Esteban Kolsky

One of the quarter’s earliest signals came from synthetic data. The enterprise has long supplemented incomplete records with additional information and context; the emerging capability is to construct information that does not yet exist. Synthetic scenarios can represent rare events, missing conditions, exceptions,…

AI remains the main catalyst, but the emerging management requirement is adaptability—the ability to redirect capital, redesign work, change technology, construct context, and adjust machine authority without another large transformation program.

September exposed a broader enterprise problem: Technology is becoming easier to deploy than to absorb and govern. Top concerns: Capital remains available but more conditional, work is changing faster than organizations, enterprises can increasingly construct context that did not previously exist, digital…

This month’s thread: AI is moving from stand-alone adoption into the operating fabric of the enterprise, changing how technology itself is managed. For 2027, the emerging model is more dynamic: modular architecture, dynamic capital allocation, workforce planning, and controls designed for systems that increasingly act…

This month’s thread: AI is exposing a broader change in how enterprises manage technology. Architecture must become more adaptable, technology investment more dynamic, workforce planning more closely tied to productive capacity, and enterprise context more deliberately preserved, and controls must be capable of…

Enterprise technology is returning to a broader operating agenda. AI remains an important catalyst, but it no longer needs to consume the entire board and executive agenda. July’s evidence points to a more durable set of priorities: growth, resilience, sustainable operations, trusted information, infrastructure…

Technology spending is accelerating, but foundations are taking the budget.

Enterprise AI is moving to operating judgment. The constraints now sit in inference economics; hybrid architecture; decision rights; institutional knowledge; cybersecurity; and the ability to reallocate money, people, and strategies while the market changes faster than the annual planning cycle.

Enterprise AI is moving to operating judgment: decisions that affect how AI is deployed and leveraged, not whether it will be used. The constraints now sit in inference economics; hybrid architecture; decision rights; institutional knowledge; cybersecurity; and the ability to reallocate money, people, and strategies…

AI has entered a post-promise phase, where the conversation has shifted from model excitement to the harder work of infrastructure, governance, data readiness, and operating model redesign. Spending continues to climb, with Gartner projecting $2.52 trillion in worldwide AI spending for 2026, yet more than half of CEOs…

This month’s thread: AI has entered the postpromise stage. The conversation is moving away from model fascination and toward infrastructure, data movement, governance, cybersecurity, workforce redesign, and operating model control.

This month’s thread: AI has crossed from novelty into controlled execution. The market still talks about models, agents, and intelligence, but spending has shifted to the infrastructure required to make AI usable: data, security, governance, compute power, and the people able to redesign work around it.