Five questions for turning AI capability into business results.
Most firms have done some parts of AI well, and most are struggling with the rest. Adoption continues while firms redesign processes, roles and products. These questions make the decisions and responsibilities explicit. This is the simplest model I can come up with, based on the firms I work with. It starts from the questions the work must answer. There are five. Answer them in order and the structure follows.
The role AI plays in how we work (automation or augmentation), and the economic goal (efficiency or growth). Decided, written down, and communicated until nobody can miss it. Map the opportunity against the business outcome.
Available is the easy half. Set up well is the real test: the current generation in everyone's hands, configured properly, with the budget to use it fully. And the next generation in pilot, so that by the time it becomes the current generation the firm already knows how to use it.
Two measures, applied at every level. Breadth: the whole firm using the tools. Depth: enough power users, found, named and amplified so that their impact and methods spread. Both need materials, content and dashboards in the hands of the people who own the outcome. And both carry a bar, set by level: a director's bar differs from a graduate's. Miss the minimum for your level and that is a tough conversation, held within your level. The bar cuts both ways: each level needs enough power users, amplified, freed up to use their skills properly, and given the budget to do so. Use the tools' own usage data alongside practical assessment. Surveys help explain behaviour; they do not establish capability or captured value on their own. Assess the quality of use.
The tools pay back when the work itself changes. Choose two or three changes by working backwards from the business number. Test them, then plan the integration, ownership and role changes needed to sustain the result. Every week should produce or update two lists: things we learned that should be rolled out, and things we should test next. Find the dependencies that could stop delivery.
More experiments: the same muscle, pointed at revenue. A technology that changes how the work is done will change what customers buy: new offers, new service lines, new ways of delivering existing work. Each test ends in a decision: scale it or drop it. Test customer need, strategic fit and commercial potential before building.
The questions clarify accountability. Delivery still needs to cross functions, with an owner who can carry the change through. Three parts, no more.
Supports questions three to five.
(Question one). Writes and communicates the strategy. Sets the bars by level and holds them.
(Question two, supporting three to five). Two halves: the AI team owns experiments and pilots; IT owns everything in production, run as products. Together they equip the firm with materials, content, dashboards and support.
(Questions three to five). Own usage, the tough conversations, the experiments that redesign the work across functions, with a named owner for each workflow, and the new products and services. Execution and its consequences live where the profit and loss lives.
None of this is exotic; each part is already working somewhere. The work is answering all five at once, each in its right court.
This is an artefact created for David's weekly email. See the others here.