments across an organisation, such as governance, risk, legal, and executive leadership. But AI comes with its own risks, meaning regulatory obligations, compliance requirements, and reputational exposure now require direct board attention.

For most of the past decade, due to its previously limited scope, organisations have treated artificial intelligence primarily as an engineering matter. It was managed by data science teams and used primarily to compute budgets, model data, and release schedules. Boards relied on the expertise of lower management involved in the IT departments to manage the detail involving AI, but AI is now being used across all sectors and departments. Its wide accessibility and readily available software like ChatGPT make it easy for any individual to use. However, this has also made the risk of AI being exploited easier, since it no longer requires specialised expertise to use.

Across the world, regulatory developments, documented corporate failures, and increased public scrutiny are placing AI governance directly within the remit of executive leadership and the board. Boards are now required to incorporate AI into the organisation while managing the risks it introduces, which requires the proper frameworks and fluency to be put in place before an incident occurs.

From infrastructure to institutional responsibility

When a bank’s lending algorithm systematically disadvantages applicants from a particular demographic, this is not a data engineering matter. When a healthcare provider’s diagnostic system produces outputs that cannot be explained to a regulator or a patient, this is not a product defect. When a recruitment platform’s screening model penalises candidates on the basis of which university they attended, the resulting liability falls to the board.

In each of these examples, all of which has occurred in recent years, the consequences are institutional rather than technical. Regulatory censure, reputational damage, legal exposure, and loss of stakeholder trust fall within the responsibilities of governance, risk, and executive leadership.

Boards that treat artificial intelligence as a technology matter rather than a governance matter are repeating the error that boards made with cybersecurity fifteen years ago, and many are discovering this only after the fact.

In the early 2000s, organisations generally regarded data security as an IT function. A series of significant breaches, together with the regulatory and legal consequences that followed, established cybersecurity as a standing item on board agendas. AI governance is following a comparable path, although the timeframe is considerably shorter, given that the pace of AI adoption has been significantly faster in recent years.

The regulatory environment

The European Union’s AI Act, which began applying in phases from 2024, constitutes the most comprehensive legal framework for artificial intelligence currently. It classifies AI systems according to risk level, imposes conformity requirements on high-risk applications, and requires transparency and human oversight measures that apply directly to the organisations deploying these systems. Non-compliance carries penalties of up to €35 million or 7% of global annual turnover, whichever is greater, which is a significant amount, especially for SMEs that are directly using AI systems in their organisation.

In the United States, federal agency guidance and state-level legislation continue to develop. The Equal Employment Opportunity Commission has issued guidance addressing employer liability for discriminatory outcomes produced by AI hiring tools. While similarly, the Federal Trade Commission has indicated that algorithmic deception falls within its enforcement remit. Additionally, several states have enacted, or are considering, legislation that requires algorithmic impact assessments for consequential automated decisions.

Regulators in the United Kingdom, Canada, Singapore, Brazil, and Japan are developing or implementing comparable frameworks; South Africa is similarly in the process of drafting national AI policies. Thus, the position taken by governments internationally insists organisations that deploy AI bear responsibility for its outputs, and express that a lack of understanding of how a system operates does not constitute a defence.

Six governance risks requiring board attention

These regulatory frameworks that governments are implementing have the aim of making AI use safer for individuals and companies alike but place the burden of compliance directly on the organisations that use AI. This requires boards to take ownership of the obligations those frameworks impose, while addressing the broader risks that AI deployment introduces across their operations. Some of these risks involved include:

Accountability gaps

When AI causes harm, who is responsible? Boards must establish clear ownership chains from development through deployment and ensure ongoing monitoring for prompts and access.

Compliance exposure

With regulations across the EU, the US, and Asia, boards risk personal liability if their governance structures are absent or performative. Hence, companies should make strict compliance goals to protect themselves and stick to them.

Algorithmic bias

Models trained on historical data can encode and amplify discrimination. Without ongoing auditing, organisations face discriminatory outcomes at scale, which can harm individuals, while damaging the company’s reputation.

Transparency deficits

Regulators, customers, and courts increasingly demand explainability. ‘The model decided’ is not an acceptable answer when consequential decisions affect people’s lives. AI hallucinations, or fake results, can also impact a company’s responses. Companies should double-check the results given by AI.

Ethical failures

Beyond legality, stakeholders expect AI use to align with the company’s stated values. Ethical misalignment erodes trust with employees, customers, and investors.

Reputational damage

AI failures travel fast. A discriminatory outcome, a biased recommendation engine, or a misused data practice can produce a reputational crisis that outpaces legal exposure.

Establishing meaningful board oversight

There is a distinct difference between performative and substantive AI governance, and boards that fail to distinguish between the two leave their organisations exposed. Issuing an AI ethics statement, or appointing a Chief AI Officer, without corresponding structural oversight does not constitute governance. Effective governance requires boards to move from passive awareness to active accountability. This does not mean every director needs to have technical expertise in machine learning, but rather it requires the board to ask the appropriate questions, to commission the appropriate structures, and to hold management accountable for providing credible answers.

Board guidelines to address in AI governance

Establish an AI risk committee or extend the mandate of the existing audit and risk committee to include AI, with dedicated agenda time and access to independent technical advisors.
Commission an internal inventory of AI and automated decision-making systems in use across the organisation, classified by risk in line with applicable regulation.
Make algorithmic impact assessments a requisite for any AI system that makes or materially influences decisions affecting customers, employees, or other third parties.
Mandate human oversight mechanisms in operational practice, not solely in policy, for all high-risk AI applications.
Approve an internal AI use policy covering third-party model use, employee use of AI tools, data handling, and incident response.
Establish an escalation and incident response pathway for AI-related failures, with defined accountability at board, executive, and operational levels.
Invest in board-level AI literacy through structured briefings and external advisors, while retaining independent judgement rather than deferring to those whose interests may not align with the organisation’s.

Internal policy as a governance requirement

Public discussion of AI governance often ignores the internal policy environment. Organisations deploy AI in two main directions: externally, in products and services offered to customers, and internally, in tools used by employees. Both carry governance obligations, but internal deployment is commonly overlooked.

Generative AI tools have been adopted within organisations at a pace that governance structures cannot catch up with. Employees routinely use large language models for drafting, analysis, research, and decision support, often without formal guidance on what data may be shared, how outputs should be verified, or what limitations apply. This represents a systemic risk affecting intellectual property, data privacy, regulatory compliance, and professional liability.

A credible internal AI policy addresses each of these areas: permissible use cases, data classification rules, requirements for human review, vendor assessment processes, and employee training. Developing such a policy requires cross-functional input from technology, legal, human resources, risk, and operations, together with board-level endorsement to ensure it carries the necessary authority.

The transparency challenge

AI presents boards with a challenge that does not arise in the same form with other technologies: the opacity of complex models. A board can review a financial model in detail, but some cannot yet review a deep learning system used for credit decisions in the same way. As a result, the systems with the greatest capability are often the most difficult for governance structures to scrutinise, while being the most consequential to scrutinise effectively.

Organisations should be able to explain how AI decisions are reached, and this requirement should be embedded directly from the point of procurement and development. Additionally, auditing AI models, whether through internal teams or external specialists, should be a standard operational requirement.

Reputational exposure

For many organisations, the reputational dimension of AI governance carries greater immediate consequence than the regulatory dimension. Public tolerance for AI-related failures involving bias, discrimination, or misuse of personal data has declined significantly as awareness of these issues has increased. Incidents that would previously have remained within specialist coverage now routinely attract public attention.

This changes the calculation boards must apply to AI risk. Reputational risk does not resolve when a case concludes. If damage is done to communities, it harms multiple parties, including the organisation accountable.

Organisations with demonstrable governance structures, rather than policies that exist only on paper, are better positioned to respond when an incident occurs. The distinction between a crisis that is managed and one that escalates frequently depends on the quality of the governance infrastructure in place beforehand.

A narrowing window for proactive governance

Boards currently have an opportunity to address AI governance proactively rather than in response to an incident. Regulatory timelines continue to advance, litigation concerning AI outcomes is increasing, and institutional investors and proxy advisors are beginning to incorporate AI governance into their assessments of board quality and organisational risk. Organisations that establish robust governance structures now are likely to gain advantages in regulatory relationships, talent retention, and stakeholder trust that will be difficult to replicate later.

In conclusion, the boards best positioned to manage this are boards that recognise AI has altered the risk landscape, that establish governance structures proportionate to that change, and that ensure those structures function in practice rather than serving a primarily symbolic purpose.

The post AI Governance Is Becoming a Board-Level Responsibility appeared first on SmartTechNXT.

Analysis of automation’s role in the UN SDG’s

Joshua van Aswegen — Mon, 04 May 2026 13:12:53 +0000

Analysis of automation’s role in the UN SDG’s

How Intelligent Automation is Shaping a Better Future: An Analysis of Automation’s Role in the UN SDGs

Executive Summary

Core Question: How do Intelligent Automation (IA), Artificial Intelligence (AI), and Robotic Process Automation (RPA) impact the United Nations Sustainable Development Goals (SDGs)?

Key Findings: Intelligent Automation acts as a critical accelerator for the UN 2030 agenda. It directly enhances global healthcare diagnostics (SDG 3), personalizes inclusive education (SDG 4), promotes gender equity in curricula (SDG 5), and optimizes renewable energy grids (SDG 7 & 13). However, its deployment introduces socio-economic friction, including a documented 12% increase in the Gini income inequality index and rising data center energy demands. To ensure AI and RPA serve as genuine instruments of social progress, organizations must couple deployment with strategic governance, ethical bias auditing, and explicit renewable energy commitments.

The World’s To-Do List: The UN 2030 Agenda

In 2015, the United Nations set out what is arguably the most ambitious to-do list in human history. The seventeen Sustainable Development Goals (SDGs) cover everything from ending poverty and hunger to tackling climate change and ensuring quality education for all. These were adopted by every member state, with a 2030 deadline. That deadline is now very close, and progress, while real, has been uneven.

Into this picture steps one of the most influential technologies of our time: Intelligent Automation (IA). AI and RPA are no longer confined to corporate efficiency projects. They are already reshaping healthcare, education, agriculture, energy systems, and public services around the world, with measurable effects on the very challenges the SDGs were designed to address.

This article makes the case that AI and Automation are genuine instruments of social progress, with real potential to accelerate the SDGs in ways that matter to ordinary people. Despite its complications, automation has a significant impact on society today.

Defining Intelligent Automation: Two Technologies Worth Understanding

AI (Artificial Intelligence) refers to computer systems that can carry out tasks that normally require human intelligence: recognising images, understanding language, making predictions, and finding patterns in large datasets. It powers everything from medical diagnosis tools to climate models to personalised learning apps.

Rather than simulating human thinking, RPA (Robotic Process Automation) mimics human actions on a computer: clicking buttons, entering data, moving files, and filling in forms. By centralising a company’s data to create big datasets, it acts as a digital administrative layer supporting operational efficiency and AI readiness. It is exceptionally good at the kind of repetitive, rules-based administrative tasks that consume enormous amounts of human time in organisations like hospitals, universities, and government departments.

The two technologies are commonly used together as Intelligent Automation (IA), with AI providing the intelligence to understand complex, unstructured information, while RPA provides the hands to act on it quickly and consistently.

Improving Global Healthcare (SDG 3)

Healthcare is one of the clearest examples of AI doing something genuinely useful for people outside of the business world. AI-powered diagnostic tools are helping doctors detect diseases earlier and more accurately than was previously possible. Machine learning algorithms trained on thousands of medical images can identify early-stage cancers, diabetic retinopathy, and other conditions that might otherwise be missed until they become harder to treat.

In public health, AI models can predict how infectious diseases will spread through a population, helping health authorities allocate resources before a crisis peaks rather than after. During the COVID-19 pandemic, AI tools were used to track transmission patterns and support vaccine distribution logistics, which is a real-world demonstration of the technology’s potential in global health emergencies.

RPA is making healthcare systems more efficient behind the scenes. Hospital administrative processes, such as appointment scheduling, prescription management, insurance claims, and supply chain logistics, are being automated, freeing healthcare workers to spend more time with patients rather than paperwork. Research confirms that AI-enabled remote monitoring, virtual consultations, and automated data analysis are simultaneously improving patient outcomes and reducing costs.

The equity dimension here matters. SDG 3 is not only about improving healthcare for those who already have good access; it is also about universal health coverage. AI-powered tools, particularly telemedicine platforms and diagnostic aids, have a genuine capacity to reach the people the current healthcare system routinely fails.

Advancing Inclusive Education and Gender Equality (SDGs 4 and 5)

Education is another domain where AI’s social impact is tangible and growing. Adaptive learning platforms powered by AI can adjust the pace, style, and content of instruction to match each student’s individual needs and learning patterns. AI’s language translation is expanding access to education for many communities where language barriers have limited students’ opportunities. For students in under-resourced schools, or those who have historically been left behind by one-size-fits-all teaching, this kind of personalisation can be transformative for their learning.

Gender equality (SDG 5) is also in play. AI tools can analyse educational content to identify and flag gender biases in textbooks, curricula, and career guidance materials. They can surface patterns, such as the underrepresentation of women in science textbooks, that human reviewers might overlook. AI-powered career-matching and skills-development tools can help women identify and access labour market opportunities in fields where they have historically been underrepresented.

On the institutional side, RPA is helping universities and schools run more efficiently. Research from universities in India found that RPA adoption significantly improved operational performance by reducing processing times for attendance management, certificate generation, and student enrolment. Staff freed from these repetitive tasks can redirect their time towards teaching, advising, and supporting students.

Driving Economic Growth, Productivity, and Market Inclusion (SDGs 8 and 9)

When people talk about AI and the economy, the conversation tends to focus on productivity gains and GDP growth. Research analysing global data from 2000 to 2023 found that automation contributed to a 25% increase in labour productivity across sectors. AI-driven innovation is accelerating product development, optimising supply chains, and enabling entirely new categories of economic activity.

But the more interesting question is, who benefits from that growth? SDG 8 is about decent work and economic growth. SDG 9 focuses on inclusive and sustainable industrialisation. Both goals are premised on the idea that the fruits of economic progress should be widely shared.

However, the same longitudinal research that found productivity gains also found a measurable increase in income inequality correlated with automation—specifically, a 12% rise in the Gini index over the study period. The pattern is consistent with what other economists have found: automation tends to benefit high-skilled workers and capital owners disproportionately, while placing the greatest pressure on low-skilled and routine workers.

This is not an argument against automation but an argument for getting the policy response right. Automation has the potential to greatly improve these conditions, but it needs a focus on reskilling and upskilling programmes, transitional support for displaced workers, and deliberate investment in making automation accessible to small and medium-sized enterprises, not just large corporations. These measures are essential if the economic benefits of AI are to serve the SDG agenda rather than undermine it.

Nevertheless, there are clear benefits. In manufacturing environments, Intelligent Automation is replacing the most dangerous, physically demanding, and repetitive tasks, which is improving worker safety and well-being. In agriculture, AI-powered tools are helping smallholder farmers access weather forecasts, pest detection, and market price information that were previously only available to large commercial operations. These are examples of AI democratising access to knowledge and capability.

Optimising Climate Action and Resource Sustainability (SDGs 7, 13, 14, 15)

Climate change is the SDG challenge where AI’s contribution is promising but complicated to develop. On the promising side: AI is being used to optimise the output of renewable energy installations, managing the complex interplay of solar panels, wind turbines, battery storage systems, and grid demand in ways that would be impossible to do manually. Research also confirms that AI integration in renewable energy can significantly improve efficiency and output, directly supporting SDG 7’s goal of affordable and clean energy for all.

AI-powered environmental monitoring systems are tracking deforestation, ocean health, air quality, and wildlife populations with broad coverage and high precision. Machine learning models trained on recent climate data are helping scientists and policymakers better understand the dynamics of climate change, informing more effective adaptation and mitigation strategies. Smart city applications, such as AI-managed traffic systems, waste collection, water distribution, and building energy management, are improving heavily polluted urban areas and maximising resource use at a meaningful scale.

But here is the complication that deserves honest acknowledgement: AI is also an energy-intensive technology. Training large AI models consumes enormous amounts of electricity, and data centres powering AI applications are among the fastest-growing sources of electricity demand globally. As such, there is a well-documented ‘rebound effect’: as automation makes industrial processes more efficient, those efficiency gains are often offset by increased overall economic activity and energy consumption. AI deployment must be coupled with stringent renewable energy commitments to curb this consumption.

Automation in the Office: Evaluating RPA’s Contribution

RPA’s contribution to sustainable development deserves recognition, especially in institutions like hospitals, universities, and government agencies that sit at the heart of public service delivery.

For example, when a university automates its administrative workflows with RPA: attendance records that once required hours of manual data entry are processed in seconds, certificate generation that took days happens instantly and accurately, and staff who were spending a third of their time on routine data tasks are freed to focus on student support, teaching quality, and the kind of human human interaction that makes education valuable. One documented implementation of RPA in a university setting reduced the time required to process attendance by 99.9%. RPA is removing friction in work in ways that can cumulatively improve the services people depend on.

The sustainability dimension of RPA extends to environmental impact as well. By moving institutions away from paper-based workflows to fully digital processes, RPA reduces paper consumption, printing, and physical storage requirements. When implemented thoughtfully, through using energy-efficient servers, reusable software components, and responsible hardware lifecycle management, RPA can contribute to SDG 12’s goals around responsible consumption and production.

That said, sustainable RPA adoption is not automatic. Research highlights real barriers: the cost of integrating automation with legacy systems, and the need to involve and train staff rather than simply imposing new technology on them. Institutions that have successfully adopted RPA share a common characteristic: they treated it as a change management process, not just a technology installation (we explore this in “From Paper to Pixels: The Digital Revolution”).

Risk Assessment: What Could Go Wrong?

No credible analysis of Intelligent Automation can skip the challenges SDG implementation faces. They are real, they are significant, and pretending otherwise would undermine the case for responsible IA adoption.

1. Bias and Socio-Economic Inequality

AI systems learn from data, and data reflects the world as it is, not as it should be. A hiring algorithm trained on historical hiring decisions may learn to discriminate. A healthcare diagnostic tool trained primarily on data from one demographic group may perform worse for others. A credit scoring model may embed existing patterns of economic exclusion. These are not hypothetical risks; they exist in the real world and can become documented problems in AI deployments.

For the SDGs, the goals around reducing inequality, promoting gender equality, and ensuring health and education for all can be actively undermined by AI systems that perpetuate or amplify existing disparities. Getting the data right, auditing AI systems for bias, and ensuring diverse representation in AI development teams are necessary prerequisites for automation that would serve the SDG agenda.

2. Privacy Rights and Data Security

AI runs on data, and much of the most valuable data is personal. Healthcare AI needs patient records. Educational AI needs learning data. Public health AI needs location and behavioural data. Each of these use cases raises legitimate questions about privacy, consent, and the security of sensitive information. While in-house automation models are subject to stronger management oversight and pose lower security risks, robust data governance, clear regulatory frameworks, and privacy-by-design principles are essential to maintain public trust; without which, AI applications in sensitive domains cannot function.

3. Environmental Impact & The Energy Problem

As discussed in the climate section, AI’s energy footprint is a genuine concern that the technology community has been slow to address honestly. Responsible AI deployment must include a serious commitment to sourcing renewable energy, designing energy-efficient models, and transparently reporting the environmental costs of AI infrastructure. This is an area where the industry can do better.

What Good Looks Like: Frameworks for Responsible AI Adoption

The good news is that none of these challenges are insurmountable. The path to IA that genuinely serves the SDGs is relatively clear, even if it requires political will, institutional commitment, and genuine investment. These suggestions can help towards a strong and equitable adoption of IA:

Governance frameworks that are fit for purpose: Policymakers need to build regulatory environments for Artificial Intelligence that protect against bias, ensure data privacy, and hold developers accountable without stifling innovation.
Workforce investment that keeps pace with change: Every country adopting automation at scale needs a parallel investment in reskilling, vocational training, and educational systems that equip people for the jobs that automation creates, not just allow jobs to be replaced.
Clean energy as a non-negotiable companion: AI deployment programmes, particularly large-scale ones, should be accompanied by explicit commitments to renewable energy sourcing.
Making AI accessible, not exclusive: The SDGs are fundamentally about equity. AI tools that are only available to wealthy countries, large corporations, or elite institutions will widen global inequality rather than address it. International cooperation on AI access, including technology transfer, open-source tools, and capacity building in developing nations, is essential.
Diverse voices in Intelligent Automation development: IA implementations that are designed without input from the communities they are meant to serve tend to fail those communities. Meaningful participation from women, marginalised groups, developing-world institutions, and civil society is how you build IA that actually works for everyone.
Honest evaluation of what works: The SDGs are measurable. IA’s contribution to them should also be measured rigorously and honestly. Programmes that work should be scaled; those that do not should be redesigned. This includes keeping companies that use automation accountable for their actions.

Conclusion: A Tool Worth Using

AI and RPA are not going to solve the world’s problems on their own. But dismissing them as irrelevant to the SDG agenda, or as purely corporate technologies with no social benefit, would be equally mistaken, and increasingly hard to sustain as evidence accumulates.

The picture that emerges from serious research is more nuanced and more interesting than either the hype or the backlash suggests. AI and Intelligent Automation are making a difference in remote healthcare, access to information in agriculture, improvements in education, energy efficiency, and climate management. However, simultaneously, automation is widening income inequality in measurable ways, creating real risks for workers in vulnerable roles, consuming significant amounts of energy, and, if poorly designed, could embed and amplify the biases of the societies that built it.

This requires deliberate choices: about governance, investment, equity, and the values we want built into the systems we are developing. It requires institutions, such as universities, hospitals, the business community, and government agencies, to think carefully about how they adopt automation and to measure not just efficiency gains but also social outcomes. And it requires the technology community to take its responsibilities to the SDG agenda seriously, not as a marketing exercise, but as a genuine commitment.

By approaching Intelligent Automation strategically, ethically, and inclusively, organisations will improve their own operational performance, while meaningfully contributing to a market that is more economically and socially resilient.

Sources and Further Reading

Almusharraf, A.I. (2025). Automation and Its Influence on Sustainable Development: Economic, Social, and Environmental Dimensions. Sustainability, 17, 1754.
Baig, M.A., Huang, J., & Xu, J. (2025). AI for United Nations Sustainable Development Goals.
Frey, C.B., & Osborne, M.A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.
Khan, S., Saif, M., & Sharma, N. (2025). Sustainable Development Practices for Adoption of Robotic Process Automation (RPA) at the Universities. Journal of Interdisciplinary Knowledge, 8, e01651.
Lainjo, B. (2024). The Role of Artificial Intelligence in Achieving the United Nations Sustainable Development Goals. Journal of Sustainable Development, 17(5), 30–42.
Vinuesa, R. et al. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1), 1–10.
World Bank Open Data (2000–2023). GDP, Gini Index, Energy Consumption, CO2 Emissions datasets. https://data.worldbank.org/.

The post Analysis of automation’s role in the UN SDG’s appeared first on SmartTechNXT.

Motivating the Workforce in a New Era: AI’s Impact on Human Thinking

Joshua van Aswegen — Thu, 19 Feb 2026 13:46:33 +0000

Motivating the Workforce in a New Era: AI’s Impact on Human Thinking

AI is already deeply engrained in the workspace: in workflows, decision-making, performance tracking, and product development. Amidst their competitors, companies have shifted to using AI to focus on the scale and speed of their outputs and gain a competitive advantage. However, as much as AI has impacted the workspace, it has also affected its workforce.

For many, the introduction of AI is not experienced as a single disruptive event, but as a slow reorientation. Tasks that once demanded judgment are now suggested by models. Personal creation and work have subtly turned into prompts and validations of work generated by AI. Over time, this alters how people understand the value of their own efforts and, ultimately, their own motivation.

This matters because motivation is not merely a function of compensation or culture but is deeply psychological. People stay engaged when they feel competent, autonomous, and meaningfully connected to their results. Because AI is so deeply ingrained in society, it has the power to support these conditions, but it can also undermine them when its presence diminishes the visibility of people’s efforts, the authorship of their work, or their personal growth.

A worker’s psyche can have a significant impact on the type and quality of work they produce. Creating memorable and top-quality work can be difficult when a worker is distracted by feelings of boredom, insecurity, and/or demotivation. The introduction of AI into the business world has left many feeling this way. Too many fears surrounding AI and the impression it’s made have influenced the output of work and achievements.

Unlike earlier waves of automation, AI operates at the cognitive level. It has the ability to not only replace physical or repetitive tasks, but also to participate in thinking itself. As a result, workers are not just adjusting their workflows; they must also rethink their professional identities. Questions can now arise, such as: What is my role when a system can generate ideas? Where does my judgement still matter? Am I building expertise, or merely supervising it?

This is not a question of whether AI will replace jobs, because in many cases, it has not (for more information, see: The Human Algorithm: Automating a More Ethical Future and When Leaders Fear AI and Automation: How Myth-Making Undermines Your Chances of Success). The question is now about how AI can, and is, changing how people experience their work, how they motivate themselves, and how much of their identity and skills they can still attach to what they do.

Why Motivation Is a Leadership Problem

In the tech sector, motivation has often been treated as a personal trait. High performers are assumed to be intrinsically driven and are awarded as such. Naturally, the more motivation a person has for their work, the higher the quantity and quality of their results. Consequently, leaders often focus on boosting motivation and morale, not only for the individual worker but also for the entire team to achieve greater success.

When motivation declines, leaders often misdiagnose the cause. They see slower innovation, risk aversion, or reduced initiative and assume it to be personal issues. However, this is not because people have lost their capacity for passion, ambition, or pride. It is often because automation is reshaping the psychological foundations of work faster than individuals and institutions can adapt to it.

The central tension of the automated workplace is not productivity versus employment. The question is whether people can continue to motivate themselves when their role in value creation becomes less visible, less tactile, and less emotionally legible. Many AI tools shift engineers, analysts, and operators from creating outputs to supervising them instead. The work becomes less about problem-solving and more about validating, approving, or correcting machine-generated results. This shift in responsibilities matters because when people create results themselves, they feel more connected to their work. When they only supervise work done by systems or others, they often feel less involved and less motivated.

Over time, workers may feel that their expertise is no longer essential but merely contributory. They become accountable for outcomes they did not fully author and their motivation weakens when their contribution becomes indirect.

Skill Atrophy from AI

One of the least discussed psychological consequences of widespread AI adoption is not job displacement, but skill displacement. As attention, trust, and responsibility shift toward focusing on intelligent systems, human competence can slowly atrophy.

Skills are not static assets and require regular use, challenges, and feedback to remain sharp. When AI systems take over core cognitive tasks, such as drafting, coding suggestions, data analysis, prioritisation, or decision-making, workers may stop directly exercising those skills. While this shift can increase short-term productivity, it reduces opportunities for deep skill engagement. Over time, workers may notice that tasks they once handled confidently now feel unfamiliar without AI assistance.

High reliance on AI often feels like progress when we see faster outputs and fewer errors. But this can lead to dependency on these systems. A recent MIT study found that “excessive reliance on AI-driven solutions” may contribute” to “cognitive atrophy”; this serves as a warning for our critical thinking (Kosmyna et al., 2025; Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task). When teams default to AI suggestions without challenging their thinking, they miss the opportunity to test assumptions and refine their intuition. Over time, workers may hesitate to act without AI confirmation, even in areas where they once had more confidence.

This creates a subtle form of learned dependence. While people remain employed and productive, their internal sense of capability narrows. And so, motivation suffers not because work is harder, but because self-trust is weaker.

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Skill Development from AI

Although over-reliance on AI carries genuine risks, it would be a mistake to see the resulting skill shift as entirely negative. When appropriate conditions are met, AI can also serve as a catalyst for the development of new skills.

AI is very good at generating options, patterns, and preliminary solutions. As explained, this shifts the human role from execution toward evaluation. But, when workers are encouraged to critique, compare, and refine AI outputs, they develop higher-order cognitive skills like judgment, discernment, and strategic reasoning. These skills are harder to automate and become more valuable over time through continuous use. The issue then becomes how to frame this shift in skill development.

Motivation grows when people feel their thinking is being sharpened rather than bypassed. This reframing requires a deliberate emphasis on judgment, interpretation, and decision-making as the core human contribution. Instead of valuing speed alone, organisations should value their workers’ insight. This means also recognising reasoning, discernment, and learning alongside their outputs and profits.

Employees who use AI to explore alternatives, test ideas, and deepen understanding demonstrate a form of cognitive growth that is essential for the company’s long-term resilience. When managers recognise improvements in reasoning, systems thinking, or cross-domain understanding, they reinforce the idea that development itself is a meaningful outcome rather than a distraction from performance.

AI usage itself can become a signal of discernment. Rather than measuring only whether AI tools increase efficiency, organisations can observe how thoughtfully they are used. Workers who regularly verify outputs, catch subtle errors, refine prompts, or add contextual constraints contribute to quality and trust, even when those contributions are not immediately visible in output metrics.

Outside the Office

Still, AI does not only shape behaviour in the office. AI has become engrained in daily life: showing up in recommendation systems, search engines, social media feeds, navigation tools, and generative assistants. This increasingly mediates how people find information, make decisions, and even span attention in their personal lives. Nevertheless, this constant interaction with automation subtly trains cognitive habits that can also follow individuals into the workplace.

When AI consistently removes friction from everyday choices, people may practice fewer skills related to memory, exploration, and independent problem-solving. Automation options like ChatGPT make it very easy for people to run their lives; automation has become so advanced, scholars use it to write theses, businesses use it to run marketing campaigns, many people even use it to help them decide on basic questions like what to have for lunch. Information arrives instantly, recommendations replace discovery, and summaries stand in for deeper engagement.

Over time, this can encourage a default reliance on external systems to think, choose, and prioritise. In the workspace, this can show up in workers as reduced patience for ambiguity, quicker acceptance of first-pass answers, or discomfort with slow, complex reasoning.

Despite this, everyday AI use can cultivate valuable capabilities when engagement is more dynamic rather than passive. Regular interaction with AI-driven tools can strengthen the ability to frame precise questions, evaluate competing information, and iterate on ideas quickly. Exposure to diverse content and perspectives can expand conceptual range and support creative thinking, particularly when users pause to understand and decode what they encounter.

For many individuals, AI lowers the barrier to curiosity. The ability to explore unfamiliar topics, test ideas, or clarify uncertainty on demand can build confidence in people wanting, and being able, to learn. People who feel capable of learning quickly are more likely to engage with challenges at work rather than withdraw from them.

The difference between dependency and development lies in the way in which automation is used. Passive consumption can narrow skills over time, while reflective engagement can sharpen them. People who question AI’s outputs, compare sources, and reflect on its relevance continue to exercise critical thinking, even outside of work.

For organisations, this impact highlights the importance of creating an atmosphere that accepts and encourages deliberate AI engagement at work. Cultures that value curiosity and reflection help counterbalance the passive habits formed elsewhere and amplify the skills people are already developing in their daily lives. Thus, everyday AI use becomes less of a threat to workplace capability and instead helps workers use skills that organisations can choose to strengthen.

At the end of the day, AI alone does not determine which human skills endure. AI has the capability to expand possibilities, accelerate workflows, and reshape how work is done, but it does not replace human choice.

Each interaction with AI presents an opportunity for each individual to decide on: accept an output as a shortcut, or to use it as a prompt for deeper thinking; to lean on convenience, or to actively refine one’s own expertise. Over time, as can be seen, these everyday choices shape how skills evolve. While organisations influence the environments in which people work, it is important to remember that individuals remain active participants in their own development. Skills can only grow when they are used with intention. Hence, automation is a tool that simply responds to how thoughtfully people engage with it.

In this context, motivation becomes the driving force behind whether individuals choose active engagement or passive reliance. When people feel that their thinking still matters and that their abilities are being exercised rather than replaced, they are more likely to remain motivated, curious, and committed to their work.

However, when AI is experienced as a shortcut that bypasses human effort, motivation can gradually weaken, even if productivity appears to increase. A subtle difference that business leaders would be wise to remain cognisant of and maintain open communication with their teams.

The post Motivating the Workforce in a New Era: AI’s Impact on Human Thinking appeared first on SmartTechNXT.

The Human Algorithm: Automating a More Ethical Future

Joshua van Aswegen — Tue, 25 Nov 2025 09:44:16 +0000

The Human Algorithm: Automating a More Ethical Future

Over the past decade, automation has become one of the most defining forces reshaping how societies function and how economies grow. Artificial Intelligence (AI) and Robotic Process Automation (RPA) are no longer abstract technologies discussed in research papers or confined to film scripts, they have quietly become part of everyday life. From online customer service systems to energy management in global corporations, automation now influences the way people work, communicate, and even make decisions.

The potential benefits of these technologies are enormous, allowing people to focus on more creative or complex tasks. However, this rapid shift toward an automated world also raises important questions about its wider consequences. AI consumes large amounts of energy, contributes to electronic waste, and relies on resource-intensive hardware. It changes the structure of the job market, creating opportunities for some while displacing others, while also impacting the way businesses face accountability for their decisions.

Because of this, discussions about automation can no longer focus only on efficiency or innovation. Rather, they should also address sustainability and responsibility. Companies and policymakers are turning to Environmental, Social, and Governance (ESG) principles and the United Nations Sustainable Development Goals (SDGs) to guide this transformation. These frameworks provide a foundation for understanding how automation can serve not only economic progress, but also environmental protection, fair labour practices, and ethical governance.

This essay explores automation’s environmental and social consequences, while highlighting the growing role of AI and RPA in shaping both challenges and solutions. It argues that automation, if developed and managed responsibly, can become a tool for achieving long-term sustainability. The question is not whether automation will define the future, but whether humanity can guide its development toward outcomes that are equitable, ethical, and sustainable for all.

Environmental Impacts of AI as Automation

With environmental concerns growing worldwide, the impact of artificial intelligence (AI) on the planet is now impossible to ignore. AI systems are often celebrated for their efficiency, creativity, and problem-solving power, but running these massive models comes with real environmental costs. Behind every AI-generated image, chatbot reply, or recommendation algorithm is an energy-hungry infrastructure that keeps it all running.

Recent studies, including one from MIT News, show that data-centre energy demand has exploded in just the past few years, primarily because of AI’s growth. Just in North America alone, total capacity jumped from 2,688 MW in 2022 to over 5,341 MW by 2023. Globally, data centres consumed around 460 terawatt-hours (TWh) in 2022, roughly the same as France’s total consumption. If this trend continues, that number could rise to 1,050 TWh by 2026, making data centres one of the world’s top five power consumers. Since most electricity still comes from fossil fuels, this kind of digital expansion is clearly unsustainable without a major shift toward renewable energy.

Even a single AI model can have a shocking footprint. Training OpenAI’s GPT-3, for example, required about 1,287 MWh of electricity and emitted 552 metric tons of CO₂, enough to drive a car over a million miles, purely to train the model. Every single query to ChatGPT, or to other large models, consumes significantly more power than a normal Google search, about five times more. Multiply that by millions of users and billions of queries, and the scale of energy use becomes enormous.

But it’s not only about electricity. AI systems also demand huge amounts of water to keep their processors cool. According to MIT’s analysis, about two litres of water are used per kilowatt-hour of data-centre energy. In water-scarce regions, this can put major pressure on local supplies and ecosystems. On top of that, manufacturing AI hardware like GPUs depends on rare minerals such as lithium, cobalt, and tungsten, which are mined and processed in energy-intensive ways that also create toxic waste.

And then there’s the growing mountain of electronic waste. As AI hardware evolves so quickly, data centres frequently replace entire racks of GPUs and servers to keep up. One IEEE Spectrum report estimates that AI-related hardware upgrades could generate 2.5 million tons of e-waste each year by 2030. Considering the world produced around 62 million tons in total in 2022, AI could soon account for a sizable chunk of it. This waste contains hazardous materials like lead and mercury, which can pollute soil and water when dumped in landfills, especially in countries without strong recycling systems.

Where RPA Fits In

This is where RPA (Robotic Process Automation) enters the picture. While AI often grabs the spotlight, RPA offers a quieter, more sustainable kind of automation. Instead of training massive neural networks, RPA relies on smaller, rule-based systems that automate repetitive office tasks, things like invoice processing, payroll, compliance checks, or data entry. These systems don’t require vast computing power, which means they consume far less energy and generate less electronic waste.

RPA can also support sustainability goals directly. For example, companies can use RPA bots to automate the collection and reporting of environmental data by tracking energy use, emissions, or supply chain sustainability metrics. This helps organisations meet the reporting requirements of ESG (Environmental, Social, and Governance) standards, which are now becoming central to corporate accountability.

From an SDG (Sustainable Development Goals) perspective, this type of automation supports several of the UN’s key targets, especially SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), and SDG 9 (Industry, Innovation, and Infrastructure). RPA can streamline data gathering for sustainability reports, improve energy monitoring in production lines, and reduce paper waste by fully digitising workflows. Basically, RPA shows that automation doesn’t have to come at a huge environmental cost.

When combined thoughtfully, AI and RPA together can create a more balanced approach to digital transformation. RPA can handle predictable, rule-based work while AI focuses on higher-level problem solving, ideally using “green AI” models that are optimised for energy efficiency. Companies integrating both technologies under a strong ESG framework could significantly reduce their environmental footprint while still benefiting from automation.

ESG and Corporate Responsibility

However, all of this depends on corporate decision-making. Most of AI’s environmental impact comes not from individual users but from the choices made by big tech companies. Platforms like X (formerly Twitter), for instance, have built AI chatbots like Grok directly into their user interfaces, making energy-intensive AI usage part of everyday social media behaviour. This creates the illusion that individuals are to blame for rising energy demand, when, rather, corporate-level integration drives the problem.

This tactic echoes what oil companies did decades ago. BP’s famous “carbon footprint” campaign in the early 2000s encouraged people to think about their individual emissions, cleverly distracting from the industry’s much larger responsibility. Today, we see a similar strategy in tech: users are told to “limit prompts” or “reduce screen time,” while corporations keep embedding AI deeper into their infrastructure without transparency about its environmental cost.

This is exactly why ESG frameworks are so crucial. They push companies to measure and disclose their full environmental impact, from data centre emissions to supply chain minerals to hardware disposal. Some major tech firms, like Google and Microsoft, have committed to running their data centres entirely on renewable energy and even becoming carbon negative in the next decade. Commitments such as these align directly with SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action).

But these pledges need to be backed by action. RPA tools should be implemented to track compliance, automate sustainability audits, and flag inefficiencies in real time. When RPA is used to monitor ESG progress, it helps ensure that sustainability isn’t just a marketing slogan but a measurable, ongoing process.

Moving Toward Responsible Automation

If we want automation to truly serve humanity rather than harm it, we need a more holistic approach. That means designing AI systems that prioritise energy efficiency, extending the lifespan of hardware, and embracing circular economy principles through recycling and repurposing electronics rather than discarding them. It also means using RPA and AI to make sustainability management itself more efficient, transparent, and data-driven.

Balancing Progress with People

In guiding the development of automation towards a more ethical service, its direct effects on people cannot be ignored. However, this is a tricky subject to approach as the social impact of automation is one of the most debated issues of our time. On one hand, technologies like AI are transforming the way we work, promising higher productivity, fewer errors, and even entirely new kinds of jobs. On the other hand, these same systems threaten to displace millions of workers and deepen social and economic divides.

Economists have long argued that automation ultimately leads to more wealth and, eventually, more jobs. As the Stanford Encyclopaedia of Philosophy points out, productivity growth doesn’t automatically mean job loss. History shows that innovation often creates new opportunities even as it destroys old ones. The invention of the tractor reduced the need for farm labour but led to the rise of the automotive and industrial sectors.

Similarly, today’s automation may eliminate certain office or factory jobs, but it’s also spawning new roles in data analysis, AI ethics, and systems maintenance. The problem isn’t the change itself, it’s the transition, which can be slow, uneven, and painful for workers caught in between.

The Polarised Workforce

Current research suggests we’re entering a new era of labour market polarisation. High-skill jobs, particularly those involving technology, creativity, or complex decision-making, are thriving. Meanwhile, many routine office or manufacturing roles, especially those that involve repetitive tasks, are being automated out of existence. This has created what economists call a “dumbbell-shaped” job market: a concentration of well-paid technical and managerial jobs at the top, a growing pool of low-wage service work at the bottom, and a shrinking middle class in between.

This pattern reflects a phenomenon known as skill-biased technological change. In simple terms, automation increases the productivity of skilled workers while replacing less-skilled ones. As a result, people with technical, analytical, or creative expertise see their value rise while others face declining job security and fewer advancement opportunities. The social outcome is widening income inequality.

How AI and RPA Create (and Save) Jobs

Yet the story isn’t entirely bleak. When designed thoughtfully, AI and RPA can actually enhance human work rather than replace it. For example, Intelligent Automation systems can handle routine data processing while humans focus on strategic decision-making, innovation, or customer relations, tasks that still require emotional intelligence and judgment.

RPA, in particular, represents a more balanced form of automation. Because it handles rule-based, repetitive tasks, such as processing invoices, updating records, or managing compliance forms, it often removes the most monotonous parts of a job rather than eliminating the job entirely. Consulting firms like SmartTechNXT describe RPA as a “digital co-worker” that enhances human capabilities. When companies introduce RPA ethically, they can actually improve employee satisfaction by freeing people from tedious work.

Of course, this doesn’t happen automatically. It requires investment in reskilling and upskilling. A worker who once spent hours entering data into spreadsheets could, with the right training, become a “bot manager,” responsible for supervising RPA workflows or analysing the insights those bots generate. This human–machine collaboration creates a hybrid workforce and can become one that’s more adaptive, efficient, and resilient.

The Human Side of ESG

These transitions highlight why the “Social” pillar of ESG (Environmental, Social, and Governance) is becoming increasingly important. Companies are being held accountable not just for their profits or carbon emissions, but for how they treat their employees and communities. The UN’s Sustainable Development Goals (SDGs), particularly SDG 8 (Decent Work and Economic Growth) and SDG 10 (Reduced Inequalities), call for “inclusive and sustainable economic growth” where automation improves, rather than erodes, job quality.

To meet these goals, companies using AI and RPA need to communicate transparently with workers about upcoming changes, involve them in automation decisions, and invest in their career development. Ethical implementation means making sure people feel part of the process, not victims of it. When handled well, automation can support decent work by removing drudgery and opening doors to new, more creative and fulfilling forms of employment.

UNESCO’s Recommendation on the Ethics of Artificial Intelligence emphasises that human rights and dignity must remain at the core of every technological deployment. For example, the International Labour Organisation (ILO) and other global institutions are now advocating for a human-centred approach to automation. This means ensuring fair labour conditions, preventing discrimination through algorithmic bias, and protecting workers’ privacy as data-driven systems become more common in the workplace. Automation is a deeply social issue, tied to values of fairness, inclusion, and justice.

Governments and corporations also need to consider governance, the “G” in ESG, as part of their automation strategy. Transparent reporting on job impacts, labour diversity, and training investments should become standard practice. This builds trust and aligns corporate goals with global frameworks like the UN SDGs.

The future of automation doesn’t have to pit technology against humanity. When aligned with ESG principles and the UN SDGs, AI and RPA can work together to advance both sustainability and social equity.

Environmentally, automation can transition toward “green AI” models and low-energy RPA systems powered by renewable resources, contributing to a circular digital economy. Socially, automation can empower workers by reducing repetitive labour, creating new hybrid roles, and supporting lifelong learning.

But to achieve this vision, companies must embed sustainability and ethics at the heart of their automation strategies. Governance, the often-overlooked “G” in ESG, plays an important role here. Transparent reporting, stakeholder engagement, and accountable leadership ensure that automation supports long-term well-being rather than short-term profit.

Automation’s story isn’t just about faster algorithms or smarter machines but about the kind of society we want to build around them. If we combine innovation with accountability, technology with empathy, and efficiency with environmental care, automation can become a cornerstone of a more sustainable, fair, and human-centred world.

The post The Human Algorithm: Automating a More Ethical Future appeared first on SmartTechNXT.

When Leaders Fear AI and Automation: How Myth-Making Undermines your chances of success

Joshua van Aswegen — Mon, 24 Nov 2025 14:29:45 +0000

When Leaders Fear AI and Automation: How Myth-Making Undermines your chances of success

How leaders talk about change can have a profound impact on the chances of success. Automation, for example, is often talked about as if it’s about to change everything, everywhere, overnight. From self-driving cars to AI-powered offices, people tend to imagine a future where machines take over all the work and humans are left behind. Automation and AI have become two of the most spoken buzzwords in modern business.

Yet, for all the attention they receive, they’re often misunderstood. Many organisations still hesitate to explore Robotic Process Automation (RPA) and Artificial Intelligence (AI) because of myths that make them sound complicated, expensive, or dystopian, when in fact these are among the most practical tools for improving efficiency.

In fact, many of the things we believe about automation, like the idea that robots will steal every job, or that technology always makes life easier, are myths that oversimplify what’s really going on. The truth is that automation doesn’t just replace people; it changes the way we work, the skills we need, and highlights the value we place on human judgment.

Understanding the myths around automation isn’t just about separating fact from fiction, but more about recognising that the future of work depends as much on our social and political choices as it does on the technology itself. Only by questioning these myths can we shape automation into something that truly benefits everyone, rather than letting fear or hype decide its impact for us.

If your business is exploring automation, or if you’re sceptical about its promises, this guide will help separate fact from fiction by debunking the most common RPA myths perpetuated by those who fear the journey ahead.

Myth #1: Robots Will Take All Our Jobs

One of the most common and persistent fears surrounding automation is the idea that machines will replace human workers entirely, leading to massive unemployment and social collapse. It’s a fear that resurfaces whenever a new technology emerges, from the textile machines of the Industrial Revolution to the rise of computers and, most recently, artificial intelligence. This fear seems logical at first: if a robot or algorithm can do a task faster, cheaper, and without rest, why would employers keep hiring people?

However, history tells a more nuanced story. When new technologies emerge, they do eliminate some jobs, but they also create others, often in ways that are difficult to predict. For example, while automation has reduced the need for certain manual or routine roles, it has also generated demand for technicians, data analysts, designers, and other professionals who build, maintain, or work alongside these systems.

Moreover, automation tends to change jobs rather than erase them. Instead of replacing human workers, machines often take over repetitive or dangerous tasks, freeing people to focus on more creative, social, or problem-solving aspects of their work. This shift can make jobs more engaging and even more productive if workers are given the right training and support.

The real issue, then, is not that automation will make human labour irrelevant, but that societies and companies often fail to manage the transition fairly. When workers are displaced without opportunities to retrain or adapt, automation becomes a threat instead of a tool for progress. The myth that “robots will take all our jobs” overlooks this reality. It’s not the technology itself that determines whether automation helps or harms people, but the choices we make about how to use it.

Myth #2: RPA is Only for Large Companies

Another widespread misconception about automation is that it is a privilege reserved for large corporations with vast budgets, cutting-edge technology, and dedicated IT departments. Many small and medium-sized enterprises (SMEs) assume that automation is too expensive, too complex, or simply not possible for their scale of operations. This belief has long discouraged smaller businesses from exploring tools that could streamline their work and make them more competitive. Automation is not an exclusive luxury and is becoming increasingly accessible to organisations of all sizes.

The growth of affordable cloud-based platforms and user-friendly digital tools means that small businesses can now automate routine tasks. This includes the help of low-code platforms that enable users to create applications using visual interfaces with minimal coding, as well as no-code platforms that facilitate the creation of automation workflows through simple templates and actions. Scheduling, inventory management, customer service, and data entry are offered without the need for massive financial investment or technical expertise.

More importantly, automation can have a greater impact on smaller companies precisely because their resources are limited. By automating repetitive administrative tasks, small business owners and employees can focus more on strategic work, innovation, and customer relationships, areas where human creativity and flexibility still matter most. Far from being a threat, automation can level the playing field, allowing smaller companies to compete with larger ones in terms of efficiency and responsiveness.

The myth that automation only benefits big corporations overlooks this potential. When smaller businesses embrace automation thoughtfully, they can amplify their human touch, using technology to enhance productivity and make better use of the skills and ideas that only people can provide.

Myth #3: RPA and AI are the Same Thing

As automation technologies have become more advanced, terms like RPA and AI are often used interchangeably, leading to confusion about what each actually does. Many people assume that RPA and AI are the same because both involve machines performing tasks that humans once handled. However, this myth oversimplifies two very different technologies.

RPA (Robotic Process Automation) is rule-based automation: it follows strict, predefined instructions to complete repetitive, structured tasks like data entry, invoice processing, or report generation. It cannot “think” or make decisions on its own; it simply executes what it has been programmed to do, quickly and accurately.

AI (Artificial Intelligence), on the other hand, is designed to simulate aspects of human intelligence, such as learning, reasoning, and pattern recognition. Through techniques like machine learning (ML) and natural language processing (NLP), AI can analyse data, adapt to new information, and even make predictions or recommendations. More information on this can be found in our article The Dual Power of RPA and AI in the Modern Enterprise.

Understanding the difference between RPA and AI is important because they serve different purposes and have distinct implications for the workplace. RPA is ideal for improving efficiency and reducing manual errors in repetitive tasks, while AI enables more complex cognitive functions that support decision-making and innovation.

When these technologies are combined, what’s often called “intelligent automation”, businesses can achieve far more powerful results, blending the speed of RPA with the adaptability of AI. The myth that they are the same limits how people imagine automation’s potential and can lead to misguided investments or unrealistic expectations.

Recognising their differences helps organisations choose the right tools for their needs, empowering workers rather than replacing them.

Myth #4: RPA is Too Complex and Requires IT Experts

Another common misconception is that RPA is too complicated for the average worker or small business to understand. Implementing RPA certainly requires some initial training and a clear understanding of how business processes work. However, modern RPA tools are becoming increasingly user-friendly, often designed with visual interfaces and low-code or no-code options that make them accessible even to non-technical users. The real challenge lies not in the software’s complexity, but in how well organisations support employees in learning and adapting to it.

However, closely related to this is the belief that automation automatically makes work easier and more efficient for everyone involved. While automation can certainly reduce tedious manual tasks and streamline processes, it doesn’t always simplify work in practice. In many cases, automation transforms how effort is applied rather than removing it, meaning workers may find themselves needing to monitor, manage, or correct automated systems rather than performing the tasks directly. This shift can create new kinds of stress and responsibility, especially when employees are not properly trained to use these tools.

Automation is not a “set and forget” solution; it requires human oversight, adaptation, and sometimes significant rethinking of how work is organised. When companies fail to prepare their teams for these changes, the promise of efficiency can quickly turn into frustration and confusion.

When introduced and integrated correctly, RPA can significantly reduce the overall workload, making tasks feel less overwhelming. The myth that RPA is too complex discourages many from exploring its potential, but it can be a practical and empowering tool for improving everyday work, provided businesses invest in proper training and change management.

Myth #5: RPA is Too Expensive

One of the strongest convictions preventing companies, especially smaller ones, from implementing automation is the belief that it is too expensive to use. For many, automation seems like complex software systems and long, disruptive installation periods that only large companies can afford.

While that may have been true in the early days of industrial automation, this has changed entirely. Today, many automation solutions are cloud-based, subscription-driven, and scalable, meaning businesses can start small and expand as they grow. Tools like RPA software, chatbots, and workflow automation platforms have become far more affordable and accessible, allowing even startups and small businesses to benefit from them. In many cases, the initial cost of automation is quickly offset by the savings it generates through reduced manual errors, faster processing times, and greater performance consistency.

However, it’s still important to acknowledge that automation isn’t completely cost-free. Effective implementation requires planning, investment in training, and ongoing maintenance to ensure systems stay up to date and secure. But these costs are strategic investments rather than unnecessary expenses. The myth that automation is too expensive often overlooks the hidden costs of not automating such as inefficiency, burnout, and missed opportunities for innovation. When approached gradually and thoughtfully, automation can deliver a strong return on investment.

The real question is not whether companies can afford automation, but whether they can afford to be left behind by those who use it wisely.

Myth #6: RPA isn’t Secure

Another widespread concern is the belief that automation inherently makes systems more vulnerable to cyberattacks or data breaches. Because automation relies heavily on digital platforms, cloud infrastructure, and interconnected software, many people assume that introducing automated tools automatically increases security risks. While it’s true that automation can introduce new technical vulnerabilities when implemented carelessly, the idea that automation is inherently insecure is misleading.

In fact, automation can greatly enhance security when it is properly designed and managed. Automated systems can perform continuous monitoring, detect irregularities faster, and respond instantly to potential threats, significantly reducing the window of opportunity for cybercriminals. For example, automated security tools can flag unusual logins, encrypt sensitive data, and even isolate compromised systems before damage spreads.

The real security risk lies in how automation is deployed and maintained. Like any technology, automated systems require responsible oversight, regular updates, and strong cybersecurity practices. When organisations neglect these elements, they expose themselves to threats, not because automation is insecure, but rather because it has been poorly managed.

Ironically, many of the same myths that discourage companies from adopting automation, such as fears of complexity or high cost, also prevent them from taking advantage of tools that could make their systems safer. By combining automation with human expertise and ethical governance, organisations can strengthen their defences rather than weaken them.

The myth that automation isn’t secure overlooks the fact that security is a matter of design and responsibility, not the technology itself.

Myth #7: RPA is Only for Simple Tasks

Another common misconception about automation, and RPA specifically, is that it can only handle basic, repetitive, or low-level tasks such as data entry or scheduling. While that may have been true in the early stages of automation, modern technology has developed beyond these limits.

Today, automation can carry out more complex functions that involve analysis, decision-making, and even creativity. Advanced types of RPA, such as data reconciliation, virtual assistants, or fraud detection, enable systems to recognise patterns, interpret human language, and generate insights from large volumes of data. Meaning, automation can now be found in many areas such as financial forecasting, medical diagnostics, customer service, and even content creation.

However, this does not mean that automation can replace human intelligence. The most effective use of automation lies in combining humans and machines, where technology handles repetitive or data-heavy tasks, and people contribute empathy, creativity, and ethical judgment. The belief that automation is only useful for simple work underestimates its potential and ignores the ways it can enhance human capability rather than diminish it.

Myth #8: RPA is Just Another Tech Trend

Perhaps one of the most short-sighted myths about automation is the belief that it is simply a passing trend, one that will fade once the excitement wears off. This perception often appears because new tools and platforms seem to develop constantly, each promising revolutionary change. So, some people dismiss automation as hype rather than a lasting transformation.

This was the same for the wireless radio when it first came into commercial use in the 1920s, when most people thought it was a fad that would never become commonplace.

However, this view overlooks how deeply automation has already reshaped the modern world. From manufacturing and healthcare to education and public administration, automation has become embedded in daily operations, influencing everything from how we produce goods to how we communicate and make decisions. Far from being temporary, automation represents a long-term shift in how societies function, much like the Industrial Revolution or the rise of the internet.

Automation continues to evolve because it is driven by ongoing needs, not by short-term fascination. Even when specific technologies or platforms become outdated, the underlying goal of automating processes to improve performance remains constant.

Dismissing automation as a trend risks missing opportunities to adapt, innovate, and stay competitive in an increasingly digital economy. Understanding automation as an enduring force rather than a temporary phenomenon allows businesses, governments, and individuals to better prepare for its challenges and harness its benefits.

Conclusion

These myths about automation often say more about our worries than about what’s really happening. Ideas like “robots will take all our jobs,” “automation is only for big companies,” or “it’s too expensive and not safe” create fear and confusion that stop people from seeing its real potential.

Automation isn’t an unstoppable force that replaces humans, nor is it a passing trend; instead, it’s a set of tools we design, control, and improve. When used wisely, it can make work more meaningful, efficient, and creative, not less.

Of course, it comes with challenges and requires training, planning, and responsibility. But the more we understand what automation can and can’t do, the better we can shape it to work for everyone. By moving past the myths and looking at the bigger picture, we can build a future where technology supports human potential instead of threatening it.

The post When Leaders Fear AI and Automation: How Myth-Making Undermines your chances of success appeared first on SmartTechNXT.

From Paper to Pixels: The Digital Revolution

Joshua van Aswegen — Mon, 10 Nov 2025 15:14:23 +0000

From Paper to Pixels: The Digital Revolution

We live in a time when technology is changing the way we work faster than ever before. From automation to artificial intelligence, digital tools are transforming nearly every aspect of business operations. For many organisations, having to keep up with this pace of change is both exciting and challenging. The idea of digital transformation often sounds promising, bringing greater efficiency, fewer errors, and smarter systems, but the reality can be more complex.

Integrating new technologies like these into existing structures requires careful planning, strong leadership, and a clear strategy. This article looks at some of the key factors that influence Robotic Process Automation’s (RPA) successful implementation to help you and your company adapt to the 21st century, focussing on effective integration strategies, data protection, and sustainable implementation.

Why This Topic is Relevant

In today’s rapidly evolving digital landscape, organisations across all sectors are undergoing significant transformations driven by the implementation of new technologies. Digital transformation is more than just a trend; it is an essential strategy for companies wanting to remain competitive. However, while embracing innovations such as automation, cloud computing, and artificial intelligence, businesses can often face major challenges when integrating these technologies into their existing systems and workflows. Merging legacy infrastructure with modern digital solutions can be complex and risky, particularly when it comes to ensuring data security, system compatibility, and user acceptance.

A key component of this transformation is the use of Robotic Process Automation (RPA), which allows companies to streamline repetitive processes, increase operational efficiency, and reduce manual error. Yet, implementing RPA successfully requires more than just technical deployment, it also demands a holistic approach that aligns technology with business goals.

Creating an integration-first strategy is fundamental to ensuring that RPA and other digital tools complement existing technologies rather than disrupt them. This strategy requires careful planning, system compatibility, and scalability to be able to work across departments. At the same time, your business will need to remain vigilant of data security and compliance regulations. Because your company will automate workflows and handle large volumes of sensitive information, maintaining strong cybersecurity measures and adhering to compliance standards is important to building trust in the technology and minimizing risks.

By understanding these core elements, your company can unlock the full potential of RPA and other emerging technologies, and ensure a smooth transition toward a more connected, intelligent, and efficient digital future.

Why Some Businesses Struggle with RPA

While RPA holds immense potential for improving efficiency many organisations still struggle to implement it successfully. The challenges businesses face when adopting RPA are often rooted in a combination of human, technical, and organizational factors.

One of the most common barriers to RPA adoption is the fear of change among employees. Automation can create anxiety in the workplace, particularly when workers perceive robots as a threat to their job security. Concerns about being replaced by machines or having to learn unfamiliar digital skills can lead to resistance and slow down adoption. Overcoming this fear requires transparent communication and a clear explanation of how RPA can complement human roles rather than eliminate them. When employees understand that automation is intended to reduce repetitive tasks and free up time for more meaningful work, they are more likely to embrace the technology.

A lack of technical knowledge is another key challenge. Limited expertise or understanding of RPA can prevent organisations from realising its full benefits. Implementing automation involves not only setting up bots but also redesigning workflows, identifying suitable processes, and maintaining the technology over time. Without trained staff or external support, companies may struggle to select the right tools, troubleshoot errors, or optimise performance. This is why training and partnering with experienced RPA professionals is important to reduce this knowledge gap and allow for a smoother implementation.

RPA also depends heavily on the quality of existing business processes. Disorganised or poorly defined workflows can hinder automation because bots operate best when tasks are standardised, repetitive, and rule-based. If a company’s internal procedures are inconsistent or lack clear documentation, it becomes difficult to automate them effectively.

Along with this, is the issue of outdated technology. Many organisations still rely on legacy systems that were not designed to integrate with modern automation tools. These systems may lack the necessary interfaces or compatibility for bots to function properly, leading to disruptions or costly custom solutions. Overcoming this challenge often requires a broader digital modernisation effort, ensuring that your company’s IT infrastructure can support automation securely and efficiently.

Data security and privacy concerns also play a significant role in RPA hesitancy. Because bots often require access to sensitive company information, such as financial data or customer records, any security flaw can pose serious risks. Without strong cybersecurity measures, businesses expose themselves to data breaches, compliance violations, and reputational damage. Establishing strict access controls, continuous monitoring, and compliance with data protection regulations are essential to building trust in automation initiatives.

Additionally, many businesses face legal and regulatory challenges related to RPA. Industries such as healthcare, finance, and government operate under stringent data handling and reporting laws. Your company may hesitate to automate processes for fear of violating regulations or facing penalties. To mitigate these concerns, keep in mind any regulations when incorporating automation solutions.

Creating an Integration-First Strategy for RPA

A successful digital transformation is more than simply adopting new technologies; it needs thoughtful planning to ensure that RPA work harmoniously within your existing organizational ecosystem. RPA relies on interacting with a variety of software systems to perform its functions, and so an integration-first strategy is essential. This plan ensures that automation enhances, rather than disrupts, current processes and technologies.

An integration-first approach to RPA is about aligning automation initiatives with your company’s broader digital infrastructure from the very beginning. This means assessing the current technological landscape, identifying potential compatibility issues, and planning how bots will communicate with existing applications. Many organisations still operate with a mix of legacy systems and modern cloud-based solutions, but without careful integration planning, this mix can lead to fragmented data flows or operational constraints.

One of the first steps in developing an integration-first strategy is conducting a comprehensive systems audit. This process helps identify which applications, platforms, and workflows are suitable for automation, and which might require upgrades or modifications before bots can interact with them effectively. The audit also highlights potential risks, such as outdated systems that lack proper interfaces or inconsistent data structures that could complicate automation. Seeing these early on can help your company avoid expensive reworks and minimise disruption during rollout.

Equally important is ensuring that RPA aligns with organisational goals and IT governance policies. RPA should not operate in isolation but should fit into a larger digital transformation company plan. Hence, collaboration between IT departments, business units, and automation specialists helps ensure that automation efforts address your business’s real needs while adhering to security and compliance standards. Keep clear communication channels between technical and operational teams to prevent misunderstandings and promote your long-term success.

A phased implementation is one of the most effective strategies for RPA deployment, rather than automating everything at once. By beginning with a small set of well-defined processes that are easy to automate and measure, you can test the technology, identify potential challenges, and refine workflows before expanding automation across the enterprise. Early successes will help build confidence among stakeholders and employees, showing both you and your company the tangible value of RPA. Once proven effective, you can gradually extend automation to more complex or high-impact areas of the business.

Finally, an integration-first strategy requires a commitment to continuous improvement. Technology evolves rapidly, and integration points that work well today may need to be updated or optimised in the future. Regularly reviewing your system’s performance, monitoring its compatibility, and adapting to new technological trends ensures that RPA remains efficient and aligned with business priorities over time.

Managing Change and Adoption

One of the most underemphasised aspects of digital transformation is not the technology itself, but the people who use it. The success of RPA, and digital transformation, depends largely on how well your organisation manages change and encourages the technologies’ adoption among your employees. Even the most advanced automation systems can fail if users are resistant, unprepared, or disengaged. Therefore, managing the human side of technological transformation is just as important as managing the technical one.

Adoption management in the context of RPA involves preparing, supporting, and guiding employees through the transition from traditional workflows to automated processes. This transition can be scary. A common belief is that automation may replace people’s jobs. Such fears are understandable, as automation fundamentally changes the nature of work. However, most people don’t understand that the goal of RPA is not to eliminate human contribution but to enhance it by reducing repetitive, time-consuming tasks and allowing employees to focus on more strategic and creative responsibilities.

To address resistance, your company must communicate openly and consistently about what automation means for your workforce. Your employees should understand how RPA will affect their daily work, what new opportunities it may bring, and how the company plans to support them throughout the change. Leadership plays a key role in shaping attitudes toward digital transformation. When leaders frame RPA as a tool for empowerment rather than replacement, they help foster a positive culture of innovation and learning.

Beyond communication, you need to train and educate your employees to successfully adopt RPA. You’ll need to understand how automation works, how to collaborate with bots effectively, and how to handle exceptions or system updates. Investing in hands-on training programs, workshops, and ongoing learning opportunities builds confidence and competence across the organization. Companies like SmartTechNXT can help train you and your employees when purchasing our bots. Your company will need to assess what training is needed and help build your employees knowledge and tolerance to these technologies.

Effective adoption management recognises that not all employees adapt at the same pace. Personalised support and continuous feedback channels allow your organisation to identify pain points and provide targeted assistance where needed. Make sure to encourage feedback as it also gives employees a sense of involvement in the transformation process, making them feel that their voices and experiences matter. When workers become active participants in change, they are more likely to embrace and sustain it.

Another critical factor in managing change is establishing realistic expectations and clear success metrics. Set achievable goals for RPA adoption, such as measurable improvements in efficiency, error reduction, or employee satisfaction. By tracking progress and celebrating milestones, you can reinforce a sense of accomplishment and demonstrate the tangible benefits of automation. Recognising team and individual contributions throughout the process further motivates employees and strengthens commitment to long-term transformation.

Conclusion

RPA is not limited to a single industry or type of organization but rather, it is a versatile tool that can be adapted to various operational needs. As such, the key to success lies in thoughtful integration, robust governance, and employee engagement. Businesses that view automation as part of a broader digital transformation strategy, rather than a quick fix, are the ones that achieve sustainable and scalable results.

Real-world applications of RPA show that when implemented effectively, automation can transform organizations by enhancing accuracy, accelerating workflows, and empowering employees to focus on high-value activities. From finance and healthcare to manufacturing and public administration, RPA continues to reshape the way organizations operate, reimagining how work is done to create smarter, more agile, and more human-centred workplaces.

Digital transformation is redefining the way organizations operate, compete, and deliver. At the heart of this transformation lies Robotic Process Automation (RPA), a powerful enabler of efficiency, accuracy, and innovation. But of course, as this article has shown, achieving success with RPA requires much more than simply installing new software. You need a holistic strategy that balances the needs of technology, security, people, and your company.

An integration-first approach ensures that automation works in harmony with existing systems, while robust data security and compliance measures protect sensitive information in an increasingly digital world. Exceptionally important, however, is the human dimension. Managing change effectively and promoting user adoption will be the reason your company sees technological progress into lasting organisational growth. By following our best practices, such as phased implementation, clear communication, and continuous improvement, you’ll be able to navigate the challenges of automation with confidence.

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The Dual Power of RPA and AI in the Modern Enterprise

Joshua van Aswegen — Fri, 01 Aug 2025 12:57:59 +0000

The Dual Power of RPA and AI in the Modern Enterprise

As the world progresses into an innovative era of the 21st Century, new technologies are being made available for companies to strengthen their business operations. With the fast-paced changes, two automation technologies, Robotic Process Automation (RPA) and Artificial Intelligence (AI), have revolutionised business operations.

RPA is the use of software to streamline repetitive, rule-based tasks, which acts as a digital workforce that mimics human actions to allow human workers to focus on more important tasks. It is especially beneficial for handling processes like invoices and data migration. AI simulates human intelligence, bringing more advanced problem-solving, data analytics, and decision-making capabilities to the table. Together, these technologies represent a new era of intelligent automation and give companies the opportunity to innovate, improve their efficiency, and adapt to a constantly evolving economy.

Their benefits and limitations are numerous, and these can be difficult to comprehend. Thus, questions can arise over what technology is better for you and your company.

For example, with AI progressing faster and making its way into many different facets of life, including new technology like ChatGPT, has RPA become redundant? How distinct are these two processes, and in what situations can you use them? This article will explore those questions and help share some insights into the world of automation.

What is RPA?

Starting with a definition for Robotic Process Automation, RPA is a software technology that builds, deploys, and manages software robots that mimic human interactions to process data and work on digital systems. These robots are capable of navigating computer drives, extracting data, and more, while additionally being more consistent than human workers.

The foundations of RPA, back in the 1990s, came from early forms of automation tech like screen scraping and workflow automation tools. This tech involves extracting data from user interfaces and enabling very basic automation of repetitive tasks. It was very limited in its functionality, but it laid the groundwork for what we know as RPA today. More modern RPA evolved around the 2010s, when early RPA platforms emerged as a blend of screen scraping, business process management, and artificial intelligence to automate processes in a rule-based routine, using the existing IT infrastructure available to companies. Companies like Fortra, Blue Prism, UiPath, and Automation Anywhere pioneered these modern RPA solutions. These robots could interact with computers very similarly to humans; they could interact with existing applications, clicking, typing, and copying data, and more.

The COVID-19 pandemic also saw a massive spike in RPA adoption in numerous businesses, as there was a need to reduce dependency on human labour due to the lack of freedom that the virus brought to the world. Since then, SmartTechNXT has helped multiple companies land on their feet after the pandemic, and RPA has become a significant driver of their economic production.

RPA is used in numerous ways in the world, and most people are unaware of how common these robots are within everyday processes. For starters, resetting your password is done with RPA. Before these bots, when a request came in to reset a password, an employee would need to find the time to manually reset it while trying to juggle other tasks. Now, a robot intercepts the request, recognising the rule-based tasks involved, and resets the password for you quickly and easily. Another example involves the delivery status of your online purchases. Robots now automatically track drivers and packages to allow you to see your shipment status whenever you like and receive updates in real time.

RPA’s benefits are countless. They streamline workflows, increase employee satisfaction, engagement, and productivity by removing mundane tasks. Moreover, one of the greatest benefits is that it can make your company more profitable by eliminating wasted time on repetitive tasks. Your employees can then have the freedom to work on more important ideas, focus on innovation, and interact with other employees and customers. RPA is non-invasive and can quickly be put in place to advance your company’s digital transformation. While these bots are ideal for automating workflows that involve big data, virtual desktop infrastructures, and database access, RPA is incredibly versatile and can be applied to many different sectors.

In short, RPA follow the rules set by you to streamline routine tasks and enhance efficiency across digital systems. After evolving significantly in the past few decades, RPA now plays a crucial role in transforming workflows, boosting productivity, and freeing employees to focus on more creative and important pursuits.

What is AI?

Artificial Intelligence (AI) is essentially about teaching machines to think and act in ways that mimic human intelligence. It’s the technology that powers some of the most exciting advancements we see today, helping machines understand language, recognise patterns, solve problems, and make decisions. At its core, AI relies on data and algorithms to learn, adapt, and get smarter over time. In other words, it’s what allows systems to not just perform tasks but also improve at them as they process more information.

AI isn’t just one thing; it’s a huge field with different areas that serve different purposes. For instance, Machine Learning (ML) is a branch of AI that focuses on enabling machines to learn from data without needing to be explicitly programmed. Think about how Netflix recommends your next binge-worthy series, it’s analysing your preferences and learning what you’ll enjoy. Then, there’s Natural Language Processing (NLP), which is all about helping machines understand and respond to human language. That’s what makes tools like Siri, chatbots, or even those spam filters in your email so effective. Other types of AI include Computer Vision, which helps machines “see” and analyse visual information (like facial recognition or checking product quality in factories), and Deep Learning, which uses neural networks to tackle more complex tasks like real-time language translation or creating hyper-personalised experiences.

In the business world, AI has become a game-changer. It’s helping companies work smarter, not harder, by analysing mountains of data, spotting trends, and making predictions that would take humans forever to figure out. For instance, AI-driven tools are being used for things like financial forecasting, risk assessment, and even supply chain optimisation. AI also transforms how businesses interact with customers, whether it’s through personalised shopping recommendations, virtual assistants, or chatbots that answer questions at lightning speed. Essentially, AI is taking care of the heavy lifting behind the scenes so teams can focus on creativity and innovation.

That said, AI isn’t perfect. Building and implementing these systems can get pretty complicated and expensive. You need skilled developers and high-quality data to make it work, which can be a challenge for smaller organisations. Plus, there’s the ethical side of things to consider. AI can inherit biases from the data it’s trained on, and if that data isn’t diverse or accurate, the results can be skewed. For instance, an AI-powered hiring tool might unintentionally favour certain candidates over others, which is a big problem.

Still, the potential of AI is incredible. It’s not just a tool, it’s a partner in transforming how businesses operate. From streamlining operations to delivering jaw-droppingly accurate insights, AI isn’t just the future; it’s here, and it’s changing the game in ways we’re just beginning to grasp fully.

This text was generated by ChatGPT, a newly functioning AI site that has taken the world by storm. While the site is still in its beginning phase and there are kinks and bugs to work out, AI technology such as this is becoming more advanced at a very fast rate.

Since the 1990s, AI shifted from rule-based systems to machine learning, where they learned patterns from data rather than following predefined rules, like RPA does. The development of technology at this time and the increasing availability of computational power and data helped AI systems become more flexible and capable of handling complex problems. In 1997, IBM’s Deep Blue AI system famously defeated the world chess champion Garry Kasparov, which showed how AI was becoming tremendously more powerful in problem-solving.

Today, AI is a frequently brought up topic and is becoming a central part of the technological landscape. Self-driving cars, content generation, and chatbots have all been showcased in various sectors of life, especially in social media and the economic industry. However, concerns over AI have not diminished. Since its invention, AI has been a popular theme in pop culture, especially in films where it has been portrayed as both a powerful ally and a terrifying enemy. AI is seen as a tool that is entirely capable of making its own choices, which could lead to it turning on humanity. Pop culture has certainly painted a very dramatic picture of AI, but these are over-exaggerated storylines designed to capture audiences. While these stories do highlight important issues like the risks of misaligned objectives and unintended consequences, today’s AI systems are highly specialised tools which serve more benefits to your company and do not have the self-awareness to “turn on” humanity.

Synergy between AI and RPA

Intelligent Automation (IA) uses AI and technologies like RPA and ML to reimagine how your business operates. Combining AI’s brainpower with RPA’s ability to transform complex processes allows Intelligent Automation to succeed in end-to-end automation capabilities. The benefits of using IA are numerous; these resources reduce costs by augmenting your company’s workforce and improving both productivity and accuracy. Your business can enjoy higher quality service, giving consistent and faster responses to data, market research, and enhancing your customers’ experience.

RPA and AI can work together as complementary technologies. The rule-based, repetitive tasks by RPA can work in conjunction with the learning and decision-making of AI. RPA alone struggles with unstructured data, like emails, handwritten documents, or images, which AI can extract and classify, making it actionable for RPA workflows. It also allows bots to comprehend speech patterns and conversations, transforming chatbots into more easily accessible tools. AI-powered systems will also learn over time from historical data and user interactions to continuously optimise their processes over time. This adaptability makes RPA more effective in dynamic environments and can be scaled across functions to analyse patterns in multiple sectors and identify inefficiencies.

As AI continues to become more prevalent, accessibility to these technological resources for all becomes more important. Businesses, particularly larger corporations, are already benefiting from introducing AI into their operations, and smaller businesses (SMEs) and local communities can also benefit from these advancements. A lower entry barrier to using these tools can help level the playing field against large corporations.

Many RPA and AI tools are now available as Software-as-a-Service (SaaS) platforms, which means businesses can implement them without costly upfront investments. Additionally, while these technologies are often seen as a replacement for human labour, their implementation can also foster new roles in local communities. Local tech consultants and service providers can specialise in helping businesses with the education and resources needed to get them started with AI and RPA.

Conclusion

In conclusion, as we move into the more innovative landscape of the 21st century, integrating RPA and AI is not just reshaping business operations; it’s redefining how we work and how businesses compete. These technologies that inject cognitive capabilities into everyday practices empower companies of all sizes to drive efficiency, innovate, and stay agile in a fast-paced economic market.

Of course, more ethical questions can be asked about intelligent automation. Such as, If AI can replace human workers, will unemployment become a more widespread issue? What will happen to those whose jobs are replaced? This impact on labour markets can also spread to more overarching issues. As workers are losing their jobs and AI-related expertise is in higher demand, higher salaries and education are focused on a limited number of positions, widening the skill gap for those without access to education or resources to develop these skills.

This is why the thoughtful implementation of intelligent automation paves the way for a more inclusive future. Regulating and managing intelligent automation ensures that human values guide its development. While these ethical concerns remain, by investing in reskilling and fostering collaboration between humans and machines, organisations can ensure that the benefits of automation extend far beyond their company. By including automation across the board, we can fuel local economies, create new job opportunities, and build a future where technology serves as a partner in progress for everyone.

The post The Dual Power of RPA and AI in the Modern Enterprise appeared first on SmartTechNXT.

Gen Z in the Workforce: Shaping, Challenging, and Redefining the Future of Work

Joshua van Aswegen — Wed, 11 Jun 2025 13:54:57 +0000

Gen Z in the Workforce: Shaping, Challenging, and Redefining the Future of Work

The future of the economy is in the hands of the next generation.

By 2025, Gen Z (those born roughly between 1996 and 2010) will comprise over a quarter of the global workforce, according to the World Economic Forum. Gen Z has had the privilege of growing up immersed in technology, with constant access to the internet, smartphones, and social media, making them the first digitally native generation. This familiarity makes them fluent in digital communication and more connected to the rest of the world than ever before. Gen Z is also deeply attuned to concepts such as mental health and work-life balance, which makes them more active in trying to find equality for all.

Finding ways to work with them has become more important as we strive for innovation. However, attracting Gen Z can be a struggle, as they are quite different to previous generations. Their identity, beliefs, and their place in the world is constantly being questioned and constantly changes, hence learning about Gen Z is an important step in helping companies find ways to attract them. Thus, this article will explore Gen Z, their attitudes and their opportunities for growth, especially in the context of automation.

The Digital-First Generation: Preferences and Expectations

Being so connected with the internet, and growing up with the rise of social media, Gen Z has become tech-dependant. The rise of social media has also helped to connect individuals to the rest of the world at a much higher rate than ever, which at the same time has allowed individuals to see the inequality within different communities, something which Gen Z seems largely focused on. Thus, they prefer mission-driven organisations that defend and bolster employee well-being, community impact, and environmental responsibility.

They expect companies to keep up with the times, meaning they also expect businesses to adopt modern technologies like robotic process automation (RPA) and artificial intelligence (AI) to eliminate mundane tasks and optimise creativity and strategy.

This generation is not content with the status quo. They demand that employers foster inclusive, digitally enabled environments and actively support mental health initiatives. For them, digital tools aren’t just conveniences, they’re essential components of a modern workplace. In fact, Gen Z is likely to be drawn to companies that utilise automation, provide flexible working options, and communicate their mission with clarity and purpose.

Despite it being brand new, ChatGPT and AI has revolutionised Gen Z. Almost all their tasks and questions are being directed to AI, even for simpler assignments. AI has become an important asset in their daily lives and so will be utilised in every function in the workspace too. Hence, having it readily available in your company to help make business processes easier is a great way to invite Gen Z into your company!

The Business Imperative: Why Gen Z Matters

As the years go by, Gen Z will become the main percentage of employees in the workspace, thus attracting Gen Z is not optional; it’s a necessity. In a recent Blue Prism-sponsored webinar, Edwin Klimkeit (Enterprise Executive Director, Blue Prism EMEA) and Melina Friedrich (Innovation Designer, Digital Impact Labs) highlighted how embracing Gen Z’s values and working style is vital to future-proofing business.

Klimkeit emphasised the importance of “bringing in new, fresh ideas” and aligning with emerging needs. Gen Z with its new values and standards for the world can bring in ideas that revolutionise the market as we know it. Friedrich added that innovation thrives in diverse teams, and so combining generational knowledge with new-age digital insights is important for growth within your company.

However, Gen Z’s adaptability can also raise challenges for employers and brands. This generation is known for their fluid identities, tailoring their behaviour to social contexts and expecting brands to do the same. A rigid or overly traditional brand image risks hostility. Gen Z wants to be seen and heard but also feel safe from criticism. As such, they often seek to align with brands that take a stand for something meaningful, without requiring them to take personal risks themselves.

Interestingly, while Gen Z values diversity, they also prefer not to stand out too much. They want inclusive representation in marketing, but excessive emphasis on “making a difference” can make them uncomfortable. Brands and employers need to strike a delicate balance: celebrating diversity while creating unifying messages that don’t polarise marginalised communities.

Despite the clear advantages Gen Z brings to the workforce, their entry is not without its challenges. Growing up immersed in rapid digital feedback loops, many Gen Zs struggle with sustained focus. The average Gen Z user unlocks their phone around 280 times per day and exchanges hundreds of messages, which can lead to digital exhaustion and decreased productivity if not carefully managed.

They carry high expectations but often have a low tolerance for outdated systems. They anticipate quick decision-making, constant feedback, and seamless digital efficiency, some of which can create friction in hierarchical or traditional top company structures. Their strong desire for purpose and transparency also means they are more likely to leave employers that fail to align with their personal values.

Their hyper-connected lives have contributed to increased levels of anxiety, burnout, and loneliness. Exacerbated by the Great Recession and financial crisis of 2008, and the impacts of COVID-19, 1-in-3 Gen Zs have reported symptoms related to depression and anxiety. Although Gen Z is vocal about the need for mental health support, businesses still face difficulties addressing these issues on a large scale. Traditional HR models may no longer be sufficient, and companies need to rethink how they approach wellness in the digital age.

Additionally, many Gen Zs are conflict-averse and tend to favour indirect communication. While this may promote harmony on the surface, if not done correctly, it can weaken feedback loops and strain team dynamics, particularly in high-pressure or fast-paced environments.

While it can seem overwhelming, especially to older generations, to stay competitive, companies must go beyond mere accommodation and evolve in step with Gen Z’s values and working styles. This includes creating digitally optimised environments, where tools like RPA and AI-powered platforms become core to operations, enabling employees to focus on more strategic, creative work.

Mental wellness initiatives are also essential: normalising mental health days, investing in digital well-being tools, and allowing flexibility without sacrificing responsibility. At the same time, businesses must demonstrate authentic values and ethical leadership. Gen Z can quickly detect performative gestures, so walking the talk on issues like sustainability and inclusion is non-negotiable.

Finally, agility and continuous learning must be built into the organisation’s foundation. As a generation known for job mobility and a hunger for growth, Gen Z expects opportunities to learn, upskill, and advance, especially within structures that are flexible, collaborative, and forward-looking.

Gen Z’s integration into the workforce is catalysing a broader transformation in workplace culture, tools, and expectations. Their presence is forcing businesses to become more agile, values-driven, and employee-centric. But the journey won’t be easy. Bridging generational gaps, managing digital overload, and redefining leadership for a more empathetic era are challenges yet to be fully solved.

Still, if companies are willing to listen, adapt, and invest in both technology and human capital, they’ll not only attract Gen Z, but they’ll also future-proof their organisations for the decades to come.

Written By Joshua Van Aswegen

The post Gen Z in the Workforce: Shaping, Challenging, and Redefining the Future of Work appeared first on SmartTechNXT.

Deciphering SaaS – Automation that can grow with you!

Joshua van Aswegen — Tue, 22 Apr 2025 13:08:09 +0000

SaaS (Software as a Service)

Streamlined Processes, Maximized Impact: The Future of SME Automation

In our technological age, many services have become available to companies to boost their productivity. Software as a Service (SaaS) is one of these services offering a unique way to transform business operations. These Cloud-based solutions can enhance efficiency, scalability, and cost-effectiveness.

Software as a Service (SaaS) provides businesses with on-demand access to software applications without the need for extensive infrastructure, available online 24/7 for your company to access whenever you want. While this service has many benefits, it can be especially helpful for RPA, allowing an easy and seamless way to introduce your company to automation.

This article will explore the evolution and impact of Software as a Service, its benefits and how useful it is alongside Robotic Process Automation solutions.

What is SaaS?

Software as a Service, or SaaS, is a solution to the cumbersome local installation of software services, by offering a connection and subscription to IT software services on a shared platform via the internet. Likely, you’ve already been using SaaS for a long time. Services like Gmail, Google Docs, Dropbox, and Zoom are all common examples of internet-based services. As long as you have an internet connection, you can access these services on any device, anywhere you want. This is a major help to companies who use terminals to access their data, which is now a very outdated and inefficient way of working. Additionally, while the costs vary per service, instead of paying a one-time license which can be expensive, you’re able to pay a fixed rate monthly or yearly, which can significantly reduce the upfront investments for your company.

Subscription-based services are not a new concept in technology; in fact, it was common in the 1960s because of the extraordinary costs of computers at that time. The highest level processor by IBM had 2MB of RAM; 1 GB of hard drive capacity cost $200 000 in 1980¹. Renting these processors for much less money from a provider was much easier. Time-sharing, as it was called, allowed companies to have data located in a different place than their own workspace. This mitigated the effects of companies having to install software on mainframe computers and access it via terminals (which meant you were forced to be physically present at your company, since laptops and mobile phones were not invented yet). Companies no longer had to purchase expensive licenses and maintain complex infrastructures, which was not efficient in the long term.

By the 1980s and 1990s, software vendors introduced “on-premise enterprise applications” like Oracle, which required local installation and dedicated IT management. But time-sharing became less popular in this time because of the development of personal computers. But in the 1990s, Salesforce’s use of SaaS changed the game by helping companies manage the data of hundreds of computers that these companies were running. SaaS significantly helped to get business applications on computers without using too much expensive hard drive space. Since then, SaaS has only grown and so has its reputation.

Today, advancements in cloud computing have made SaaS a dominant model and much more useful than before. Companies like Google and Microsoft have revolutionised software accessibility. SaaS is now a $200 billion industry and is a perfect way to boost your company’s performance.

How can it help your company?

SaaS can provide many ways to give your company a boost, especially in streamlining business operations and reducing costs. Without the significant upfront investments needed into hardware or software licenses, the subscription pricing option allows businesses to pay only for what they use.

Since it’s all internet-based, updates and support are handled by the provider, meaning you do not have to do extra work managing this application maintenance.

The scalability of SaaS is exponential. Your company can quickly scale operations by upgrading your plans, and new features can be added without complex installations. This is particularly advantageous for companies facing fluctuating demands, as you can change your subscription as you wish.

Accessibility is also extremely flexible, as your SaaS applications can be accessed anywhere with an internet connection. In a post-COVID era, this helps remote teams to collaborate efficiently without worrying about commutes.

Online services mean your data is easier to manage, faster, and up to date. Providers handle security patches and ensure the latest cybersecurity measures are in place. Frequent updates on software mean your company will always use the best version of your applications. Since they are designed to integrate with popular business software, your company can automate workflows without disrupting existing IT infrastructure.

The Match between SaaS and RPA

Traditional RPA required supplementary investment in infrastructure and licensing, which SaaS changes by allowing businesses to pay per bot, task, or user at a specific subscription rate. In conjunction with advancing technology, RPA has benefitted from many new technological solutions, to make automation simpler for companies to incorporate into their operations. Introducing SaaS with RPA has taken away the complex configurations and high maintenance needed to keep these automations running. Now, RPA can be incorporated and maintained from anywhere!

This is especially useful for small and medium businesses (SMEs) to benefit from automation, even when your company has not yet had the chance to explore robotic processing. SaaS can help companies start small with automation; by limiting some factors, such as the number of runs available per month, you can get the look and feel for RPA without investing heavily. As you become more aware of how RPA works, and how it can help you, your provider can easily increase your accessibility to automation to benefit more sectors of your company.

Standardised integration APIs allow SaaS RPA tools to integrate seamlessly with other cloud-based applications and enable workflow automation across multiple software environments. This makes RPA more applicable than ever, allowing these solutions to even automate Outlook emails.

RPA can become as scalable as SaaS, depending on workflow needs. Big data that companies must manage can easily be sorted by automation solutions, making your company more efficient, boosting productivity, and allowing a solid base for AI implementations. There is no need to invest in additional servers, as your provider can manage those for you, depending on your contract and your company’s needs. The right SaaS providers can also offer enterprise-grade security, with encrypted data handling and compliance with regulations, so your company can feel safe knowing your data remains private.

However, there are some challenges with SaaS RPA that your company should consider. The most significant challenge is that SaaS operates purely on the internet, and as such, an internet outage or a place with no service will struggle with these automation workflows. Additionally, some companies, especially long-standing companies, may still have older on-premise systems which may not be fully compatible with cloud-based automation, requiring additional hardware. Some companies can also prefer to keep these tools behind their own firewalls, and as such, the licencing agreement can require an upfront investment.

SaaS RPA offers many solutions to companies in this new era of technological advancement.

Conclusion

SaaS has become a fundamental solution to any business’s operations. This service has transformed how businesses employ automation workflows in their operations. The benefits are extraordinary, and can help your company reduce costs, become more accessible, and boost efficiency. Without the burden of extra IT infrastructure, SaaS offers a compelling way to enable businesses of all sizes to automate their tasks.

As more businesses embrace solutions such as these, SaaS and RPA will continue to drive efficiency, innovation, and competitive advantage in an increasingly automated world.

Source

https://www.ai-bees.io/post/history-of-saas-its-challenges-and-bright-future

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