Rapid Five
Redesign a team around AI through five recurring transformation stages.
- Difficulty
- Expert
- Time to result
- ~months to results
- Steps
- 5
- Confidence
- 99%
Rapid Five moves an organization from superficial tool adoption to an AI-native operating model. Reveal establishes the baseline by examining actual workflows, maturity, and the jagged frontier where AI helps or harms performance. Architect converts that diagnosis into workflow-level before-and-after designs supported by appropriate technology and change management. Prove tests the designs in two-week sprints using real work and three measurement horizons: efficiency, capability, and transformation. The fourth stage embeds AI through peer learning, AI-first defaults, identity change, and performance systems. Dynamize then repeats the assessment every 90 days so the operating model evolves alongside rapidly changing AI capabilities.
Origin
Extracted from Marketing Against The Grain after researching 20 existing transformation frameworks, real-world cases, failure patterns, and guidance from organizations including McKinsey, HBR, and Wharton.
Core principles
- 01Assess real work before prescribing AI.
- 02Redesign workflows instead of merely adding tools.
- 03Prove value through live, measured pilots.
- 04Make AI adoption part of team identity and performance.
- 05Reassess the operating model as capabilities change.
How to run it
- 1
Reveal
Assess the team's real workflows and current AI maturity. Map the jagged frontier by identifying the tasks where AI improves work and those where it degrades quality or introduces risk.
Pro tip Use recordings and transcripts of employees performing normal work rather than relying only on retrospective descriptions.
Watch out A generic industry-level assessment can conceal important differences between individual workflows.
- 2
Architect
Design the target AI-native operating model workflow by workflow. Specify before-and-after processes, technology choices, role changes, and the change-management support required.
Pro tip Make each proposed change concrete enough that a team can compare the old and new workflow directly.
Watch out Do not preserve an obsolete process merely because it is familiar.
- 3
Prove
Implement the proposed workflows through two-week sprints on real work rather than synthetic demonstrations. Measure results across efficiency, new capability, and deeper transformation.
Pro tip Choose pilots with meaningful work, observable baselines, and bounded operational risk.
Watch out A successful demo is not evidence that a workflow performs reliably in production.
- 4
Shift the Identity
Move the team from optional tool usage to an AI-first working identity. Reinforce the shift through peer learning, default practices, and integration into performance expectations.
Pro tip Let credible internal practitioners demonstrate successful workflows to their peers.
Watch out Mandating tools without learning support or role clarity can create resistance and shallow compliance.
- 5
Dynamize
Reassess workflows and the operating model every 90 days. Update the system as models, costs, risks, and practical capabilities change.
Pro tip Treat each quarterly review as a new Reveal stage rather than a minor software update.
Watch out A static transformation plan will age quickly in a fluid AI market.
In the wild
A support team documents its highest-volume workflows, identifies where AI can classify and draft safely, and redesigns escalation paths around those capabilities. It pilots the new process for two weeks on real tickets, measuring resolution speed, answer quality, and the types of cases agents can now handle. Successful practices become team defaults, and the workflow is reassessed after 90 days as models and costs change.
→ The team gains a measured, maintainable AI operating model rather than an isolated chatbot experiment.
A sales team maps prospect research, account planning, outreach preparation, and CRM updates. It designs AI-assisted versions of each workflow, pilots them with one sales pod, and compares time saved, research quality, and meeting conversion against the old process. Peer demonstrations and performance reviews then reinforce the practices that work.
→ AI adoption becomes embedded in repeatable sales operations and is revised quarterly.
Common mistakes
Starting With a Favorite Tool
Selecting a model before mapping the work reverses the framework and encourages teams to force unrelated tasks into the tool.
Testing Only Synthetic Tasks
Synthetic examples can hide integration, quality, and change-management problems that appear during real work.
Treating Transformation as Permanent
A one-time redesign becomes obsolete as model capability, economics, and risk boundaries change.
Is it for you?
Best for
It is best for teams that want to become AI-native but need a structured transformation process rather than another collection of tools.
Not ideal for
It is not ideal for teams unwilling to document workflows, fund experiments, or change roles and operating practices.
From the transcript
“The new framework is called Rapid Five. And R is reveal. Assess the team's actual workflows, maturity, and map the jagged frontier of where AI…”
“A architect design the target AI native operating model with workflow by workflow before and after designs, technology selection and change management. P prove, implement…”
“Shift from tool adoption to identity shift through peer learning, AI first defaults, and performance integration, and Dynamize, build a 90-day reassessment cycles because AI…”
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