Building Your First AI Strategy: A Step-by-Step Framework

Executive Summary

• An AI strategy is a documented plan that aligns AI investments with business objectives and defines how AI will create value
• Strategy comes before technology selection—not after. Organizations that skip strategy waste resources on disconnected initiatives
• This framework covers six phases: business alignment, opportunity identification, capability assessment, prioritization, resource planning, and governance
• Effective AI strategies are living documents, reviewed quarterly and revised annually
• Small organizations need strategy too—the scope scales, but the principles remain the same
• A complete strategy addresses the "why," "what," "how," and "who" of AI adoption
• Most strategies fail not from poor analysis, but from lack of execution commitment

Why This Matters Now

Every organization is being asked—by boards, customers, competitors, and employees—"What's your AI strategy?"

The wrong answer is a shopping list of AI tools. The right answer is a clear articulation of how AI will advance business objectives, what capabilities you'll build, and how you'll manage the associated risks.

Organizations without strategy face three problems:

1. Scattered investment: Multiple teams pursuing disconnected AI initiatives, duplicating effort and creating technical debt
2. Disappointed stakeholders: Executives expecting transformation while teams deliver incremental improvements
3. Unmanaged risk: AI deployment without governance, creating compliance exposure and reputational risk

A strategy provides direction. Without it, AI becomes a series of experiments that don't compound into organizational capability.

What Is an AI Strategy?

An AI strategy is a documented plan that answers four fundamental questions:

A strategy is not:

• A list of AI tools to purchase
• A technology architecture document
• A project plan for a single initiative
• A mandate from IT to the business

A strategy connects AI to business value. Everything else is implementation detail.

Phase 1: Business Alignment (Weeks 1-2)

Before considering AI capabilities, establish clarity on business context.

Key Activities

1.1 Review business strategy

• What are the organization's strategic priorities for the next 2-3 years?
• What competitive pressures require response?
• What operational challenges constrain growth?

1.2 Identify strategic themes for AI

• Where could AI accelerate strategic priorities?
• What problems, if solved, would have material business impact?
• What capabilities do competitors have that we lack?

1.3 Establish success criteria

• How will we measure whether AI investments succeeded?
• What's our investment appetite (budget, risk tolerance)?
• What timeline is realistic for meaningful outcomes?

Output

A documented strategic context that AI must serve—not the other way around.

Common Mistake

Starting with "We should use AI for..." instead of "Our business needs..."

Phase 2: Opportunity Identification (Weeks 2-4)

With business context established, systematically identify where AI could create value.

Key Activities

2.1 Conduct use case discovery

• Interview business unit leaders about pain points and opportunities
• Review operational processes for automation potential
• Analyze customer feedback for unmet needs
• Benchmark competitors and industry leaders

2.2 Categorize opportunities

• Efficiency: Automating existing processes (cost reduction)
• Effectiveness: Improving decision quality (better outcomes)
• Experience: Enhancing customer or employee experience (satisfaction)
• Innovation: Enabling new products or business models (growth)

2.3 Document candidate use cases For each candidate, capture:

• Problem statement
• Proposed AI approach
• Expected business impact
• Data requirements
• Technical complexity
• Risk profile

Output

A catalog of 10-20 candidate AI use cases with preliminary assessment.

Common Mistake

Limiting discovery to what IT thinks is possible, rather than what the business needs.

Phase 3: Capability Assessment (Weeks 4-5)

Understand your current capabilities and gaps before committing to specific initiatives.

Key Activities

3.1 Assess current state

• Data: What data exists? What's the quality? Where are the gaps?
• Technology: What infrastructure exists? What integrations are possible?
• People: What AI skills exist internally? What's the training status?
• Process: What governance structures exist? What policies apply?

If you haven't done a formal readiness assessment, now is the time. See our AI Readiness Assessment guide.

3.2 Identify capability gaps For each candidate use case:

• What data would we need?
• What technology would we require?
• What skills would we need to develop or acquire?
• What governance would we need to establish?

3.3 Evaluate build vs. buy vs. partner For each capability gap:

• Can we build this internally? (Time, cost, expertise)
• Should we buy a solution? (Vendor options, integration complexity)
• Should we partner? (Strategic value, resource constraints)

Output

A capability gap analysis that informs prioritization and resource planning.

Common Mistake

Assuming current capabilities are sufficient, or that any capability can be quickly acquired.

Phase 4: Prioritization (Weeks 5-6)

Not all opportunities are equal. Prioritization ensures resources focus on highest-value initiatives.

Key Activities

4.1 Score each use case

Use a 2x2 matrix or weighted scoring model:

4.2 Select initial portfolio

• 1-2 "lighthouse" projects: High visibility, manageable risk, clear success metrics
• 1-2 foundational initiatives: Data quality, governance, skills development
• Clear "not now" decisions with rationale

4.3 Sequence initiatives

• What must happen first? (Dependencies)
• What can run in parallel? (Resource constraints)
• What's the 12-month view? 24-month view?

Output

A prioritized portfolio of AI initiatives with sequencing logic.

Common Mistake

Trying to do too much at once, or selecting "cool" projects over strategically valuable ones.

Decision Tree: AI Strategy Approach Selection

Phase 5: Resource Planning (Weeks 6-7)

Strategy without resources is wishful thinking. Quantify what you need to execute.

Key Activities

5.1 Estimate investment requirements

• Technology: Infrastructure, platforms, tools, licenses
• People: Hiring, training, contractors, consultants
• Process: change management, governance, documentation
• Contingency: Buffer for unknown requirements (typically 20-30%)

5.2 Develop funding approach

• Centralized AI budget vs. business unit funding
• CapEx vs. OpEx considerations
• Phased funding tied to milestones

5.3 Identify resource constraints

• What can't be solved with money? (Talent scarcity, data limitations)
• What organizational changes are required?
• What partnerships might accelerate capability?

Output

A resource plan that's realistic and funded, or clearly identifies what must be true for execution.

Common Mistake

Underestimating change management and governance costs, which typically exceed technology costs.

Phase 6: Governance and Execution Framework (Weeks 7-8)

Define how strategy will be executed, monitored, and adapted.

Key Activities

6.1 Establish governance structure

• Who owns AI strategy overall? (Typically CTO, CDO, or designated AI executive)
• Who approves new AI initiatives? (governance committee composition)
• How are decisions made? (Criteria, process, escalation)

6.2 Define execution model

• How will AI projects be staffed? (Center of excellence, embedded teams, hybrid)
• What's the project methodology? (Agile, stage-gate, hybrid)
• How will business and IT collaborate?

6.3 Create measurement framework

• What metrics track AI value creation?
• What's the reporting cadence? (Quarterly executive review, monthly operational)
• How will strategy be revised? (Annual planning cycle, trigger-based updates)

Output

A governance and execution framework that ensures strategy translates to action.

Common Mistake

Creating strategy documents that live in a folder, never referenced in actual decisions.

Checklist: AI Strategy Development

Foundation

• Business strategy reviewed and understood
• Executive sponsor identified and committed
• Strategy development team assembled (cross-functional)
• Timeline and milestones defined

Discovery

• Use case discovery interviews completed
• 10-20 candidate use cases documented
• Capability assessment completed (or scheduled)
• Competitive and industry analysis reviewed

Prioritization

• Scoring criteria defined and weighted
• Use cases scored consistently
• Initial portfolio selected (2-4 initiatives)
• "Not now" decisions documented with rationale

Planning

• Resource requirements estimated
• Funding approach determined
• Key hires or partnerships identified
• 12-month roadmap developed

Governance

• Governance structure defined
• Executive review cadence established
• Success metrics defined
• Strategy document published and communicated

Common Failure Modes

1. Technology-First Thinking

Starting with "We should use [specific AI tool]" instead of "We need to solve [business problem]" leads to solutions without problems.

Fix: Always start with business context. Technology is an enabler, not a strategy.

2. Boiling the Ocean

Attempting to transform the entire organization simultaneously overwhelms resources and creates fatigue.

Fix: Start with 2-3 focused initiatives. Expand only after demonstrating success.

3. Strategy Without Commitment

Creating a beautiful strategy document without executive commitment to fund and support it.

Fix: Secure resource commitment before finalizing strategy. A strategy you can't execute isn't a strategy—it's a wish.

4. Static Documents

Treating strategy as a one-time exercise rather than a living framework.

Fix: Schedule quarterly reviews and annual updates. Strategy must adapt to changing conditions.

5. IT-Isolated Strategy

Developing AI strategy within IT without meaningful business partnership.

Fix: Ensure business unit leaders are co-owners of strategy, not just consumers.

Metrics to Track

Frequently Asked Questions

Next Steps

A well-crafted AI strategy is the foundation for successful AI adoption. It aligns investment with business value, provides direction for execution, and establishes governance for risk management.

If you're starting your AI journey or resetting after scattered initiatives, strategy development is the place to begin.

Book an AI Readiness Audit with Pertama Partners to build a strategy grounded in your specific context and capabilities.

References

1. Harvard Business Review. "Building the AI-Powered Organization." 2023.
2. McKinsey & Company. "The State of AI in 2024." McKinsey Global Survey, 2024.
3. MIT Sloan Management Review. "Winning with AI." Research Report, 2024.
4. Gartner. "AI Strategy Framework." 2024.

Related Reading

• What Is an AI Readiness Assessment?
• Creating an AI Roadmap: From Vision to 18-Month Plan
• 7 AI Strategy Mistakes That Derail Implementation