Vision to Results: Crafting an AI Strategy that Delivers w/Geoff Livingston

By Dan Sanchez · Episode published

An AI strategy connects a business objective to a measured workflow, a responsible owner, and adoption support so experiments can become useful operating systems.

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Connect AI to a business objective

An AI strategy should not begin with a list of tools. It should begin with the business result the organization needs and the work that prevents it from getting there. Geoff Livingston came to AI through years of writing about its role in marketing, watching Siri and Alexa become ordinary, and helping companies build machine-learning systems. That perspective makes the strategy question practical: what should the organization do differently because this capability exists?

The generative AI shift became much more visible with GPT in November of 2022. Since then, many teams have rushed to experiment, but experimentation alone does not create value. A department needs a clear priority, an owner, a measure of success, and a way to move a useful pilot into normal operations.

Move from pilot to operating system

Start by mapping the customer and employee workflows where information, decisions, or repetitive production create friction. Choose one use case that can be measured. Define the current baseline, the expected improvement, the data the system needs, and the risks that require review. Then run a contained pilot before expanding it.

The strategy should also account for adoption. People do not change because a leader announces a technology. They change when the new process helps them do meaningful work and when they have support learning it. Give teams examples, training, and a safe way to report what is failing.

Build for the gap between vision and behavior

The gap between an executive vision and daily behavior is where many AI programs stall. A strategy document can sound ambitious while nobody knows what to do Monday morning. Translate the vision into a small number of workflows, assign responsibility, and review results often enough to learn.

Geoff’s work through Cognitive Path reflects that bridge from strategy to implementation. The goal is not to chase every new model. It is to help an organization cross the chasm from interest to capability. Keep the human judgment that protects the customer, but remove avoidable friction where AI genuinely helps.

A useful AI strategy is therefore a living operating plan. It connects a business outcome to a tested workflow, gives people a reason to adopt it, and improves through evidence. That is how vision turns into results.