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From Business to AI Strategy
Business Consulting and Services > Executive and Strategic LeadershipDescription
From Business to AI Strategy is the practical ability to connect what an organization is trying to achieve with how AI can help it get there. It starts with reading a business — mission, value chain, competitive forces, customers, financials — and using that picture to shape an AI strategy and operating model that genuinely supports it. In applied work it shows up as maturity assessments, prioritized initiative portfolios scored on value and feasibility, business cases with credible costs, ROI and risk mitigations, and executive presentations that win funding. The capability deepens through repetition: drafting a strategy, testing it against peer and expert critique, revising the numbers and the story, and eventually designing and leading an enterprise AI capability.
Expected Behaviors
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LEVEL 1
Fundamental Awareness
Working alongside strategy and AI leaders, reads an organization's vision, mission, strategy and core values and explains how an AI strategy and operating model support them. Talks through value chain, Five Forces, SWOT and business model canvas elements, distinguishes leading from lagging and financial from non-financial metrics, and pulls company and industry facts from 10-K filings, investor materials and analyst sources to describe competitors, customers and readiness signals for AI.
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LEVEL 2
Novice
Supports AI strategy work by linking use cases to cost leadership, differentiation and focus moves, sorting applications as predictive, generative or agentic, and classifying how each creates advantage. Rates the seven building blocks against the five maturity levels for the organization, labels the profile as unhealthy, at risk or healthy, and benchmarks it against peers. Drafts an initial AI strategy from the situational analysis, ties each element to a business outcome, and reviews peers' drafts.
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LEVEL 3
Intermediate
Runs opportunity identification and funding work across functions and business units, using driver trees, value chain mapping, Five Forces and SWOT to surface candidate use cases. Builds weighted scoring frameworks and portfolio spreadsheets, plots benefit against ease, sequences quick wins with high-impact bets, and refreshes scores on trigger events. Develops the business case: costs, benefits, ROI, payback and NPV, team and timeline, categorized AI risks with mitigations, then presents for approval.
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LEVEL 4
Advanced
Leads how the AI strategy is packaged and sold. Drives story, storyboard and deck build under the Pyramid Principle, writes storyline titles that argue the case in sequence, sets structure, citation and quality conventions, and edits for stand-alone, non-redundant slides. Tailors content to each stakeholder group, delivers with command of pacing and presence, concedes and refutes opposing points, and compresses the full analysis into a 10-minute executive story while shaping the AI capability build.
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LEVEL 5
Expert
Sets the enterprise AI operating model: three-tier local, regional and global structure, orchestration choice and rationale, governance bodies, roles and bimodal delivery standards. Directs findings through executive interviews, documents and surveys, judges enterprise maturity against best practice, and defines target state, target architecture and the people-process-technology roadmap. Argues for shared platforms, defends the strategy under executive challenge, and drives organization-wide buy-in.
Micro Skills
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LEVEL 1
Fundamental Awareness
Business Strategy Core Components & Hierarchy
AI Strategy & Operating Model Fundamentals
AI Leadership Core Focus Areas
Leading vs Lagging Indicator Classification
Describe smart descriptive, predictive and prescriptive KPIs and how AI improves measurement
Explain how the value chain describes a company's internal activities and sources of advantage
Explain how Porter's Five Forces describe a company's competitive landscape
Describe the four quadrants of a SWOT analysis and the internal and external factors each captures
Identify the building blocks of a business model canvas (value proposition, segments, channels, revenue, costs, partners)
Recognize whether an organization's business strategy states how AI fits into it
Describe how a metrics causality chain links an AI initiative through data, analysis and process to business impact
Give examples of leading and lagging metrics for measuring an AI project's success
Identify how geographic, business-unit and functional scope affect AI deployment (regulation, localization, BU maturity, data silos)
Describe the factors that signal organizational readiness for AI-driven innovation
Explain how benchmarking against industry peers and best-in-class reveals improvement opportunities
Explain why AI strategies must align with business goals and competitive advantages
Identify the elements of a situational analysis (economy, industry, competitors, company, customers)
Identify sources for company and industry research (SEC EDGAR 10-K filings, financial databases, industry analysts, corporate websites)
Explain the financial measures used in background research (revenue, revenue growth, earnings/EBITDA, gross margin)
Identify the economic and industry information that frames a business background (structure, growth, trends, news)
Identify the competitor attributes to compare (product lines, market share, rankings, geography, key KPIs)
Recognize the company information found in annual reports, 10-K filings and investor materials (products, drivers, executives, financials, technology)
Describe customer segments, channels, personas, trends and key points in the customer journey
Identify the organization's stated vision, mission, strategy and core values
Explain how market dynamics, competitive positioning, organizational capabilities and technology readiness inform AI strategy
Explain how a situational analysis points to where analytics and AI opportunities exist
Recognize how business backgrounds and strategy statements differ across Leadership Circle peers' organizations
Recognize whether a proposed AI initiative supports stated business priorities
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LEVEL 2
Novice
Relate AI use cases to cost leadership, differentiation and focus strategies
Classify AI use cases by how they create competitive advantage (efficiency, decision-making, new business models, customer-centric innovation)
Classify AI applications as predictive, generative or agentic by the capability each provides
Sort observations about an AI program into the seven building blocks (vision, strategy, metrics, governance, people, processes, technology)
Complete a seven-block AI maturity assessment, rating each block against the five-level descriptions (Aware, Reactive, Proactive, Managed, Optimized)
Identify transformative predictive, generative and agentic AI applications in a given industry
Match business strategies to corresponding AI strategies and use cases (e.g., cost leadership → operational efficiency and automation)
Identify industry-specific AI opportunities that support an industry's business strategy
Read the shape of a maturity profile to label program health (unhealthy, at risk, healthy) and check whether vision and strategy lead the other blocks
Gather examples of how competitors have used AI to create competitive advantage
Use strategic tools (SWOT, value chain, driver trees, Five Forces) to link each AI initiative to a business outcome
Compare an organization's maturity ratings with industry peers using available benchmarks
Identify ways AI could enable new business models and revenue streams for the organization
Draft an initial AI strategy for the organization from its situational analysis
Assign a maturity level (1–5) to each of the seven building blocks for own organization
Check a draft AI strategy for clarity and consistency with the business strategy
Gather evidence on own organization's technical capabilities, cultural readiness, data infrastructure and business integration for the maturity assessment
Label own organization's maturity profile as unhealthy, at risk or healthy
List emerging AI trends and technologies relevant to the AI strategy
Propose a target maturity level for each building block based on business need
Compare own maturity ratings with Leadership Circle peers and industry competitors
Give feedback on peers' AI strategies for clarity and consistency with their business strategies
Link each element of the draft AI strategy to a key business outcome
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LEVEL 3
Intermediate
Use Five Forces and SWOT analysis to surface AI opportunities
Translate common industry challenges (e.g., fraud, demand forecasting, predictive maintenance, personalization) into candidate AI use cases
Decompose profit into revenue and cost drivers with a driver tree to locate AI opportunities
Map representative analytics and AI use cases onto the stages of a value chain
Score an AI opportunity on business-benefit factors, reversing the scale for legal, reputational and change-management risk
Score an AI opportunity on ease-of-implementation factors (technology reusability, data availability, quality, variety, complexity, lifecycle, solution and vendor maturity)
Write an opportunity definition covering description, value, strategic moves supported, business capabilities and technology maturity
Develop a customized, weighted scoring framework for evaluating AI projects
Build a portfolio scoring spreadsheet with weights, dimensions and measure thresholds
Map scored opportunities on the benefits-versus-ease portfolio (breakthrough, low-hanging fruit, no-go zone, minimal incremental benefit) to select use cases
Sequence initiatives strategically across easy wins, visible quick wins and harder high-impact bets
Update opportunity scores when ease and benefit shift with new technology, regulation, acquisitions, executive focus and competitive action
Schedule portfolio reviews and define the trigger events that prompt rescoring
Build a prioritization plan that balances immediate wins with long-term AI strategy
Identify AI opportunities for own organization systematically across functions and business units
Adapt available industry use cases to own organization's context
Select three to five AI initiatives that fit the AI strategy, using business frameworks
Articulate the business impact of each selected initiative
Score own initiatives on strategic/business impact and ease of implementation using a rubric
Map own initiatives on a 2×2 portfolio and set an expected sequence
Align own AI initiatives with business goals, competitive advantages and strategic priorities
Involve the right evaluators for business impact and for ease of implementation
Reconcile rubric-based scores with high-level judgment and explain differences
Prioritize initiatives on strategic goals, value and feasibility, including when not to choose the top-scoring one
Confirm an AI opportunity has the prerequisites to proceed (clear business need, acceptable ROI, strategic alignment, right team)
Follow the five-step business case process (prepare, know your audience, build the case, crunch the numbers, present)
Select the financial method (ROI, payback period, NPV, IRR, breakeven) that fits the investment decision
Identify the specific AI risks in a business case and categorize them (technical, operational, ethical/reputational, strategic, financial, regulatory)
Define the roles an AI project needs (e.g., data engineer, ML engineer, product manager, AI ethicist, change management)
Plan an AI development timeline across the data science pipeline (define, collect, model, rationalize, deploy)
Identify beneficiaries, stakeholders, subject matter experts, the business need and alternatives before building a case
Map the decision makers, influencers and supporters for an AI investment
Document the need: pain points, beneficiaries, process, solution requirements and success measures
Estimate project, capital and operating costs and tangible and intangible benefits, excluding sunk costs
Calculate ROI, payback period and NPV for an example AI project
Determine how the organization evaluates investments (process, timing, case-by-case vs portfolio, level of detail, phased approval)
Generate and narrow solution options, including an "as-is" option, by cost, speed, risk and revenue impact
Outline a directionally correct implementation plan (pre-switch work, owners, rollout, training, new operating costs, retired systems)
Assign a mitigation (governance, pilot, monitoring and model validation, stakeholder engagement) to each identified risk
Assemble the business case document (schedule and team, cost/benefit impact, financials, risks and mitigation)
Line up stakeholder support by working with a champion, anticipating concerns and sizing up competing proposals
Present the business case concisely, handling naysayers and closing on value and need
Coordinate alignment, resources and approval to move an AI opportunity to a funded project
Build a cost-and-benefit matrix with tangible and intangible components
Build a spreadsheet of key cost and revenue categories over time
Staff an AI initiative with development, business-support and run-state resources
Build a cross-functional team including beneficiaries, finance, customer-facing staff and external experts
Create a development and deployment timeline that includes a pilot and rollout across organization and geography
Calculate the overall NPV of own top initiative
Identify the AI risks of own initiative and a mitigating strategy for each
Produce a business case for the top initiative (cost/benefit, NPV financials, timeline and team, risks and mitigation)
Identify the key resource constraints and development time challenges for the initiative
Weigh significant intangible costs and benefits in the investment decision
Flag risks that could stop the initiative and plan how to address them
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LEVEL 4
Advanced
Review decks against the standard slide architecture (header, storyline title, body, takeaway, footer) and fix structural gaps
Lead the plan–write process from story to storyboard to slides for a strategy deck
Structure the AI strategy deck with the Pyramid Principle, from situation analysis to opportunities to business case, so horizontal and vertical logic both hold
Write storyline titles that carry the full argument when read in sequence
Set the deck's structure and style conventions (title page, agenda, breadcrumbs, page numbers, sources, appendices)
Enforce consistent source citation on slides (document, author, date, website, own analysis)
Quality-check decks against the details checklist (legible, one message, parallel construction, no typos, accurate data)
Develop tailored communication strategies for diverse stakeholders, adjusting content and detail with a context checklist
Develop a storyline that sets objectives, states key messages and organizes them into a logical flow
Edit a deck to be complete but non-redundant so each slide stands on its own
Build a persuasive argument using the six elements of persuasion, with a strong opening, a clear body and a close that requests a specific action
Deliver with command of word choice, clarity, volume, pace, gestures, eye contact and time
Establish credibility (ethos) by sourcing facts, avoiding extreme positions and taking opposing views seriously
Address opposing points using MECE structure, conceding weak points and refuting significant errors
Engage the audience by inviting participation, seeking feedback and pacing to their mood
Gain stakeholders' buy-in for the AI strategy through effective presentations
Synthesize prior assignments into a 10-minute final presentation covering situational analysis, business strategy, AI strategy, maturity, prioritized initiatives and business case
Shape the story, key points and closing "drop the mic" statement for an executive audience
Structure own AI capability across the different levels of the organization
Lead the build of a well-defined AI capability driven by business needs
Review AI capabilities from other organizations and adapt them to own context
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LEVEL 5
Expert
Design a three-tier (local, regional, global) structure for operating AI across the enterprise
Set the enterprise orchestration model for AI (central, parent-led, federated, local) and the rationale for it
Design the AI operating model, establishing governance bodies, roles and processes (steering committee, demand governance, architecture review, stewardship, program lead)
Set bimodal delivery standards that match the degree of rigor (adaptability vs reliability) to each AI deployment
Direct the findings effort through executive and stakeholder interviews, documentation review and surveys
Translate lessons from organizations that built AI capabilities (e.g., JPMorgan Chase, UPS, Starbucks) into enterprise strategy
Embed lessons from AI failures (e.g., biased hiring tool, Zillow iBuying, Watson for Oncology) into enterprise risk management
Decide where the AI capability sits in the organization across the seven building blocks
Judge enterprise AI maturity from findings, themes and comparison to best practice
Define the target state for the enterprise AI capability, driven by business need
Set the technology target state architecture that coordinates the capability build
Own the strategic roadmap for building the AI capability across people, process and technology
Make the case for shared AI platforms and tools that no single initiative can fund
Present and defend the organization's AI strategy to an executive committee within a 10-minute window
Communicate technical concepts and business cases to executive stakeholders with clarity and impact
Critique peers' AI strategy presentations on story, clarity, takeaways and delivery
Field executive committee challenges to the AI strategy and adjust the case in response
Drive organizational change and foster buy-in for AI-driven strategies at all levels of the organization