In boardrooms everywhere, the prevailing wisdom is that artificial intelligence (A.I.) will reshape business. But a fresh set of data suggests that only a fraction of companies are really getting it done. In Kyndryl’s global Readiness Report, which polled 3,700 senior executives in 21 countries, only the pacesetters — a mere 13% of respondents who turn talk into scaled execution — are clear.
The Readiness Gap Undercutting The AI Hype
The headline paradox is stark. Eighty-seven percent of leaders believe a year from now, AI will have revolutionized roles and accountabilities, yet just 29% say their workforce has the skills, training, and support to make it work. High confidence in innovation mechanics — 90% have faith in their tools and processes to enable them to test and scale new ideas — but 57% acknowledge they continue to get bogged down by underlying technology issues on a regular basis.
The result is a broadening “readiness gap.” The energy proof of concept for many players exists, but the plumbing to support it doesn’t. Even though 54% claim to see measurable ROI from AI pilots, 62% admit efforts are still stuck in pilot mode. That mix — early success without a solid enterprise foundation — is the reason why momentum seems to disappear between a cool demo and a changed operating model.
These findings are consistent with those of other studies. Atlassian has seen increased use of AI tools by the workforce but without an organization-wide productivity uptick. Cisco’s AI readiness work also highlights data security, network capacity, and governance as common blockers to scale.
What the 13 Percent Do Differently to Scale AI
The pacesetters marry vision with investment and discipline. Instead, they’ll likely try to steel their data foundations, lay the foundation for robust MLOps, and codify model governance on day one as it relates to algorithms. Their own employees use A.I. more on a weekly basis — some 66 percent do, compared to 63 percent for followers and 56 percent for laggards — but the real dividing line is what that usage translates into in terms of business results.
They like funding AI in the way of product development, not technology experiments. That’s portfolio management, stage gates tied to value, and cross-functional ownership that includes data, security, compliance, and business unit P&L. Incentives match the goal: teams are measured on cycle time, defect rates, or customer resolution (risk reduction), not just model accuracy.
Pacesetters also standardize building blocks and reclaim development cycles:
- Retrieval-augmented generation on regulated data
- Shared feature stores
- Reusable connectors
- Observability dashboards
- Unbundling engineering work from one-off projects
On a stable path with clear guardrails, they get there faster because there’s less reliance on chance.
Why Everyone Else Stalls on Enterprise AI Scaling
For most organizations, the hurdles are less about algorithms and more about architecture, talent, and change management. Even with reasonable use cases, fragmented data estates, brittle legacy systems, and a lack of clear ownership bog customers down. Security and compliance teams have no shared risk frameworks with which to slam on the brakes. Procurement cycles and vendor sprawl jack up prices before the value ever arrives.
Skills are another fault line. Leaders tend to more easily overestimate the readiness for work when a few power users adopt and go above and beyond, but then the median employee hasn’t been trained at all, and frontline managers have no idea how to re-engineer workflows. Without role redesign, AI is instead a bolt-on that raises load, rather than a spark plug that lifts toil.
Finally, many strategies end up chasing today’s shiniest models rather than the most tractable or important problems. Unconstrained chatbots in serious domains only beg for failure. More constricted tasks — claims triage, agent assist, code review, knowledge retrieval — are better initial bets because they come with clean data, clear metrics, and less risk.
How to Bridge the AI Readiness Gap Right Now
Start with value, not novelty. Identify three to five use cases that have a clear line of sight on revenue, cost, or risk. Specify pre-AI baselines and target deltas in order to measure handle time, backlog reduction, or fraud catch rate.
Create a slick and tough platform. Favor training over production: data quality pipelines, secure connections to source systems, identity and access controls, as well as real-time monitoring of model drift and misuse in output. Standardize patterns like retrieval-augmented generation and human-in-the-loop review before rolling out to additional teams.
Design for the worker. Give us task-level copilots within the tools we already use with in-context guidance and examples. Train managers on how to do workflow redesign so AI is replacing steps instead of adding them. Link the inclusion of AI to performance goals and future recognition.
Govern with proportionate risk. Develop model cards, approval workflows, and incident playbooks. “I would say that you want to best align legal, compliance and security because what you don’t want is surprises too late.” Create a small “red team” function to test for prompt injection, data leakage, and bias.
Make costs transparent. Track unit economics that might feel like cost per thousand tokens, per transaction, or per ticket resolved. Where possible, consolidate vendors and cache or distill models to minimize run costs as volume increases.
The Bottom Line on Turning AI Pilots into Impact
The potential of A.I. is real but the transformation isn’t automatic. The 13% who execute at scale aren’t smarter about models; they’re simply more disciplined on data, operations, governance, and change. Winners will be the ones that act nimbly on modest but valuable problems at the same time as they systematically prepare to scale. Everybody else will continue to learn that enthusiasm is not a plan.