2026 Decision Intelligence Benchmark — Special AI Report
How DEI leaders are adopting AI — or not — to achieve their business objectives
Table of ContentsDEI leaders are among the more active AI adopters in the benchmark: 57% report their teams using public GenAI daily — tied for the highest rate in the benchmark and 9 points above the 48% cross-functional average, while 50% report their teams using enterprise AI daily — 13 points above the 37% benchmark average. This level of daily adoption outpaces most peer functions, including L&D and CSR.
What the adoption numbers do not surface is the proficiency gap sitting beneath them. DEI ranks 10th of 11 on enterprise AI proficiency, with only 50% of leaders rating their teams competent or above — 10 points below the 60% cross-functional average, and no advanced users on enterprise or company-owned AI. Teams are using enterprise AI at the highest rate in the benchmark while developing advanced capability in it at one of the lowest rates. That combination — high use, low advanced proficiency — is the central challenge ahead in DEI's AI story.
The other defining characteristic of DEI's AI position is the nature of its skepticism. Data privacy is the top mistrust driver on public GenAI at 82%, consistent with the benchmark average. But bias in data or training models ranks 2nd at 77% — the highest bias concern of any function surveyed and 18 points above the 59% cross-functional average. It is also the only function where bias outranks inaccuracy (73%) as the second-ranked concern. DEI leaders understand specifically how training data shapes outputs, and they are responsible for the organizational outcomes that biased AI could distort.
For the purposes of this report, and to better understand how leaders think about different kinds of AI, we grouped tools into three categories:
DEI leaders have set some of the most ambitious AI adoption expectations in the benchmark, particularly for public and enterprise tools. The data also shows that actual daily use is keeping pace better than in most peer functions — a sign that individual leaders are driving informal adoption even without well-developed programs.
DEI leaders set above-average expectations for public and enterprise AI, while company-owned AI expectations sit slightly below the benchmark. 67% expect their teams to use public generative AI daily, compared to the cross-functional average of 60% — placing DEI 3rd among 11 functions on this measure. Enterprise AI expectations are nearly as high at 65%, above the 59% benchmark average. Company-owned AI expectations are lower at 47%, slightly below the 50% cross-functional figure.
Breaking down by level, directors were the largest sample in this group and their expectations for public GenAI daily use are the highest within the function, suggesting an environment of practitioner-level adoption.
Actual daily use in DEI is above the cross-functional average for both public GenAI (57% vs. 48% overall) and enterprise AI (50% vs. 37% overall), making DEI one of the stronger-performing functions on realized adoption for these two tool types. Company-owned AI actual daily use sits at 27%, exactly at the benchmark average. AI use in this function is likely driven by individuals using readily available tools for writing, research, and analysis.
Members describe AI as a frequent subject of strategic discussion, but governance and formal enablement have not caught up.
The expectation-to-actual gaps in DEI are smaller than in most peer functions. The public GenAI gap is 10 points (67% expected, 57% actual), and the enterprise AI gap is 15 points (65% expected, 50% actual), while the company-owned AI gap is larger at 20 points (47% expected, 27% actual). For comparison, the cross-functional average gaps are 12, 22, and 23 points respectively. Part of this is a product of alignment, but it also reflects something fundamental about DEI teams: They tend to be small (sometimes just teams of one), making alignment easier or even trivial.
DEI leaders are clear about what AI should do for their teams. The tighter-than-average expectation gaps on public and enterprise AI point to individual practitioners driving adoption through personal use rather than waiting for organizational guidance. DEI leaders say they are rarely included in enterprise AI decisions from the outset — often brought into problem-solve after tools are already deployed, not to shape them from the beginning. The next level of AI integration requires identification of the best opportunities for enhancing the work of DEI using technology.
DEI leaders are not heavily reliant on AI, and they do not anticipate that large portions of their work will be automated in the next two years. This response reflects both the human-centered nature of most DEI work and a realistic read of the function's modest process optimization goals.
77% of DEI leaders predict slight disruption or no disruption if AI became unavailable — a conservative read that places DEI in the lower half of the benchmark on AI dependence. Only 18% predict moderate disruption and 5% major disruption.
At the role level, directors' disruption expectations skew even lower. Among the small senior leader cohort, there is slightly more variance of opinions.
DEI leaders are pessimistic about future automation potential for AI. This group had the second-highest share among the 11 functions surveyed to believe that fewer than 20% of their function's work could be automated by AI over the next 24 months, at 77%. The cross-functional average has 63% in the under-20% band, and 10% seeing 40% or more automatable — so DEI is notably more conservative on both ends of the scale. Only 5% see 41–60% automation potential.
These results portray the relational nature of DEI work: relationship-building, facilitation, culture change, and navigating organizational politics are inherently human activities. Where AI automation potential is more credible — data analysis, reporting, content creation, employee listening synthesis — the function has expressed some interest but could probably use a more refined vision.
The low disruption and low automation outlook in DEI reflects a sentiment that the most important parts of their work cannot be delegated to an algorithm. While usage has been strong, it is not transformative and leaders are perhaps not seeking technology-driven transformation. DEI leaders are also involved or concerned with AI adoption across the enterprise, an opportunity to gain more exposure on the capabilities of emerging technology.
DEI leaders rate their teams as broadly competent in using public generative AI and company-owned AI, but expert-level proficiency across any of the three tool types is very low.
DEI's proficiency profile is uneven across the three AI types. On public GenAI, 71% of DEI leaders rate their teams competent or above — right at the 70% cross-functional average, ranking 6th of 11 functions. On company-owned AI, 67% rate teams as competent or above, ranking 3rd of 11 and 10 points above the 57% benchmark average — a genuine strength that likely reflects the smaller, more deliberate deployments of internally governed tools.
Enterprise AI is the outlier in the wrong direction: only 50% of DEI leaders rate their teams competent or above on enterprise tools, ranking 10th of 11 functions surveyed and 10 points below the 60% cross-functional average. That gap is particularly notable given that enterprise AI records the second-highest daily use rate in DEI at 50% — teams are using these tools more than their proficiency levels would suggest.
Advanced proficiency is nearly absent across all three types. DEI records 6% advanced on public GenAI, below the 16% cross-functional average, and 0% advanced on both enterprise and company-owned AI, against benchmark averages of 13% and 14% respectively.
The role-level cut on proficiency shows directors as the level most likely to rate their teams as competent on public GenAI — consistent with directors being the most active users and the most direct managers of team AI behavior. What is consistent across levels is the near absence of advanced proficiency ratings. This appears to be a ceiling that exists at every level of the function.
Competent is a good place to be, but it is also the plateau for most DEI practitioners. The absence of advanced proficiency on enterprise and company-owned AI — tools that organizations are investing heavily in — is the most actionable finding in this section. DEI has an opportunity to improve the ability of existing, competent users, to realize greater gains from new technology.
DEI leaders show a trust gradient that moves in the same direction as the broader benchmark — higher trust in internal tools, more skepticism toward public GenAI — but the reasons for their skepticism about public GenAI are meaningfully different. Bias is the dominant concern, not privacy.
DEI leaders express moderate or significant trust in enterprise AI at 55%, slightly above the cross-functional average of 51%. Company-owned AI earns the highest trust share at 68% (moderate or significant), well above the 50% benchmark figure — a strong result for a community that often does not own the AI tools they use.
Public GenAI sits at 27% moderate or significant trust, ranking 6th of 11 and just below the 28% benchmark average.
The mistrust driver profile in DEI highlights the function's mission. Bias in data or training models is cited by 77% of DEI leaders as a driver of public GenAI mistrust — a figure that is 18 points above the cross-functional average of 59% and the highest of any function. Inaccurate outputs comes in at 73% and data privacy at 82% (consistent with the benchmark pattern), but the elevation of bias above peer functions is distinct to DEI.
The bias concern extends into enterprise AI as well: 50% of DEI leaders cite bias as a driver of enterprise AI mistrust, above the 45% cross-functional figure. For company-owned AI, bias drops to 36%, consistent with greater organizational control over training data and model governance. Members say they understand well how AI systems can replicate and amplify historical patterns of bias, and work to remove that bias and hold organizations accountable when AI tools produce inequitable outcomes.
Enterprise AI earns genuine confidence — likely because employer-governed tools feel more accountable to the organization's stated values. Public GenAI earns skepticism that is specific and grounded: DEI leaders understand first-hand how training data shapes AI outputs. HR and business leaders can consider addressing trust gaps with bias evaluation frameworks and governance processes that give DEI leaders a formal role in reviewing new tools before they are deployed at scale.
Compared with peer functions, DEI holds an above-average position on actual AI adoption and AI trust — both meaningfully ahead of the cross-functional average. Team proficiency lands close to the benchmark on aggregate, but DEI's advanced-tier proficiency remains among the most restricted in the benchmark — a narrower, more specific gap than the overall proficiency picture suggests.
DEI's cross-functional position reflects a function that is using AI more than many peers but has not yet built the depth of capability that would make that usage transformational. On AI adoption — the share of leaders reporting daily use across all three tool types — DEI performs above the benchmark average, driven largely by strong public and enterprise AI use rates. Trust is a genuine strength as well: DEI ranks 4th of 11 on the aggregate trust measure, well above average, driven by strong enterprise and company-owned AI trust. These are real advantages. The function has not turned away from AI or treated it as irrelevant.
Team proficiency is closer to the benchmark than the underlying story might suggest: DEI's aggregate competent-or-above rate sits almost exactly at the cross-functional average. The real gap is narrower and more specific — advanced-tier proficiency, which is nearly absent across all three AI types. That distinction matters for how leaders respond: general AI enablement programs will move the aggregate number, which is already fine, but won't produce the advanced practitioners the function currently lacks. On dependence and automation outlook, DEI sits modestly below the benchmark, consistent with a function that is using AI without having woven it deeply into its operational foundation. And while trust is strong overall, the mistrust that does remain is rooted in concerns — particularly bias — that are harder to resolve through general AI enablement than through targeted, bias-specific governance.
DEI's position in the benchmark captures a function in an earlier stage of AI integration than its adoption numbers alone might suggest. Usage is real, but it is individualistic and informally acquired. Trust in enterprise tools is genuine, but the governance infrastructure that would make that trust durable is not yet in place. The bias concern that animates DEI's AI skepticism is not an obstacle to be overcome — it is a form of domain expertise that should be formalized into the function's institutional role. The functions that are furthest along in AI are not the ones where adoption happened fastest; they are the ones where adoption was paired with governance, fluency, and clear accountability. DEI has the adoption. The governance and fluency are the next chapter.