Geopolitics Is Rewriting Global AI Strategy: What CIOs Need to Do Next
Markets may be "excessively optimistic" about AI's profit upside, according to the International Monetary Fund. At the same time, fresh data from Gartner's 2026 CIO & Technology Executive Survey (2,500 respondents) shows AI spend is set to rise, yet decision patterns are splintering under regulation, data sovereignty, and hard ROI expectations.
The headline: 89% of global CIOs plan to increase AI budgets in 2026 (87% in Europe). Interest is high, but so is scrutiny. Western Europe reports slower progress from pilots to scaled deployments, pressured by tighter budgets, tougher rules, and a more volatile geopolitical backdrop.
Vendor portfolios are going regional
Isolationist currents aren't a blip. Gartner's data shows 22% of Western European CIOs expect to reduce reliance on international tech vendors, while 27% plan to deepen ties with regional providers. That's a meaningful shift in selection criteria.
Chris Howard, Distinguished VP Analyst and Chief of Research at Gartner, says: "Influenced by the geopolitics of AI, CIOs and technology executives should recognise that vendor geography and data sovereignty risks are now viewed by many of their peers as a critical consideration in developing a global vendor portfolio." He adds: "This criterion is likely to rise in importance based on growing geopolitical risks and cost pressures."
The transatlantic divide is widening
According to the survey, 50% of non-US CIOs and technology executives anticipate changes in vendor engagement due to regional factors, versus 31% in the US. One in three non-US leaders plan to shift focus to regional vendors; only 16% of US leaders signal the same.
Howard warns: "All technology vendors, but especially those in the US, should be aware of this because it is a threat to their ability to serve as 'vendors of choice' across a global market. This may be the beginning of a change in hegemony that will play out over the coming years."
From pilots to agentic AI ROI
Board conversations have moved past experiments. Kris van Riper, Practice Vice President at Gartner, says: "2025 was about AI pilots, discovery and experimentation. 2026 will be about delivering agentic AI ROI."
"Gen AI will continue to be important but agentic AI will soon become the main investment focus. Agentic AI offers a more direct path to business value than previous Gen AI initiatives," she says. Gartner reports 64% of technology executives plan to deploy agentic AI within 24 months. The window for open-ended experimentation is closing.
Execution now depends on internal capability across five value pillars:
- A business-aligned AI roadmap
- Clear, measurable value targets
- Upskilling initiatives for workforce readiness
- Strong data governance practices
- The ability to reprioritise resources
Van Riper adds: "Technology executives have advanced AI and Gen AI deployments and are now expanding into agentic AI… We are seeing a prioritisation of AI investments which are expected to grow by more than 35% year-over-year. This is in the context of a very constrained IT budget environment."
What this means for your 2026 plan
- Rework vendor strategy by geography: Score suppliers on data residency, export controls, auditability, and continuity risk. Build regional redundancy where exposure is high.
- Contract for volatility: Add clauses for data localization, model rehosting, escrow of critical assets, and price-protection tied to regulatory shifts.
- Architect for sovereignty: Prefer architectures that let you swap models, endpoints, and inference locations without replatforming.
- Double down on value proof: Stand up 3-5 agentic AI use cases with line-of-business owners. Tie each to a baseline, a target, and a 90-day payback hypothesis.
- Train for new work patterns: Focus upskilling on prompt-to-process design, human-in-the-loop supervision, exception handling, and safe use of data.
- Tighten data controls: Enforce consent and lineage. Limit sensitive data exposure in prompts, logs, and agent memory. Automate PII redaction.
- Reallocate, don't just add budget: Sunset low-yield pilots. Move spend into agentic workflows tied to revenue, margin, or risk reduction.
Agentic AI: metrics that actually move
- Cycle time reduction per process (hours to minutes)
- Cost per transaction or ticket
- Automation coverage (% of steps executed by agents)
- Exception rate and rework
- Customer effort score / NPS delta on assisted journeys
- Revenue per rep (if agents assist sales/service)
- Model update cadence and incident count
Questions to put in front of your vendors
- Where is training, fine-tuning, and inference executed today? What are my options by region?
- What happens to my data if cross-border transfers get restricted? Show the fallback plan.
- Can I move models between your stack and alternatives without refactoring flows?
- How do you log, store, and purge prompts, outputs, and agent states?
- What performance and value guarantees can you commit to in production?
A practical 90-day playbook
- Weeks 1-2: Build a one-page AI value thesis. Pick 3 agentic use cases with clear baselines.
- Weeks 3-6: Create thin-slice pilots with production data and human oversight. Define pass/fail gates.
- Weeks 7-10: Lock vendor contingencies (regional options, contract clauses). Establish data controls and audit trails.
- Weeks 11-13: Scale the 1-2 winners. Publish a scorecard weekly to the exec team.
Context and further reading
For macro expectations on AI's profit outlook, see the International Monetary Fund's analysis here. For regulatory direction in Europe, review the European Union's AI Act materials here.
Upskilling your team
If you're standing up the "people" pillar, map roles to focused training paths. Explore role-based programs here: AI courses by job.
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