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For enterprise leaders, preparing for IT transformation in 2026 is less about buying a single new technology than about connecting AI and data to business operations—and making that change governable, secure, visible and adaptable. Surveys published in 2025 and 2026 point to the same management challenge: technology ambitions are growing, but strategy, oversight, skills and execution capacity must keep pace. Their findings are snapshots of particular respondents, not universal forecasts or a prescription for every company.
What is changing in enterprise IT priorities?
AI is rising alongside familiar responsibilities such as cybersecurity and aligning technology work with business goals. In the 2025 SIM IT Issues and Trends Study, AI ranked first among IT management issues reported by respondents, followed by cybersecurity and IT-business alignment. The study included 704 IT executives, among them 211 CIOs, representing 344 organizations. Its rankings describe those respondents; they do not establish a universal order of priorities for every enterprise. Read the study in MIS Quarterly Executive.
That combination matters: adopting AI without a clear business purpose can add cost and operational complexity, while pursuing alignment without addressing security and governance can expose the business to unmanaged risk. Leaders should therefore evaluate transformation proposals as business changes with technology components, not as isolated technology deployments.
Why should technology leaders be closer to business strategy?
McKinsey’s Global Tech Agenda 2026 describes CIOs in leading organizations as integrating AI and data into operating models and taking a more strategic role. Its survey collected responses from 632 C-level executives and IT professionals in 69 nations and 24 industries between September 29 and November 10, 2025; responses were weighted according to each respondent region’s contribution to global GDP. McKinsey defined “top performers” as organizations whose respondents reported at least 10% average growth in both revenue and EBIT over the preceding three years; 114 respondents met that definition. At nearly two-thirds of these top-performing companies, technology leaders were very involved in enterprise strategy, compared with 52% of other organizations. That is a survey comparison, not proof that closer CIO involvement by itself caused stronger growth. Read McKinsey’s Global Tech Agenda 2026.
The practical implication is to give technology leaders a role in setting business priorities early enough to shape them. When a proposed AI or data initiative is considered, executives should be able to explain what business outcome it is meant to improve, which workflows or decisions will change, and how results will be measured. This makes it easier to distinguish a strategic investment from a technically interesting pilot that lacks a route to adoption.
How can enterprises scale AI without losing control?
An IBM Institute for Business Value study, announced June 8, 2026, indicates a gap between the pace of AI deployment and organizations’ ability to oversee it. The study surveyed 2,000 senior executives responsible for IT, technology or AI decisions across 33 geographies and 19 industries from January through April 2026, in cooperation with Oxford Economics. In that sample, 77% of organizations said AI adoption was outpacing current governance capabilities, and 70% said business teams were deploying technology faster than IT could track. These are respondent reports, not measured rates for all enterprises. Read IBM’s study announcement.
The same study reported an average of 54 AI-agent incidents in surveyed organizations over the preceding year. IBM defined an incident as an unintended or harmful occurrence that required human correction. It reported that 17% of incidents were high severity and took more than four hours to contain. Within the high-severity breakdown presented by IBM, 37% involved data exposure or security breaches, 33% cascading system failures and 17% compliance issues. Those percentages refer to IBM’s reported incident categories and should not be read as shares of incidents across all enterprises.
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These findings make visibility and controls an operating requirement for scaling—not paperwork to add after deployment. Before expanding agent use, leaders should establish who can authorize an agent, what information and systems it can access, which actions require human approval, how activity is logged, and who can stop or roll back an action. Controls built into the systems and workflows can make oversight more immediate than relying only on manual review. In IBM’s study, organizations embedding controls directly into AI systems reported 25% fewer incidents than those relying on manual governance; this association is not a guarantee of the same reduction for an individual company.
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Agents can interact with data, applications and business processes, so deploying them exposes weaknesses in the underlying operating environment. Before broad rollout, examine the dependencies that make it difficult to see, constrain or change how automated work is performed.
- Data access: Map the data an agent can reach, its sensitivity and the rules governing its use. Limit access to what the task requires.
- Identity and permissions: Give agents identifiable credentials and bounded permissions rather than allowing actions through broadly privileged user accounts.
- Workflow boundaries: Define which actions an agent may complete independently and which require a person’s review, especially where an action could materially affect customers, finances, compliance or service availability.
- Logs and monitoring: Make it possible to reconstruct what the agent accessed, what it decided or attempted, and what systems or people were affected.
- Recovery: Decide how to pause an agent, revoke access, correct an outcome and restore a service if a workflow fails or produces harmful results.
These checks are useful whether a company is buying an agent-enabled product or building its own. They focus modernization on the capabilities needed for safe operations rather than assuming that a newer platform alone will resolve governance or integration problems.
How should leaders choose an adaptable architecture?
Transformation choices should be assessed against business value, governance and visibility, portability, integration, and the organization’s ability to execute. There is no evidence here establishing one vendor or architecture as the universal winner. The relevant question is whether a design lets the organization change models, components or workloads without losing control of its systems and data.
IBM’s study reported that organizations designing for adaptability early—keeping workloads portable and models replaceable rather than tied to hard dependencies—reported 10% higher AI return on investment in 2025. That is an association in the study, not a promised return or proof that portability alone produced the difference. Still, it supports treating replaceability as a design consideration when selecting platforms and planning integrations.
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- Governance and security: Confirm that controls, access boundaries and monitoring can be applied across the intended workflow.
- Portability: Identify whether workloads and models can be moved or replaced without an unacceptable rebuild.
- Integration: Determine whether data, platforms and existing workflows can work together reliably.
- Execution capacity: Check whether internal teams have the skills and time to operate the design and maintain its controls.
How should budgets and expectations be set?
Budget decisions should follow the organization’s strategy and capacity to execute, not a survey projection treated as a spending target. IBM reported that surveyed organizations expected AI agents to increase by 38% by 2027 and projected AI spending to rise from just under 15% of IT budgets in 2025 to nearly 25% by 2027. These are respondents’ expectations, not observed future outcomes or a recommended allocation for every company.
A useful budget case separates the costs of experimentation from those of production operation: integration, data preparation, security and governance, monitoring, training, and ongoing support all affect whether a deployment can deliver sustained value. Tie each investment to an accountable business owner, an outcome measure and a decision point for continuing, changing or stopping the work. That discipline can help prevent a large portfolio of pilots from becoming a larger portfolio of unowned operating costs.
Geography and sector also matter. Gartner reported in November 2025 that 52% of government CIOs outside the United States expected their IT budgets to increase in 2026. Its result came from 284 non-U.S. government CIOs within a 2,501-respondent CIO and Technology Executive Survey fielded May 1–June 30, 2025. It is not an estimate for all enterprises or for U.S. organizations. Read Gartner’s survey release.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should companies address security skills and delivery capacity?
AI and cloud security plans depend on people who can design, operate and assess controls. PwC’s 2026 Global Digital Trust Insights survey included 3,887 business and technology executives in 72 countries, with fieldwork from May through July 2025. PwC identified knowledge and skills gaps as the top two barriers respondents faced in implementing AI for cyber defense over the past year. Among approaches they were exploring, 53% cited AI tools, 48% security automation, 47% cyber-tool consolidation and 47% upskilling or reskilling. These are survey responses about approaches being explored, not proof that each approach has been deployed or delivered a specific result. Read PwC’s 2026 Global Digital Trust Insights.
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PwC also says organizations are prioritizing specialized managed services, particularly those that had experienced a major attack; 48% of that group were prioritizing them. External services may help address a specific capability or capacity gap, but they do not remove the need for internal ownership of risk, access, business decisions and incident response. A sensible plan identifies which capabilities must remain in-house, where training can close gaps, and where outside support could provide expertise or operational coverage.
What is a practical preparation sequence?
Use a sequence that makes strategic intent, operating readiness and control visible before expanding deployments. Adapt the timing to the organization’s risk, systems and governance structure.
- Set business priorities. Name the outcomes technology investment is expected to support and assign a business owner alongside technology leadership.
- Inventory current AI use. Include sanctioned tools, embedded product features, agents, and business-led deployments that IT may not yet track. Record their owners, data access and connected systems.
- Assess the control environment. Check identity, permissions, logging, human approvals, incident escalation and the ability to pause or reverse consequential actions.
- Choose a bounded use case. Start where expected value is measurable and the organization can observe performance, constrain access and recover from failure.
- Test integration and replaceability. Check dependencies, data quality and whether models or components can be changed without losing necessary visibility or control.
- Fund the operating model. Account for skills, security, governance, support and monitoring as well as implementation. Decide who is accountable when a system behaves unexpectedly.
- Set scale gates. Expand only when the business outcome, control performance, operational ownership and recovery process meet criteria agreed in advance.
This sequence is a management framework, not a claim that every enterprise should follow identical steps or adopt AI agents on a fixed timetable. The decision to scale should depend on what the organization can demonstrate in its own environment.
What should executives take from the 2026 evidence?
The surveys point toward an integrated transformation agenda: align technology with business strategy, connect AI and data to real operating processes, make governance and visibility part of system design, preserve the ability to adapt, and invest in the skills needed to run the result. Their samples and measures differ, so their percentages should not be combined into a single forecast. The strongest planning response is to use them as signals of management issues to test against the company’s own strategy, exposure and capacity.
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