The assembly line was designed to maximise operational efficiency. Jobs are broken into discrete tasks and mastered in isolation. Businesses adopted this model and applied it to their workflows, assigning functions to dedicated teams to master their respective portions of the process. Digitisation has since supercharged a business's ability to maximise operational efficiencies; cloud databases give immediate access to data that was once trapped in filing rooms, APIs instantly connect information across systems, while logic models manipulate data and handle tedious calculations.

Historically, the digitisation of workflows has been financially justified by measuring time savings, framing ROI in terms of reduced headcount. Until recently, the upfront investment required for automation made it challenging to demonstrate short-term ROI, slowing adoption. Today, with a few well-crafted prompts and targeted training, AI exponentially reduces the investment required. 

Organisations now face a fork in the road: will they use AI to continue automating and reducing headcount, or will they use it to unlock innovation at scale?

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Path 1 – Full-Scale Automation and Headcount Reduction

There’s a general concern amongst the people we interview that they will lose their jobs to automation. Realistically, if companies pursue AI with the agenda of reducing headcount, redundancy could be on the cards within the next 5 years. A single person may be orchestrating the work of a hundred, making for a promising financial report for the first year or so. 

But when you strip people out of your organisation, you’re also stripping out the creative capacity that makes you interesting. That capacity is built from lived experience, intuition, and the mix of perspectives you’ve intentionally hired for. It’s messy, it’s human, and when it comes together, it produces the kind of originality you can’t automate.

AI can gather and filter perspectives, but it still operates inside an echo chamber made from the same data everyone else has access to. That’s why AI-led creativity already has a detectable “tang”; the ads where the smiles look a bit off, the punchlines that feel pre-fabricated, the ideas that give de ja vu. Until AI can truly match the depth and contradiction of being human, removing human creative capacity will only weaken your ability to evolve, differentiate, and stay competitive in a shifting market.

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Path 2 – Human-Centred Automation and Innovation at Scale

The winning organisations will use AI to amplify human potential rather than replace it. Repetitive, menial tasks will be automated, freeing teams to focus on strategy, solution design, market exploration, and customer experience. AI will accelerate learning, aid decision-making, and enable agility, while preserving the creative and adaptive thinking that drives long-term success.

Teams will function as innovation labs, applying structured methods, such as design thinking, to solve complex challenges. They will focus on outcomes rather than tasks, empowering members to collaborate openly, take risks, and deliver meaningful, high-impact results.

Companies like Canva, Atlassian, and many modern startups are already operating this way. They integrate AI to speed delivery, expand imagination, and continuously improve. Their operating models focus on unlocking human creativity, not reducing it. Outcomes, not tasks, define success.

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The Shift Required

Choosing Path 2 will demand more than technology. It will require a deliberate shift in structure, culture, and mindset:

• Outcome-focused thinking: Success is judged by the real impact of the work, not by how many tasks get completed.
• Creative capacity as currency: Investment and recognition go to ideas and imagination, not to cutting people for short-term gains.
• Flexible team composition: Teams flex around the work, not hierarchy, so people can contribute where they’re most effective.
• Fluid ways of working: Processes stay light and adaptable so teams can shift quickly, respond to change, and get to better solutions without the drag of rigid systems.
• Design thinking a practical approach to rapid problem-solving: Design thinking helps teams understand problems fast, test ideas early, and make smarter decisions with less waste.
  
  

Conclusion

The assembly line was revolutionary for its time. It created predictability, speed, and efficiency. But it treated humans as cogs in a machine. Organisations that follow Path 1 will repeat the same pattern, swapping one technology for another while losing what makes them alive: human creativity.

One day, AI may rival human creativity, generating ideas, art, and solutions that feel indistinguishable from our own. But until that moment, we cannot rely on it to originate truly novel thinking. And even when it can, we must pause to ask if human insight should be entirely replaced by algorithmic thinking?

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At Fabric, we combine our expertise in strategy, design, and software engineering to deliver exceptional value for our clients. If you're looking for support in accelerating your digital journey, we would love to hear from you.