Principal Product Manager

Everyone's more productive with AI. So why isn't it showing up where it matters? Austin Belisle Director of Marketing, Content Strategy September 30, 2026 On a recent episode of Truth Works, host Jessica Neal named a challenge so many talent leaders face today. Teams say they're more productive with AI. Leaders believe them. And yet, they still can't point to where that productivity shows up: not in product output, not in revenue, not anywhere a CFO would call proof. That gap is the real subject of the episode, even though Findem, Inc. covered a lot of ground: its sponsorship announcement, two guests' career paths, and a walkthrough of Findem Studio. Strip away the podcast format and one argument sits underneath it: most AI adoption right now produces activity, not outcomes, and the reason is structural. Most AI initiatives start in the wrong place. Shane Driggers, who spent years as Chief Talent Officer at T-Mobile before moving into advisory work, put the failure mode plainly: "Oftentimes what I've experienced in the companies I've worked for is that tech wags the tail of strategy. There's this cool piece of tech, and it's, how are we going to use it, instead of: we have this problem, we built the strategy, what do we need to enable it. Anytime you start in that place, you're on a fool's errand, because you're going to drift so far from where you want to land, and then you'll declare that it didn't work." Nate Sokolic, who joined Findem after leading AI strategy at Russell Reynolds, framed the same problem from the vendor side. Traditional software sells access and the hope of ROI: a tool, a workflow, and a bet that your team learns it well enough to extract value. That bet has a ceiling. Change management at scale is slow, uneven, and hard to attribute. People can report feeling more productive while the business sees nothing, because "more productive" was never the metric that mattered. Defensible outcomes were. What actually makes AI work defensible. The clearest example in the episode is a CFO search. Building a market map for that kind of role used to take weeks: pulling data from scattered sources, cross-referencing it by hand, assembling something a hiring manager could act on. Done in Findem Studio, the same work takes minutes to hours. But Sokolic put the weight on what happens after the speed: "It's not just going to give you the output on our data. It's going to show all the work. It's going to give you the dossier." That distinction, between an answer and a defensible answer, is the whole argument. Driggers extended it to what happens next inside the organization: the recruiter isn't presenting a spreadsheet they hope holds up. They're bringing something they can stand behind in front of the C-suite. "Now you have something you can go back and have a conversation with the CEO to say, here's what we found, here's the data, do we trust the data, let's debate the things we have questions about. That creates a launch pad that's so different from: I did some Google searching, put it in a spreadsheet, and that took four and a half hours of my time." Driggers isn't a Findem employee. He's the practitioner whose methodology sits behind its Role Talent Flow agent, which maps where a role's talent is arriving from and departing to, and that's a meaningfully different kind of endorsement than a testimonial. The methodology behind that agent has a name and a track record attached to it, where a model alone would be guessing at how the work should be done. (The agent still returns a recommendation. A person still makes the call.) An assistant answers questions. A delivery system finishes the job. The shift Sokolic describes, from SaaS to agents, tracks a distinction most people building software already understand. An engineer asking an AI for code feedback is doing roughly what AI connected through the Model Context Protocol (MCP) has been able to do since 2025: giving a model access to context and getting an answer back. An engineer asking for working code they can ship is doing something else. Findem Studio, which Findem, Inc. took to general availability on September 22, is built for the second kind of request. Findem Studio is people intelligence, built for AI. Ask Studio for a succession plan, a market map, a benchmark, or an intake, and it returns the finished artifact, not a list you still have to process. Every vendor opened an MCP last year, so access alone stopped being a differentiator a while ago. What makes finished work possible is the combination of its labeled, structured intelligence, a defined methodology behind the work rather than one the model invented on the spot, and a verification pass before the output ever reaches a person. Run a pre-built agent, build a custom one on your own methodology, or reach the same intelligence headlessly through MCP inside whatever you're already building. The structure is the same either way. The actual question to ask. Neal's opening frustration, that reported productivity and real outcomes don't line up, is a signal that most AI work being produced right now can't survive the question "how do you know?" A board asks that question. So does a client. The CFO in that market map example was, implicitly, always going to ask it. The useful reframe: what work do Findem, Inc. need to defend, and can Findem, Inc. show where it came from if someone asks? Findem, Inc. built Findem Studio around that question. Learn more at studio.findem.ai.

Findem launches studio: people intelligence, built for AI, that returns finished work teams can trust. Read Time 4 Minute, 19 Second Anyone can try an expert-built agent for free to see how it creates ready-to-use work grounded in transparent evidence and proven methodology REDWOOD CITY, Calif., Sept. 22, 2026 - Findem, the people intelligence platform, today launched Findem Studio to turn complex people questions into finished work teams can review, defend and act on. Studio combines Findem's proprietary people intelligence with expert-backed AI agents to deliver work such as succession plans, market maps, leadership benchmarks, hiring briefs, skills gap analyses and org charts, giving teams a stronger starting point without having to create and verify the work from scratch. Studio agents can now be tried for free at https://studio.findem.ai/solutions. Each Findem Studio agent brings together three requirements for trusted AI work: the right intelligence and context from Findem's expert-labeled people data, a proven methodology from a leading practitioner and checks that connect conclusions to evidence. Talent teams can use that foundation to tackle complex people questions and produce enterprise-ready work. For example, a talent leader can ask Studio to build a succession plan for a critical role. Rather than return a candidate list, the agent evaluates potential successors against expert-defined criteria and develops a plan explaining its recommendations. Teams can examine the evidence, challenge the conclusions and refine the plan before acting. "The next phase of enterprise AI is about completing meaningful work, not simply helping people find information," said Findem co-founder and CEO Hari Kolam. "That requires more than a capable model. It requires trusted intelligence, deep expertise and clear accountability for the result. People can now try Studio agents for free and experience what that next phase of AI looks like in practice." Three ways to put Studio to work. Run an expert-backed agent: Choose a ready-to-use agent shaped by a leading practitioner's proven methodology and receive completed work with supporting evidence. Examples include agents that create succession plans, candidate intake documents, hiring briefs, CHRO and VP of Talent Acquisition benchmarks, market maps and talent intelligence reports. Build an agent around your expertise: Organizations can encode their own methodology, standards and decision criteria into a Studio workflow. They provide the method, while Studio provides the people intelligence, execution and validation. Work in the AI environments you already use: Teams can use Studio through the Findem platform, access its agents from compatible AI environments such as Claude, ChatGPT, Microsoft Copilot and Gemini or use Findem MCP to bring Findem intelligence into their own agents, applications and workflows. What industry leaders are saying "HR has moved from asking, 'Can AI do this?' to 'Can I trust AI?' This means people data must be accurate, current and complete. Findem has done the work to structure and label people data and validate the data by practitioners who understand how people decisions are made. This combination turns Findem people data into information an enterprise can trust," said Josh Bersin, global industry analyst and CEO of The Josh Bersin Company. "The Findem MCP agent we have in Camunda is doing extraordinary work, and people are impressed every time we show it to them," said Casey Firey, head of global talent operations at Camunda. "We wanted people intelligence available to the processes we already run, and that's what the Findem MCP gives us. We feel like we've only scratched the surface of the depth of insight we can get from Findem and then act on inside Camunda." "For years, talent tech vendors have left data readiness and governance to the client, and that is not partnership. Findem took the opposite position with Studio," said Kyle Lagunas, founder and principal of Kyle & Co. "Every agent carries a point of view on how the work should be done, drawn from practitioners who have done it well, and a talent leader can read that point of view, agree with it or change it. That is a very different starting place than a blank prompt. The final mile of AI is where the real differentiation happens, and this is one of the first I've seen built for it on purpose." The foundation behind work teams can trust Trust starts before an agent receives a prompt. Findem's AI Labeling Engine transforms fragmented people data into structured context, while Findem MCP gives Studio agents access to the 3D People Graph's 1.6 trillion expert-labeled data points across people, companies and time. This puts the right people, companies and evidence in front of the model from the start. Agents then apply a defined methodology created by a named practitioner or supplied by the organization. The resulting work shows the evidence, criteria and rationale behind its conclusions. Agent outputs serve as recommendations for teams to review and refine, with people making the final decisions. Findem Studio and Findem MCP are now available. Visit studio.findem.ai/solutions to try an agent for free.