The Tool Desk
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Why more AI spending does not automatically mean more business impact
AI can make individual work feel faster without yet improving an organization’s financial results. In McKinsey’s 2026 State of AI survey, 80% of respondents said AI improved their individual productivity, while 37% said it had contributed positively to organizational EBIT. The latter share was essentially unchanged from 2025. These are survey respondents’ reports, not causal estimates or a forecast for a particular company.
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That gap is a reason to fund outcomes rather than adoption alone. A proposal should explain what work will change, what the change is expected to improve, and how the organization will tell whether the improvement happened. Buying additional seats or expanding model access may be part of the plan, but it is not a business case by itself.
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McKinsey’s 2026 survey points to different functions for cost and revenue opportunities. Treat these as places to investigate—not proof that a project in any one function will pay off in your organization.
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| Business aim | Functions where respondents most often reported benefits | What to test locally |
|---|---|---|
| Reduce cost | Supply chain management, service operations, and manufacturing | Whether AI can reduce a specific source of delay, manual work, waste, or service cost without shifting costs elsewhere. |
| Grow revenue or develop offerings | Marketing and sales, product and service development, and software engineering | Whether a measurable improvement in conversion, product development, customer experience, or engineering throughput is plausible. |
Start with a workflow and its current baseline, not with a preferred model or vendor. Identify the task, the people and systems involved, the bottleneck, and the outcome that matters. Then check whether the data and process are ready for change. A technically impressive demonstration is weak evidence if it does not fit how work is performed or if its benefits cannot be distinguished from other changes.
Fund the operating model as well as the use case
McKinsey’s 2026 survey found that high-performing respondents more often redesigned workflows and combined efficiency goals with growth or innovation objectives. That association does not show that spending more—or copying a particular practice—causes stronger performance. It does point to work that should be considered in a serious proposal: redesigning the process, assigning a leader who owns the outcome, and involving the people responsible for delivery and adoption.
- Workflow redesign: specify which steps, handoffs, or decisions will change, and who will own the redesigned process.
- Implementation and adoption: account for integration, training, support, and the effort needed to change established work.
- Governance: identify relevant risks and controls, including how outputs will be reviewed and how access or sensitive data will be managed.
- Measurement: define the baseline, the outcome metric, the measurement period, and the point at which the project will be reviewed.
These are budget items, not incidental overhead. If they are omitted, the project may appear cheaper while leaving the main barriers to business impact unfunded.
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Budget for the full cost of using AI
Model access is not the whole bill. Usage can rise as a pilot expands, and costs may vary with the volume and type of work handled. In McKinsey’s May 2026 Enterprise AI FinOps survey, 93% of respondents said their organizations exceeded AI budgets. McKinsey also reported that AI spending rose nearly fourfold as organizations moved from isolated use cases to enterprise-wide adoption. Those findings describe the surveyed groups; they are not a universal forecast for every company.
In McKinsey’s 2026 State of AI survey, one in five respondents said operating costs, including token costs, constrained their organization’s AI use. Budget for visibility and control alongside rollout:
- Track usage and cost by project, workflow, or business owner where practical.
- Forecast costs at expected operating volumes, not just at pilot scale, and revisit assumptions as usage changes.
- Compare model and workflow choices against the required quality and service level; do not assume the highest-cost option is necessary for every task.
- Set a review point and clear thresholds for investigating unexpected usage or cost.
Cost visibility also makes it possible to compare a project’s operating expense with its measured value. Without attribution and forecasting, a successful pilot can become a poorly controlled production service.
How to compare AI proposals and allocate the budget
There is no standard scoring formula in the cited evidence. A practical comparison should make the same questions visible for every candidate so leaders can weigh potential value against readiness, cost, effort, and risk.
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| Decision dimension | Question for the proposal |
|---|---|
| Business outcome | Is the aim lower cost, higher revenue, better quality, or innovation—and which specific result is expected? |
| Evidence and baseline | What is the current performance, what evidence supports the expected improvement, and how will it be measured? |
| Full operating cost | What are the expected model-usage and other recurring costs at realistic scale? |
| Workflow and data readiness | Can the process and its data support the proposed use, and what must change first? |
| Implementation and organizational change | What integration, process redesign, training, and adoption effort is required? |
| Risk and governance | What controls, reviews, and accountable owners are needed for this use? |
| Scalability | What must be true to extend the project, and what evidence would justify further funding? |
Compare AI proposals within the broader technology budget, too. McKinsey’s run-versus-change framework distinguishes spending needed to operate existing infrastructure and applications—including cybersecurity, compliance, and cloud platforms—from change spending for modernization, application development, data and analytics, and AI. The decision is not to starve essential operations to create an AI line item; it is to check whether the organization is protecting what it must run while putting enough change investment behind its goals.
McKinsey and Serviceware’s 2026 analysis describes at least one third of technology expenditures for change as a modeled benchmark for “deliberate modernizers.” It draws on technology leaders at 17 global companies, and the authors note that appropriate allocations depend on industry needs, technology maturity, and value goals. It is contextual evidence about technology budgets—not a recommended AI percentage or a rule for every organization.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Set a realistic return horizon and review point
Do not assume that every AI project will repay its costs within a year. In Deloitte’s 2025 survey of 1,854 executives across Europe and the Middle East, respondents typically reported two to four years to achieve satisfactory ROI on an AI use case; 6% reported payback in under one year. Deloitte contrasted that AI-use-case result with a typical 7–12 month payback expectation for technology investments generally. These are reported survey results, not guaranteed timelines or a company-specific forecast.
The same Deloitte survey found that 85% of respondents said their organizations had increased AI investment over the preceding 12 months, while 91% planned another increase in the following year. These figures indicate reported momentum among those executives; they do not establish that increasing investment is the right decision for every organization.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteFor each proposal, put the expected outcome, baseline, direct and indirect costs, measurement plan, review date, and conditions for expansion or discontinuation in writing. Set a review schedule appropriate to the work: early checks can catch adoption or cost problems, while the final value assessment may need a longer period than a pilot. If the underlying workflow or measurement changes, record that rather than presenting unlike periods as a clean before-and-after comparison.
What the surveys can—and cannot—tell a budget owner
McKinsey’s 2026 State of AI survey found that 28% of respondents said AI represented more than 10% of their enterprise ICT budget, and 60% expected their organization to increase AI investment in the next year. These figures describe respondents’ spending and plans; the 28% figure is not an optimal allocation, and the 60% figure is not a recommendation.
The cited evidence is mainly executive or company survey research and strategic analysis, not controlled experiments. It can help identify common opportunities, constraints, and planning questions, but it cannot determine the right allocation or expected return for a particular organization. McKinsey’s March 30, 2026 article, “Recalibrating technology budgets for the AI era,” puts the distinction succinctly: “it won’t be enough for companies to spend more; they will also have to spend differently.”
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