There are a lot of videos floating around the social media sphere. All of them sound the same: “This is how I created the perfect AI business strategy,” “All you need to know to make your own AI strategy,” and so on. 

They are all trying to sell you something. So how do you trust anyone? And more importantly, how do you trust AI for such a big task?
The real question is not whether AI can do something clever. It’s whether it will change how your company wins. Building an AI business strategy means answering that question. It means choosing a small number of business problems and proving value, and then converting those proofs into durable capability. 

Below, we make that big task of making a strategy using AI easy, using six evidence-forward steps: each with the practical setup a leader needs and the precise actions teams should take. 

Understand business objectives and map AI to value. 

“We have data and models. Where do we start?” If this is you, then here is your answer: You start with this: 

Use that data and translate corporate goals into measurable AI outcomes. AI is still and probably will be prone to hallucinations, so you must define your goals and make the AI work product measurable. 

If the CEO cares about margin, focus on cost-to-serve. If growth matters, target conversion lift or retention. 

Use an AI-first scorecard (à la Iansiti & Lakhani) to align readiness to ambition: score AI adoption, architecture, capability, and everything else you use or need. The scorecard forces you to stop at one question: which of our strategic goals will AI measurably move in 6–12 months? That becomes the north star for your artificial intelligence strategy. 

Conduct a data audit and readiness check. 

“But our data is messy — do we even have what we need?” 

As Sherlock Holmes said: “Data, data, data.” In AI, it’s the foundation of everything. So treat the data audit as your first real project. This means systematically reviewing: 

• Inventory sources: Where is your data coming from? Internal systems, third-party platforms, customer interactions? 
• Data owners: Who controls or manages each dataset? This helps with access and accountability. 
• Sample quality: Is the data clean, complete, and representative of the problem you’re solving? 
• Latency: How fresh is the data? Real-time vs. batch updates can make or break AI performance. 
• Labeling needs: For supervised learning, do you have labeled data? If not, how much effort will labeling require? 

Next, identify silos, places where data is trapped in isolated systems, and assess the minimal plumbing (i.e., integrations, pipelines, APIs) needed to enable repeatable experiments. You’re not building the final product yet; you’re checking whether you can run a credible pilot in 8–12 weeks. 

• If yes: Build the pilot and start learning fast. 

• If no: Quantify the remediation work (what needs fixing) and estimate the ROI of fixing it. This helps justify investment and prioritize effort. 

Build an ethical, governance, and risk framework early. 

“Governance will slow us down.” 

For most founders and managing-level staff, compliance, regulation, or other such buzzwords look like the biggest hurdle and the tallest wall for them to cross. But there is a man for everything, or, for this context, an AI. For your AI business strategy to be complete, you need to make sure that you define the rules for sensitive use cases: bias checks for hiring or credit decisions; explainability thresholds for regulated workflows; data-access controls and audit logs—document decision criteria for model retirement and human review. Framing ethics as a business control protects trust and unlocks scale. A credible, successful AI strategy includes these protections by design. 

Choose technologies, vendors, and the build/buy split. 

“Should we buy a model or build one?” 

Make that decision against the business value and the scorecard from step 1. Take this as a rule of thumb: 

• Build where the model is core IP and differentiates the product. 
• Buy or partner where commoditized stacks speed time to value. 

Favor modular, cloud-native architectures and MLOps so you can monitor, retrain, and replace components without rework. This keeps building an AI strategy pragmatic and reversible. 

Assemble teams, close skill gaps, and design the operating model. 

“Who runs this — a central team or the business units?” 

Design the operating model to match your culture. Most organizations land on a hybrid: a central COE that supplies platform, governance, standards; product-facing squads that own use cases and outcomes. 

Hire a small core of senior engineers and translators (product managers with data fluency)—Upskill broad teams with short, applied workshops. 

Pilot, measure, and scale with discipline 

“How do we know when to scale?” 

Run time-boxed pilots with control groups and clear success metrics mapped to business value (revenue lift, time saved, churn delta). If the pilot demonstrates causal lift, productize the components: data pipelines, model APIs, and monitoring. Create a lightweight platform to avoid rebuilding. Set a quarterly review cadence to retire underperforming models and to reprioritize the portfolio.  

If you want a short starter sequence: (1) pick one business metric to move, (2) run a 10-day readiness check, (3) launch a single 6–8 week pilot with a named business sponsor and two KPIs, and (4) require a go/no-go decision based on business impact, not just model fit. That simple loop turns experiment into strategy.