AI strategy and capability development

Executive learning should equip leaders to choose projects, challenge proposals and support implementation. My programs combine concepts from machine learning, AI and data science with showcases and implementation case studies, and with discussion of projects suited to the participants' own organization.

Participants examine what an application would require, how it would fit existing work and how much time and effort it would take to develop. Leaders and technical teams leave with priorities they can act on.

I start with the business decision or process, its current performance and the consequences of error, and then compare approaches by the total work each requires: building it, checking its results, correcting errors, handling exceptions and maintaining it. A language model can produce a first answer quickly and still cost more to verify than a task-specific model or ordinary code, so I compare complete costs rather than setup effort alone.

The work produces a prioritized assessment of opportunities, a documented comparison of approaches, the data and infrastructure each requires, and a scope that can be tested. Assumptions about benefits, costs and operating ownership stay explicit. Where a decision depends on forecasts, customer models or experiments, it continues as Predictive Analytics; where it depends on document systems, agents or integrated workflows, it continues as AI Workflows & Agents.

If you already have a strategy, I review it with your team rather than starting again. I assess its assumptions, project selection and evidence, and whether each proposed application has suitable data, an accountable owner and a way to judge the complete result.

I compare the full operating plan with the current process, including implementation, checking, correction, fallback and maintenance. The review distinguishes time released for other work from a reduction in spending, and tests whether downstream capacity can turn faster production into more completed work.

Engagements can focus on a single decision, a leadership program or continuing advice as projects develop. Continuing advice keeps the original assumptions in view as a project meets real data and operating constraints: whether the expected benefit still holds, what the checking effort turns out to be, and when a simpler approach would serve. I can work with your engineers on requirements and technical choices, or take responsibility for a defined implementation.

I bring a background in quantitative economics, business leadership, machine learning and software engineering to this work.

For senior leaders at a large insurer, I designed and delivered AI and data-science programs that combined these concepts with showcases and implementation case studies, and with discussion of projects suited to the organization. Participants identified projects worth the time and effort to develop, and selected projects moved into implementation.

I have also delivered corporate data-science training and contributed to curriculum development for professionals in consulting, financial data and technology. That experience informs programs that connect leadership understanding with the analytical and engineering capabilities teams need to deliver.