A forecast released last month puts a genuinely large figure on global AI spending, and the warning attached to it deserves at least as much attention as the number itself. Gartner’s September 16, 2026 forecast projects worldwide AI spending will reach two point seven trillion dollars, while warning that corporate buyers could face acute risks around runaway costs and vendor lock-in as that spending accelerates.
Why a Growth Forecast Coming With a Risk Warning Is Worth Noticing
Analyst forecasts projecting rapid growth in a technology category are common, and most arrive without much accompanying caution about the buyer-side risk that growth creates. This one is different specifically because the warning is attached directly to the same forecast driving the growth headline, rather than appearing separately or later. That pairing is worth reading as a genuine signal that the pace of AI spending itself, not simply the technology’s usefulness, is now something analysts consider a distinct risk category worth naming explicitly to enterprise buyers.
What Runaway Cost Actually Looks Like in Practice
Runaway cost in this specific context typically does not arrive as a single, dramatic overrun. It accumulates through a pattern that is by now familiar across the software categories already covered in this trend: consumption-based AI pricing that scales faster than initially modelled, feature adoption that spreads across a user base more quickly than budget planning anticipated, and default settings that enable usage-based billing without requiring the deliberate activation step that used to prompt a cost review before spend began.
Why Vendor Lock-In Compounds the Cost Risk Specifically
The lock-in half of Gartner’s warning is worth reading alongside the cost half rather than treating as a separate concern, since the two reinforce each other in a specific way. An organisation that adopts a vendor’s AI capability broadly, integrating it into genuine business-critical workflows, faces increasingly limited practical ability to challenge that vendor’s pricing at renewal, precisely because switching cost has grown alongside adoption. That dynamic means the window for negotiating favourable AI-related terms is often widest early in adoption, before genuine dependency has developed, and narrows specifically as usage grows, which is the opposite of when most organisations feel ready to negotiate seriously.
What This Means for How Budgets Should Be Built Right Now
Given this specific pairing of rapid growth and explicit risk warning, building AI-related software budgets around a genuine, evidence-based consumption forecast, rather than a vendor-supplied estimate or last year’s spend extrapolated forward, is worth treating as a standard planning discipline rather than an optional extra step. That forecast should explicitly model what happens if usage grows faster than expected, since Gartner’s own warning suggests that scenario is a real, foreseeable risk rather than a worst-case tail event unlikely to actually occur.
Putting a Concrete Figure on What Lock-In Actually Costs
The lock-in half of Gartner’s warning is easy to treat as an abstract concern until an actual figure gets attached to it. Independent research puts the average enterprise technology migration project at roughly three hundred fifteen thousand dollars, a figure covering data migration, application refactoring, retraining, and downtime, and separate 2026 survey data found that eighty one percent of enterprise decision makers are at least somewhat concerned about becoming too dependent on a single AI vendor specifically.
That gap between how many organisations report genuine concern about AI vendor dependence and how few have actually priced what a migration away from a locked-in vendor would cost is precisely the gap Gartner’s warning is pointing toward. Concern alone does not reduce switching cost. Only a deliberate architecture and contract strategy, built before dependency deepens rather than after, actually does.
Why Confidence About Switching Rarely Matches the Reality
A separate, independent survey of enterprise executives puts a further, sobering number against the lock-in half of Gartner’s warning specifically. A survey of five hundred forty two US enterprise executives at organisations with active paid AI vendor contracts found that only six percent believed they could stop using their primary AI vendor without any operational disruption, while eighty nine percent simultaneously expressed confidence they could complete a full migration to a new vendor within four weeks, a confidence gap the researchers themselves flagged as a clear mismatch between perceived and actual switching difficulty.
That gap between confidence and reality is worth taking seriously as its own distinct risk signal, separate from the spending and lock-in figures already discussed. An organisation that believes it could migrate quickly if it needed to, without having actually tested or priced that migration, is operating on an assumption rather than a verified fact, and Gartner’s own warning suggests that assumption is exactly the kind most likely to prove costly once it is actually tested under real conditions.
Why This Warning Deserves a Direct Response From Procurement, Not Just IT
Given that this warning was framed explicitly around corporate buyers rather than technical teams alone, it is worth treating as a prompt for procurement and finance leadership specifically, not something to leave entirely to whichever team happens to be evaluating a given AI tool technically. A procurement function that has historically reviewed software purchases primarily for price and contract terms needs to extend that same scrutiny to consumption forecasting and exit cost specifically for any AI-related purchase, since those are precisely the two risk categories Gartner’s warning names directly, and neither is well captured by a traditional procurement checklist built around fixed, predictable licence costs.
Conclusion
Gartner’s forecast of two point seven trillion dollars in global AI spending, paired directly with an explicit warning about runaway costs and vendor lock-in, signals that the risks of rapid AI adoption are now being named as clearly as the growth opportunity itself.
Building AI-related budgets around an evidence-based consumption forecast that explicitly models faster-than-expected growth, and negotiating vendor terms early in an adoption cycle before genuine dependency narrows that leverage, are the practical responses to a warning that arrived attached to the same forecast celebrating the category’s growth.
