Defining AI Strategy Checklist
A practical guide to help leaders define what AI should mean in their organization, align priorities, and strengthen the data, governance and operating foundations needed for measurable outcomes.
Are You Defining AI Strategy Around Business Outcomes or Just Deploying Tools?
AI is moving fast, but speed alone does not create value. Many organizations are pushing ahead with pilots, platforms and point solutions before they have aligned on what AI is supposed to do, how success will be measured or what data and governance foundations are needed to support trustworthy outcomes.
When AI strategy starts with tools instead of shared definitions, business priorities and data realities, it often leads to fragmented investments, weak adoption and “confident” outputs that people do not fully trust. In fact, 50% of CEOs surveyed said that rapid investment has resulted in disconnected technology within their organization.1 A stronger approach starts by defining what AI means for your organization, what outcomes matter most and what minimum viable guardrails are required so people will trust the results and keep using the sanctioned tools. That is where strategy becomes practical.
Use this checklist to evaluate where you stand today and identify the areas where CDW can help you move from AI exploration to trustworthy execution.
Four Areas to Strengthen Your AI Strategy
Strategy and Alignment: Define What AI Means and What It Needs to Deliver
Before you scale AI, align leaders on what AI actually means in your environment and which outcomes matter most. A strong strategy creates a shared definition, a clear business case and a realistic sequence of priorities.
- Have you aligned executive, business and technology stakeholders on what “AI” means in your organization, such as copilots, predictive models, optimization, decision support or agentic workflows?
- Have you identified the business outcomes that matter most, such as revenue growth, cost savings, risk reduction, service improvement or workforce productivity?
- Have you prioritized a short list of use cases with clear owners, success metrics and a realistic path to measurable impact?
- Have you defined where AI should support people, where human review is required and where automation should not be the goal?
Data and Trust Foundations: Make Better Inputs Possible Before Expecting Better Outputs
AI strategy and data strategy are inseparable. If your data lacks ownership, quality, context or lineage, AI can produce polished answers that still miss the truth. Trustworthy execution starts with minimum viable governance.
- Have you identified the data sources, owners and business definitions required for your priority AI use cases?
- Do teams agree on the meaning of critical terms and metrics so AI is not pulling from conflicting definitions across the business?
- Can you trace where data comes from, how it was transformed and which policies or constraints should govern its use?
- Have you established practical standards for data quality, access, security and retention that support AI use without waiting for a multiyear governance overhaul?
- Do you have guardrails in place to reduce the risk of inaccurate, incomplete or context-poor outputs shaping decisions?
Workload, Architecture and Platform Choices: Map What Runs Where and Why
Early decisions about tools, models and architecture can shape costs, flexibility and risk for years. AI strategy should account for the workload, the infrastructure it depends on and the long-term tradeoffs of build-versus-buy and vendor decisions.
- Have you mapped your priority AI workloads to the models, data, integrations and infrastructure they require?
- Do you know which workloads are best suited for cloud, on-premises or hybrid environments based on cost, latency, data gravity and compliance needs?
- Have you evaluated build-versus-buy decisions in light of your current skills, operating model and timeline to value?
- Do you understand the long-term implications of vendor lock-in, pricing model changes and proprietary dependencies before committing to a path?
- Have you accounted for the security, networking, storage and performance requirements needed to support AI workloads at scale?
Operating Model, Adoption and Next Steps: Build a Strategy People Will Use and Sustain
A good AI strategy does not end at selection or deployment. It creates the conditions for adoption, iteration and trust over time so sanctioned tools become useful habits instead of shelfware or workarounds.
- Do you have a current-state assessment of skills, processes and technical debt that could slow adoption or carry legacy problems into new AI environments?
- Have you defined how AI initiatives will be governed, monitored and refined once they move beyond experimentation?
- Do you have a change management and enablement plan to help employees understand when to trust AI, when to verify outputs and when to escalate concerns?
- Are you prepared to measure business impact over time and adjust priorities when a use case is not delivering value?
- Have you identified the next best step, whether that is a strategy workshop, readiness assessment, governance review or workload mapping exercise?
Source: 1 IBM, “CEOs Double Down on AI While Navigating Enterprise Hurdles,” May 2025
Why CDW
CDW helps organizations turn AI ambition into a practical strategy that leaders can trust, teams can adopt and the business can measure.
- Business-first strategy guidance: We help align stakeholders on what AI should mean in your organization, which use cases matter most and how to connect them to measurable business outcomes.
- Trusted data and governance foundations: We help validate the data quality, lineage, security and guardrails needed to reduce risk and support trustworthy execution.
- Practical roadmap and workload clarity: We help assess your current state, map workloads and models, and define the infrastructure, controls and next steps required to move forward with confidence.
- Breadth across the enterprise: CDW can bring together specialists across data, cybersecurity, infrastructure, networking and services to support AI initiatives that span more than one domain.
Request an AI Strategy Workshop From CDW
Our experts can help you assess your current state, identify gaps and define the next best step toward a more trusted, measurable AI strategy.