Insights

The conversation surrounding Generative AI has decisively shifted from “what is it?” to “how fast can we change?” While GenAI is often framed as a technological leap, its true disruption lies in how it rewrites the rules for talent, training, operating models, and leadership culture, especially in knowledge-driven industries. Leaders pointed to a generational divide: early-career employees adopt GenAI as naturally as spreadsheets, while mid-level professionals - often gatekeepers of process and institutional knowledge - remain hesitant or unsure how to apply it. This "messy middle" is now the greatest barrier to widespread adoption.

As digital transformation reaches a new inflection point with the mainstreaming of predictive, generative and now agentic AI, enterprises are reassessing both their past progress as well as future direction. We’ve already invested significantly in digitizing operations, modernizing infrastructure, and experimenting with emerging technologies – and yet the journey has only really begun. The definition of “digital” is also evolving - from discrete, tech-led initiatives to embedded strategies that are inseparable from core business and growth priorities. And this redefinition of Digital now means managing significantly higher complexity, balancing incredible speed and agility with stability and leverage, integrating sustainability, ethics and responsible AI governance. Request here.

This session underscored that agentic AI is no longer a theoretical construct but an operating reality, with enterprises deploying it across clinical trials, engineering, logistics, procurement, manufacturing, and customer engagement. Enterprises should treat agentic AI not as a technology overlay but as a driver of operating model redesign, workforce transformation, and measurable business outcomes. Priorities should be clear data governance, explainable trust frameworks, and vertical use cases that deliver demonstrable value.

This is a summary of learnings on the three pillars of success in AI in the Enterprise: AI Strategy, Enterprise Architecture and Organizational Design & Change. We also reflect on a deep dive example amongst our membership and take away key learnings from the journey. AI-generated audio summary available.

Cross-industry leaders compared AI transformation across very different starting points - from greenfield platform roll-ups to century plus-old, regulated incumbents. Despite variance in maturity, themes converged on: simplify and standardize first; keep the core thin; push differentiation to data products and AI at the edge; and treat culture as the hardest constraint.

The Executive Technology Board Innovation Index is a most practically innovative startups list - curated by our members, and spotlights early-stage companies shaping AI in the enterprise. This year’s list features private, AI-native startups that are gaining traction across large enterprises - we started with sourcing lists from top tier VCs and Industry Analysts; then rationalized down for focus, fit and size; and then invited members to down select the companies to include on the list