2026 Decision Intelligence Benchmark — Special AI Report
How talent marketing leaders are adopting AI — or not — to achieve their business objectives
Table of ContentsTalent marketing is not one function on AI — it’s two. Directors report high AI dependence, high trust in governed tools, and lower assessments of their teams’ AI capability. Managers report more modest disruption if AI disappeared but rate their teams’ proficiency higher and show stronger actual adoption of enterprise tools. These two layers of the same function are not experiencing AI the same way.
The 39-point gap between expected and actual daily use on enterprise AI captures the problem at its sharpest. Leaders expect 71% daily team use of enterprise platform AI. Actual daily use sits at 32%. The tools are embedded in the ATS, CRM, and programmatic stack — and they are not embedded in daily practice.
Where the function does show strength, it concentrates at the top. Directors trust company-owned AI at 82%, and talent marketing ranks 2nd of 11 on AI dependence overall. That dependence signal — and the confidence behind it — is a foundation to build from.
To better understand how leaders think about different kinds of AI, we grouped tools into three categories used throughout this report:
Talent marketing leaders set the highest expectations for enterprise AI of any type surveyed. Actual use falls far short across all three categories, and the enterprise gap is the most severe. Directors are outpacing managers on public GenAI adoption while lagging on enterprise AI — an inversion worth understanding.
Talent marketing leaders carry unusually high ambitions for enterprise AI. 71% expect their teams to use enterprise platform AI daily — the highest expected daily use of any AI type in this function and above the 60% expectation for company-owned AI and 48% for public generative AI. That ordering is notable: most functions rank public GenAI highest on expectations, treating it as the most accessible AI category. Talent marketing’s inversion reflects how deeply integrated vendor-embedded AI has become in the function’s mental model of how work should be done.
The role-level cut on company-owned AI exposes the sharpest expectation divergence. Directors expect their teams to use company-owned AI daily at a rate of 83%, compared with 53% of managers. That 30-point gap suggests directors are significantly more optimistic about internal AI deployment than the teams they manage — a pattern that sets up the adoption story that follows.
Actual daily use trails expectations across all three AI categories. Public GenAI lands closest to its target: 41% actual versus 48% expected, a 7-point gap. Company-owned AI shows a 22-point gap, with 38% actual daily use against a 60% expectation. Enterprise AI shows the widest shortfall in the function: 32% actual daily use against a 71% expectation, a 39-point gap.
The director-manager split inverts depending on which AI type is measured. For public GenAI, directors report higher actual daily use at 60% versus 37% for managers — a 23-point difference in the direction of directors leading. For enterprise AI, that advantage disappears: only 27% of directors expect daily use, below the 35% managers report. Members describe an environment where vendor AI features are accumulating faster than teams are building the habits to use them, and where the gap between expectation and reality is widest in the tools most directly under organizational control.
Public GenAI’s 7-point gap is narrow. Company-owned AI’s 22-point gap mirrors the cross-functional pattern for internally governed tools: They take longer to activate because they require rollout, training, and workflow integration that public tools do not. Enterprise AI’s 39-point gap is in a different category. Teams expect a lot more usage of enterprise AI features than what teams are currently implementing.
The director-level enterprise AI adoption figure of 27% is particularly important context. Directors in talent marketing are often the primary vendor managers and ATS owners. Their actual daily use being lower than managers’ suggests the tools with the most AI investment are not reaching the level of the organization where vendor relationships are managed and requirements get set.
Talent marketing ranks 4th of 11 functions on expected AI usage, 2 points above the cross-functional average of 58%. But the internal story is less uniform than the aggregate suggests. The enterprise AI expectation of 71% is the function’s defining ambition — and its defining gap. Members recognize the adoption lag on vendor-supplied AI as tied to a familiar challenge: getting new capabilities into daily practice is harder than acquiring them.
Talent marketing ranks 2nd of 11 functions on AI dependence. The aggregate figure masks a sharp internal split: 63% of directors would face moderate or major disruption if AI disappeared, compared with 33% of managers and 0% of senior leaders. Automation expectations are consistently moderate across levels.
Talent marketing’s 38% moderate-or-major disruption rate ranks 2nd of 11 functions and sits well above the cross-functional average. Directors report 63% moderate or major disruption if AI disappeared, the highest director-level dependence figure in Assemble's benchmark data. However, managers sit at 33% and senior leaders report 0%.
At the senior level, SVP and VP-level respondents report no disruption to their function if AI disappeared — the only role level in talent marketing at zero. That may create a potential governance blind spot: The leaders with the most authority over tool investment and strategy currently report much lower operational dependence on AI. The director layer, by contrast, will most acutely feel disruption in the absence of AI.
Talent marketing leaders’ automation expectations are moderate. 36% expect more than 20% of the function’s work to be automated by AI in the next 24 months, essentially at the cross-functional average. Nearly two-thirds of respondents expect 20% or less automation.
These expectations are consistent across role levels: directors and managers both land near 36–39% expecting more than 20% automation. That alignment, combined with the wide divergence in actual dependence, points to a function where the leadership view of AI’s strategic ceiling is shared, but daily reliance on the tools is distributed very unevenly.
Talent marketing is one of the most AI-dependent functions in the benchmark by the moderate-or-major disruption measure, ranking 2nd of 11. That ranking is almost entirely driven by the director layer, where 63% report meaningful disruption. Directors report the highest dependence on AI. The zero-dependence reading for senior leaders suggests AI has been positioned as a practitioner tool rather than a strategic input.
Talent marketing teams show a broadly competent profile on public GenAI and a more beginner-weighted picture on enterprise and company-owned AI. Managers consistently rate higher competence for their teams than directors across all three AI types, a reversal of the typical seniority-proficiency pattern we see across the benchmark.
Talent marketing teams report the strongest proficiency on public GenAI, where 74% rate their teams as competent or above, six points above the cross-functional average. That falls to 57% for enterprise AI and 54% for company-owned AI — a gradient that reflects the same pattern seen across most functions: longer exposure to public tools produces higher competence, while enterprise and internally governed tools require structured enablement that has not yet fully arrived.
While competency is common, advanced or expert proficiency is near or below the average across all three categories. 14% of teams reach advanced or expert on public GenAI. That figure falls to 11% for both enterprise AI and company-owned AI. Talent marketing ranks 6th of 11 functions on advanced or expert proficiency in Public AI, and 8th in the other two. The function isn't behind — but it's not far ahead of peer functions, either.
The pattern holds across all three AI types: directors rate their teams lower than managers do, and the gap is largest on enterprise AI (45% vs. 66% competent-or-above) and smallest on public GenAI (72% vs. 78%). On company-owned AI, directors rate 43% of their teams competent-or-above with no advanced-level ratings, versus 61% competent-or-above and 17% advanced for managers.
The data suggests that enterprise AI tools in talent marketing are often activated at the platform level but not embedded in team workflows — a pattern consistent when other departments choose the tool but may not use these features themselves.
Talent marketing ranks 6th of 11 functions on overall proficiency, essentially tied with the cross-functional average. Members describe teams that are using AI in their day-to-day work while their managers are still forming opinions about what the tools can do. That gap will constrain both adoption targets and vendor evaluation quality until leaders close it. Directors consistently rate their teams’ AI proficiency lower than managers do. Whether that reflects genuine capability differences, more demanding assessment standards at the director level, or lower personal familiarity with the tools, the gap is consistent across all three AI types and will constrain both adoption targets and vendor evaluation quality until leaders close it.
Talent marketing shows moderate-to-strong trust in enterprise and company-owned AI, and mixed trust in public GenAI. The most notable finding is the director-manager split on company-owned AI: 82% of directors express trust, while managers sit at 50%.
Talent marketing’s trust gradient runs in the expected direction — company-owned AI is most trusted, enterprise AI closely follows, and public GenAI earns the lowest trust. All three of those are near the cross-functional average and middle-of-the-pack among the 11 functions surveyed. Talent marketing ranks 5th of 11 functions on overall trust across all three AI types, 2 points above the cross-functional average of 43%.
The director-manager split on company-owned AI is the most pronounced trust divergence in this part of the data. Directors express 82% trust in company-owned AI while managers sit at 50%. Members note that directors who manage vendor relationships and data privacy reviews tend to develop stronger trust in tools they have had a hand in approving. Managers, who may have less visibility into governance structures, hold a more cautious view.
Data privacy or security concerns drive public GenAI mistrust at 91% in talent marketing — the leading concern by a wide margin, and among the highest rates in the benchmark. Talent marketing handles sensitive candidate data, compensation information, and hiring pipeline details. The risk of entering that information into a public AI environment is concrete, not theoretical, and leaders in this function are aware of it. Inaccurate or hallucinated outputs follow at 76% and misalignment with internal policy was next at 69%.
For enterprise AI, mistrust concentrates differently. Inaccurate or hallucinated outputs along with bias in data or training models are tied for first at 53%. Data privacy concern drops sharply from 91% to 31% when moving from public to enterprise AI — a signal that the vendor relationship and associated data agreements resolve most of the privacy anxiety. Company-owned AI shows further reductions across all drivers.
Talent marketing’s trust picture is coherent: concerns are institutional and data-driven, they ease substantially as AI moves into governed environments, and the function is slightly above average in its overall trust posture. Directors’ 82% trust is a meaningful organizational asset — it means the people with purchasing authority and governance responsibility are confident in the tools the organization has made. The gap to close is manager trust — bringing the practitioner layer into the governance picture.
Talent marketing’s most distinctive cross-functional position is its AI dependence ranking: 2nd of 11 functions, driven almost entirely by the director layer. On expected usage, trust, adoption, proficiency, and automation outlook, the function clusters close to the cross-functional average.
Talent marketing’s position in the benchmark is defined by standout AI dependence. At 38% moderate or major disruption, the function ranks 2nd of 11 — only employee experience is higher at 63%. That dependence ranking is not matched by a corresponding lead in adoption, proficiency, or trust, which puts talent marketing in a distinctive position: a function where AI has become structurally important to a key layer of the organization, even as overall usage and capability remain average.
On the five other vectors, talent marketing hovers close to the cross-functional center. Expected usage at 60% ranks 4th, two points above average. Trust at 45% ranks 5th, two points above average. Actual adoption at 37% is one point away from the average. Proficiency at 62% ranks 7th — tied with healthcare social media — essentially at the 61% average. Automation outlook at 36% ranks 7th, two points below average. The function is not behind on any dimension; it's not a leader either.
Talent marketing's benchmark profile shows uneven AI adoption across organizational layers. Managers have integrated enterprise AI more deeply into daily workflows, while directors express stronger confidence in governed AI environments but lower enterprise AI adoption and proficiency. Closing that gap represents the function's clearest opportunity for improvement.