Most AI investment decisions in planning are being made under pressure to act rather than in response to a clearly identified problem. The organisations seeing returns are the ones that started differently.
The pressure is real. Your board has asked what you are doing about AI. Competitors appear to be moving. Vendors are presenting compelling roadmaps. And somewhere underneath all of that noise is a genuine question you have not yet been able to answer cleanly: what would AI actually do for your planning capability, given where you are right now?
The difficulty is not a lack of options. It is that most of the available guidance comes from people with something to sell, calibrated to organisations with data foundations and architectural maturity that bear little resemblance to yours. The result is a landscape where the gap between what AI can theoretically do and what it will realistically deliver in your specific context is large, and almost nobody is being straight with you about it.
The most consistent pattern is starting from the wrong question. Organisations are being asked what they are doing with AI — a supply-side question — rather than which specific decisions would genuinely be better if AI were applied to them — a demand-side question. The first question produces projects that are defensible in a board presentation but hard to justify once the initial enthusiasm settles. The second produces a shorter list of initiatives with a clearer path from experiment to embedded operation.
A related pattern is the pilot that does not reach production. The gap between a working experiment and a justifiable investment at scale turns out to involve considerably more infrastructure than the initial pilot suggests — including how decisions get executed back into the systems that run the operation day to day. That infrastructure is consistently underestimated in early pilots, and its absence is one of the main reasons promising experiments do not reach production.
Data readiness is also consistently underestimated. Not in the sense that organisations lack data, but in the sense that data exists somewhere without being available in usable form at the moment of decision. The distinction between existence and usability is where assumptions most often break down, and it is rarely visible until something external forces it into view.
The assumption most organisations are operating on is that the primary question is which AI use case to prioritise. Pick the right one, sequence the investment correctly, and the returns will follow.
The evidence suggests the question is upstream of that.
Most AI investment in planning is being driven by the need to demonstrate activity... the board has identified AI as a strategic priority, and the organisation is being asked what it is doing rather than which specific decisions would genuinely be better if AI were applied to them. Those two questions pull in opposite directions. The first produces initiatives that are defensible in a board presentation but hard to justify once the initial enthusiasm settles. The second produces a shorter list with a clearer path from experiment to embedded operation.
What separates the organisations seeing returns from those stuck in pilot purgatory is not the sophistication of the technology. It is whether the problem was identified before the tool was selected and whether the data foundations and organisational conditions required to take a working experiment to production were honestly assessed before the investment was committed.
BPC's outside-in view on this pattern comes from practitioners who have navigated AI investment decisions in planning at comparable organisations. Tell us about your context and we can find the most relevant comparisons.
Timing: Wed 29 Jul · 15:00 BST · 60 minutes
Focus: Supply chain and planning leaders examining how to sequence AI investment in planning against their actual current position rather than an idealised future state.
Format: Consultant-supported discussion session grounded in practitioner experience
AI tooling is lowering the barrier to building internally and changing the architecture direction question before any vendor enters the room. This session examines what that shift means in practice for a mid-market supply chain function.
Examines the strategic context that should sit upstream of any capability investment, including operating model design, partner ecosystem constraints, and the shift toward AI-enabled best-of-breed components.
BestPractice.Club is not a consultancy and does not provide advisory services based on full organisational discovery.
What you see here reflects pattern recognition drawn from many years of conversations with supply chain and operations leaders facing real, high-stakes decisions. It is intended to help you orient yourself, clarify your decision position, and understand what often proves useful at similar points — not to provide definitive advice tailored to your specific circumstances.
Any suggestions are indicative, not exhaustive, and are made without full visibility of your organisation, constraints, or risk profile. Decisions remain yours, and should be tested against your own data, context, and governance processes.
If a pattern doesn’t quite fit, that’s normal. They are distilled from many examples from varying contexts. Decisions rarely move in straight lines with teams often revisiting earlier stages as new information emerges. If it would help to talk through your situation and sense-check where you are, you’re welcome to schedule a short conversation.