Buyer journey

Implement and evaluate an AI agent

Choose a narrow business job, select an approach, evaluate real tasks, and decide whether an agent is ready to operate.

For: Product, engineering, data, and operations leaders moving from an AI-agent idea to a governed pilot.
Problem: Teams choose architecture or tooling before defining the job, evidence standard, ownership, and scale-or-stop decision.
Outcome: A practical path from use-case selection through evaluation, adoption, and operating value.
Includes: Build-versus-buy, architecture, evaluation, adoption, operating models, and ROI decisions.

2026.08.01

How to evaluate an AI agent for business data

Build a task-specific test set and production acceptance scorecard for correctness, grounding, permissions, usefulness, and failure recovery.

2026.07.21

A practical six-step guide to choosing one business job, defining approved context, testing the agent, and making it part of an existing workflow.

2026.08.01

Decide which parts of an enterprise AI-agent system to build, buy, or combine using business, data, governance, and lifecycle criteria.

2026.08.28

Choose between deterministic workflows, single agents, and multi-agent orchestration by matching coordination complexity to a real business job.

2026.08.24

A practical 30-day plan for testing one AI agent on a real business workflow with clear scope, shadow mode, human review, and a go-or-stop decision.

2026.08.04

Build a defensible AI-agent business case using a real baseline, quality-adjusted outcomes, full operating cost, and clear scale-or-stop gates.

2026.08.01

A practical guide to agentic BI, how it differs from dashboards and copilots, and what teams need before adopting it.

2026.07.20

Define where AI belongs in recurring work, who owns it, which data it can use, and how teams review its answers before scaling adoption.