Is your business ready for AI? How automation without strategy leads to faster failure

The pressure to adopt AI is enormous – within the boardroom, competitors already using it, analysts are predicting adoption. According to Grand View Research the global AI market, valued at USD 279.22 billion in 2024, is predicted to have an expected annual growth rate of 36.6% between 2023 and 2030.

But rushing headlong into AI without careful preparation can end in disaster. Many leaders overlook the less creative, more difficult work, such as auditing the data estate, considering legacy constraints, streamlining processes that enable them to design for the future, not the past.

It’s an all-too-common story when a leading retail brand launches its expansion into a new market: with big expectations and big investment; millions across hundreds of stores. A key part of the plan is a state-of-the-art inventory and supply chain system, designed to automatically keep shelves stocked and logistics seamless. But when the automated systems work with bad data and untested assumptions, customers can walk into the new stores only to discover empty shelves.

In short, businesses can learn a harsh lesson if they automate without first understanding and preparing the system they are automating. Applying automation or AI on top of those broken systems doesn’t fix the problem – it amplifies it.

Understanding the system: Why automation goes wrong

There are some classic mistakes businesses make in trying to bolt AI onto already chaotic systems. If their data is untrustworthy, processes are poorly understood, and if systems are rigid and siloed, then automation will only get you to the wrong destination faster.

For instance, an organisation may want to speed up approvals, automate reporting, implement AI or digitise customer onboarding. Instinctively, they reach for tools: low-code platforms, robotic process automation bots, cloud APIs, AI integrations. These are powerful tools, yet are rendered relatively worthless when used without a clear understanding of how the business actually functions end to end. Instead they create an illusion of progress. Suddenly approvals are flying through faster, only to hit a backlog in finance. AI models generate insights based on incomplete or poor quality data. Reports are automated, but no one trusts the numbers.

There’s significant investment and yet nothing really changes – except now the system is even harder to debug. This is all a result of optimising in isolation, without considering how the system flows, how the data moves, or where the real bottlenecks lie. Most organisations aren’t struggling because of a lack of automation or AI. They’re failing because their underlying systems: processes, data structures, team responsibilities are complex, fragile, and full of blind spots. These are the foundations needed for any tech strategy to work.

Benefits of building solid foundations for AI

With the right foundations in place, you unlock business potential such as:

·       Hyper-personalised customer experiences

·       Real-time insight that drives smarter strategy

·       Seamless collaboration across teams and ecosystems

·       Freedom to adopt emerging technologies without fear of collapse

In addition to the insight gained from AI, there are other benefits from reviewing and optimising infrastructure. For instance, the business stops reacting to problems and can start to anticipate them. It can use AI to augment decision-making, not just as a tool for analysis.

Integration isn’t a painful retrofit; it’s an engine to create new value streams. Ultimately, people across all departments stop battling systems, and instead start using them to do their best work.

Getting your business ready for AI

While AI can completely transform a business, only fundamental prep work will ensure it is  effective. Business leaders must understand their own context, build a strategy and get preliminary systems in order to harness their data.

Understanding the importance of zooming out before you speed up is essential. If you start with clarity – about the system, the constraints, the flow of work, and bring in the right expertise to design for flexibility and scale, then automation and AI become game-changers.

But first of all, having the right team to undertake this work is essential. It’s not just about rounding up a group of developers or automation tools. This transformation team will require experienced architects, transformation partners, and system thinkers. The right specialists will build a roadmap that aligns technology with real outcomes.

Once the team is in place, some essential steps to get systems into the right order for AI implementation include:

·       Systems Thinking – This is an important discipline. It’s a way of stepping back to see the whole picture: how work flows, where delays occur, where feedback loops exist (or don’t), and what unintended consequences are lurking beneath the surface.

·       Getting data in order - your data needs to be clean, reliable, and well-understood.

·       Undertaking a tech architecture review - The architecture needs to be suitably scalable for the organisation’s needs, as decoupled as possible, and observable.

·       Educating the entire transformation team - People need to understand not just how tools work, but how the business works as a system.

Ready for take off

Automation, at its most basic level, is a time saver. But when it’s built on solid foundations: clean data, well-designed processes, and thoughtful system architecture – it becomes something far more powerful. It becomes a launchpad.

But in CIOs’ need for speed, the one crucial step that often gets skipped is understanding the system. Applying AI on top of broken systems is just faster failure.

Only organisations that make time to do the groundwork and look within to their tech infrastructure and systems will genuinely transform how the business operates. That future isn’t reserved for the tech elite - It’s available to any business that chooses to zoom out, see the system, and build with intention.