article / Context Engineering
Operational AI resources
Make AI useful where the work actually happens.
Practical guidance for choosing the right opportunity, giving AI the context it needs, redesigning the workflow, and operating the system long after launch.
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Start with the decision in front of you
Whether you are deciding where to invest or trying to get a pilot over the line, begin with the question your team needs to answer next.
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AI Strategy
Where can AI create the most value first?
Prioritize the workflows with a measurable outcome, a viable path to production, and a business owner ready to act.
Explore AI Strategy → Measure readiness and valueAI Maturity
Is AI changing performance—or just changing tool usage?
Measure readiness, adoption, engineering impact, governance, and business value with evidence leaders can trust.
Explore AI Maturity → Make business context usableContext Engineering
What must AI know—and be allowed to do—to complete the work?
Connect the knowledge, permissions, evidence, and systems AI needs to make useful decisions inside a real workflow.
Explore Context Engineering → Redesign how work movesAI Workflows
Where should AI act, where should people decide, and how should the work move?
Redesign end-to-end work around clear agent responsibilities, human judgment, system actions, and measurable outcomes.
Explore AI Workflows → Move beyond the pilotAI Execution Gap
What will make this AI system dependable after the demo?
Build the evaluations, controls, observability, security, and operating discipline that production AI requires.
Explore AI Execution Gap → Organize the team around outcomesAI Delivery
Who will own the outcome—and who can actually deliver it?
Make better decisions about partners, internal ownership, build versus buy, delivery pods, and long-term operations.
Explore AI Delivery →Point of view
Arguments for building AI that lasts
Clear thinking for leaders making consequential choices about AI, organizations, and the future of work.
AI Strategy
The Best AI Use Cases Change the Work, Not Just the Task
AI value shows up when the workflow changes. A faster draft is useful, but the bigger upside is when context, review, approval, and system updates move together.
Context EngineeringYour Data Moat Is the Context Your Agents Can Actually Use
A data moat is not a pile of records. For AI agents, the moat is the context the workflow can retrieve, trust, explain, and improve over time.
AI MaturityStop Measuring AI in Tokens: The Enterprise AI Value Scorecard
Tokens are an input metric. Enterprise AI value shows up when a workflow produces trusted, accepted, economically useful work.
AI DeliveryThe New Enterprise AI Role: Agent Operations Lead
The AI talent gap is not only a shortage of AI engineers. It is a shortage of people who can operate agents inside real workflows after launch.
Latest field guides
Practical guidance for the next decision
article / Context Engineering
Unified Context Layer: How to Make Business Data Usable by AI
Read the guide article / AI Delivery
What Is an AI Delivery Pod? A Mid-Market Guide to Humans Plus Agents
Read the guide article / AI Delivery
Who Should Own an AI Workflow? The Operating Model for Mid-Market Teams
Read the guide article / AI Maturity
Why AI Productivity Metrics Fail Without Workflow-Level Measurement
Read the guide article / AI Delivery
AI Center of Excellence vs Embedded Workflow Teams
Read the guideMore ways to learn
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