Continuous Intelligence

Score all 15 nodes honestly, as a team where possible. Independent scoring before sharing avoids anchoring on whoever speaks first.

• Use the Self-Assessment in Section 03 of this page
• Score each node 1–5: 1 = not started, 5 = embedded
• Have team members score independently before comparing results
• Pay attention to the gaps between individual scores, they are often more revealing than the averages
• Output: a shared, honest view of your current AI adoption across all 15 nodes

Look at where your scores are lowest. These are the nodes with the most headroom and often the greatest impact on the rest of the lifecycle.

• Find the 3–5 nodes with the lowest scores
• Check whether the low scores cluster in one loop or spread across all three
• Ask: which low-scoring node, if improved, would create the most downstream benefit?
• Consider dependencies: some nodes unlock others (Context Orchestration affects everything downstream)
• Output: a shortlist of nodes worth targeting first

Spreading effort across all three loops at once dilutes progress. Pick the loop with the highest leverage for your current context and put your energy there.

• Review your scores by loop: Context, Dev, and Ops
• Identify which loop has the most room to improve and the most immediate impact
• Most teams benefit from starting with Context: weak foundations limit what AI can do in Dev and Ops
• If your Context loop is already solid, target the loop where your team spends the most time
• Output: one loop as your primary focus for this cycle

For each priority node, design a small, bounded experiment. Define what success looks like before you start, so you know when you have learned what you need to learn.

• Pick one or two nodes within your focus loop to experiment on
• Define the experiment: what will you try, for how long, and with who?
• Set a clear definition of done before you begin
• Keep the scope small enough to complete in 2–4 weeks
• Output: a written experiment brief with a named owner, a timeframe, and a success criterion

Ownership is what separates experiments that ship from ones that stall. Every pilot needs a named person who is accountable for seeing it through.

• Assign one owner per pilot, not a team or a committee
• The owner does not have to do all the work, but they are responsible for progress
• Give the owner the authority to make decisions within the experiment scope
• Check in weekly, not monthly
• Output: a named owner for each active pilot, with agreed check-in points

Sharing what you are learning, including failures, builds collective knowledge and creates accountability. Progress that stays in one team tends to stay in one team.

• Set a regular cadence for sharing updates, even if brief
• Share what's working, what's not, and what you're changing as a result
• Include other teams at a similar stage of AI adoption
• Document the learning, not just the outcome
• Output: at least one shared update per pilot cycle, with learnings captured

Pilots that succeed need to become practice. Use Context and Skills Refinement to feed what you have learned back into your AI tools, processes, and team norms.

• For each successful pilot, define what it looks like as standard practice
• Update your AI context, prompts, or guardrails to reflect what worked
• Raise the baseline score for that node in your next Self-Assessment
• Look for adjacent nodes where the same learning might apply
• Output: at least one node moved from experiment to embedded practice

Run the full cycle each quarter. The value compounds over time. Each cycle should start from a higher baseline than the last.

• Re-run the Self-Assessment with the full team
• Compare scores to the previous cycle: where has adoption improved?
• Identify the next set of priority nodes based on the updated scores
• Pick your next focus loop and repeat the cycle
• Output: an updated score, a new focus, and a clear starting point for the next cycle