Business Knowledge

AI Strategy: Core Concepts

Kieran F. Noonan
Artificial Intelligence AI Strategy Digital Transformation Business Strategy

Summary

An Artificial Intelligence (AI) Strategy provides a structured framework for businesses to make impactful decisions regarding AI implementation. It helps clarify choices, evaluate trade-offs, and use evidence to move from vague intentions to clear, actionable plans. This guide explains what an AI strategy entails in practice, when to apply it, and how to implement it step-by-step.

The Concept in Plain English

Developing an AI Strategy is like creating a blueprint before building a house. Instead of randomly adopting AI tools, this strategy ensures every AI initiative is deliberate, aligned with business goals, and supported by data. It’s about making smart, evidence-based choices on where to invest in AI, what problems to solve, and how to measure success. This structured approach helps avoid costly mistakes and ensures that AI adoption delivers real value to the organization.

When to Use It (and When Not To)

Use it when:

  • The decision has a material financial or operational impact on your business.
  • You need to get key stakeholders (like department heads and investors) aligned on a shared vision for AI.
  • You are faced with multiple AI-related options and the trade-offs between them are unclear.

Do not use it when:

  • The decision is easily reversible and the associated costs are low.
  • You are simply looking for a way to justify a decision that has already been made.
  • The quality of your data is too poor to support a meaningful comparison between different options.

How to Apply It (Step-by-Step)

  1. Define the Decision Scope: Start with a clear goal statement. The output should be a well-defined boundary for your decision-making process. What specific problem are you trying to solve with AI?
  2. Map the Current State: Establish your baseline by gathering relevant metrics. This will give you a snapshot of your current performance before any AI implementation.
  3. Generate Options: Brainstorm potential AI solutions, considering your constraints and resources. The result is a set of viable options.
  4. Evaluate Trade-offs: Define your evaluation criteria and assign weights to them. Use this to create a ranked list of your options based on their potential value and costs.
  5. Decide and Implement: Make your decision based on the evaluation. Create a detailed action plan with a designated owner and a timeline for implementation.

Worked Example

A mid-sized retail firm uses this AI Strategy framework to compare two potential AI initiatives.

  • Option A (AI-powered inventory management): Costs £120,000 to implement and is projected to deliver £450,000 in value through reduced waste and optimized stock levels. The net benefit is £330,000.
  • Option B (AI chatbot for customer service): Also costs £120,000 but is expected to yield £420,000 in value. Based on the agreed-upon criteria of maximizing net benefit, Option A is ranked higher and selected for implementation.

Risks and Limitations

  • Weak Assumptions: Over-reliance on flawed or overly optimistic assumptions can heavily bias the outcome.
  • Stakeholder Misalignment: If key stakeholders are not in agreement, execution can be significantly delayed or derailed.
  • Unnecessary Complexity: For small, reversible decisions, applying this extensive framework can be overkill and waste valuable time and resources.