--- title: "People People Summit 2026: AI, Investors, Lean Hiring | Findem" description: "What 160+ People and talent leaders said at The People People Summit 2026 in Toronto about investor expectations, AI fluency, and hiring with lean teams." url: "https://www.findem.ai/blog/people-people-summit-2026-takeaways" --- # What people leaders said at The People People Summit 2026 ← Back to blog Elan Keshen Director of Product Marketing, Funds and Networks October 23, 2026 On October 20, more than 160 HR, People, and talent leaders filled MaRS Waterfront in Toronto for [The People People Summit](https://luma.com/tppgsummit2026). Most of them work at venture- or PE-backed companies somewhere [between seed stage and a few hundred employees](https://www.findem.ai/blog/ai-candidate-sourcing-companies-under-300-employees). Nearly all of them are being asked to hire well with fewer people and adopt AI without a roadmap. They're also expected to explain their function in finance's language. We sat in on four sessions: a CEO and Chief People Officer on running a company through change, an investor roundtable on what VCs and PE firms want from People leaders, a panel on AI at work, and [a live build of an AI sourcing workflow](https://www.findem.ai/blog/autonomous-ai-sourcing-agents). Speakers asked for candour, so we've left names and companies out. One pattern ran through all four. People leaders are now judged on business terms, and AI is changing the shape of the work faster than it's changing how many people do it. ## The seat at the table is earned in business metrics A software CEO on the opening panel put the stakes in one number: headcount and salaries make up 70 to 80 percent of a software company's P&L. During fast growth, a business can absorb a lot of inefficiency, and investors expect it to. When growth slows, the cracks show. Every role gets examined, and the People leader sits in the middle of those conversations. His company has gotten meaningfully smaller over the past year. His executive team now works toward one shared set of business KPIs instead of a set per function, and his Chief People Officer tells town halls plainly that no one is locking the door. A company in transformation needs people who want to be in their seat. Investors at the afternoon roundtable described that expectation from across the table. From a head of People, during diligence and after, they want: - **A story that matches the CEO's and CFO's.** They expect a growing company to have problems. They want the leadership team to describe those problems the same way. If the CEO says sales is hitting quota and the attrition data says otherwise, the gap is the problem. - **Accurate numbers, even if reconciling them takes a day.** Investors will happily wait a day; a number that changes later costs trust. - **Risks raised before they find them.** One investor said the only thing that kills a deal at the HR diligence stage is suspected fraud. Weak employment agreements or open severance become a line item they plan to help fix. - **Context.** Zero attrition at 175 people isn't good news until someone explains it. Ask for the diligence checklist before the meeting, because investors have one. After the round closes, ask to read the investment memo: it tells you whether the investor expects growth at all costs or margin discipline, which changes who you hire and who you keep. Learn the equity plan well enough to teach it. One panelist built an "Options 101" session with their CFO after hearing a recruiter offer candidates "option dollars." Then connect programs to valuation. "We hired four salespeople" is an activity report. "We hired three reps who ramped to full quota by Q2, and that's in the revenue forecast" is a business result. If you don't know which metrics your investors weigh most, ask them what their best portfolio companies report. ## AI is changing job shapes faster than headcount The People leader at an enterprise AI company said her headcount hasn't changed much because of AI. The skills she hires for have, and so has the way each person on her team spends the day. Two skills matter more than they used to: critical thinking, and the ability to judge quickly whether an AI output is any good. [An early-stage investor's talent partner](https://www.findem.ai/solutions/vc-pe-talent) said she hasn't had a founder ask for a pure people-manager role in more than a year. New leaders are expected to stay hands-on and reach for tools and automation before they reach for headcount. ### The candidate everyone wants barely exists Hiring managers keep describing the same person: 20 years of operating experience and deep, current AI fluency. One search firm maps every search on those two axes, and the corner where both are high is nearly empty. Every company with that wish list is fishing in it. The panel's advice was to decide which one the role actually needs. Seniority helps when someone has to see around corners. AI aptitude can come from a senior leader who reskilled recently, or from someone with three years of experience who has been building with these tools daily. Several early-stage companies in one portfolio now have young technical leaders with no management experience on paper and strong hands-on skills in practice. Hiring is shifting toward potential and learning speed over a playbook someone has run before. ### Test AI fluency instead of asking about it One talent leader described a recent process that separated real capability from vocabulary: - A screening question: describe something you built with AI, at work or at home, that changed how you operate. - A case study where candidates were told to use AI and given 20 minutes. - An in-person interview with no AI at all, built on situational questions, such as what you'd do when a venue calls to say it can't fit your event. The third step matters as much as the second. It shows how someone thinks on the spot when there's nothing to prompt. Neither panelist has cut junior hiring. Founders are more interested in [early-career talent](https://www.findem.ai/blog/early-talent-in-peril-sap-findem-research): newer hires bring creativity and have fewer habits to unlearn. ### Keep AI governance short enough to remember The enterprise AI company's [internal policy](https://www.findem.ai/blog/ai-governance-in-hr-guide) fits on one page and has three rules. [AI doesn't make decisions, so no one can blame it for one](https://www.findem.ai/blog/who-is-accountable-for-ai-in-hiring). Be careful with what data you share; imagine it leaking. You own the quality of the output, and a long document isn't a better document. For lean teams, the same logic applies to build-versus-buy: build small tools where the risk is low, and buy from a reputable vendor where the data is sensitive or the workflow is core. ## Lean teams automate the repeatable work and keep the human parts human The most practical session of the day was a live build. An independent recruiting practitioner took a real job description for an AI engineer at a global financial services company and turned it into a scored shortlist in about 45 minutes, using a coding agent connected to public people data. The agent's first useful move was to point out that the job description asked for two people: a hands-on LLM builder and an enterprise finance governance operator. Those rarely show up in one profile. In a real search, he said, that's the moment to [go back to the hiring manager](https://www.findem.ai/blog/hiring-manager-candidate-requirements) and agree on a tradeoff before anything else happens. For the demo, LLM depth became a must-have and finance experience a nice-to-have. From there, the workflow ran in four steps: 1. Map the job description to search filters, and flag the requirements that can't be filtered. "Reacts well to shifting priorities" isn't something public data can confirm. 2. Run several searches, starting wide and narrowing, then merge the lists and remove duplicates. 3. Build a scorecard. Candidates missing a must-have drop out; nice-to-haves add weighted points. 4. Score the list and attach the evidence behind each score, so a person can check the reasoning. His method is worth copying even if you never open a terminal. Give the agent all the context you have, let it take the first step, and correct it on every turn. After two or three rounds, when the output matches how you'd have done it, ask it to write the process down as a reusable instruction set. Next time, you start from there. ### The 80/20 split He estimates he can automate about 80 percent of his work. The other 20 percent stays human on purpose. He reaches out to 30 to 50 people a day, and an agent drafts every message, but he reads each one before it goes out. He records a short personal video for every candidate before a call. The time saved elsewhere is what pays for that. An investor's talent partner splits her work along a similar line. Her number of introductions per year hasn't changed, because the bottleneck is still how many conversations one person can have. What changed is quality: AI helps her write a specific note to each founder about why a candidate is worth their time, and more of those introductions get accepted. Volume changes the math. At high volume, [AI screening can be fairer to candidates](https://www.findem.ai/blog/can-ai-be-a-bias-interruptor), because no recruiter can give real attention to 10,000 applicants who look alike on paper. For senior roles, it doesn't hold. One panelist said an AI candidate filter on a major professional network did a poor job on a recent search of hers. A senior hire is closer to an enterprise purchase than a transaction, and people still want a human making the call. ## Where Findem fits for lean, venture-backed teams The workflow from the live build is the one most small teams want and few have time to build and keep running themselves. [Findem ReadySlate Lite](https://www.findem.ai/blog/findem-readyslate-ai-recruiting-agents) packages it for companies without a dedicated TA function: HR runs hiring, founders and hiring managers run their own searches, and there may be no ATS or recruiting CRM at all. It runs self-serve in [Findem Studio](https://www.findem.ai/studio). You paste a job posting and the agent drafts the requirements, which you confirm one by one. It's the point where the live demo paused for a hiring-manager decision, built into the first step. The agent then builds a long list of people who fit and adds new matches on every run. You pick the shortlist. Two steps after that are optional. The agent can email candidates from your own mailbox, and it can run a 5-minute voice screen with the people who say yes. Candidates know in advance that an AI is conducting the screen. You choose what the agent does on its own and what waits for you, and ReadySlate never rejects candidates or makes hiring decisions. It matches the first rule on the panel's one-page AI policy. The investor roundtable's advice to ask your backers who they can introduce you to is the other half. [Findem for startups](https://www.findem.ai/solutions/startups) makes that network searchable: describe a role in plain language and get a ranked shortlist from 850M+ profiles, with [warm paths](https://www.findem.ai/why-findem/relationship-signals) through your team and your investors' portfolio surfaced alongside cold ones. [Success Signals](https://www.findem.ai/why-findem/success-signals) show who's likely to do well in an environment like yours, and the [Authenticity Suite](https://www.findem.ai/products/authenticity-suite) flags fake or inflated profiles before you spend time on them. For the searches the panel said should stay human, like a first head of engineering or a founding GTM leader, Findem Fast Track puts an experienced recruiter on the search, working from Findem's data, and hands you a vetted slate. ## Pick one workflow before next week The AI panel closed by asking each speaker for one thing a small People team could start next week. One answer was a personal daily brief that pulls new roles, layoffs, and news into a single email each morning. Another was to find the people in your company already using AI well and get them talking about it, because polished posts from peers make everyone else afraid to start. Both come down to one decision. Choose one repeatable piece of your hiring work, run it by hand with an agent until the output is right, and keep the judgment calls for yourself. Then show your CEO where the hours you got back went. If sourcing is the piece you'd pick, bring an open role to [ReadySlate Lite](https://www.findem.ai/blog/findem-readyslate-ai-recruiting-agents) and see the long list it builds, or see how [Findem works for startups](https://www.findem.ai/solutions/startups). ## Recommended next reads October 23, 2026 ### [From the black box to the boardroom](https://www.findem.ai/blog/ai-transparency-black-box-to-boardroom) Tina Shah Paikeday October 21, 2026 ### [Stop paying for tools, start paying for outcomes: Meet Findem ReadySlate](https://www.findem.ai/blog/findem-readyslate-ai-recruiting-agents) Ash Ahluwalia October 20, 2026 ### [The person behind the resume: Candidate fraud and the advance decision](https://www.findem.ai/blog/candidate-fraud-detection-authenticity-suite-pro) Ash Ahluwalia