2026 Decision Intelligence Benchmark — Special AI Report
How learning and development leaders are adopting AI — or not — to achieve their business objectives
Table of ContentsL&D leaders are approaching AI with unusual trust, clear ambition, and a pragmatic sense that the technology is already changing the function. But the strongest through-line is not that AI has arrived fully formed. It is that leadership expectation is ahead of team adoption, trust is ahead of proficiency, and dependence is forming before governance and enablement have fully caught up.
That makes this an L&D story in the deepest sense. The function is not only being changed by AI; it is responsible for teaching the organization how to change with AI. The opportunity is to model what disciplined adoption looks like: clear use cases, responsible review, role-specific practice, and a practical path from belief to capability.
To better understand how leaders think about different kinds of AI, we grouped tools into three categories used throughout this report:
L&D leaders have ambitious targets for how often their teams should be using AI. Reviewing the data on expectations, actual use, and the gap between them reveals where the function currently stands.
L&D leaders have ambitious targets for AI adoption across all three categories assessed in the benchmark. Public GenAI, enterprise platform AI, and company-owned AI all land in the same narrow band of expected daily use, with roughly two-thirds of leaders saying their teams should be using each type every day. This consistency of expectations across categories is meaningful. Leaders are not treating AI as a narrow use case or a single-tool experiment. They are instead imagining a standard operating expectation across all three types of AI tools.
The data further reveals that senior-most leaders (EVP/SVP/VP) hold the highest expectations for public GenAI: all of them expect daily use, compared with 56% of directors and 56% of managers. On enterprise AI the pattern shifts — directors expect daily use most often (67%), ahead of managers (57%) and senior leaders (62%). The ambition sits highest where organizational visibility is greatest.
Actual use tells a different story. Public generative AI is the leveraged most, with 48% reporting daily use. Enterprise platform AI sits at 29% daily use, despite being embedded in systems that organizations already own. Meanwhile, company-owned AI is lower still, with only 21% reporting daily use (sometimes limited to specialty use cases).
The more governed, integrated, or organization-specific the AI environment becomes, the more actual use trails leadership expectation. Public tools are easier to access, and teams are using them both at work and at home. Enterprise and company-owned tools require rollout, training, workflow design, and confidence on the part of users that the tool is worth using.
Gaps between expectations and actual usage appear consistently across all three types of AI, and, ironically, widen for the tools organizations have built themselves. Public GenAI shows a 17-point gap between expected and actual daily use. Enterprise platform AI shows a 33-point gap. Company-owned AI shows the most extreme divergence: 64% expected daily use versus 21% actual, a 43-point gap. Internally governed or company-specific AI may be where leaders see the greatest value, but it is also where teams appear furthest from the intended usage pattern.
The root cause of the adoption gap is capability and culture, not tooling. L&D leaders have set a clear ambition for frequent AI use, especially in enterprise and company-owned environments, but those tools are not yet embedded in the way teams work. That gap should feel familiar: this community has seen before that access does not create adoption, and adoption does not create proficiency.
L&D is meaningfully dependent on AI today — and the function has a stronger view than most leadership peers of how far that dependency will grow.
AI is not yet fully mature inside L&D workflows, but it is already present enough that most leaders would sorely feel its absence if removed.
Senior leaders report the deepest dependence: 44% of VP-and-above leaders say AI disappearing would create "moderate" disruption, compared with 21% of directors and 31% of managers. That may reflect senior leaders’ visibility into strategy, planning, content scale, and productivity expectations — the places where AI is beginning to reshape how work gets organized, and with an understanding of future realities for the business to which others may not have access.
As dependent as they are on AI in their workflows, L&D leaders are not predicting wholesale replacement. Most expect AI to automate less than 40% of the function’s work over the next 24 months. That said, a smaller cohort is more bullish: 15% of L&D leaders (and 22% at the VP-and-above level) expect 40% or more of the work to become AI-powered, above the cross-functional benchmark average.
L&D leaders are neither dismissive nor breathless — but are pragmatic in their outlook. They see AI changing the work — especially content production, curation, personalization, synthesis, and administrative lift — while still recognizing the judgment-heavy nature of learning strategy, facilitation, stakeholder influence, and culture change.
L&D leaders are not saying AI can run the function, but they are also not treating it as optional. The practical implication is not to slow down — it’s to get deliberate. The function is already dependent enough that unmanaged AI loss would hurt; that’s an argument for governance and enablement now, not later.
Across all three AI types, most L&D leaders rate their teams as (baseline) competent. Expert-tier proficiency is rare. And the gaps between senior leaders and managers on specific AI types point to uneven deployment and access across the organization.
Across all three AI types, the L&D proficiency rating is broadly "competent" — teams are able to use these tools but have not yet developed the expertise to redesign work around them. Public generative AI shows the strongest "advanced" tier at nearly 20%, reflecting longer exposure and more self-directed practice. Company-owned AI and enterprise AI trail significantly, with advanced proficiency at 16% and 8% respectively.
The role-level cuts reveal a more uneven picture. Directors rate their teams’ enterprise AI proficiency lower than either senior leaders or managers — 50% describe teams as “beginner,” compared to 22% of senior leaders and 21% of managers. Directors, often overseeing the rollout work of these tools, see more work ahead to achieve proficiency than their counterparts do.
While strong, L&D’s proficiency base may not yet be enough to support the daily-use expectations leaders are setting. Competent teams can experiment. Advanced teams can redesign. The gap between those two states is where most L&D organizations currently sit.
Teams can say they trust a tool and use it occasionally — but competence scores show what the daily impact actually is. The beginner concentration in enterprise AI in particular points to a specific training gap that adoption mandates alone won’t close. L&D is the function best positioned to design the instructional solution to its own proficiency problem.
L&D ranks #1 among all functions on AI trust. The mistrust drivers that do exist — output accuracy, data privacy, policy alignment — concentrate on public tools and ease considerably for enterprise and company-owned AI.
The trust picture is uniquely favorable for L&D. Public generative AI — the type that produces the most skepticism across other departments surveyed — earns a majority trust response here. Enterprise and company-owned AI perform even better, reinforcing the idea that L&D leaders are open to AI when it feels closer to the organization’s systems and governance.
Distinguished somewhat from the overall group, senior-most L&D leaders are the most skeptical. Among senior leaders (EVP/SVP/VP), 22% report significant mistrust of public GenAI — compared with 0% among directors and 6% among managers. That level of concern is practical: senior leaders carry more exposure to AI governance risk and more personal accountability for what goes wrong when public tools surface sensitive organizational data.
L&D teams are focused on the risks that most directly affect whether AI can be used responsibly in learning work: data privacy and security, inaccurate outputs, and bias. These concerns concentrate on public tools and ease considerably for enterprise and company-owned AI. Zooming in on senior leaders, we see mistrust concentrated around public GenAI and its potential conflicts with internal policies and data security risks.
For L&D, those concerns are not abstract. Learning content carries organizational values, compliance expectations, role-specific guidance, and employee experience implications. A flawed output can be more than inconvenient; it can become bad guidance at scale.
Trust and mistrust in this data are not opposites. L&D leaders can have moderate trust in a tool and simultaneously cite near-universal concerns about its output accuracy. That is a realistic picture of what responsible L&D AI use requires. Moving AI behind the firewall reduces some risks but does not eliminate the need for human review, quality standards, and governance. The function’s job is to design workflows that honor both sides of that equation.
Compared with other functions, L&D stands out on trust — #1 of all functions on public GenAI trust. Where it trails is capability: advanced proficiency sits in the teens across all three AI types, and actual daily use runs well behind the function’s own expectations. We score every function on the same six AI vectors, each measured the same way, then plot L&D’s distance from the cross-functional average — bars to the right mean L&D leads its peers.
Most peer functions show a more cautious AI posture. They may use AI heavily, but they often pair that usage with skepticism, especially around public tools. L&D is different. The function shows a broader trust base across public, enterprise, and company-owned AI — and that trust advantage is reinforced by a higher-than-average sense of current dependency and a more expansive view of what AI can do for the function over the next two years.
That position is an advantage, but it also creates pressure. Trust and dependence can accelerate adoption, but they can also get ahead of competence and governance. L&D’s benchmark position suggests a function ready to move — provided leaders invest in the skills, standards, and workflow changes required to make that movement productive.
L&D has bought into AI more fully than most peer functions — on trust, on dependency, and on automation outlook. That is a meaningful signal for a community responsible for helping organizations learn, adapt, and build capability. But trust and dependence are only the starting line. The next phase is whether L&D can turn that posture into disciplined use: enough structure to manage risk, enough practice to build fluency, and enough specificity to make AI part of the work rather than an idea hovering above it.