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Why AI ROI gets hidden
AI activity is not the same as business value. Usage can be spread across teams, applications, models, and workflows, while benefits may take different forms: time saved, better quality, reduced risk, or additional revenue. Without an agreed outcome and baseline, leaders can count licenses or prompts but cannot tell whether a workflow improved performance enough to justify its costs.
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Survey findings illustrate the measurement challenge, but they are not universal benchmarks. TechRadar Pro reported that a 2026 ShareGate survey of 851 IT leaders across seven countries found 51% cited cost visibility as a barrier to measuring AI ROI and 47% cited governance complexity (TechRadar Pro). The underlying survey methodology is not established here, so treat those figures as reported responses rather than prevalence estimates for all organizations.
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IBM’s June 2026 survey of 2,000 technology executives across 33 geographies and 19 industries found that 77% said AI adoption had outpaced governance capabilities, 84% had not fully operationalized AI financial management, and 85% lacked full visibility into real-time AI spend (IBM Institute for Business Value). These are IBM-published survey findings, not independent estimates of every organization’s experience.
IBM’s 2025 C-suite study, as summarized by IBM, found 25% of AI initiatives delivered expected ROI and 16% scaled enterprise-wide (IBM Institute for Business Value). Those figures are a study result, not a definitive cross-industry success rate. They do underline why organizations need to test outcomes rather than infer value from adoption.
Where governance gaps create cost or risk
Unmapped use and unclear ownership
If AI use is distributed across approved tools, embedded features, and agent workflows, a license inventory alone will not show what work is being done or who is accountable for its result. Without an owner for the business outcome, no one may be responsible for verifying quality, tracking costs, or deciding whether a workflow should continue.
Rank #2
Incomplete cost attribution
A workflow’s cost can include licensing, model consumption, infrastructure, validation, rework, and human oversight. Agentic workflows need particular care: one task may involve multiple model calls, information retrieval, tool use, and actions. Depending on the pricing model and platform, that chain can add usage, infrastructure, and review costs beyond the apparent cost of a single request.
Permissions and information that do not match the task
An AI tool can make existing information easier to retrieve, so access that was already too broad may become more consequential. In figures reported by TechRadar Pro from the 2026 ShareGate survey, 93% of respondents perceived Microsoft 365 governance as ready to support AI responsibly, yet 29% said AI tools had surfaced sensitive internal data that should not have been accessible, while 8% did not know whether that had happened. Perceived readiness is not evidence that controls work in practice.
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Unclear rules for meeting assistants can also expose sensitive material: recordings, transcripts, and notes need appropriate access, privacy, and retention rules. Clean, current, authoritative information can make it easier to ground AI outputs, but it cannot guarantee that those outputs are correct.
Shadow AI and weak incident readiness
In IBM Security and the Ponemon Institute’s 2025 Cost of a Data Breach Report, one in five organizations said they had experienced a breach due to shadow AI, and only 37% reported having policies to manage AI or detect shadow AI. Among the 600 organizations studied globally, those with high shadow-AI levels had average breach costs $670,000 higher than organizations with low or no shadow AI. The report covered breaches from March 2024 to February 2025; the cost difference is an association in that study, not proof that shadow AI alone caused the difference (IBM Security and Ponemon Institute).
Build an ROI and governance plan before rollout
- Choose a specific workflow and accountable owner. Describe the task, where AI enters it, the people and systems involved, and who can approve changes or stop the workflow. Keep the unit of measurement concrete—for example, a defined class of support cases or a particular review step—rather than measuring an entire department’s AI activity.
- Set the baseline and success threshold. Record the current outcome before launch: time, quality, error rates, risk exposure, revenue, or another relevant measure. Set a threshold for improvement and a review date. A baseline makes it possible to distinguish an actual gain from increased activity or a change in workload.
- Map costs to the workflow. Include licensing and, where applicable, model consumption, infrastructure, validation, rework, and oversight. For agents, account for repeated model calls, retrieval, tool invocation, and human review. Attribute costs at a level that lets the owner compare the workflow with its baseline and intended benefit.
- Review data and permissions. Identify what information the workflow can retrieve or send, whether those permissions are appropriate, and how privacy and retention rules apply. For meeting assistants, explicitly cover recordings, transcripts, and notes. Use authoritative, maintained sources where practical, and include output checking because good source information does not ensure correct results.
- Set operating controls. For agentic tasks, define spending limits, human escalation rules, allowed tools and actions, and monitoring for outputs and incidents. Make sure access follows the permissions that apply to the underlying data, not just the convenience of the AI interface.
- Review evidence and decide. At scheduled checkpoints, compare observed outcomes and full costs with the baseline and threshold. Scale when evidence supports the case; change the workflow or its controls when results fall short; stop when the value or risk case is unacceptable. Record the decision and who made it.
Use a framework without mistaking it for proof
NIST describes its AI Risk Management Framework (AI RMF) as “a voluntary framework to help individuals, organizations, and society manage AI’s risks and promote trustworthy development and responsible use of AI systems.” Released in January 2023, it is designed to be flexible across organization sizes and sectors (NIST AI RMF). It can structure risk work, but adopting a framework does not itself demonstrate that controls operate effectively, guarantee ROI, or establish regulatory compliance. Tailor the controls to the use case and applicable jurisdiction.
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Questions to answer before scaling
- Can you see usage and spend by workflow? If not, the owner cannot reliably compare cost with outcome.
- Is one person accountable for the outcome and decision? Name who verifies results, responds to incidents, and approves changes.
- Are data access, privacy, retention, and information quality appropriate? Check both the source permissions and the material the AI tool can surface.
- Can you monitor operation and produce risk evidence? Define what is monitored, how incidents are handled, and what records support review.
- Does the control approach fit the existing AI stack? Consider how systems, tools, and workflows connect rather than assuming a single inventory covers them.
- Can you compare results with a baseline? If the threshold and measurement are missing, the organization cannot make a defensible scale, change, or stop decision.
Matt Lyteson, CIO of IBM, described the challenge this way: “For CIOs and CTOs, the challenge now is scaling AI systems that operate continuously and autonomously, often within governance models and architectures designed for a far slower, more predictable environment,” (IBM Institute for Business Value). For a business owner, the practical response is to make each workflow legible: its purpose, costs, permissions, accountable decision-maker, and measured result.
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