5% vs 57%: Exec AI Skills Gap Keeps Pilots From Going Live

AI Skills Gap Is Stalling ROI: Why Executives Must Close It Now

AI isn't failing you. The executive skills gap is. According to Coupa's Clarity AI Impact Report, done with Incisiv Research, only 5% of executive decision-makers use AI daily, while 57% of their technical teams do. The people funding multi-million-dollar initiatives don't have enough hands-on fluency to set targets, pick platforms, or kill bad ideas fast.

"This research is a wake-up call. The days of funding AI based on hypothetical results or unproven potential are over," says Dennis Bruder, chief product officer of AI at Coupa. "Execution now depends on platform selection more than many other factors."

What the numbers say

• Only 5% of executives use AI daily vs. 57% of technical teams.
• 86% say AI is essential for survival, yet only 29% have a clear company-wide strategy.
• 72% of AI initiatives are stuck in pilot mode, but 47% of executives expect payback in 6-12 months.
• 77% cite data quality and system integration (including legacy IT) as the main blockers.
• 80% prefer buying AI via unified platforms; only 10% choose custom internal builds.
• Only 2% of AI spend goes to orchestration, even as 77% prioritize simple task automation.
• 65% want human-in-the-loop oversight, yet 56% aren't sure a governance policy even exists.
• 69% expect substantial AI-powered automation by 2030, and 83% by 2035.

Why pilots stall

• Fluency gap at the top: Without daily use, leaders set vague goals and accept vanity wins.
• No company-wide strategy: Teams chase tools, not outcomes. Effort gets fragmented.
• Data and integration debt: Dirty data and legacy systems block production.
• Underfunded orchestration: Tasks get automated in isolation. End-to-end value never shows up.
• Governance confusion: Human-in-the-loop becomes a bottleneck without clear rules and thresholds.

A 90-Day Executive Plan to Convert Pilots into Profit

1) Set the operating model and outcomes

• Pick 3-5 business outcomes with dollar targets and deadlines. Tie each to a single owner.
• Define a common intake and prioritization process for AI use cases across the company.
• Adopt a simple budget mix: core data/integration, platform(s), use cases, orchestration, and governance. Publish it.

2) Choose platforms over point tools

• Follow the market signal: 80% prefer unified platforms for speed, security, and scale.
• Selection criteria: native governance and audit, connectors to your systems, model flexibility, clear TCO, and workflow orchestration built in.
• Kill "tool sprawl." Consolidate to one primary platform per domain (procurement, finance, customer ops, etc.).

3) Fund orchestration, not just tasks

• Shift budget to orchestration so automations link across systems, data, and approvals.
• Make "end-to-end value" the rule: no greenlight unless the workflow reaches production with tracking.

4) Fix data paths that block production

• Pick the top 10 systems needed for your priority use cases. Map data flow and integration gaps.
• Stand up data contracts, quality thresholds, and monitoring. Production-ready or it doesn't ship.

5) Establish governance without slowing down

• Adopt a known framework (e.g., the NIST AI Risk Management Framework) and cut it to fit your org.
• Publish approval rules: when human-in-the-loop is mandatory, when it's sampled, and when it's automated.
• Track bias, data use, incident handling, and audit logs from day one. Keep it simple and enforceable.

6) Close the executive fluency gap

• Require weekly hands-on use by the leadership team. Review live demos, not slideware.
• Run short, focused training on prompts, evaluation, and decision frameworks for AI investments.
• If you need a fast start, see curated options by role and skill at Complete AI Training and relevant AI certifications.

What Good Looks Like in 6 Months

• Company-wide AI strategy in place, with a single intake and governance process.
• Two unified AI platforms live, integrated with priority systems.
• At least five end-to-end automations in production with measured savings and cycle-time cuts.
• Orchestration funded and staffed. Less than 30% of initiatives stuck in pilot.
• Clear human-in-the-loop rules. Policy audited and enforced. No shadow AI.
• Leaders use AI weekly and can explain the business case in plain language.

Metrics that matter

• Percent of use cases in production (not pilot)
• Cycle time reduction and cost per workflow
• User adoption and satisfaction
• Error rates and incident count
• Data quality pass rates and integration coverage
• Time-to-value vs. plan (e.g., 6-12 months)

Vendor and platform checklist

• Embedded governance and audit, plus role-based access
• Connectors to your core systems and data sources
• Choice of models, with monitoring and fallback
• Clear pricing, transparent TCO, and unit economics
• Security, compliance, and privacy controls that your risk team can verify
• Native orchestration and human-in-the-loop capabilities

Stop doing this

• Running endless pilots without production criteria and owners
• Buying isolated tools that don't integrate into workflows
• Custom-building models where a proven platform works
• Letting governance live in a PDF instead of in your systems
• Approving budgets without clear, measured business outcomes

Bottom line

The constraint isn't AI. It's leadership fluency and execution structure. Close the skills gap, pick platforms that can scale, fund orchestration, and move fast with clear guardrails. That's how you shorten the path from pilot to profit-and meet the automation targets your board expects.