Enterprise AI Agent / Problem navigation

If an enterprise wants to apply AI to the business, which problem should it start with?

Conclusion first:Don't start by asking which model is the strongest. Start by finding a real, recurring piece of work with someone accountable for acceptance, then determine whether you need knowledge organization, a workflow, an Agent, or no build for now.

This is not a tool directory, but a decision briefing for business leaders, business managers and professional services teams. Each page answers one question first, then gives the next step for judgment.

Start with your problem

Start from what the customer is asking and where the team is stuck.

These questions can be read on their own, or you can continue along the path of "problem → scenario → method → service." The answers do not make business decisions for you; they only help you clarify boundaries, materials and confirmation points.

Question 01

Which scenario should an enterprise AI Agent start with? Criteria and first steps

First find a piece of work that recurs, has clear inputs and outputs, has someone who can accept it, and can be rolled back if something goes wrong. Get that piece running smoothly first, then decide whether you need a knowledge base, tool invocation, automation, or greater autonomy.

Read the judgment path →

Question 02

What's the difference between AI Agents and workflows? How should enterprises choose for their projects?

Workflows fix known steps, while Agents handle uncertain interpretation of information, proposing plans or selecting tools. Enterprise adoption is usually not either-or: use workflows to constrain definite steps, hand judgment-requiring steps to Agents, and keep human confirmation at high-risk nodes.

Read the judgment path →

Question 03

What's the difference between an enterprise knowledge base and RAG? Why do answers still come out wrong when all the materials are there?

A knowledge base handles "how materials are stored, organized and managed"; RAG handles "which materials to retrieve and put into context when answering a question." Uploading materials does not mean answers are grounded; when versions, permissions, relationships and citation verification are not handled, the system can still retrieve or fill in the wrong thing.

Read the judgment path →

Question 04

How can sales, pre-sales and delivery share the same business context?

The key is not giving each role its own robot, but enabling customer facts, confirmed scope, unresolved issues and next-step responsibilities to be handed off along the business chain. The Agent handles preparation, coordination and recording; pricing, scope, resources and external commitments are still confirmed by the responsible person.

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Question 05

How can large models more easily understand a professional services firm?

First write "who you are, whom you serve, what problem you solve, how you collaborate, what you do not do, and why you are credible" into a public page that can be understood independently, then link scenarios, concepts, cases, and service pages to each other. The goal is not to pile up keywords, but to let real capabilities be accurately identified and verified.

Read the judgment path →

A low-risk starting point

Bring one piece of work first, not a whole system.

Prepare three sentences: what you are currently delivering; what materials you need to find and organize each time; which item must be confirmed by a human. First produce a verifiable result for this piece of work, then decide whether to expand.

Identify recurring tasks
Write down the inputs, outputs and acceptance owner clearly
Restrict materials, permissions, and manual takeover

Continue to next step

If you already have work in progress, start right there.

First look at the service approach, or contact Andy with a specific question.

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