AI strategy and capability development

An AI strategy starts with the work your organization needs to improve: the decisions people make, the information they use and the results they are responsible for. I work with leaders and technical teams to identify worthwhile applications and decide which ones the organization can deliver.

I compare opportunities against the current process, the consequences of error and the data and capabilities available. The result is a prioritized set of initiatives, with a business purpose, an accountable owner and a first implementation scope that can be tested. Choosing between generative AI, a task-specific model and ordinary code is part of that assessment, rather than a decision made in advance.

If a strategy is already in place, I review its assumptions and project choices with your team. We examine whether the expected benefits follow from a workable operating plan, including the implementation, integrations, review, correction and continuing maintenance.

The economic question is what changes in the complete process. Faster production can release time without reducing spending, and more output only helps if the next part of the business can use it. I compare alternatives on the same workload and quality requirements, and identify which assumptions a pilot needs to resolve. A worked example of whole-workflow economics develops that distinction.

For systems already running, the operating-cost resource and technical analysis of inference and review costs help assess where optimization would make a material difference.

Choosing projects also exposes what the organization needs to learn. My executive programs combine concepts from machine learning, AI and data science with implementation case studies and discussion of applications in the participants' own organization.

Leaders examine what an application requires, how it fits existing work and how to judge a proposal. Technical and business teams develop a shared understanding of data, implementation choices, operating responsibilities and acceptable results. The discussion connects learning directly to the projects they may undertake.

I can advise on a specific decision, work with a leadership team on its wider programme or provide continuing support as projects develop. The work moves from selecting an opportunity to establishing requirements, testing the approach and deciding what should enter operation.

Forecasts, customer models and experiments continue through Predictive Analytics. Document systems, integrated workflows and agents continue through AI Workflows & Agents. I work alongside your analysts and engineers or take responsibility for an agreed implementation.

My background in quantitative economics, business leadership and production software connects the investment decision with the work needed to deliver it.

For senior leaders at a large insurer, I designed and delivered programs combining AI and machine-learning concepts, showcases and project discussions. Participants examined applications within their own organization, and selected projects moved into implementation.

My corporate data-science training and curriculum work has also served professionals in consulting, financial information and technology. Across these settings, the purpose is to help people understand the choices well enough to make and support practical decisions.