By John Veltri / 15 Jun 2026 / Topics: Artificial Intelligence (AI) , Modern infrastructure , Change and training , Generative AI
Enterprise AI adoption fails when organizations treat deployment as the finish line. The real work starts after the license is purchased: training employees on foundational capabilities, uncovering governance and security gaps, and identifying which agentic workflows will deliver the highest return. Without that layered approach, adoption stays low and AI investments become expensive shelf-ware.
John Veltri, Managing Director of Insight's Google AI go-to-market, describes a Launchpad framework that treats AI adoption as concentric circles rather than a sequence of dominoes. The first circle is foundational education — showing employees where to find the tool, how to build a Gem, how to share a Gem. As that training unfolds, it organically surfaces security vulnerabilities, governance gaps, and opportunities for deeper integration. Only then does the conversation move to third-party connectors, MCP tooling, and agentic workflows that deliver measurable efficiency or revenue growth.
The pace of AI change makes sustained engagement essential. Prompt engineering — the dominant training topic less than a year ago — is already obsolete. A single-engagement deployment model can't keep up. Veltri advocates for consultative partnerships lasting 90 days to six months, where the partner stays alongside the organization as products evolve and new capabilities emerge.
Measuring success in the short term means focusing on adoption rates rather than productivity metrics. When adoption reaches 75–85% across an organization, utilization organically yields new opportunity and new capability. The 85/15 rule applies to every deployment: 85% of an organization uses AI tools the same way, but the remaining 15% is what makes that enterprise unique — and that's where custom enablement matters most.
Leaders play a specific role in making adoption stick. Veltri argues that the most effective thing a leader can do is personally use AI tools, build something visible, and be willing to fail in front of their team. That psychological safety — demonstrated through action, not announcements — is what gives employees permission to experiment.
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John Veltri
Managing Director, Insight's Google AI Go-to-Market
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Managing Director, Google AI Go-to-Market, Insight
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