Riding the waves: how to build a successful AI strategy
AI promises to boost productivity and growth and to make businesses more efficient and innovative. But how do business leaders ensure they have a strategy that can fully unlock AI’s potential?
To begin with, we need a framework to map out the potential impact of AI on businesses. The Board of Innovation (BOI) elegantly visualizes it unfolding in three distinct waves. First, AI enhances how we do things by making existing processes more efficient. Next, it improves what we achieve by delivering better outcomes. And, finally, it transforms what we do altogether by introducing entirely new methods.
Right now, most businesses are navigating the first wave, using GenAI to do more with less. This is a natural starting point and a critical one, but it is not where AI’s full potential lies.
Is AI a product or feature?
To define the right AI strategy, we first need to draw a distinction between AI as a standalone product or an embedded feature. This distinction helps to define the product roadmap critical for investment, strategy and go-to-market execution.
- AI as a product: AI is the core value proposition, shaping both the very essence of the product and experience. Take Midjourney, for example – the text-to-image AI generator replaces traditional graphic design workflows and offers a fundamentally different approach to content creation. These products exist solely because of AI, they often require dedicated R&D, proprietary models and continuous innovation to remain competitive.
- AI as a feature: By contrast, AI as a feature enhances existing experiences, processes, or workflows, providing automation, intelligence and personalization without completely redefining the product itself. Grammarly, for instance, improves writing without replacing the act of writing. This approach allows companies to gradually integrate AI without disrupting business models, making adoption smoother and more sustainable.
Some companies, like Microsoft, have successfully balanced both approaches. Microsoft Copilot functions as an AI feature when integrated into Office 365, providing users with elevated experiences and making Microsoft's products stickier. Microsoft also offers AI products such as Copilot Studio that allow enterprises to build their own AI assistants from scratch. The key lesson here is that Microsoft prioritizes end-user value over AI capabilities – its AI offerings are purpose-driven and designed to solve real problems rather than simply showcasing technology.
Building AI with purpose
The most important question business leaders and product teams should ask isn’t: “How can we use AI?” but rather: “How does AI solve a real problem for our users?”
To build meaningful AI solutions, decision-makers should:
- Ensure AI enhances – not replaces – the user experience
- Use data-driven insights to personalize AI applications without overwhelming users
- Continuously test and refine AI interactions based on real user behavior
The ultimate measure of AI’s success isn’t just automation or efficiency – it’s how seamlessly it enhances experiences, improves decision-making and creates new possibilities.
There are many philosophies from the product world that can be applied to AI integration. A well-integrated AI solution should feel intuitive and empowering. And that can translate into completely different strategies as they need to align with the organization's strategy.
One of the global brands we're working with started its AI adoption with high-value areas at an organizational level. The areas identified require automation, intelligence and change management, all at scale, and the brand’s commitment is to accelerate its competitiveness in its sector and redefine value creation.
Another organization we work with is taking a more incremental approach, with AI-driven features introduced in stages onto its existing platform and resources allocated to user adoption in order to avoid major disruptions and employee resistance.
Both are strategic choices that maximize business value based on their respective operational realities and business objectives.
For challenger brands, prioritizing AI as a product, reimagining how users interact with this technology, and offering something completely new, can accelerate market differentiation. From an operational optimization perspective, taking a cautious, controlled approach and ensuring AI investments align with business priorities, security and scalability is recommended.
Act short-term but think long term
Another important aspect to AI adoption is to take a dual focus: companies must think long-term but act short-term. This approach ensures that businesses can capture immediate value from AI integration while simultaneously laying the groundwork for larger, more transformative changes.
Unlike traditional digital transformation efforts, which often focus on delivering incremental improvements and short-term ROI, AI is a self-reinforcing technology – it learns, evolves and compounds over time. This means that decisions made today will have exponential consequences in the future. Without a clear long-term strategy, companies risk building fragmented AI solutions that may not scale or integrate effectively into future-proofing business and operating models.
Technology and product leaders can use BOI's three waves to set the strategic vision with key milestones. For example, a company might begin by integrating AI-powered chatbots for customer support to reduce response times. However, this can be part of a larger strategy to develop AI-driven customer services that extends into hyper-personalized customer experiences at scale.
Human-first transformation
AI’s future isn’t just automation – it’s transformation. Its true power lies in maximize human potential, not just optimizing processes. While businesses implement AI for efficiency in the short-term, they must also view it as a catalyst for industry reinvention. Whether AI is integrated as a feature to optimize workflows or developed as a product to unlock new possibilities, its success ultimately depends on how well it serves the end user.
As AI advances, finding answers are becoming easier to obtain – but framing the right questions is what truly matters. As OpenAI’s Sam Altman puts it: “Figuring out what questions to ask will be more important than figuring out the answer.” AI strategies must be built on more than just digital products and technological capabilities; they must be grounded in deep empathy and understanding of what people and societies need.
