What Can the Industrial Revolution Teach Us About AI Adoption?

Starting around 1760, the Industrial Revolution drastically transformed the world from agrarian, hand-crafted societies to mechanised, urbanised manufacturing economies. It was also a societal shift but the biggest ripple was felt in how businesses were operated.

This revolution is the foundation of modern society and offers many valuable lessons, beyond merely the upgrading of machinery. Despite the Industrial Revolution predating the academic birth of Artificial Intelligence (AI) by a whopping 19 decades, it can teach us a lot about adopting new technology, getting the workforce ready and embracing organisational change.

The introduction of steam-powered machinery during this time drastically changed manufacturing, transportation and commerce, but enterprises themselves didn’t transform overnight. This was because there was an adjustment period where companies had to rethink how they produced goods, managed their workforce and distributed products before the full potential could be realised.

AI has the same problem. Companies are investing in AI tools to increase productivity, automate tasks and help with major decision making. However, adopting the technology is only one step in a long journey to successfully integrating AI in the business world.

The organisations that are most likely to benefit from AI adoption are those prepared to change how they operate from within to accommodate the technology. When looking back at the Industrial Revolution, it reveals five important lessons that enterprise leaders should consider as AI becomes a more significant part of business operations:

1. Technology Adoption Requires More Than Buying

When machinery was introduced during the Industrial Revolution, it allowed manufacturers to produce goods faster and in greater quantities. The best example of this is the British textile industry. Small-scale household operations that used to produce textiles like cotton and canvas materials were gradually replaced by more powerful spinning machines and power looms.

But simply introducing the machinery wasn’t enough. Businesses then had to reorganise production, change working arrangements and develop new management structures to make using the mechanisation worthwhile.

AI adoption faces a similar challenge currently. Many organisations are quickly introducing AI assistants, AI chatbots and automation tools to improve existing processes. These applications do indeed save time but as long as businesses continue operating in the same way as before, their benefits remain limited.

For example, plugging an AI assistant into an existing customer service process might help employees respond to enquiries faster. But, if the entire customer service workflow were to be redesigned around the AI, it could allow for routine enquiries to be handled automatically. This will provide employees with the opportunity to focus on more complex customer needs.

According to Deloitte's 2026 State of AI in the Enterprise report, 34 per cent of surveyed organisations are using AI to deeply transform their businesses, while 30 per cent are redesigning key processes around the technology. Another 37 per cent are using AI on a more superficial basis, with little or no change to existing processes.

This distinction matters. Businesses that treat AI as just another software purchase may improve individual tasks but the wider organisation remains the same. Those that fundamentally rethink how work is performed will have more opportunities to achieve meaningful transformation.

2. Evolve the Workforce Alongside the Technology

Industrialisation changed the nature of work. In other words, the core daily tasks, responsibilities, and conditions that define specific roles. Mechanised production, as a result of the Industrial Revolution, reduced the need for certain traditional skills (a process called de-skilling). It instead created a demand for factory workers, machine operators, engineers and other specialised occupations.

These changes weren’t always welcomed. The Luddites, an English textile worker movement in the early nineteenth century, famously destroyed machinery to protest the loss of skilled work, declining wages and deteriorating working conditions.

Their resistance was an outward expression of a wider and growing concern about technological change. Workers saw the change as their livelihoods being threatened and didn’t necessarily see a clear path towards new opportunities.

As most of us have experienced, AI is raising similar questions about employment and skills. Automation can take over repetitive tasks previously done by humans, while generative AI can assist with writing, research, analysis and software development. This doesn’t necessarily mean every affected role will disappear. It does however mean that responsibilities and skill requirements are slowly starting to change and evolve.

This means businesses need to consider how to change the work environment so that employees can work alongside AI rather than placing the focus on which tasks can be automated.

The 2026 Deloitte research report identifies insufficient employee skills as a major barrier to successfully integrating AI into existing workflows. Its findings also show that organisations are placing considerable emphasis on improving AI literacy and developing upskilling strategies.

Training employees to use AI tools should only be a part of the solution. Businesses must also consider how roles, responsibilities and career development will develop as the technology becomes more capable.

The lesson from industrialisation is that technological progress can create new opportunities, but workers need support from organisations to participate in them. AI adoption should therefore include workforce planning, practical training and opportunities to develop new, complementary skills.

3. Early Adoption Doesn’t Guarantee Long-Term Success

The Industrial Revolution didn’t provide benefits to every business equally. Some manufacturers did recognise the commercial potential of mechanisation and adapted their operations accordingly, while others struggled to compete when production methods changed.

There was also a significant investment aspect of introducing new machinery. Businesses had to consider whether the equipment suited their operations, whether they could afford the infrastructure to support the new equipment and whether the expected productivity improvements justified the expense.

Modern AI adoption involves similar decisions. Many enterprises are still experimenting with AI, but experimentation doesn’t automatically translate into measurable business value. A company might introduce several AI tools without establishing where they will have the greatest impact or how to evaluate their performance to match the new processes.

This can lead to disconnected pilot projects, overlapping software investments and systems that employees rarely use (thus wasting opportunities and money).

Businesses need to approach AI adoption with clear objectives. Rather than implementing tools just because competitors are doing so or the market is pressuring them into doing so, they should identify what operational challenges they want to tackle and then determine whether AI is the tool that can realistically address them.

This also means measuring outcomes beyond the number of employees using AI or the number of automated tasks. Useful measures to look at might include:

• Reduced processing times and operating costs.
• Improved accuracy and fewer manual errors.
• Better customer experiences and response times.
• Increased employee productivity.
• New products, services or revenue opportunities.

Deloitte's 2026 research highlights the difference between current productivity improvements and longer-term commercial ambitions. While 66 per cent of surveyed organisations reported efficiency and productivity gains from AI, only 20 per cent said they’re already increasing revenue through the technology.

The findings suggest that businesses are beginning to realise operational benefits, but turning those improvements into new revenue sources remains a greater challenge.

The lesson is that adopting AI early isn’t necessarily a competitive advantage on its own. Organisations need to understand where exactly the technology creates value and build a strategy around those opportunities.

4. Infrastructure Determines How Far Innovation Can Go

The Industrial Revolution depended on much more than the mere invention of individual machines. The new steam-powered factories that popped up required reliable fuel supplies, transport networks and supporting infrastructure to operate at scale.

The expansion of railways, for example, allowed manufacturers to move raw materials and finished goods more efficiently from point A to point B. Improved transport connections also opened new markets, helping businesses expand beyond their immediate locations.

Similar dependencies are being created when it comes to AI adoption. Enterprise AI systems rely on access to and management of reliable data, computing resources, cloud infrastructure and secure networks. They also need to be able to integrate with existing business applications and systems. Without these foundations, organisations may struggle to move beyond isolated AI experiments.

Poor data quality can undermine the accuracy of AI-generated insights, while fragmented systems can make it difficult to connect and communicate information across departments. Legacy infrastructure can also limit the ability to deploy more advanced applications.

Security and governance introduce further considerations. Businesses must understand what information AI systems can and are allowed to access, how sensitive data is handled and how automated decisions are monitored (when human intervention is needed).

These challenges become particularly important as organisations explore specifically agentic AI, where systems can perform tasks and take actions with limited human oversight and interference.

Deloitte's 2026 research highlights this preparedness gap. Although 42 per cent of surveyed organisations considered their AI strategy highly prepared, confidence was lower across areas such as infrastructure, data management, risk and governance, and workforce readiness.

The historical parallel appears in the way machinery couldn’t transform manufacturing without the infrastructure needed to support it. Similarly, AI cannot deliver its full potential if the underlying enterprise environment isn’t fully ready.

5. The Biggest Opportunities Come From Rethinking Business Models

Perhaps one of the most significant lessons from the Industrial Revolution is that technological change can reshape and shift entire industries rather than simply improve a few existing processes.

Mechanised production allowed manufacturers to increase output, reduce production costs and serve larger markets. The expansion of factories, railways and industrial supply chains created new business opportunities that would’ve been difficult to achieve using traditional production methods. Likewise AI could also have such a broad impact.

Businesses are already using AI technology to improve customer service, accelerate product development, analyse large datasets and automate administrative work. However, its longer-term significance may lie in the new products, services and operating models it enables.

For example, AI could allow financial services companies to offer more personalised customer support, manufacturers to develop more responsive production systems and software companies to introduce features that adapt to individual users.

Agentic AI could further extend these possibilities by allowing systems to coordinate tasks across different applications, departments and business processes. At the end of the day, though, these opportunities require organisations to think beyond efficiency.

Using AI to complete an existing task faster may be useful, but using it to create a service that was previously too expensive, complex or time-consuming to offer could represent a much greater business opportunity.

This is where AI adoption becomes a question of strategy. Enterprise leaders need to consider not only how the technology can improve existing operations but also what new capabilities it makes possible.

What Enterprise Leaders Can Learn From the Industrial Revolution

The Industrial Revolution demonstrates that transformative technologies can rarely deliver their full potential through adoption alone. Businesses need to make an effort to change the systems, structures and processes around them as well.

For enterprise leaders navigating AI adoption, this means looking beyond the technology itself. The most important considerations include:

• Organisational change: Redesign workflows and processes rather than simply adding AI tools to existing operations.
• Workforce development: Help employees develop the skills needed to work alongside increasingly capable AI systems.
• Measurable outcomes: Evaluate AI investments based on business results rather than the number of tools deployed.
• Infrastructure readiness: Strengthen data management, computing resources, integration, security and governance.
• Business transformation: Explore how AI could support new products, services and operating models.

These considerations are particularly important because AI is not a single-purpose technology. Like steam power and electricity, it has the potential to influence multiple industries and functions.

The Next Industrial Revolution Will Reward Those Who Adapt

The Industrial Revolution changed more than the machinery used to manufacture goods. It transformed how production was organised, the type of workers employed and the skills they need and how products are moved.

AI could bring similarly far-reaching changes, but its success will depend on more than the capabilities of the technology itself.

Businesses that focus exclusively on acquiring AI tools risk only improving individual, smaller tasks but never addressing the wider opportunities or challenges. Those prepared to rethink their processes, infrastructure and workforce may be better positioned to turn AI investment into lasting business value.

History suggests that the organisations that benefit most from transformative technology aren’t necessarily those that adopt it first. They are the ones that understand how to adapt around it. The early bird may get the worm but the second mouse gets the cheese.

For more insights into how AI is changing enterprise technology and how your organisation can be better prepared, explore EM360Tech's Emerging Technologies feed.