Stop making every team learn the same lessons.

People decide what matters. The system remembers why.

Every campaign, customer call, product decision, and workflow teaches your company something. Most lessons remain trapped in the team that learned them—in someone’s head, a meeting, Slack, a local AI project, or an unowned document.

The next team reconstructs the context before it can act. A new agent receives the answer but not the evidence and reasoning that made it trustworthy. As AI spreads across the organization, shared context becomes the collaboration bottleneck.

The operating machinery

1. Evidence: a call, campaign, result, decision, or observation enters the system.
2. Decide: a named owner determines what the evidence means and what should change.
3. Review: the proposed change is checked before other teams depend on it.
4. Release: an approved version becomes current company context.
5. Use: people and agents begin from that release.
6. Learn: downstream results become evidence for the next version.

Ready for everyone is a decision, not a save button.

What I install

Context architecture and ownership

I map who knows what, who depends on it, and where evidence and approved context should live. Every shared package has a canonical home, named owner, known consumers, and explicit dependencies.

Lifecycle and governance

Every package receives reviewers, version history, and a release event. Together we define who can read, propose, approve, and publish so unfinished work does not silently become company truth.

Access and permissions

Canonical context can live in company-owned GitHub or an equivalent environment without requiring everyone to use Git. People and agents reach approved releases through the tools appropriate to their role: Claude, ChatGPT, files, web, search, API, or MCP.

Adoption and capability transfer

The work starts with one team and one complete loop. We observe real friction, improve the system, prove a second team can consume the first release, and transfer the operating cadence to internal owners.

How an engagement unfolds

1. Choose outcomes and audit dependencies. Select two or three immediate business outcomes and trace the context beneath them.
2. Design and install. Fit packages, ownership, permissions, lifecycle, repository, and access surfaces to the organization.
3. Prove one complete loop. A named owner takes real evidence through decision, review, and release.
4. Prove learning travels. A second team uses the approved release instead of reconstructing it.
5. Scale and transfer. Land additional teams deliberately and transfer the rollout playbook.

When this is relevant

• Useful AI workflows already exist, but adoption and operating habits vary by team.
• Multiple functions depend on the same buyer knowledge, strategy, product truth, standards, or evidence.
• Ownership, permissions, drift, review, or adoption is harder than building another agent.
• An internal operator wants to own the system after the engagement.

This is probably not the right intervention when the need is one isolated automation, no internal owner can participate, or a simple shared file or connector solves the actual problem.

Start with the dependency

Name the two or three outcomes that matter. We can locate the shared context underneath them and determine whether the problem is structural—or whether a smaller fix is the better answer.

Start a fit conversation with Jacob Dietle.