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Industrial AI often looks far more convincing in presentations than in production environments. Manufacturers have been promised predictive maintenance, autonomous production lines and fully optimised supply chains, yet many organisations remain stuck in the experimental phase. In practice, AI rarely arrives through a single dramatic rollout. More often, it begins with small trials that prove they work, build confidence among staff and gradually spread across the business.
A useful way to look at this shift is through the “crawl, walk, run” model outlined in IFS’ AI Adoption Playbook, which presents AI transformation as a staged process instead of a single technology rollout. The framework highlights a simple but often overlooked reality: successful industrial AI projects begin by solving a single, well-defined problem, not by attempting to rewire an entire organisation at once.
The crawl phase focuses on building the simplest possible AI model that delivers results only slightly better than existing methods. That benchmark is important, as many AI projects fail when they aim for perfection at the outset rather than incremental improvement.
In industrial settings, this early stage often involves using historical operational data to detect patterns that humans might miss. For example, a manufacturer may analyse vibration data from machinery or maintenance logs to identify early signs of equipment failure. The initial model does not need to be flawless; it only needs to demonstrate that data-driven predictions can outperform reactive or schedule-based maintenance.
The guidance is to test models on historical data first, checking whether they can identify problems more reliably than routine inspections or gut instinct. Doing that lets companies test whether AI is actually useful without diving straight into a full deployment.
Choosing the right starting point matters just as much as the technology itself. Early projects tend to succeed when they focus on a genuine operational problem, have usable data behind them and are backed by someone inside the business prepared to drive the work forward. Without that mix, even strong models can struggle to gain traction.
The crawl stage also exposes a problem manufacturers know all too well: their data isn’t nearly as tidy or accessible as they thought. Information is typically spread across spreadsheets, ageing databases, and systems that don’t talk to each other. Tackling smaller projects first gives teams a chance to address those issues gradually, rather than launching massive data overhaul programmes that struggle to get off the ground.
Once a basic model shows promise, organisations can move into the walk phase. At this point, attention turns from simple testing to improving accuracy. Teams often bring in additional data sources and check predictions against recent or real-time information rather than relying solely on historical records.
This is usually the point at which AI begins to deliver measurable operational benefits. In predictive maintenance, bringing in data such as temperature, pressure or electrical current can help models move beyond spotting unusual behaviour to warning that a failure may be coming. That shift allows maintenance teams to intervene earlier rather than react after equipment has stopped.
A Practical Path to AI Value
Manufacturing leaders know AI matters. But between the promise and the performance lies a gap, filled with complexity, confusion, and competing priorities. This playbook provides a clear path forward. It outlines a structured approach to pilot your first AI use case and demonstrate measurable value.
At this stage, the challenge is as much about people as it is about software. Projects often slow when frontline staff are unsure whether automated advice is reliable, especially if it challenges familiar routines. Running predictions alongside existing processes lets teams see whether the system works before they depend on it. This parallel testing approach is essential for developing confidence among frontline teams.
When engineers and operators can see how predictions align with real-world events, they are far more likely to incorporate AI insights into daily decision-making.
Another result of the walk phase is a clearer view of whether AI is delivering real business benefits. Organisations begin tracking indicators such as downtime, defect rates, energy use and labour efficiency. Showing measurable improvements is important because it helps teams justify further investment and win support from leadership.
The run phase is where AI stops being an experiment and becomes part of normal production. Systems are integrated into routine workflows and teams begin relying on predictions when planning maintenance, scheduling work or managing output.
Even then, most organisations do not replace existing processes straight away. Conventional methods often run alongside AI tools until teams are confident in the results. Moving carefully helps limit operational disruption and keeps human judgement central to decision-making.
By this stage, many organisations realise AI works best when it supports human judgement rather than replacing it. Industrial systems are particularly effective at processing large volumes of data simultaneously, spotting patterns across multiple variables, and applying the same logic consistently, without the distractions or fatigue that affect people.
Yet it still relies on experienced engineers and operators to interpret results, validate anomalies, and decide when to override automated recommendations.
The run phase also marks the point where organisations begin scaling AI adoption. Rather than focusing on a single use case, manufacturers typically launch additional pilots across different operational areas. Over time, this creates internal expertise, reusable templates, and governance structures that enable faster AI deployment.
The crawl, walk, run approach helps companies avoid some well-known AI pitfalls. One of the most common is trying to tackle too many problems at once. Splitting effort across several use cases often slows progress, whereas focusing on a single pilot tends to produce faster lessons and a clearer view of whether the investment is worthwhile.
Teams also tend to get bogged down in tidying their data before they start. Clean data is useful, but waiting for everything to be flawless can keep projects on the drawing board. In reality, many AI pilots begin with gaps or inconsistencies and become more accurate over time.
Security and organisational change also represent significant barriers. Involving IT teams early in projects helps prevent compliance delays later, while including operational staff from the beginning increases adoption and trust.
Industrial AI rarely delivers dramatic overnight change. Most manufacturers see progress through smaller improvements that build on one another. A crawl, walk, run approach gives organisations room to test new tools, learn what works and expand adoption at a pace that matches how people actually operate.
Starting with tightly defined problems often produces the strongest results. When early pilots prove useful, companies are more likely to expand AI into everyday operations. Over time, success tends to depend less on technical sophistication and more on whether teams can use the technology comfortably within existing workflows.
For most manufacturers, AI adoption develops gradually through practical experience. Each project adds knowledge, improves data use and builds confidence in automated insights, helping organisations move forward without disrupting day-to-day production.
