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Highlights
- Why artificial intelligence (AI) pilots stall at scale: Most AI initiatives fail not because the technology underperforms, but because operational teams, systems, and incentives are not designed to absorb it into day-to-day work.
- The hidden cost of operational misalignment: Fragmented systems, manual workarounds and misaligned key performance indicators (KPIs) result in significant waste of time, effort, energy, and compute that organisations rarely measure.
- What enables AI at scale: Organisations that succeed start with operational reality, redesign workflows first, and invest in operational infrastructure with the same seriousness as AI capability.
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AI’s impact across industries
Artificial intelligence (AI) is already delivering significant value in the travel and logistics industry. Predictive maintenance in aviation, optimisation in transport and logistics, demand forecasting, workforce planning, and customer service automation are all powered by AI.
In many cases, the models are accurate and the technology performs exactly as designed. Yet, too many initiatives remain trapped in pilot mode, unable to scale into everyday operations. In other words, the real value of AI remains unrealised.
We see the same pattern repeatedly. A client runs an AI pilot. It works. People are excited. Then it stalls. Six months later, it is still a pilot.
The problem is not AI. The technology does what it is supposed to do. The problem is that nobody checks whether operational teams can actually use it.
For example, an airline built a predictive maintenance model. The predictions were accurate. But maintenance engineers used paper-based work orders. To see the AI insights, they had to log into a separate system. They did not have the time. The AI worked perfectly, but nobody used it.
This is not a technology issue. It is an operational reality issue.
Reality check
Across transport and logistics, the pattern is consistent.
Operational staff juggle multiple disconnected systems. They spend large parts of their day searching for information that should be easy to access. They switch between tools, reconcile data manually, and coordinate workflows that technology should handle.
Organisations invest in AI to solve these problems. They run pilots. The AI performs well. But nobody designs the solution to work inside the fragmented operational environment that actually exists.
These pilots do not fail because the models are wrong. They fail because operations cannot support them.
Why POCs scale and why they do not
Three problems typically appear when moving AI from a successful pilot to production.
You can build an impressive AI model, but if it does not fit into the way people really work, adoption will not happen. A logistics operator built a strong capacity planning tool, but the warehouse system could not consume the recommendations. Staff manually entered outputs back into their operational system. The efficiency gains disappeared
This is not only about integration. It is about whether systems allow people to act on AI insights. A transport operator developed an optimisation tool, but operational teams were measured on compliance with old procedures, not efficiency. AI recommended one thing, but performance metrics rewarded another. Nobody used it.
You can have the right technology and the right process design, but if people are measured against something different, the AI solution will not be adopted.
None of these issues appear during the proof of concept (POC). They appear when scaling from 10 engaged pilot users to a 1,000 operational staff who need to get their work done.
The waste nobody talks about
Operational inefficiency is not just a productivity issue. It is a waste and it is a burden that compounds.
When operational teams have to spend hours searching for information across systems, that is wasted effort. When teams have to rely on multiple disconnected systems, that is wasted human effort. When AI outputs have to be manually copied into another system, more work is created and resources are consumed with no benefit.
One logistics client generated thousands of unnecessary system queries each day because the staff could not find information in the primary tool. At scale, the computational waste was significant. More importantly, it highlighted deeper structural issues—a technology landscape not designed to support efficient operations or absorb new AI capabilities.
AI pilots that never reach production create another layer of waste. Compute is used to build them. Staff time is spent testing them. Organisations lose the opportunity to fix the operational fragmentation that stops deployment in the first place.
What actually works
Results change when organisations stop leading with technology and start with actual operational workflows.
For one client, we focused first on how day-to-day operations really worked. Once this was understood, the technology design became clearer and more effective. Adoption was immediate because the solutions were built for the operational environment from the start.
The difference was not better AI. It was a better understanding of operations.
An operational readiness checklist for AI
Before approving AI investment, organisations need to assess operational readiness across four dimensions.
Can teams use AI in the normal flow of work without adding steps or creating manual workarounds?
Benefit: Adoption happens because the solution fits into the way work actually gets done.
Do existing systems allow teams to act on AI’s recommendations within their usual tools?
Benefit: Teams can act on AI recommendations without manual workaround steps.
Are there practical limits such as capacity, regulation, or time pressure that could block use at scale?
Benefit: Late-stage surprises get addressed before rollout.
Are investments in AI capability matched by investment in workflow platforms, data access, and change capacity?
Benefit: Investment covers both the AI technology and the operational changes needed to make it work.
If any one of these dimensions is weak, AI initiatives are likely to stall when moving from pilot to production, regardless of the technical performance.
Many organisations struggle not because of a lack of AI capability, but because they cannot articulate whether operations can support what AI promises.
Customer experience is a priority for most organisations. But execution fails when operations, technology, and frontline teams work in silos using different tools, accessing incomplete data, and following misaligned incentives.
What needs to change
Organisations succeeding with AI at scale are not those with the most ambitious pilots.
They are the ones building the operational capability to deploy and sustain AI in production.
This means understanding operational reality before designing solutions. It means investing in operational systems at the same level as AI technology. It means bringing operations, technology, and business teams together from the beginning. It means treating operational efficiency as core infrastructure, not a nice-to-have.
When you remove the waste of information chasing and system fragmentation, you are not only improving productivity. You are building the operational foundation that makes AI scalable.
The year ahead will separate organisations that pilot from organisations that scale. The difference will not be AI capability. It will be operational readiness.