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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchAI sprawl is a growing risk, not an unavoidable outcome. As agents and built-in AI features spread across teams, organizations can lose track of what is running, what data it can reach, who owns it, and what it costs. The answer is not to ban experimentation: it is to make approved experimentation visible, bounded, and accountable.
Why AI sprawl happens—and why it matters
Teams adopt AI to solve local problems, often faster than central IT can assess each tool. Different groups may build agents that do the same work, connect them to separate data sources, or grant permissions without a shared view of access. Shadow tools add another blind spot; shared systems can create conflicts; and costs can accumulate without clear attribution.
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The scale of the risk is uncertain, but the warning signs are real. Gartner forecasts that an average global Fortune 500 enterprise will have more than 150,000 agents in use by 2028, up from fewer than 15 in 2025. That is a forecast, not a measured outcome or a prediction for every organization. Gartner analyst Max Goss says organizations need to balance governing agents with safely empowering employees to innovate. Gartner’s forecast and guidance.
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IBM Institute for Business Value’s 2026 survey of 2,000 senior technology executives in 33 geographies and 19 industries, conducted January–April 2026, found that 70% said teams were deploying technology faster than IT could track, while 77% said AI adoption was outpacing current governance capabilities. These are respondents’ reports, not universal rates. IBM’s survey and analysis.
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How to stabilize AI use without stopping useful work
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Build an inventory and name an owner
Discover both sanctioned and shadow agents, as well as AI features embedded in existing services. For each, record its purpose, business owner, technical contact, model or service, data sources, connectors, permissions, deployment status, and retirement plan. An inventory is useful only if someone is responsible for keeping it current.
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Classify risk by data boundary and blast radius
Identify whether an agent handles regulated or sensitive information, reaches across business units, can take consequential actions, or could affect many users or systems if it fails. Keep low-risk work confined to approved data and actions. Require stronger review when access crosses organizational boundaries, exposure is regulated, or the potential impact is broad.
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Control identity, access, and lifecycle
Treat an agent as an identity-bearing system, not just a model choice. Grant only the permissions needed for its task, review connected accounts and data sources, and monitor for oversharing. Set expectations for review when purpose, data, or access changes; retire agents that are redundant, unowned, or no longer needed. Gartner recommends centralized inventories and adaptive controls, while emphasizing management of agents’ access and lifecycle. Gartner’s agent-sprawl guidance.
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Monitor outcomes, incidents, and spending
For each use case, define what success means before deployment, then track adoption, cost, incidents, and results against that goal. IBM’s 2026 survey found 84% of respondents’ organizations had not fully operationalized AI financial management and 85% lacked full visibility into real-time AI spending. Those findings indicate why cost attribution belongs in governance, not just in finance after a bill arrives. Matt Lyteson, IBM’s CIO, argues for embedding control and visibility from the start. IBM’s findings on governance and AI spending.
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Make the approved path practical and teach employees how to use it
Explain which tools and data are approved, how to request access, and where to report an unexpected agent or problem. Train teams to recognize sensitive data and risky permissions. AWS cautions that employees may work around governance if the approved route is too slow; its guidance supports local self-service within boundaries and stronger central oversight for cross-unit access, large blast radius, or regulatory exposure. AWS guidance on scaling agents across business units.
Choose an operating model that fits the risk
Most organizations do not need to choose between total central control and unrestricted local autonomy. A hub-and-spoke model can centralize standards, visibility, and high-risk decisions while letting teams experiment within defined limits. AWS recommends central governance with accountable leads embedded in business units. The right division depends on data sensitivity, cross-unit access, potential impact, and the organization’s ability to monitor and retire what it deploys.
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| Decision area | Central governance | Federated hub-and-spoke |
|---|---|---|
| Regulated data or cross-unit access | Central review and tighter controls are appropriate. | Local work can proceed within boundaries; cross-boundary cases escalate. |
| Potential blast radius | Use central approval and oversight when an error could affect many users or systems. | Bounded, low-impact experiments can remain with the accountable team. |
| Speed of local experimentation | Consistent decisions, but review can become a bottleneck. | Teams move faster when self-service routes and guardrails are clear. |
| Visibility and cost attribution | Central inventory and reporting make enterprise-wide oversight easier. | Business-unit leads contribute local context to shared reporting. |
| Monitoring and retirement | Central standards define minimum review and retirement expectations. | Named local owners maintain agents, with central visibility and escalation. |
This is a practical synthesis of AWS’s governance guidance and the portability and oversight concerns raised by IBM and Microsoft Research, not a validated ranking of operating models. If the approved route is difficult to use, the model may encourage workarounds rather than safe adoption.
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Design for change, not a single permanent AI stack
Interoperability helps limit the cost of fragmented ecosystems. Microsoft Research’s position paper argues that minimal standards for agent messaging, interaction, state, and discovery are essential to open, secure, web-scale agent ecosystems. This is the authors’ position, not a finalized standards mandate; the architecture and standards landscape is still developing. Microsoft Research’s position paper on agent interoperability.
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At the application level, favor designs that keep data access, business rules, and workflows from becoming inseparable from one provider’s model. IBM’s 2026 survey analysis associates early workload portability and replaceable models with stronger reported returns. That association is not proof that portability alone causes better returns, but it is a reason to consider replacement and migration before a system becomes difficult to change. IBM’s analysis of portability and reported returns.
Measure whether governance is working
Count more than deployments. A useful review asks whether the organization can find its agents, identify accountable owners, understand data and permissions, detect incidents, attribute costs, and retire systems that no longer justify their risk or expense. Then compare actual outcomes with the goals set for each use case.
Measurement is a broader governance challenge, not just an enterprise problem. In its 2026 report on public-sector AI across OECD countries, the OECD found that 10 of 36 countries (28%) reported any financial or non-financial impact-measurement studies of government AI use cases. The report also found that 14 of 36 (39%) required pre-deployment AI risk assessments, 12 of 36 (33%) had internal review committees, and 11 of 36 (31%) conducted post-deployment audits. These figures concern public-sector AI in OECD countries and should not be read as estimates of private-sector practice. OECD’s report on governing with AI.
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