Why MAP (Minimum AI-Ready Product) Is The New MVP

Forbes

a day ago

Ashay Satav is a Product leader at eBay, specializing in products in AI, APIs, and platform space across Fintech, SaaS, and e-commerce.
What makes AI-driven products so distinct that we need a new term like MAP? The difference lies in how AI-first products are conceived compared to traditional products. In a conventional minimum viable product (MVP), AI may be seen as a "nice-to-have" feature added later to automate certain functions. In contrast, a minimum AI-ready product (MAP) strategically integrates AI from the beginning, resulting in intelligent, adaptive and anticipatory products right from day one.
What Is A Minimum AI-Ready Product?
A minimum AI-ready product can be viewed as the next-generation minimum viable product designed for an AI-focused world. It represents the smallest functional product with essential components to harness artificial intelligence effectively.
A MAP is designed to be viable and ready for AI enhancement. While it may not yet have a complex AI model in place—sometimes the "AI" in a MAP may involve a manual process or a basic algorithm—the key idea is that the product's architecture and team are equipped to integrate or upgrade to genuine AI as more data is collected. If PMs overlook data and AI factors during the MVP stage, making adjustments later can be challenging.
Decomposition Of A Minimum AI-Ready Product
Data is at the core of any AI-ready product. AI systems learn from data, so a MAP must be designed to collect and utilize it continuously from day one. A robust data pipeline is essential and should not be an afterthought. Identify critical data early, such as user behaviors and transactions, and ensure your product captures it effectively. Design your MAP to collect both explicit feedback (like forms) and implicit feedback (such as usage logs and click streams).
An AI-ready product should integrate machine learning (ML) models seamlessly into the user experience and system architecture. For instance, if your app plans to personalize content with an ML model, the MAP could start with a basic heuristic while calling a service for recommendations. This early planning helps avoid future refactoring. Consider a MAP as having "hooks" for intelligence, allowing the architecture to support ongoing updates and easy rollbacks if needed.
Building an AI-ready product involves focusing on both technology and collaboration among people. Unlike a traditional MVP team of just a few developers and a product manager, a MAP needs a cross-functional team. This includes PMs, engineers, data scientists, machine learning experts and UX designers. A team should have product managers who align efforts with business goals, designers who ensure usability, engineers who build scalable systems and AI specialists who develop models.
Any discussion of AI in products must include ethical and security considerations. AI can introduce various risks, including biased decision-making, privacy leaks and opaque systems that users may not trust. If your product collects user data for AI, ensure that you obtain consent and provide a clear privacy policy. Use encryption and secure data storage, even if your user count is small—breaches at an early stage can be just as damaging to trust, if not more so, than breaches that occur later.
Guidance To The Product Manager On Building AI-Ready Products
Integrate AI into the heart of your product strategy rather than treating it as an experiment on the side. When crafting your PRDs or setting your OKRs, it's crucial to incorporate AI-driven goals immediately. For example, a goal could be, "Use machine learning to improve personalization and increase user retention by X%." This emphasizes the importance of AI to your team and helps ensure that resources are allocated effectively.
The development of AI features involves a degree of experimentation. Product development should necessitate more hypothesis testing, prototyping and iteration than a typical software project. Before making meaningful investments, run a pilot to determine if an AI model delivers value. Embrace a prototype-and-test mentality, where you validate the impact of potential AI features with a simplified version before scaling them up.
Realistically, not all features in your backlog should be AI-powered. Part of an effective strategy is to select the correct problems for AI to address. Identify use cases where AI can significantly improve user experiences or automate labor-intensive processes and concentrate your efforts there. A prudent approach is to start with one or two high-impact AI use cases. "Start small but smart," as one framework suggests.
Product managers must allocate resources effectively and measure success. They should also focus on metrics like AI recommendation accuracy and prediction latency on top of traditional product metrics, such as monthly active users (MAUs) or conversion rates. Tracking these metrics ensures that the AI component receives adequate attention. Strategically plan for slightly longer development cycles or dual-track development (one track for model development and another for feature development).
To summarize, a product manager should fundamentally consider AI readiness at the strategic level, which, as needed, would be integrated into the product's vision, road map and team culture. This involves anticipating the data, talent, and integration needs and addressing them in product development. By doing so, project managers position their products for successful launches and continue to innovate, ultimately increasing their value.
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