# Data & AI Strategist Certification > The Data & AI Strategist Certification is a six-week live course taught by Vin Vashishta that teaches you to assess a business, run AI opportunity discovery, defend workflow-level ROI to a CFO, and earn a C-level mandate for AI transformation. URL: https://www.datascience.vin/course-ai-strategist.html Format: Live cohort Price: $2,400 Enroll: https://app.acuityscheduling.com/schedule.php?owner=22867384&appointmentType=92765255 Updated: January 2027 - **Format:** 6 weeks, live - **Schedule:** Mon & Fri · 8:00–9:30am PT - **Q&A:** About an hour after each session - 1-hour 1:1 session with Vin - A year of drop-in office hours, twice a week - Recordings and slides after every session - 1-year access to the self-paced companion ## Why this course Most AI strategy starts with the technology and hopes value follows. This course builds the Disruptor's Mindset: start from the business, find where information and agents change the economics, and build a strategy that earns a C-level mandate. ## The shift | Old pattern | Disruptor's Mindset | |---|---| | Which AI tools should we buy? | Which decisions and workflows create advantage for us? | | Bolt AI onto the current workflow | Redesign the workflow first, then decide where AI belongs | | Promise ROI someday | Defend workflow-level ROI to the CFO upfront | | Put dates on work that has never been done | Define gates instead of dates and fund each one separately | | Wait for a mandate from the C-suite | Build the coalition that earns one | | Strategy as a plan you write every few years | Continuous transformation, run as a standing opportunity pipeline | **If you're technical: Learn the business side of the board.** Strategy, economics, and C-level communication, taught in a way engineers can use. Expect it to feel like the first time on a surfboard for a week or two. Then it clicks. **If you're on the business side: Learn the technology side of the board.** The technology model, platforms, and agents, and where each is heading, so you can see the opportunities and judge feasibility yourself. No technical prerequisite. ## Problems this course solves ### Problems you're facing - **You don't have access to the C-suite.** The most common constraint in every cohort. Bottom-up opportunity discovery and Coalition Building map the roughly six-month path from “no one knows who I am” to a C-level mandate: start with frontline teams, stack two or three wins, and earn the meeting. - **You bring data and they wave it away.** Dolphin Data. You'll learn the four causes (literacy gap, no budget or mandate, misaligned goals, or a unit that survives on opacity) and which one is your mistake. - **You think you can't get buy-in because they don't believe in AI.** They believe. CFOs have watched cloud migrations deliver little and peers get fired over it. Your gap is credibility, and credibility is built, which is what this course teaches you to do. - **Your role feels replaceable.** Opportunity discovery is the Trojan Horse: own it and you're tied to the P&L. The Three Talent Categories are blunt about where the ground is shifting: laborers follow processes, knowledge workers use frameworks, strategists build them. - **You freeze when a C-level leader challenges you.** Framework Certainty: hear the challenge, name the framework, position it as the bridge, and position yourself as the implementer. Week six runs live pushback drills. - **You feel like you're always behind and can't tell what matters.** Stay current on what matters to the value you're creating, and let the rest go. Benchmark deltas are rarely your barrier. - **You got a mandate, but the people who have to help you didn't.** The Halfway Mandate. The fix is structural: budget line items for the other units and a clear answer to what's in it for them. - **You sit inside IT or finance and structurally can't own strategy.** You'll learn where the role has to sit to work at all, and how to argue for moving it there. - **As a consultant, your outreach keeps landing at the wrong level.** You can't telephone-game your way up from a director. You'll rebuild the communication strategy around the level you actually need. - **Outside consultants present better than you do.** You hold three things they spend months building: internal relationships, cultural understanding, and domain expertise. Learn to use that advantage instead of apologizing for it. - **Pushing harder makes resistance worse.** Increasing the pain of resistance rarely works. Decreasing the pain of acceptance is the whole approach. - **Your VPs hit “so what?” and stop listening.** Assertion/Proof, narrative design, and answer-first structure match how executives actually decide, so you stop trying to out-argue them. - **You have to say something politically dangerous.** Let data be the villain. Plus the “because AI” card for addressing years of accumulated dysfunction without anyone asking why you didn't fix it sooner. - **“My team already tried that. Why will you succeed?”** Answerable if you did the Five Jobs and found out what was tried and why it failed. Not answerable if you didn't. - **You're the only person in the building who cares about data.** The course includes a documented case from an organization where “data” was a four-letter word, and the way in from there. - **You're technical and strategy feels like the first time on a surfboard.** Expected. Everything is taught twice, the ideal version and the version that survives your constraints, and half-formed questions are welcome. - **You're moving into a domain you don't know yet.** Name your vertical in week one and case studies get swapped to match it. - **You're a team of one, maybe two.** Constraints are a first-class input to every framework: intent, desired outcomes, constraints, optimizations. - **You keep getting handed work you can't refuse and can't scope.** “I was just told I have to do this. How much can you give me?” is treated as the success condition: the business is pulling your lever instead of routing around you. - **You don't know how to sell something with no timeline.** Week three is largely about this. You'll bring an opportunity with baggage, barrier after barrier, because those are the ones competitors won't touch. - **You tried to build a knowledge graph by interviewing experts.** Your users will revolt. They don't have time, and knowing how to do the work differs from knowing how to explain it. Build by observation instead. - **You want to automate and expect a fight.** Frame it as work no one wants (email, information requests, after-hours calls) and the pushback disappears. Everywhere else, lead with what's in it for them. - **You have to tell a room full of executives they're wrong.** Rational vs. irrational resistance tells you which fights are winnable, with a documented case of an entire C-suite turned around. ### Business problems - **“We ran the pilot, it worked, and nothing changed.”** The workflow never changed, so no value could be created. You'll learn the diagnostic test for bolt-on AI and the Perfect Workflow method for redesigning the work. - **“We can't calculate ROI, so finance is cutting us.”** You can't convert tokens into outcomes. The AI ROI Problem framework puts the calculation at the workflow level, where it's defensible upfront. - **“We're stuck between proof of concept and production.”** Gates and Balances gives you five gates with explicit abort criteria. The AI 80/20 warning: if a build is “80% done,” 80% of the cost is still ahead of you. - **“We have 200 candidate use cases and no way to choose.”** The Opportunity Pipeline narrows to five to ten by selecting for the profile of outperformance, instead of whoever lobbied hardest. - **“The CFO won't fund anything without a timeline.”** Phases = Gates: define the gates instead of the dates, and fund each one separately. - **“Our competitor will make us obsolete before we finish transforming.”** The No-Win Situation, and how Transformation Dominance and Learning Rate decide who survives it. - **“Our AI costs more than the people it was supposed to help.”** Simplify, Standardize, Automate, Continuously Improve, plus the “costs scale faster than returns” test that tells you when to stop. - **“We bought the platform and adoption is 5%.”** A value proposition and opportunity discovery problem, fixed upstream in how opportunities get selected. - **“Automation keeps failing on work we thought was simple.”** The deterministic vs. stochastic workflow model tells you in advance which workflows AI can carry. - **“Leadership came back from a conference and now we have to do something with AI.”** The Four Questions bring the conversation back to reality without making you the obstacle. - **“We keep chasing the newest model instead of the biggest opportunity.”** Functional, Reliable, Affordable shows where a technology is and where to enter. An older, cheaper model often delivers 90% of the available value. - **“Every initiative is a one-off.”** Replaced by a quarterly Opportunity Discovery Workshop and a standing pipeline. - **“We infuse AI everywhere and get thin results everywhere.”** Complexity and Uncertainty: the two-category test for when AI is the right tool, and when software already does the job. - **“We can't justify R&D spend that might return nothing.”** The Profitability Tax and the Product Arrow make the economic case for investing at the peak, when shareholders tolerate it. - **“No one will staff an innovation project.”** If failure gets you laid off and only success gets you promoted, no rational person signs up. Incentive design is treated as part of strategy. - **“We took one big swing at transformation and it collapsed.”** Replaced by incremental delivery (the Bridge), a North Star, and quick wins. - **“We have data everywhere and can't say what any of it is worth.”** The Data Monetization Catalog links each data set to the use cases it serves. The With-and-Without method attaches a number. - **“Our data was built for dashboards and our models can't use it.”** BI always had a human supplying context. Agents don't. Learn to gather data contextually, with provenance and workflow linkage. - **“Every department defines customer differently.”** The Multi-Domain Problem, and why Customer 360 rarely survives contact. Whose 360? - **“Our systems don't talk to each other, so agents break.”** The platform onion, workflow-centric ontologies, and knowledge graphs built by observation. - **“We're drowning in reporting with the lowest ROI in the building.”** Get off the reporting flywheel and make it self-service. - **“We can't hire the skills we need.”** Structured career paths, learning paths, and internal promotion aligned to capability maturity. - **“Every team bought a different tool and runs a different process.”** The COE model: consolidate centrally for about a year so everyone learns the same way, then distribute deliberately. - **“Only about 60% of our business behaves like one company.”** The initial assessment expects acquisitions and satellite units at different maturity levels. - **“Our engineers maintain data infrastructure they never wanted.”** Three questions for every data process found in the wild: why does this person own it, do they still want to, and are they effective at it. - **“No one owns our KPIs.”** C-Level Transformation Metrics put ownership into leaders' actual goals, with Goodhart's Law as the guardrail. - **“Consumption pricing is breaking down for us.”** Outcomes-based and ecosystem business models, the problem SAP, Oracle, and Salesforce are all working through. - **“Customers are demanding outcomes-based pricing.”** You can only price an outcome you control enough of the workflow to deliver, which takes KPI maturity levels three to four. - **“We're getting commoditized in the middle.”** The barbell: value accrues at the action surface and the outcome layer. Touch neither and you need an ecosystem, and you need to know that before you build. - **“Leadership announced a huge investment and the stock tanked.”** The missing piece was the opportunity narrative that explains why the investment is required. - **“We solved the problem, so we stopped.”** The most common ceiling. Without a pipeline that explains why the business must keep going, transformation ends at the first win. ## Course outline Mondays cover concepts and models. Fridays cover application, mechanics, and communication. Every framework is taught twice: the ideal version, and the version that survives your constraints. ### Week 1: Why transformation is forced, and what strategy is *Mon: Foundations · Fri: Making it actionable* - Continuous transformation and transient advantage - The Business/AI Maturity Model - The Robotics Decision-Making Framework - Holistic AI strategy and the Decision Flywheel - Experiment, product, scale, transform - Transformation dominance - The Disruptor's Mindset, and how incentives make disruptors valuable - Outcomes-based business models and the Action Surface - Exercise: Name your domain and start placing your organization on the maturity model. ### Week 2: The three-model view of the enterprise *Mon: Simulation and decision advantage · Fri: The technology model* - Outcomes Engineering: dictate the outcome, work backward - Information Advantage, Decision Dominance, Transformation Dominance - Digital twins and intelligent twins - Business Model, Operating Model, Technology Model - Functional, Reliable, Affordable: where to enter a technology cycle - The AI Factory Floor and Assembly Line - The barbell: action surface, commoditized middle, outcome layer - The no-win situation - Exercise: Find where a simulation would pull future information into a present decision. ### Week 3: Innovation economics and opportunity discovery *Mon: Funding the work · Fri: Managing what you can't schedule* - The Innovation Mix and the Profitability Tax - Simplify, Standardize, Automate, Continuously Improve - Complexity and Uncertainty: when AI is the right tool - Top-down and bottom-up opportunity discovery - Persuasion mechanics: let data be the villain, Dolphin Data, coalition building - The Product Arrow - Gates and Balances in full - The AI 80/20 rule and POC Purgatory - Exercise: Bring an opportunity with baggage, and the strategic driver behind it. ### Week 4: Discovery in practice, and the platform *Mon: Running the discovery conversation · Fri: Architecture, ROI, and the big decisions* - Five Whys as the engine of discovery - The three-slide opportunity structure - The Halfway Mandate - The AI Strategy Chain: driver to KPI to ROI to workflow - Knowledge graphs, ontologies, and structural causal models - The AI ROI Problem: workflow-level ROI - Platforms are an onion; the Intelligent Core and Manual Rim - The Three Big Decisions - Exercise: Tally your application switches for a day. Each one is a consolidation opportunity. ### Week 5: The strategy document and the engagement *Mon: Assessment and data monetization · Fri: Earning the mandate* - Vision and Scope, and the full strategy document - The Data Monetization Catalog - The Initial Assessment Framework - Proof of Value: the three-meeting engagement - Coalition Building: the six-month path to a C-level meeting - The Five Jobs - The COE model and the Opportunity Discovery Workshop - The Data Point That Changed Everything - Exercise: Draft the vision and scope for a real engagement, and run the reveal question on a leader. ### Week 6: Outcomes, workflows, and where this goes *Mon: Framework certainty under fire · Fri: Outcomes-based business and the future of work* - Framework Certainty under live pushback - The opportunity pipeline: 200 use cases down to five - KPI Maturity, levels one through four - Bolt-on AI and the Perfect Workflow - Consolidation and Compression - Deterministic vs. stochastic workflows - Outcomes-based pricing and the named pipelines - Learning rate and the three talent categories - Exercise: Define the perfect version of your highest-value workflow, then measure the gap. ## By the end, you'll be able to - Define an AI strategy that improves C-suite decisions - Assess a business and place it on the maturity model - Run opportunity discovery, top-down or bottom-up - Estimate ROI at the workflow level and defend it to a CFO - Manage innovation without promising timelines you can't hit - Build and present a data and AI strategy document - Navigate resistance and build a coalition that earns a mandate - Answer any C-level challenge with a named framework and a next step ## Questions **What is Data & AI Strategist Certification?** The Data & AI Strategist Certification is a six-week live course taught by Vin Vashishta that teaches you to assess a business, run AI opportunity discovery, defend workflow-level ROI to a CFO, and earn a C-level mandate for AI transformation. **How much does it cost, and what's included?** $2,400 for the full certification. It includes 1-hour 1:1 session with Vin, a year of drop-in office hours, twice a week, recordings and slides after every session, 1-year access to the self-paced companion. **How long does it take?** Six weeks, with live sessions every Monday and Friday from 8:00 to 9:30am PT and about an hour of Q&A after each one. The next cohort starts Monday, January 11. Office hours continue for a year after the course ends. **Do I need a technical background?** No. There is no technical prerequisite. If you come from engineering, expect the strategy material to feel unfamiliar at first. If you come from the business side, expect the same in reverse during the technology model and platform sessions. **Can I expense it?** Some students do, and approval depends entirely on your employer's policy. A reimbursement guide written to forward to your manager is linked on this page. **Will it teach me the machine learning?** No. None of the four core courses teach model evaluation, MLOps, training or serving operations, SRE, or security implementation. They teach the other half of the job: deciding what is worth building, proving it will create value, pricing it, and getting an organization to act on it. **Which course should I take?** If you have to decide what gets built, start with AI Opportunity Discovery. If you own enterprise strategy and need a mandate, take the Data & AI Strategist Certification. If you have the mandate and need a roadmap that makes money, take AI Product Management. If you own a platform P&L, its pricing, and its governance, take AI & Agentic Platform Monetization. The course matcher scores 40 job titles against all four. ## Instructor Vin Vashishta is the author of From Data to Profit (Wiley) and has applied every framework in this course with clients including Airbus, Walmart, Siemens, and JPMorgan Chase. More than 9,000 professionals in 47 countries have taken his courses.