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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, not tools. He recommends evaluating teams in the order of people, product thinking, engineering practice, and technology—and approaching generative AI through hands-on, cross-functional experiments tied to real business needs.
Who is Kory Jeffrey?
Jeffrey is a Principal and VP 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 building technology and product organizations.
His career path ran from English literature and philosophy, including epistemology and metaphysics, into a startup technology accelerator and then Google. At Google, he led developer relations in Canada, worked in emerging markets including Indonesia, India, and Brazil, and later served as chief of staff of engineering for Google Canada. He says that organization grew from about 200 people to just over 2,000 during his tenure.
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Jeffrey discusses that experience and his advice for technology leaders in Episode 156 of CIO Leadership Live, hosted by Lee Rennick, CIO.com’s Executive Director of CIO Communities. The 29-minute episode was published on February 20, 2025. Listen or watch through CIO.com, which links to Apple Podcasts, YouTube Podcasts, and Spotify.
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How does Jeffrey evaluate a technology company?
Jeffrey’s diligence framework puts the organization’s capabilities ahead of its tools. He orders the assessment this way:
- People: Look first at the people responsible for execution and whether the organization invests in trust, talent, and ownership.
- Product and product thinking: Assess the team’s strategic insight, empathy for users, and ability to execute on a coherent product direction.
- Engineering practice: Examine how the organization builds and operates its products.
- Technology: Consider the tools and platforms chosen to implement the work.
The order matters: a technology choice cannot make up for a team that lacks customer understanding or disciplined execution. Jeffrey identifies three recurring weaknesses to watch for:
- Scrappy teams that iterate quickly but lack strategic depth.
- Technically proud teams that optimize for the technology rather than customer outcomes.
- Sales-led organizations that change roadmaps so frequently they lose a coherent view of the market.
What makes a high-performing technology team?
Hire and empower people who take ownership
Jeffrey calls people who see a problem and take responsibility for fixing it “drivers.” They do not wait for a task to be assigned or limit themselves to their own team’s boundaries; they bring others together and help the organization make progress. Their impact can multiply when they enable colleagues rather than simply completing more work themselves.
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That approach depends on trust and investment in people. Jeffrey’s point is not that tools or process are irrelevant, but that teams need capable people who are trusted to act before those tools and processes can deliver their full value.
Treat product thinking as a combined discipline
For Jeffrey, product thinking combines three capabilities: strategic insight, user empathy, and executional excellence. A team needs to understand where the product should go, whose problem it solves, and how to deliver it. Weakness in any one area can distort the result: fast iteration without strategy loses direction, while technical optimization without user empathy can produce work that customers do not value.
How should CIOs begin using generative AI?
Jeffrey recommends learning through building rather than limiting AI adoption to discussion or high-level strategy. Form a small, cross-functional group with an engaged executive sponsor, a product or business-function representative, and engineers able to create prototypes. Give the group real problems to investigate, then use what it learns to identify useful internal and customer-facing applications and spread practical knowledge across the organization.
His framing is pragmatic: AI is another tool to use where it fits, not a universal solution. A prototype can help a team discover whether a use case is valuable, what implementation requires, and what the organization needs to learn. Measure a proposed application against the needs of that specific use case rather than relying only on abstract model benchmarks.
Jeffrey also emphasizes that the widely repeated claim that AI will take jobs misses the role of people who learn to use it. That is a leadership argument, not a quantified forecast: the episode does not present an independently published statistic on AI’s effects on employment, enterprise adoption, or return on investment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What is the 70/20/10 model?
Jeffrey describes a resource-allocation pattern used at Google Canada: 70% of organizational effort for core product commitments, 20% for adjacent innovation, and 10% for high-risk experiments that could materially change the business. He presents this as an organizational model, not a mandatory quota for each employee.
The rationale is to protect room for exploration without neglecting the work the business has already committed to deliver. For a CIO applying the idea, the practical question is how much capacity the organization can deliberately reserve for experiments while continuing to meet core commitments—not whether every team must reproduce the same percentages.
What did Jeffrey predict for enterprise AI in 2025?
In the February 2025 interview, Jeffrey forecast a shift from pursuing ever-greater computing capacity toward applications that are useful, secure, and tailored to specific industries or workflows. He expected buyers to pay more attention to data security, trust, and transparency, and anticipated more deeply embedded applications rather than isolated demonstrations.
He also said that meaningful enterprise deployments can take time and often require substantial implementation support. He expected application-layer reasoning and commercially useful systems capable of handling multiple steps to become more visible. These were Jeffrey’s outlook and predictions at the time of the interview, not measured results or confirmation of what subsequently happened.
During the conversation, host Lee Rennick relayed an anecdotal example of CIO 100 participants reporting productivity gains of 200% from AI. That figure is a host-reported example, not an independently verified study or a general estimate of AI productivity.
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