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Scaling AI now depends less on how many deployments an organization runs and more on three things: whether it can see the full cost of each AI use case, whether governance operates inside daily workflows rather than beside them, and whether each investment is judged by business results it can measure. The 2026 survey evidence points in the same direction. The figures describe what surveyed leaders reported, not proof that any single control produces a specific return.
Where scaling AI is breaking down
The clearest signal comes from the IBM Institute for Business Value’s 2026 global survey of technology executives. Eighty-five percent of respondents said they lacked full visibility into real-time AI spend, and 84% said they had not fully operationalized AI financial management. The same survey found that 77% of organizations said AI adoption was already outpacing their governance capabilities. These results describe that study population, not every company’s position.
The practical consequence is that leadership can approve more pilots without knowing which ones are consuming budget, which ones have a named owner, or which ones are producing anything measurable. Each of the three musts below addresses one part of that gap.
Must one: control the full cost, not just the model invoice
A model provider’s invoice or a cloud-account total is only one line in what an AI use case actually costs. A single cost view needs to cover:
- Model and token consumption across providers and internal applications
- Cloud and GPU infrastructure
- Software licensing
- Data pipelines
- Operating labor: the people who build, run, review and maintain each use case
Attribute spend to the use case that generated it
Attribution is the step that turns a total into a decision. Costs should be assigned to the business unit, product or use case that generated them, because a cloud-account total cannot show which use case earns its cost. IBM’s guidance on AI cost management describes four connected practices: comprehensive attribution and total-cost visibility, outcome-based benchmarking, cross-functional governance, and continuous portfolio optimization. Its stated premise, published on IBM Think on September 11, 2026, is direct: “AI cost management works by tracking, analyzing and governing the costs of AI workloads across the enterprise.”
Convert consumption into unit economics
Token counts and GPU hours rarely support a business decision on their own. Translate them into a unit cost that matches how the work is measured, such as cost per resolved request, completed workflow or decision. Then pair that unit cost with an outcome such as cycle-time reduction, cost avoidance, conversion lift, revenue contribution or faster incident resolution. IBM’s guidance presents these as examples, not a fixed menu.
Before claiming an improvement, put four things in writing:
- A baseline measured before the use case went live.
- A named owner accountable for the result.
- A fixed evaluation period.
- A unit cost that includes operating labor and data costs, not only model fees.
Plan for the spending trajectory
IBM’s 2026 survey projects that AI spend will rise from just under 15% of surveyed organizations’ IT budgets in 2025 to nearly 25% by 2027, which IBM describes as a 71% increase. That is a forecast from survey respondents and should be cited with that attribution. For budget planning, it means AI spend is moving from experiment funding into a recurring line that needs forecasting and attribution.
Must two: put governance into the operating workflow
IBM’s CIO, Matt Lyteson, framed the shift in the company’s June 8, 2026 newsroom announcement: “It is no longer just about deploying AI faster. It’s redesigning how organizations control, govern and invest in it and embedding control and visibility from the start, so they can scale with confidence.” A governance policy stored in a document and checked at periodic sign-off does not keep pace with how quickly use cases are deployed. The evidence points toward controls that run inside the workflow and toward clear accountability for each use case.
Assign decision rights for every use case
For each use case, record the answers to five questions:
- Who owns the business result?
- Who can approve access to data and models?
- What spending and risk limits apply, and who can change them?
- Who monitors exceptions?
- When must a person intervene before an output or action proceeds?
Build checks into the workflow and product lifecycle
Checks work when they run where the work happens: in the build pipeline, in an approval step of a business process, or in the release path of a product. IBM’s 2026 survey found that 59% of surveyed technology executives named security and compliance concerns as a top barrier to scaling AI agents, so agent controls are a sensible place to start embedding checks.
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Test whether governance is actually operating
Having a framework is not the same as running one. KPMG’s June 2026 release, based on a survey of more than 1,750 senior leaders across 20 countries, reports that only 24% of organizations had proactively integrated risk management into strategy and the technology lifecycle, and only 28% tracked operational or revenue outcomes linked to trusted AI. Adrian Clamp, KPMG International’s Global Head of Consulting Strategy & Investment, put the problem plainly: “Real value from AI requires operating as an intelligent enterprise – aligning strategy, decisions, and execution. Yet, most organizations have not redesigned themselves to do so, with complexity rising faster than performance. As a result, many risk scaling AI without delivering sustained enterprise impact or meaningful returns.”
Must three: judge the portfolio by business outcomes
Adoption counts, agent counts and usage volume describe activity, not value. Apply the outcome test before a pilot expands, and again at every review after it does. Gartner’s survey of 353 data and analytics and AI leaders, conducted November to December 2025, found that only 39% were confident that current AI investments would positively affect financial performance. That gap between activity and confidence is why the outcome test matters.
A gate before scaling a pilot
- A target outcome stated in business terms, with a measured baseline
- The full operating cost, expressed per unit of work
- Quality and risk guardrails with explicit thresholds
- An accountable business owner
- A fixed review cadence
At each review, scale initiatives that produce durable results, revise those with a credible path to improvement, and pause or redirect funding where evidence remains weak.
Look beyond efficiency
PwC’s 2026 AI Performance Study surveyed 1,217 senior executives across 25 sectors and several regions. It defines AI-driven performance using reported revenue and efficiency gains, adjusted against industry medians. Within that comparison, the strongest performers were 2.6 times as likely as their peers to say AI improved their ability to reinvent their business model. They were also more likely to have responsible-AI frameworks and cross-functional governance boards. These are comparative survey results, not a forecast for any individual company. PwC’s Joe Atkinson, Global Chief AI Officer, described the difference: “Many companies are busy rolling out AI pilots, but only a minority are converting that activity into measurable financial returns. The leaders stand out because they point AI at growth, not just cost reduction, and back that ambition with the foundations that make AI scalable and reliable.”
Rank #4
Fund the foundations
Gartner reports that organizations with successful AI initiatives invest up to four times more, as a share of revenue, in foundations such as data quality, governance, AI-ready people and change management than organizations reporting poor outcomes. Gartner also reports up to 65% greater business outcomes, including revenue growth and cost optimization, among organizations with the highest maturity of AI-ready data and analytics capabilities. Both findings are associations across respondents. They do not show that adding foundation spend will produce the same results in a given company.
Connect cost visibility to reported ROI
KPMG’s Q2 2026 Global AI Pulse found that organizations with full visibility into AI operating costs were five times more likely to report established ROI than those without, at 15% versus 3%. That is a relative association in one survey, and each respondent’s view of “established ROI” follows that survey’s own definition. It is still a strong reason to treat cost visibility as a precondition for any outcome claim.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choosing between platforms, tools and use cases
When leaders compare cost platforms, cloud tools or competing use cases, five dimensions give a consistent basis. The evidence supports these as decision criteria. It does not rank named vendors against each other.
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| Dimension | Question to ask | Evidence to request |
|---|---|---|
| Cost visibility and attribution | How granular is attribution: by model, team, product or use case? | A sample cost report that shows total cost per use case, including non-model costs |
| Outcome measurement | Are financial and operational outcomes measured against a baseline? | Baseline values, metric definitions and the evaluation period |
| Accountability and enforcement | Can a named owner set and enforce risk and spending limits? | Written decision rights and spending thresholds that can be enforced in the system |
| Workflow integration | Does it fit existing data sources and operating processes? | Documented integration points with current data and approval workflows |
| Adaptability | Can the team change models or providers without rebuilding the use case? | Portability terms and documented steps for switching models or providers |
IBM’s guidance names Apptio for central tracking of AI initiatives and for linking total cost of ownership to defined outcomes, and Cloudability for cloud and AI unit-cost optimization. These are examples of enterprise software categories cited in that guidance, not endorsements or rankings, and buyers should test any product against the five dimensions above.
Quick Recap
Reading the figures responsibly
- Each study has its own population, geography and question wording. Compare figures within a single report, and do not merge results from different studies into one dataset.
- Terms such as “successful,” “established ROI” and “AI-driven performance” are defined differently in each report. Quote the definition alongside the number.
- The figures are survey results. They describe what respondents reported and are not controlled trials of cost controls, governance boards or foundation spending.
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