C-Suite Takes Command: AI Strategy Moves From IT Project to Board Agenda
AI strategy has shifted from an IT initiative to a core business priority. New survey data shows decision authority migrating to the top table-and fast. That's good for speed and accountability, but it introduces a new risk: sidelining the people who know the tech best.
The numbers executives should pay attention to
- CIO involvement rose from 44% in 2024 to 71% in 2025.
- CEO involvement more than doubled, from 26% to 55%.
- COO jumped from 2% to 41%; CFO from 1% to 38%.
- IT/Technology managers fell from 67% to 48%.
- Data scientists/analysts slid from 44% to 13%.
Translation: executive ownership is up, hands-on technical input is down. If you want useful outcomes, you need both.
Alignment looks good-depth does not
Seventy-three percent say the C-suite collaborates on AI, and 71% are confident in their performance metrics, up from 66% last year. But confidence in leadership's grip on core AI issues is slipping where it matters.
Leaders perceived to understand infrastructure requirements dropped from 72% to 66%. Cybersecurity comprehension improved from 72% to 77%, yet only 38% believe leadership fully grasps the risk. Alignment without depth invites expensive mistakes.
Governance is the choke point
Sixty-five percent have AI governance committees, but 43% say they're bogged down and lack authority to act. Only 57% feel their data governance practices are solid.
Top blockers to AI adoption: data privacy and security (59%), followed by data quality (40%). You can't scale AI on shaky data and unclear guardrails.
A 90-day executive plan to fix this
- Set decision rights: Publish a simple RACI for AI decisions-strategy, funding, model risk, data use, vendor selection, and go/no-go. Make approval, escalation, and stop-work authority explicit.
- Tie AI to P&L and risk: Create an AI Investment Council (CEO, CFO, CIO, CRO) that greenlights use cases against three gates: business value, data readiness, and risk controls.
- Operationalize data quality: Assign named data owners and stewards. Define SLAs, data contracts, and lineage for every production AI use case. No owner, no deployment.
- Adopt a standard for risk: Map controls to the NIST AI Risk Management Framework. Add threat modeling, red-teaming, vendor security reviews, and human-in-the-loop checkpoints for high-impact workflows.
- Get clear on infrastructure: Inventory workloads, latency needs, and data gravity. Build a TCO model that compares cloud vs. on-prem vs. hybrid, including GPU availability and unit economics per inference.
- Measure what matters: Track outcome metrics (revenue, cost, cycle time), model quality (precision/recall, drift), and adoption (users, task coverage). One dashboard, reviewed monthly by the C-suite.
- Empower governance: Upgrade your AI committee from "advisory" to "decision-making." Give it authority to approve, pause, or retire models and to enforce remediation timelines.
- Rebalance talent: Re-embed architects, data engineers, and security leads into product teams. Maintain a technical bar for every executive decision with a named expert in the room.
From pilots to production: the operating model
- Portfolio, not pet projects: Fund a pipeline of use cases with clear kill criteria. Expect most to be small wins; prioritize compounding value over single bets.
- Data-first deployment: Require data quality sign-off before any model goes live. Establish a playbook for data sourcing, labeling, drift monitoring, and rollback.
- Controls by design: Privacy, security, and compliance built into workflows-not bolted on at the end. Automate documentation and audit trails.
- Change management: Train managers on process redesign and KPIs, not just tools. Incentivize business adoption, not model accuracy alone.
Board-ready checklist
- Which three AI use cases drive the most near-term value, and what is the unit economics for each?
- Do we have clear owners for data sources, quality SLAs, and incident response?
- What risks are we accepting, mitigating, or avoiding per the NIST AI RMF-and who signed off?
- How will we measure productivity and margin impact, and when will we kill underperforming deployments?
- Does our AI committee have stop-work authority and budget control? If not, why not?
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The shift to executive ownership is overdue. Now match it with technical depth, clear decision rights, and data you can trust. That's how you ship AI that moves the P&L-and stands up to scrutiny.
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