How to develop your AI strategy

Every organisation is under pressure to “have an AI strategy” from investors asking about competitiveness, employees wondering how AI will reshape their roles, customers expecting smarter experiences, and boards wanting clarity on risk. Even if a company is not ready to deploy advanced AI, it still needs a thoughtful point of view: Where can AI create value? How will we use it responsibly? What foundations must be in place?

A small sidebar:  I believe in a few years, companies won’t produce standalone “AI strategies” at all. Just as “digital transformation” became embedded into every business plan, AI will soon be part of every strategy and operating model. But today, with the pace of change accelerating, organisations still need clarity.

Why AI matters 

Several shifts in consumer behaviour and business performance show how fast AI is being adopted:

• 50% of consumers already use AI-powered search when looking for products.
• 73% identify as omnichannel shoppers, expecting seamless “phygital” experiences.
• AI in e-commerce is delivering 25%+ improvements across customer satisfaction, revenue uplift, or cost reduction.

The value is clear. The challenge is knowing where to start.

AI clearly matters but what do we actually mean by “AI”? The term covers a wide range of capabilities, from basic statistical models to fully autonomous agents. Understanding this evolution is essential because the right starting point depends on your business priorities, your data maturity and your technical capabilities. Before deciding where to focus, it helps to look at how AI has evolved and which types of AI you may want to consider for your organisation.

How AI has evolved and what that means for your strategy

AI has evolved through four major eras, each unlocking new forms of value while increasing the technical foundations required to realise it. Understanding these shifts helps leaders assess where they are today and what capabilities they must build to move forward.

The first era, starting in the early 1900s, focused on manual insights. Organisations relied on statistical methods and human analysts to identify patterns in structured data. This era enabled data-driven decision-making but depended heavily on people interpreting the results.

The second era, spanning roughly 1940 to 2020, introduced advanced analytics and machine learning. Businesses gained the ability to forecast demand, segment customers and deliver smarter product recommendations. To capture this value, organisations needed integrated datasets, trained ML models and the operational processes to use them consistently.

The third era, Generative AI (2021–2024), brought human-like text and content generation through large language models. Companies applied these tools to marketing content, customer support and internal productivity tasks such as code generation. Realising these benefits required high-quality models, strong prompt design practices and responsible governance.

We are now entering the fourth era: Agentic AI. This involves autonomous, goal-driven agents that can orchestrate actions across systems, such as managing returns, cancelling orders or even completing purchases directly through conversational interfaces. To deploy Agentic AI safely and effectively, organisations need real-time data, integrated systems, orchestrated workflows and strong governance to support end-to-end automation.

What a strong AI foundation looks like

Before deploying any of the above, organisations need the fundamentals that allow AI to perceive, decide and act:

• Integrated systems with automated data flows
  
• Single view of data, e.g., customer, product, sales 
  
• Event-level, real-time data
  
• Orchestrated workflows AI can augment or automate
  
• Analytics capabilities to measure and interpret impact
  

But technology is only one component. During a recent webinar we co-hosted with Deloitte Digital, we highlighted that AI success depends on five key organisational pillars: strategic ambition and alignment, operational integration, organisational and cultural change, governance and value realisation, and a strong data and technical foundation. Together, these ensure AI is embedded responsibly, measurably and at scale.

To help you develop an AI strategy that is practical, value-focused and aligned to your organisation’s readiness, we’ve outlined a four-step approach. It starts with building on what you already have, focuses on quick wins, and then guides you toward more advanced capabilities such as Agentic AI, all while ensuring strong governance and measurable outcomes.

A four-step approach to developing an AI strategy

Developing an effective AI strategy requires a structured approach that balances ambition with practicality. The following four steps provide a clear path for organisations to realise value quickly, build long-term capability and prepare for more advanced forms of AI, including Agentic AI:

1. Build on the foundation
A practical AI strategy starts with strengthening the foundations you already have. Rather than building from scratch, organisations should connect and elevate existing systems, data assets and operational capabilities to ensure AI can be deployed safely, quickly and cost-effectively. Getting the basics right accelerates every subsequent step.

2. Deliver quick wins
The next step is to focus on early, achievable wins that reflect your current data readiness. Prioritising use cases in insights and analytics, predictive intelligence and action-oriented automation enables teams to demonstrate value within weeks. These early successes build organisational confidence, secure stakeholder support and buy-in, and create momentum for scaling AI across your business.

3. Move toward Agentic AI
Once foundational workflows are integrated and initial automation is in place, organisations can begin to evolve toward Agentic AI. This shift enables autonomous, goal-driven agents that not only analyse information but also orchestrate actions across systems and optimise processes end-to-end. The result is faster cycle times, reduced costs and improving operations.

4. Govern, measure and scale
Sustained success requires strong governance, transparent metrics and scalable operating practices. Establishing clear guardrails, performance frameworks and accountability mechanisms ensures that AI remains aligned with business goals, complies with regulations and consistently delivers measurable value. This discipline enables organisations to scale responsibly while maintaining trust and control.

Use a framework to select your AI use cases

To support the creation of your AI strategy, we have also provided a simple framework to help you prioritise AI use cases. It guides you toward the opportunities that will create the most value, based on your readiness, cost, effort and time-to-value. This ensures your roadmap is grounded in reality – not hype – and helps you sequence initiatives in a way that delivers quick, incremental wins while building toward more transformative outcomes.

The message is: Start small. Deliver value quickly. Scale what works. Build the foundations along the way.

If you’re looking to accelerate your AI journey, whether by identifying quick-win use cases, assessing your data and technology foundation, or designing an enterprise-wide AI roadmap, we’d be happy to help. Our team works with organisations to turn AI vision into measurable business value, and we can support you at any stage of the journey.

Get in touch if you’d like to explore what this could look like for your organisation.