AI Strategist Certification

Monday · Foundations. Strategy redefined as “the study of leverage and advantage in competitive zero-sum games” — a statement of why, never the actions themselves.

1. System → Model → Framework: the teaching architecture you’ll use for six weeks
2. Continuous Transformation — one-time change → continuous improvement → continuous transformation
3. Transient competitive advantage, and why sustainable advantage is gone
4. The Anti-Patterns, and the Consolidation Cycles that fall out of fixing them
5. The Business/AI Maturity Model and the Transformation Progression: unmanaged → managed by tasks → managed by intent and outcome
6. The Robotics Decision-Making Framework: simulate → optimize → execute → feedback → learn
7. Technology cycles and waves — the twenty-year inventory
8. Where AI strategy starts and stops: strategy ends at opportunity discovery

Friday · Making it actionable.

1. Holistic AI Strategy — aligning decision-making across the enterprise
2. DIKW: Data → Information → Knowledge → Wisdom
3. The WIDA cycle and the Decision Flywheel — the same construct drawn two ways
4. Optimal analysis and optimal response, replacing the perfect-data / perfect-decision pendulum
5. Experiment → Product/Feature → Scale → Transform, and the Minimum Value construct
6. Gates and Balances and the Product Arrow (introduced) · North Star plus quick wins
7. The Disruptor’s Mindset, and how incentives make disruptors valuable
8. Meet the business where it is — how to start a flywheel from the ground floor
9. Outcomes-based business models and the Action Surface

Exercise Identify your domain and share it. Start noticing where your organization sits on the maturity model.

Monday · Simulation and decision advantage.

1. Outcomes Engineering — dictate the outcome and work backward
2. Intent · Desired Outcomes · Constraints · Optimizations
3. Time Travel, and its two lessons — including “never tell them more than they are ready for”
4. Digital twins and intelligent twins; descriptive models vs. causal and complex-systems models
5. Information Advantage · Decision Dominance · Transformation Dominance
6. “Stasis is a myth” — technology-driven growth vs. managed decline
7. The Talent Framework: structured career paths, learning paths, internal promotion
8. Ecosystem business models · Current State / Future State / Transformation

Friday · The technology model — the central session of the course.

1. Business Model / Operating Model / Technology Model. “You have a business model, you have an operating model. Do you have a technology model?” AI strategy is the act of moving parts of the first two into the third
2. Functional → Reliable → Affordable: the three phases of every technology cycle, and where to enter
3. The Trough of Investment
4. The AI Factory Floor and the AI Assembly Line
5. Orders of Optimization: zeroth, first, second, third · Core and Rim
6. The barbell: action surface, commoditized middle, outcome layer
7. Framework Certainty — who decides what
8. Decreasing the pain of acceptance vs. increasing the pain of resistance
9. The two strategic drivers — cost and trust — and the no-win situation

Exercise Think about time travel over the weekend — where in your business would a simulation let you pull future information into a present decision?

Monday · Funding the work and starting the flywheel.

1. The Innovation Mix: exploration vs. exploitation, and the budget split
2. The Profitability Tax, and the Bridge
3. The maturity model applied to data, analytics, and AI — strong definitions that answer “how is this different from data science?”
4. Simplify → Standardize → Automate → Continuously Improve
5. Intervention in the workflow — the value test for any technology insertion
6. Complexity and Uncertainty: the two-category justification for AI
7. Detection · Diagnostics · Predictive · Prescriptive
8. Top-Down and Bottom-Up opportunity discovery
9. Persuasion mechanics: transferring ownership · painkiller first, then vitamins · accelerate and redirect · let data be the villain · Dolphin Data · Coalition Building 101

Friday · Managing what you can’t schedule.

1. The Product Arrow — Economics 101, and why you invest in exploration at the peak
2. Gates and Balances in full: the five gates, and the rules — report position and potential, never certainty; never put it on a roadmap; never discuss it externally
3. The Four Questions for top-down discovery · Pragmatic Futurism
4. The AI 80/20 rule, and POC Purgatory
5. Connecting business metrics to model metrics — reliability is not accuracy
6. Costs scale faster than returns · The Data Point That Changed Everything (introduced)

Exercise Find an opportunity with baggage — one where you don’t know how you’d get there, and it’s barrier after barrier. Bring the strategic driver, the business objective, and why data and AI is the right solution.

Monday · Running the discovery conversation.

1. Five Whys as the engine of discovery
2. Pragmatic futurism applied — don’t constrain thinking to products you already have
3. The three-slide structure: opportunity and ROI → workflow and the long chain → re-orchestration
4. Revealing the long chain · The Halfway Mandate
5. The construct of timing — creating your own trigger instead of waiting for the customer’s
6. Assertion / Proof, and narrative decision-making framework alignment
7. The AI Strategy Chain: strategic driver → business objective → KPI → ROI → workflow → why AI
8. Opportunity discovery as the Trojan Horse
9. Knowledge graphs, ontologies, and structural causal models

Friday · Architecture, ROI, and the big decisions.

1. Phases = Gates: messaging innovation that has no timeline
2. The Multi-Domain Problem — whose “customer 360”?
3. The AI ROI Problem: workflow-level ROI, never token-level
4. Opportunity → Use Case → Workflow → Change → Roadmap
5. Evaluation of trade-offs — consolidation before automation
6. Action surface and the single pane of glass; chatbots, reactive agents, proactive agents
7. Platforms are an onion · The Intelligent Core and the Manual Rim — irreducible complexity
8. Build order: software and expert systems → data → descriptive models → advanced models
9. Functional vs. reliability requirements · opacity vs. transparency · The Three Big Decisions

Exercise Tally your application switches for a day. One participant logged a thousand. Every switch is a consolidation opportunity.

Monday · Assessment and data monetization.

1. Vision and Scope, and the full data and AI strategy document structure — never start an engagement without it
2. The L: opportunity → use case → workflow, then across to the roadmap
3. The four reasons a business gathers data
4. The Data Monetization Catalog, and the With-and-Without method
5. The Seven Stages of Data Grief · the AI 80/20 rule — 80% of value is in the data
6. What makes data monetizable: accessible, contextual, unique, low-cost, engineered, customer-aligned
7. The Initial Assessment Framework — seven assessment points
8. Spotting a Setup to Fail; the Reveal Question; Goodhart’s Law
9. Proof of Value: the three-meeting engagement model; the Product Rolodex

Friday · Earning the mandate.

1. Coalition Building — the six-month path from “no one knows who I am” to a C-level meeting
2. The Five Jobs — the fact-finding sequence that populates the strategy
3. Data and model literacy scoring
4. The COE model, centralization and transition planning, and the internal FDE construct
5. The Opportunity Discovery Workshop and its three first-run objectives
6. Presenting: current state, opportunities and threats — no weaknesses
7. Narrative frameworks — sequence, complication, solution
8. The Data Point That Changed Everything: starting point → challenge → data point → outcome
9. The Angel of Death · rational vs. irrational resistance

Exercise Draft the vision and scope for a real or hypothetical engagement. Run the reveal question on a leader you work with.

Monday · Framework certainty under fire.

1. Framework Certainty as a narrative-building principle — hear the challenge, name the framework, position it as the bridge, position yourself as the implementer
2. Systems → Models → Frameworks as the live explanation sequence
3. The Product Arrow as the standard counter to “you’re risking my revenue”
4. Incremental delivery as the bridge
5. Owning the decision platform — the strategist kicks off the flywheel
6. Intent-based agents · the opportunity pipeline: 200 use cases down to five
7. Ship it and find out; data space exploration; continuous transformation across the coming waves

Friday · Outcomes-based business and the future of work.

1. KPI Maturity — levels one through four, and why it’s where the flywheel begins
2. Narrative design: answer first, a little evidence, rephrase the answer
3. Bolt-on AI — “I’ve never seen bolt-on AI with positive ROI”
4. The Perfect Workflow: define the theoretically perfect version first, then measure how close you can get and at what cost
5. Consolidation and Compression — the two themes you find every time
6. Deterministic vs. stochastic workflows and transformations
7. Outcomes-based business models and pricing — you can only charge for an outcome you control enough of the workflow to deliver
8. The named pipelines: Recruit to Revenue · Content to Cash · Data to Profit · Strategy to Opportunity · Opportunity to Profitability · Transformation as a Service
9. Learning rate and the End of Human Advantage · three talent categories: laborers, knowledge workers, strategists

Exercise Take your highest-value workflow. Define its perfect version from each actor’s perspective, then measure the gap and the cost of closing it.