Most organizations attempt to drive AI adoption by “training first” and fixing the workflow later. This is a sequencing error. This article outlines a 6-step diagnostic roadmap—based on the Domino Map™ methodology—to help training managers architect the workflow, define decision rights, and remove constraints before assigning a single course.
The Sequencing Error in L&D
If you are a training manager today, you are likely under immense pressure to “solve” the AI question. The mandate from leadership is usually urgent and broad: Get our workforce ready for Generative AI.
The natural response is to mobilize content. We procure licenses, curate libraries of “Prompt Engineering” courses, and launch learning pathways. We assume that if we provide the knowledge, the application will follow.
However, in the context of AI, this “Training First” approach is often a fundamental sequencing error.
AI does not just require new skills; it needs a new operating model. When we train employees on tools without first redesigning the workflow they fit into, we create friction. Employees learn the tool, return to a rigid process that hasn’t changed, encounter undefined risks, and eventually abandon the tool.
To drive true adoption, we need to flip the sequence. We must move from a “Training First” model to a “Diagnostic First” operating system.
In my work helping organizations implement the Domino Map™—a framework for human-AI organizational design—I use a six-step roadmap to stabilize the environment before enablement begins. Here is how training managers can apply this sequence to ensure their AI initiatives actually stick.
Step 1: Define the Operational Outcome (Not the Learning Outcome)
Most training requests start with a learning objective (e.g., “Learners will understand how to use Copilot”). In a diagnostic operating system, we start with an operational objective.
Before designing content, ask the stakeholder: What specific metric is this AI initiative supposed to improve?
- Is it to reduce the cycle time of quarterly planning?
- Is it to lower the error rate in code reviews?
- Is it to increase the volume of customer support resolutions?
If you cannot define the operational outcome, you cannot measure the ROI of the training. Stop and define this metric first.
Step 2: Map the Critical Decisions
AI changes work by decoupling tasks from decisions. To build effective training, you must identify where the judgment lies.
Map the specific workflow in question. Identify the critical “Decision Points” where errors are most likely to happen or where value is created. For example, in a recruiting workflow, the task is “screening resumes,” but the decision is “selecting a candidate for an interview.”
Training must focus on improving the quality of that specific decision, not just the general usage of the tool.
Step 3: Classify the Work (The Triage)
Once you have mapped the decisions, you must classify them. This is the step most organizations miss, leading to mass confusion. Every step in the workflow should be tagged as:
- Human-Only: The “No-Fly Zone” for AI. These are decisions requiring ethics, high-stakes accountability, or complex negotiation. Training here focuses on policy and compliance.
- AI-Supported: The collaboration zone. AI provides the draft or the data, but the human makes the final call. Training here focuses on critical thinking and skepticism.
- AI-Automated: The delegation zone. AI executes the task within guardrails. Training here focuses on exception handling (what to do when the bot breaks).
Step 4: Locate the Real Constraint
Now that the map is clear, look for the bottleneck. This is the most liberating step for a training manager because it often reveals that training is not the answer.
If the team isn’t using AI, is it because they lack skill? Or is it because:
- Data Constraint: Is the data feeding the AI unstructured or messy?
- Governance Constraint: Legal hasn’t approved the use of client data?
- Incentive Constraint: They are paid based on billable hours, and AI reduces those hours?
If the constraint is structural, no amount of instructional design will fix it. Flag these issues to leadership immediately.
Step 5: Sequence the Interventions
Only after the previous steps are complete should you deploy the enablement. The correct sequence for a Human-AI rollout is:
- Fix the Process: Redesign the workflow and handoffs.
- Clarify the Rights: Publish the “Decision Rights” (who owns the output).
- Clean the Environment: Ensure data and tool access are ready.
- Targeted Enablement: Now deploy the training.
By waiting until step 4 to train, you ensure learners step into a system that works. They aren’t fighting the current; they are swimming with it.
Step 6: Measure Decision Quality
Finally, move your measurement strategy beyond “Completion” and “Satisfaction.” If you did Step 1 correctly, you can now measure Decision Quality.
Look for signals such as:
- Rework Rates: Did the team have to redo the AI’s work less often over time?
- Escalation Frequency: Did the team handle exceptions correctly?
- Speed to Decision: Did the planning cycle actually get shorter?
A Practitioner’s Example
Consider a financial services client I recently worked with. They initially requested a “broad AI upskilling” program because adoption of their planning tool was low.
Using this diagnostic roadmap, we paused the training request. We mapped the workflow (Step 2) and found that the real issue was a Governance Constraint (Step 4)—account managers didn’t know if they were allowed to send AI-generated forecasts to clients without manager approval.
We didn’t launch a course. We launched a “Decision Rights Grid” that explicitly stated: AI generates the draft; Manager approves; Account Exec sends.
Once that rule was clarified, usage spiked. We then provided targeted training on how to review the draft. The training was effective because the rules of the road were finally clear.
Conclusion
The role of the training manager is evolving. In the era of AI, we are not just content distributors; we are system architects.
By adopting a diagnostic-first operating system—defining outcomes, mapping decisions, and classifying work before we train—we protect our organization from wasted effort. We ensure that when we ask for our learners’ attention, we provide them with the tools to succeed in a system designed for them.
