AI Tightens CIO-CEO Collaboration as CIO Boardroom Influence Grows

AI Is Tightening the CEO-CIO Partnership. Turn That Momentum Into Business Results

AI isn't just changing products and workflows. It's changing who sits at the strategy table - and how often. In the past year, 31% of US tech leaders say their CEO is working more closely with the CIO, driven by pressure to deliver AI outcomes.

That shift shows up in the boardroom. According to research from Expereo and IDC, 79% of technology leaders report a higher profile with the board due to AI, and 76% are confident their teams can deliver growth and efficiency through the technology strategy.

What the data signals

• 31%: CEOs are working more closely with CIOs than a year ago.
• 79%: Tech leaders say AI raised their profile with the board.
• 76%: Confident in supporting growth and efficiency via tech strategy.
• Top 12-month priorities: Networking/connectivity (43%), security/cybersecurity (38%), data analytics/AI (37%).
• 48%: Expect their role to become even more critical in coming years.
• 78%: Say the current pace of innovation makes it an exciting time to be a technology leader.
• Survey scope: 650 IT decision-makers (CIOs, CTOs, and influencers) at companies with 500+ employees and $500M+ revenue across five countries.

Survey source: Expereo and IDC's Technology Leaders Survey 2025. Learn more about IDC's research approach at IDC.

Why CEOs are leaning in

AI has moved from experimentation to core operations - cost, speed, customer experience, and revenue. Executive teams are aligning on AI strategy, governance, and implementation, with the CIO at the center.

As one research leader put it, CEOs and boards now view AI as fundamental to business outcomes. Expectations are higher, and support is stronger - which means the CIO is now a commercial leader, not just a technology leader.

How leading CIOs are operating

• Partner with the CEO on a shared AI agenda tied to revenue, margin, and customer metrics.
• Run AI as a portfolio with clear stage gates: explore, pilot, scale, retire.
• Centralize data foundations and governance; decentralize use cases to business owners.
• Establish an AI council (CEO, CIO, CFO, legal, HR, product, sales) for priorities, risk, and funding.
• Publish a quarterly AI scorecard: adoption, productivity, quality, risk, and ROI by use case.

Real-world snapshot: CIO influence in action

At Amplitude, CIO and SVP of GTM strategy Dan Carpenter reviews AI progress directly with the CEO, focusing on internal productivity and faster customer outcomes. His remit blends CIO duties with go-to-market operations to accelerate growth through tech, process, and execution across GTM teams.

His view is clear: the CIO should lead AI to avoid duplicated teams and fragmented data sources. Operations, enablement, technology, and data teams need one coordination point for models, tools, and data.

Do you need a Chief AI Officer?

• Reality check: 86% of US businesses have not hired a CAIO, despite last year's predictions that the role would absorb many CIO duties.
• Pattern: More common in tech vendors than in end-user companies; many organizations assign AI to the CIO, CTO, or CISO depending on structure.
• Example: Nokia appointed a Chief Technology and AI Officer and stood up new teams to drive AI and corporate development.

Practical guidance: If data, governance, and AI platform strategy are fragmented, a CAIO can help. If your CIO already owns enterprise data, security, platforms, and AI delivery - and has tight ties to product and GTM - adding a CAIO can create overlap and slow execution.

Your 90-day executive action plan

• Set three board-level outcomes: revenue lift, cost-to-serve reduction, and cycle-time compression.
• Pick 5-7 use cases with clear owners (e.g., sales forecasting, support deflection, claims processing, content ops).
• Fund the data foundation: unified customer and product data, access controls, lineage, red-teaming policies.
• Stand up an AI council to make monthly calls on priorities, risk, and scaling.
• Define model and tool standards; create a gated path from pilot to production.
• Publish an AI scorecard: baseline now, target in 90 days, owner, budget, and expected ROI.

Governance that doesn't slow you down

• Policy: data usage, IP, vendor risk, human-in-the-loop, content provenance, and audit trails.
• Controls: role-based access, dataset approvals, prompt and output logging, incident response.
• Quality: bias checks, accuracy thresholds per use case, fallbacks, and model drift monitoring.
• Security: isolation for sensitive workloads, secret management, secure plug-ins and integrations.

Measuring ROI without guesswork

• Productivity: hours saved, tickets resolved per agent, code merged per engineer, content throughput.
• Revenue: win rate, average sales cycle length, upsell conversion, churn and NRR.
• Customer: CSAT, first-contact resolution, time-to-value.
• Quality: error rates, rework, model accuracy vs. baseline process.
• Cost: compute per use case, licensing per user, data pipeline costs, and unit economics.

Org model that scales

• Central team: platform, data contracts, safety, governance, vendor management.
• Federated pods: business-owned use cases with embedded data and product partners.
• RACI: central owns enablement and safety; business owns outcomes and adoption.
• Talent: prompt engineering as a skill for every role; product managers accountable for AI feature success.

What this means for executive teams

AI is closing the gap between technology and growth. CEOs and CIOs who meet weekly, share a single AI value backlog, and publish a board-ready scorecard will win market share while others run pilots that never scale.

Keep the center tight (data, governance, platform) and the edges fast (business use cases). That's how you turn interest into outcomes.

Next step

If you're building executive-ready skills and playbooks for AI adoption, explore role-based learning paths here: Complete AI Training - Courses by Job.