About Applied Enterprise AI

Durable design for value delivery

By Curtis Hibbs and Joshua Barnes

AI can help someone finish a task faster. Whether finishing faster helps the person waiting for the result depends on what happens around the task: the information available, the decisions that follow, the review required, and the work someone else must do.

Applied Enterprise AI starts from that question. It is a body of work about designing how people and AI deliver value together.

We write for people who lead AI-enabled work or design it. The aim is practical: better decisions about what to change, how to try a change, and what evidence would justify keeping it.

Why this work

Adopting an AI tool leaves many decisions unresolved. Where should the tool fit? What responsibility should a person retain? Does the current workflow still make sense? How will anyone know whether the change helped?

Consider a team using AI to prepare recommendations that an internal reviewer assesses before they reach a customer. The reviewer is the immediate recipient of the AI-assisted work; the customer is the person the overall delivery flow serves. Faster preparation may help the reviewer, but its effect on the customer depends on what follows. Does the customer receive a useful recommendation sooner? Does the recommendation help them decide better? More drafts waiting for review would tell a different story.

An internal workflow can still have a useful outcome of its own: helping the reviewer decide well. Name the recipient and the customer. Evaluate the local result, and examine how it contributes to the customer's outcome.

Beyond improving the current process, AI may make a previously impractical outcome worth pursuing. That outcome can call for a different arrangement of work, with different information, roles, and decisions. But sometimes a small improvement to an existing workflow is enough.

Applied Enterprise AI explores those choices from a systems perspective aimed at value delivery. It shares that orientation with Applied End-to-End Flow but focuses on the design and operation of AI-enabled work.

What the name means

Applied means our work should help you act. An article should clarify a decision you face, and give you a manageable way to test a possible improvement to your work.

Enterprise means considering interdependent work. Other people control the information, resources, and decisions that even a small team relies on. Those dependencies shape what a local improvement can accomplish.

AI includes several arrangements: a person using an assistant, a predefined workflow, or agents carrying out authorized work. Choose the simplest arrangement that can deliver the intended result under the conditions that matter.

The subtitle, Durable design for value delivery, names the ambition. What lasts is made explicit: the purpose, the boundaries, and the basis for judging results. Methods can evolve as capabilities expand, because each change in method can be judged against what lasts.

But the purpose and boundaries can change too. A better capability may open a new way to serve someone or make a handoff unnecessary. A changed obligation may require a different control. Each change should be deliberate, with evidence and responsibility for its consequences.

What you can expect

The articles ask questions like these. When does a workflow deserve redesign? What should remain under human control? If the AI passes its evaluation, do we know enough about the value delivered?

Three connected perspectives guide that inquiry:

• Vision: What better outcome becomes possible, for whom?
• Path: What should change first, given the dependencies and authority involved?
• Means: How should people, AI, information, tools, and controls work together?

A useful answer should make clear what remains uncertain and help you choose a bounded next action. Our examples should distinguish what was observed from what is proposed, and explore conditions where a different approach may work better. But the usefulness of this developing work needs to be demonstrated in practice.

Applied Enterprise AI is the overall body of work. This website is its canonical knowledge base, holding reusable concepts, practices, evidence, and limits. Articles on the Applied Enterprise AI LinkedIn Page explore particular arguments and applications of that knowledge. The site and the articles grow together, so you can follow an article's reasoning back to the concepts underneath.