In CIO.com’s February 20, 2025, episode of CIO Leadership Live, Inovia Principal and VP of Technology Kory Jeffrey argues that strong technology organizations start with people, then product thinking, engineering practice, and technology choices—in that order. For leaders considering generative AI, his advice is to form a small cross-functional team, build real prototypes, and judge them against useful business outcomes rather than abstract benchmarks.
Who is Kory Jeffrey?
Jeffrey is a principal and vice president of technology at Inovia, a Canada-headquartered venture capital firm that invests from company formation through pre-IPO. As a principal, he focuses on early-stage technology companies, particularly from formation through Series B. In his technology role, he works through Inovia’s CTO office with portfolio companies as they build technology and product organizations.
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His career path crossed several disciplines. After studying English literature and philosophy, including epistemology and metaphysics, he joined a startup technology accelerator and later Google. At Google, he led developer relations in Canada, worked in emerging markets including Indonesia, India, and Brazil, and became chief of staff of engineering for Google Canada. Jeffrey says that engineering organization grew from about 200 people to just over 2,000 during his tenure.
He discusses that experience and his advice for technology leaders in CIO.com’s episode 156 of CIO Leadership Live, hosted by Lee Rennick, executive director of CIO Communities at CIO.com. The episode runs 29 minutes and was published February 20, 2025. CIO.com lists listening and viewing options through Apple Podcasts, YouTube Podcasts, and Spotify.
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How does Jeffrey evaluate a technology company?
Jeffrey’s diligence framework puts organizational capability ahead of tools. He considers people first, then product and product thinking, engineering practice, and finally technology. The sequence is deliberate: a company’s people and operating choices determine whether its tools can produce useful results.
| Order | What Jeffrey looks at | What it means in practice |
|---|---|---|
| 1 | People | Whether the organization can attract, trust, and empower people who take responsibility and help others deliver. |
| 2 | Product and product thinking | Whether the team understands its market and users, sets a coherent direction, and executes against it. |
| 3 | Engineering practice | How the organization builds and operates its products. |
| 4 | Technology | Which tools and platforms the company chooses to implement its work. |
Product thinking joins strategy, empathy, and execution
Jeffrey describes product thinking as a combination of strategic insight, user empathy, and executional excellence. In other words, a team needs to understand where the market is going, what users need, and how to turn that understanding into a product that works. He says product thinking is rare, and cautions that teams can become unbalanced in ways that undermine results.
- A scrappy team may iterate quickly but lack strategic depth.
- A technically proud team may optimize for technology rather than customer outcomes.
- A sales-led organization may change its roadmap so frequently that it loses a coherent view of the market.
What does Jeffrey recommend for building high-performing teams?
He emphasizes trust, investment in people, and hiring “drivers”: people who notice a problem, take ownership of fixing it, bring others together, and increase the effectiveness of the wider group. The idea is not simply to hire individuals who work hard in isolation; drivers help a team move through problems that might otherwise fall between roles.
Jeffrey’s people-first approach also shapes his view of technology leadership. Leaders should create conditions in which capable people can make decisions and work across boundaries, then pay attention to whether the product and engineering practices support the intended customer outcome. Choosing a tool before answering those questions risks solving the wrong problem efficiently.
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How should a company begin using generative AI?
Jeffrey recommends learning through hands-on work rather than limiting AI conversations to strategy meetings. He suggests a small cross-functional group with an engaged executive sponsor, someone representing a product or business function, and several engineers able to build prototypes. That group can explore internal workflows and product opportunities, discover where AI is useful, and share practical learning with the rest of the organization.
- Choose an engaged executive sponsor. The sponsor should help the group stay connected to organizational priorities.
- Include a business or product representative. This person can identify user needs and judge whether a proposed use case matters.
- Bring in engineers who can build. Prototypes make assumptions testable and expose implementation challenges.
- Assess the result against the use case. Jeffrey favors internal benchmarks tied to the specific task over comparisons with generic reasoning benchmarks.
- Share what the team learns. The aim is not just to produce a demonstration but to build capability and identify applications worth pursuing.
His framing is practical: AI is another tool in the organization’s toolkit, not a universal answer. The question is where it adds value and what evidence shows that it does. In the episode, host Lee Rennick relays an example from CIO 100 participants who reported productivity gains of 200%. That is an anecdote presented by the host, not an independently verified study or a general estimate of AI’s effect.
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What is the 70/20/10 model for AI experimentation?
Jeffrey describes a resource-allocation pattern he used at Google Canada: 70% of organizational effort on core commitments, 20% on adjacent innovation, and 10% on high-risk experiments that could materially change the business.
| Share of organizational effort | Focus | Purpose |
|---|---|---|
| 70% | Core commitments | Deliver the organization’s existing priorities. |
| 20% | Adjacent innovation | Explore opportunities connected to the current business. |
| 10% | High-risk experiments | Test ideas that are less connected to current work but could materially change the business. |
This is an organizational pattern from Jeffrey’s Google Canada experience, not a prescribed quota for each employee. Applied to AI, the principle is to protect some capacity for exploration without letting experimentation displace essential commitments.
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What did Jeffrey predict for enterprise AI in 2025?
In the episode, Jeffrey forecast a shift from maximalist claims about unlimited compute toward a more practical question: what is AI good for? He expected enterprises to focus more on useful, embedded applications, data security, trust, and transparency, with more targeted or verticalized solutions rather than one-size-fits-all tools.
He also observed that early enterprise adoption included “toy” applications, while deeply embedded use cases take time and may require substantial implementation services. He expected application-layer reasoning and commercially useful multi-step systems to become more visible. These are Jeffrey’s forecasts in an interview published February 20, 2025—not measured results or independently established adoption trends.
One useful implication for technology and finance leaders is to evaluate a proposed AI investment at the level of its actual workflow: define the task, identify who uses the output, consider security and trust requirements, and decide what outcome would count as worthwhile. The episode does not provide a formal ROI study, adoption-rate statistic, or independent evidence about workforce displacement. Jeffrey’s line, “It’s not AI taking your job. It’s someone using AI,” is a leadership warning about adapting skills, not a quantified forecast of job losses.
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