Author(s): Adel Zaalouk Originally published on Towards AI. A Framework for Building Great AI Products Introduction The other day, I found myself reflecting on a classic concept that I was taught in business school, Maslow’s hierarchy of needs, a simple but powerful framework for understanding human motivation, with basic physiological needs at the foundation (air, food, water, shelter, sleep, clothing,…) and the pursuit of self-actualization at the pinnacle. sketchplanations.com This got me thinking, in the world of tech (especially AI) and products, what is an equivalent? I mean, users always have needs, and needs in the product vary significantly subject to the use case and the problem being solved, but a spectrum definitely exists. Is there a model or a framework we can use to identify what constitutes the “right” product for customers and what customers would expect of the product? Luckily, Geoffrey Moore’s “Crossing the Chasm” provides some answers. In his book, Moore references Levitt’s Whole Product Model, and goes further to simplify by introducing the “Simplified Whole Product Model”. In this post, we will internalize Moore’s model, expand it, and show how it can be applied specifically to AI products (applies to any product as well). We’ll dive into the trade-offs inherent in building AI applications and illustrate these concepts with real-world examples. My goal is that after you read this post, you should have a mental model and a framework for building great/usable AI products, which would help you not only think about the technology, but also how it fits in the big picture. Thanks for reading The Technomist! Subscribe for free to receive new posts and support my work. The Whole Product Primer (Plus it’s Descendants) The “Whole Product” model revolves around the idea that a core/generic product must be complemented by additional services and interfaces (aka enablers) making up the Whole Product which should provide a solution to the customer’s problem and to address their needs. In Geoffery Moore’s book, the core/generic product is defined as the fundamental offering or technology that a company produces, which may not be sufficient to fully solve the customer’s problem or meet their needs. This is where the outer ring comes into play. It represents the whole (expected) product, which is divided into sectors. This outer ring encompasses all the additional elements that customers expect or require to make the core product fully functional and valuable to them, let’s call them the “enablers”. The Adapted (Simplified) Whole Product Model In the tech industry, companies often prefer to build upon existing open-source projects or technologies rather than developing everything from scratch. These companies focus on adding unique value through layers of customization, support, consulting services, integrations, and proprietary patterns, creating a whole product that is more than the sum of its parts. Furthermore, any successful technology is bound to become commoditized over time, a strategy we often see in tech employed by competitors who gain from doing so, forcing value into higher layers in the value chain (which they usually have thus wanting to commoditize). Recognizing this, companies need to continually innovate and differentiate their offerings to maintain a competitive edge (related, see a previous post on AI market dynamics and what companies in the space focus their efforts on). Therefore, let’s adapt the simplified whole product model with two key adjustments. First, we’ll shift from fixed sectors to a more modular, petal-like structure. This reflects the interconnected yet distinct components that comprise the whole product layer. Second, we’ll introduce a new layer above the whole product layer, called the “differentiated product layer”. This layer will highlight the unique value propositions that set companies and their products apart, showcasing how they create the most value for their customers. To be more concrete, let’s show how this can be applied to Slack for example (this is just for illustration purposes, the real differentiators could very well be very different). In addition to representing the product’s enablers differently using petal-like modular components, we added a new layer to highlight the differentiators. In the example above and in the case of Slack, enablers could be threads, Slack Connect, the workflow builder, and/or Slack AI. We are very close to being done here with the adaptations, so we will add one last thing to our new framework. In addition to the differentiated layer, we would like to model customizability for products. I.e., one customer’s whole product may not be the same for another. I.e., not all customers desire exactly the same features, so it’s important to cater based on customers’ constraints/needs. For example, generically, some customers value safety/security over cost, others might value speed, etc. Let’s continue the slack example. Slack might have different customers to cater for. Enterprise customers, use it mainly as a means for company-wide communication, in that case, the focus will be security and compliance with the company’s communication policy, leading to: Prioritized Enablers: Enterprise-grade security, granular permissions, compliance features (e.g., data retention policies) Emphasized Differentiators: Slack Connect for secure external collaboration, integration with enterprise security tools Another use-case, focus area might be on developers, and Slack being part of their dev/test workflows. In that case, the focus will be on developer productivity and collaboration, leading to: Prioritized Enablers: Integrations with development tools (e.g., GitHub, Jira), code snippets, powerful search Emphasized Differentiators: Workflow Builder for automating tasks, Slack AI for code suggestions and knowledge retrieval The takeaway here is that versatility can be a core differentiator on its own because it allows for tailored product experiences. Another way to look at it is that the constraint being imposed defines the core value proposition of the product and how it is shaped to best serve and differentiate in a particular space. In our example, Slack can tailor its offering to different customer segments, highlighting the features and capabilities that are most relevant to each group. This customization not only enhances the user experience but also strengthens Slack’s value proposition in a competitive market. Towards Whole AI Products (aka Systems) Hopefully, […]
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