Domain-Specific AI Transforming Executive Search
June 9, 2026 – Mark Jacobson is the GM, executive search with Findem.ai, an AI-powered talent intelligence and recruiting platform used by companies to find, engage, and hire candidates more efficiently.
At its core, Findem uses artificial intelligence and machine learning to build deep talent profiles by aggregating publicly available data (such as career history, skills, education, and signals of experience) into a single, continuously updated view of a candidate. Mr. Jacobson recently sat down with Hunt Scanlon Media to discuss how Findem’s foundation of context-rich, longitudinal talent data is changing executive search, allowing AI to move beyond keywords and résumés to support faster, more transparent, and insight-driven talent decisions.
Mark, what inspired the creation of Findem and its focus on AI-driven talent data?
Findem was created around a simple but transformative insight: people are far more than what they choose to write in a resume or LinkedIn profile. If the industry wanted AI that could meaningfully understand talent, it needed data that reflected the full arc of a person’s career—not just titles, timestamps, or keywords. Before Findem, firms were spending enormous energy on manual research: assembling longlists, stitching together multiple databases, and rebuilding market maps for every engagement. The real problem wasn’t the effort—it was the data itself. You can’t build intelligent systems on unstructured profiles and one-off spreadsheets. Our breakthrough was to build labeled, contextual, longitudinal data—what we call 3D talent data. It captures three essential dimensions: Who a person is; where they worked and in what company context; and when they achieved specific outcomes over time. Executive search questions are inherently contextual. “Find me a sales leader who scaled a business from early stage through exit” can’t be answered with keyword search. It requires understanding what someone did, at what stage, and with what impact. That foundation of expert-labeled, context-rich data is what separates domain-specific AI from generic AI—and it’s why Findem is delivering ROI in an era where most enterprise AI projects struggle to do so.
How do you see AI changing the future of recruiting and HR?
We’re entering a decade where talent decisions must be faster, more transparent, and rooted in evidence. Boards want clarity. CEOs want agility. And teams can no longer rely on workflows driven by manual research and institutional memory. AI is fundamentally reshaping the market. The last decade was defined by point solutions—tools that solved narrow parts of the workflow but didn’t change outcomes. The next decade will be defined by intelligent systems that understand context, generate insights, and guide decisions.
What is the risk of not learning the ins and outs of AI?
For executive search, the implications are profound: Speed and scale without sacrificing quality; institutionalized intelligence that compounds with every engagement; and evidence-based communication that elevates the search experience. Crucially, this isn’t about replacing humans. It’s about shifting their focus. AI can complete mechanical research tasks 80× faster and with greater consistency, freeing researchers and consultants to focus on advisory work, client storytelling, and candidate assessment. We call this emerging role the talent engineer—someone who partners with AI to shape search strategy, evaluate outputs, identify bias, and translate insights into board-ready narratives. By 2026, we expect the shift from traditional SaaS to assistive and agentic AI to be well underway. We’re launching specialized agents built specifically for search workflows—not generic chatbots, but domain-specific intelligence tuned to what elite firms actually do.
How does Findem ensure fairness and transparency in its algorithms?
Fairness and transparency have been core to Findem from day one. Our 3D data is fully auditable. Every attribute—from company stage to role impact—is explicit and based on verifiable signals. We never infer protected classes or rely on opaque scoring. We maintain rigorous GDPR and CCPA compliance, pass external bias audits, and provide clients with controls for adjusting or disabling attributes tied to sensitive categories. We also work with organizations such as AnitaB.org, RecruitMilitary, and Glider AI to ensure our datasets treat diverse talent pools accurately and responsibly. AI has the potential to democratize opportunity—but only if built with transparency, community partnership, and continuous monitoring at the core.
How do you measure success—for both clients and your own team?
For clients, success is measured by outcomes that matter to firm leaders: Reduced time-to-fill; Lower cost-per-search; higher consultant productivity; and more transparent, evidence-based client interactions. Many firms consolidate multiple tools into Findem and often accelerate searches by 30 days or more. We also measure how much time teams reclaim from manual research so they can focus on the strategic advisory work clients pay for. For our own team, we measure success by customer impact, partner satisfaction, and the compounding quality of the intelligence we help firms create.
What’s next for Findem?
The theme for 2026 is clear: outcomes over features. We’re launching AI agents tailored to the nuances of executive search – agents that understand context, stage, and operating realities across the talent landscape. Our longer-term vision is to become the operating system for talent decisions. Our labeled data becomes the foundation for organizations to build their own agents, their own AI workflows, and their own intelligence on top of our platform. The firms that win will be those that invest in transformation – not just tools. And we’re building the infrastructure to power that future.
