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In 2022 alone, over $136 billion was invested in AI, underscoring its worldwide growth. Sequoia Capital, one of Sillicon Valley’s top VC firms, have “an order of magnitude more dollars invested at the AI application layer” than in underlying AI according to Pat Grady, a partner at the firm. Evidently, building AI products are of increasing importance. However, without a well-crafted AI product strategy, even the most advanced technologies can fail to achieve their potential…
Take for example a startup that found itself struggling to balance the demands of finding customers, product development, and managing investors. Pulled in multiple directions, the startup lacked a clear direction, could not focus its execution, and ran out of money before building a competitive product.
A well-crafted AI product strategy is crucial: it provides direction and structure for building product roadmap that compounds into a Product-Feature Flywheel: each feature compounds in value over the next to eventually create an edge in a crowded market, “WOW” customers and investors, and achieve top 1% growth among other startups, all with less time and cost. Think before your sprint.
AI product strategy is the plan to guide the design and development of AI products. It involves analyzing customer-problem workflows, scoping product features, assessing technical risks and costs, organizing a roadmap, and evaluating the competitive and partnership landscape—all while focusing on AI’s edge and crafting a data strategy to support that.
By doing this effectively, companies can reduce go-to-market (G2M) time by 50%, cut costs, and create highly impactful products. This blog will explore the components and importance of AI product strategy, showing you how to develop one for your business, reduce risks, and ultimately, build AI products that resonate with customers.
To build an effective AI product strategy, there are five primary components to consider:
In a rapidly evolving marketplace, AI can differentiate your product from competitors. AI product strategy allows businesses to leverage data and algorithms in ways that traditional products cannot, delivering superior customer experiences and insights. For example, AI-powered personalization in e-commerce or predictive analytics in healthcare can offer an edge that makes competitors obsolete.
AI product strategy helps eliminate wasted time by allowing teams to rapidly test, iterate, and eliminate dead ends. Instead of pursuing multiple directions, a solid strategy ensures that the team stays focused on the most viable approach. Knowing when to pivot, stop, or push forward is key to reducing time to market, which can be the difference between success and failure in a fast-paced market.
AI product strategy provides a structured approach to assessing technical and commercial risks. From understanding the feasibility of the technology to ensuring the business model is viable, the strategy reduces the chance of launching a product that fails to deliver results. It gives businesses a framework for balancing innovation with risk management, ensuring that resources are used wisely.
An AI product that creates artifacts and diagrams that can convey the future value of the company for attracting early-adopters, team members, and investors. The technical vision of a company is often either too general or too specific to be compelling for a wide range of audiences. AI product strategy can convey a 1000 words in a succinct informatic diagram.
The foundation of an AI product strategy starts with defining task workflows. This begins by identifying the ideal customer profile (ICP) and mapping out the problems they face — problems that your product can solve. The goal is to pinpoint the “A to B” problem: what needs to happen for a customer to exchange money for your product? Understanding this transaction is key to developing a product that meets both user needs and business goals.
AI technologies, as an industry rule of thumb, typically deliver 80% accuracy upfront with the remaining 20% costing requiring significant resources to improve. It's important to align technology choices with the task-workflow — balancing the value creation of AI while mitigating technical risk of implementing it. This alignment ensures that you're scoping AI technologies that deliver up to the expectations of your customers while not dangerously overcommitting.
Once the AI architecture is scoped, the next step is to unroll its features into a product roadmap that is calibrated to your resources. Here it is important to be precise in prioritizing features of the AI architecture so that there is a synergistic effect — the first builds on the second builds on the third — to essentially create a Product-Feature Flywheel by chaining together the low cost, high value features. Having a clear roadmap will be your filter for misaligned customers and investors and attracting aligned customers and investors so you are not pulled in 1000 directions or wasting resources in the wrong direction.
For many people, AI is quite new, we see an ‘AI awareness’ gap in the market that makes it difficult to make these decisions. It requires experience and deep understanding of AI to properly plan for each of the steps above. Customers often come to us asking for clarity on:
All of these AI-specific topics are examples of nuanced focuses that arise while defining an AI product strategy. Each application is different, requiring different deep dives in what matters.
Looking ahead, the barrier for entry for launching a company is lowering — with agile, small teams capitalizing on niches and expanding outwards from there. AI is currently a boom industry offering distribution opportunities. The combination of how to effectively build AI products with small teams is crucial, to do more with less resources. How will your team compete?
In summary, AI product strategy is essential for companies launching products with minimal cost and resources. It can be broken down into a 3 step process of defining your customer workflow, scoping your AI architecture, then translating that into a prioritized roadmap.
The result is a Product-Feature Flywheel: low cost, high value AI features that compound in value overtime… Anything else and your chances of building an uncompetitive product increases.
Now is the time to start thinking about how you can develop or refine your AI product strategy. Whether you're launching a new AI-driven product or enhancing an existing one, having a solid strategy in place will position you for long-term success.
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