Slalom 2026 AI Research: The Ambition-Execution Gap Is Widening

Originally published by Slalom Consulting. Source: AI Research Report: The Ambition-Execution Gap Is Widening – Slalom, 2026

Slalom’s 2026 AI research report—based on a survey of 2,000 business and technology leaders across the US, Canada, UK, Germany, and Ireland—documents a pattern that has become one of the defining challenges in enterprise AI: organizations are more confident, more ambitious, and more committed to AI than ever before. And the gap between that ambition and what they can actually execute is widening.

The Investment Intention Is Clear

90% of organizations plan to increase AI investment in 2026—an uptick from their already-high 2025 projections. The confidence is real. The question Slalom’s research presses is whether the underlying infrastructure can support what leaders are committing to.

The answer, across six domains of AI readiness, is consistently: not yet.

Six Domains, Six Gaps

Strategy and Governance

48% of executives continuously update their AI strategies. But only 51% of those update their metrics when their strategy changes. 87% still rely on traditional annual investment models that can’t keep pace with how rapidly AI opportunities and risks evolve.

Slalom’s Senior Managing Director Joe Berg offers a counterintuitive take: “If you’re continuously updating your AI strategy, you probably don’t have one.” Strategy that changes weekly is noise. What organizations need is a durable north star with adaptive execution.

Technology and Data

77% invest in technology modernization—but only 34% have enterprise-wide replatforming mandates. Just 42% possess robust data foundations capable of meaningful AI impact.

Senior Director of AI Strategy Tamarah Usher frames the gap plainly: “Most organizations still treat AI as a technology strategy, not as business transformation.” Without modern data infrastructure, AI deployments are building on unstable ground.

Security, Risk, and Ethics

95% of leaders are comfortable using AI copilots for strategic decisions. Yet only 50% have formal AI audit or ethics boards, and only 42% implement model or data controls such as bias and drift monitoring. 10% have no AI risk controls at all.

External regulation is currently more influential than internal readiness in shaping governance behavior—a fragile foundation that will create compliance problems as regulation matures.

Experience and Process

56% are redesigning workflows with tangible gains—62% report less manual work. But only 21% have achieved enterprise-wide AI use cases. 39% orchestrate multi-agent workflows across end-to-end processes.

The pattern is consistent: AI works well locally. Scaling it across an enterprise involves organizational complexity—ownership, accountability, integration, change management—that most organizations haven’t resolved.

Workforce and Organization

68% believe their organization keeps pace with AI. 93% report workforce barriers. This confidence-capability disconnect is one of the report’s most striking findings: nearly all organizations have leaders who believe they’re keeping up, and nearly all have significant workforce skill gaps.

Only 20% describe their reskilling initiatives as very effective. Just 9% of employees receive meaningful coaching or mentoring on AI. Senior Managing Director Ali Minnick draws the organizational implication: “AI has become a test of leadership as much as technology.”

Business Value

76% frame AI primarily as an efficiency tool. Only 48% see AI’s reinvention potential—its capacity to enable fundamentally new products, services, or business models. Only 39% have clear ROI metrics and performance tracking in place.

Mid-Market Companies Are Outperforming Large Enterprises

One of the most interesting findings in Slalom’s research: mid-market companies outpace large enterprises on aligned modernization (41% vs. 32%). Smaller organizations are moving faster because they have less legacy infrastructure to navigate, less organizational complexity to manage, and greater urgency to compete.

Large enterprises have the budgets and the data assets—but their size is working against them in the adaptability dimension that AI transformation demands.

Four Shifts to Close the Gap

Slalom identifies four critical shifts for organizations serious about closing the ambition-execution divide:

Convert confidence into capability through scaled reskilling—not one-time training, but ongoing development tied to specific role changes
Complete modernization without compromise—half-built data foundations produce half-built AI outcomes
Redesign processes rather than retrofit AI into existing workflows—the ROI difference is significant
Advance beyond efficiency toward differentiation and reinvention—treating AI only as a cost tool misses its largest value creation potential

What This Research Misses

The survey geography (US, Canada, UK, Germany, Ireland) skews the findings toward English-speaking and DACH enterprise markets. Asia-Pacific markets — particularly Japan, South Korea, Singapore, and India — have different AI adoption patterns driven by distinct regulatory environments, talent pools, and manufacturing sector dominance. TCS’s and Infosys’s global delivery models provide a different view of where AI production deployment is actually happening: in many cases, AI-enabled services are being deployed in client engagements in APAC and emerging markets faster than in the US mid-market contexts Slalom serves.

The finding that mid-market companies outperform large enterprises deserves more structural analysis than it receives. Slalom’s core market is mid-market (they’re explicit about this) — which creates potential sample bias toward reporting mid-market strengths. That said, the finding is consistent with what BCG and others observe: organizational agility, not budget size, drives AI adoption speed. What Slalom doesn’t explore is whether the mid-market advantage is durable as AI deployments scale, or whether it reflects lower baseline complexity that large enterprises have already navigated.

“Only 9% of employees receive coaching or mentoring” is the most damning finding in the report — and it’s treated as one data point among many. If the workforce is the primary bottleneck to AI value (a consensus finding across Accenture, Deloitte, McKinsey, and EY research), and only 9% of employees receive meaningful coaching, then virtually every organization is under-investing in the highest-leverage intervention. The McKinsey skills reset research quantifies the economic stakes at $2.9 trillion in US value by 2030 — a figure that makes the 9% coaching rate a strategic emergency, not a measurement category in a survey report.

The confidence-capability disconnect (68% believe their org keeps pace; 93% report workforce barriers) is a critical finding the report frames as a “paradox” rather than a failure of organizational self-assessment. Research on Dunning-Kruger effects in organizational settings consistently shows that leaders in less mature organizations rate their capability higher than leaders in more mature organizations — because they lack the reference points to recognize their gaps. The 68% confidence figure among organizations where 93% experience workforce barriers is not a paradox; it’s evidence that most organizations don’t accurately know where they stand on AI readiness.

Research Methodology: 2,000 business and technology leaders across the United States, Canada, United Kingdom, Germany, and Ireland. Survey conducted August 2025.

Source: Slalom 2026 AI Research: The Ambition-Execution Gap Is Widening