For the past year, executive talent leaders have been asking themselves the same question on AI: Am I moving fast enough, or could I be doing more? The pace of AI development feels impossible to keep up with (OpenAI just released GPT-5 last week), and no one wants to be left behind. To find the answer, we surveyed over 30 executive talent leaders to benchmark exactly how AI is being used—and where the real opportunities lie. The results reveal a surprising disconnect between daily habits and long-term strategy. AI Comfort Levels in 2025 Overall, executive talent leaders are still in the early adoption phase of incorporating AI into their daily workflows. 55% of exec talent leaders ranked themselves as having a personal comfort level of 3 out of 5, meaning they feel middle of the road on their usage. When we break this down between executive talent leaders working in three different settings– venture capital and private equity firms, in-house companies, and executive search firms– leaders at venture capital and private equity firms had a slight edge in terms of self-reported AI confidence.33% of executive talent leaders at venture capital and private equity firms indicated a comfort level of 4, compared to 22% at executive search firms and 25% at in-house firms. No participants from venture capital or private equity firms ranked themselves at a level 1. A higher self-reported comfort level with AI tools was associated with an increase in the number of AI tools adopted. Respondents who ranked themselves at a level 3 or 4 reported 1-2 more AI tools in their daily workflows compared to respondents at levels 1 and 2. It’s important to note that not a single survey respondent gave themselves a 5, indicating that everyone feels there is still more to learn about AI. Top AI Tools for Executive Talent The most common AI tool being leveraged in executive talent workflows is Chat-GPT or other LLMs (e.g., Gemini, Claude, Perplexity, and Anthropic), with over 90% adoption. Established AI tools like notetakers (e.g., MetaView, Fathom) and AI-native LinkedIn Recruiter features are tied for second, with 64% adoption respectively. While AI sourcing tools are another well-established category, talent leaders seem to be adopting these at a substantially lower rate. One explanation is that their own proprietary data is superior to what is available in current providers. After LLMs and the established tools, adoption of more specialized AI tools drops off a cliff. Very few leaders are leveraging custom-built AI or have adopted AI coding tools themselves. This is a massive opportunity area to build a competitive advantage, and many leaders haven’t seen their organizations cross the chasm yet. When we break down tool usage by workplace setting further, we get a clearer picture of how different talent leaders are leveraging a combination of AI tools in everyday workflows. AI Notetakers, for example, factor more heavily into the workflows of executive talent leaders at executive search firms (70%) and venture capital and private equity firms (67%), but are not as widely adopted by leaders at in-house companies.The native AI features in LinkedIn Recruiter, meanwhile, see the highest adoption by in-house leaders (75%), along with sourcing tools (75%) and custom-built internal AI tools (50%). Higher privacy and security concerns among in-house users likely contribute to this adoption pattern. Additionally, as these tools tend to require budget approval through a traditional procurement process, it tracks that respondents have higher adoption among tools that have gone through lengthy approvals and been thoroughly vetted by their organizations.Venture capital and private equity are also adopting custom-built AI tools for customized workflows, though at a lower rate (33%). While only 4% of leaders at executive search firms have adopted custom-built AI tools, 17% of leaders in this group are leveraging AI code tools to create custom reports and automate repetitive tasks. The AI Task Disconnect So how are leaders actually leveraging these AI tools across their day-to-day tasks? We asked leaders to share whether they never, sometimes, or always use AI across four task categories: Data Collection, Data Management, Research, and Writing. When it comes to AI adoption, executive talent leaders are more comfortable applying AI across creative and analytical tasks involving research and writing, while operational tasks surrounding data collection and management are still in nascent stages. Approximately 40% of leaders shared they “Always” use AI for writing and research tasks. When coupled with “Sometimes” responses, about 90% of respondents use AI for these tasks with some degree of regularity. When reviewing data management use cases for AI, the number of “Always” responses drops significantly, down to 9%. Interestingly, data management received the highest number of “Sometimes” responses for AI usage (70%), which could mean that AI is being used for one-off tasks, rather than being incorporated into systematic processes. Perhaps the biggest takeaway is that, while 40% of leaders ‘Always’ use AI for research and writing, nearly the same number ‘Never’ use it for data collection. If the foundation of good research is good data, this gap suggests many AI-driven insights are built on a shaky foundation, or the AI research and writing workflows are not as transformative as they could be.This presents a major opportunity for leaders who want to invest in incorporating AI within data management workflows. What’s Standing In the Way of More AI Adoption? When it comes to the barriers preventing talent leaders from leveraging AI more often, the standout response is a lack of understanding. This is especially true for talent leaders working at executive search firms, where more than half of the leaders identified it as a major concern. The primary issue may stem from a lack of organizational AI policies or a formal training program, rather than from individual interest or capabilities. The knowledge gap highlighted in the data presents a significant risk to organizations that do not equip their teams with a cohesive AI strategy. The second-leading barrier overall was “Legal or Privacy Concerns” (33%), with many leaders expressing concern over leaking proprietary information—nearly all of the responses for “off-limits” activities involving AI referenced exposing client data. Leaders from venture capital and private equity firms cited this barrier more than leaders at in-house companies or executive search firms. The most confident and comfortable group with AI tools also has the most awareness of the risks associated with sharing proprietary data. From there, data quality and budget constraints follow as the next leading concerns. These operations-oriented categories are the most important to in-house executive talent leaders due to the reality of rigorous procurement processes for corporate software purchases. This group is more likely to rely on the native AI features within pre-approved platforms like LinkedIn Recruiter, even if more powerful, specialized tools exist on the market. Conclusion Executive talent leaders are embracing AI in daily research and writing workflows, but haven’t yet cracked the code to incorporate AI into the data management practices that power those tasks. The most comfortable and confident AI adopters are not only using more AI tools, but are also more concerned about security and privacy concerns. These tensions highlight a market in transition, grappling with how to turn the immense potential of AI into a reliable and scalable advantage. The solution isn’t a new tool, but an organization-wide strategic shift that empowers talent leaders to turn their talent data foundation into a true insight engine. Those who do this successfully will come out ahead. About This Survey We collected survey responses via Typeform between July 10 and August 5, 2025, from a panel of executive talent leaders based primarily in North America who were recruited through our company newsletter and LinkedIn posts. In total, we received 33 complete responses across three workplace settings: venture capital and private equity firms (n=6), in-house companies (n=4), and executive search firms (n=23). Over 90% of respondents hold titles at the VP or Partner level. No compensation was provided to survey respondents for their participation. Given the targeted nature of this research, readers should note that the overall sample size is small and heavily weighted towards the executive search vertical when considering the findings.