AI is no longer just a research buzzword—it’s becoming the engine behind real products used by millions. From personalized recommendations to fraud detection and conversational agents, AI is shaping user experiences in powerful, subtle, and often invisible ways.
But despite all the hype around model architectures and benchmarks, the real challenge in AI product development is not choosing the best algorithm. It’s building the right foundation: one made of the right data and the right way to evaluate performance.
In this article, we’ll walk through the AI product development cycle—from initial idea to deployment—and focus on two stages that often receive less attention than they should: data collection and evaluation. While they might seem routine, they are central to building AI systems that actually work in practice.
An AI product is a solution that uses artificial intelligence to address specific business problems or enhance processes. Unlike traditional software, AI products can learn from data, make predictions, create content, and automate tasks typically requiring human intelligence. These capabilities are powered by AI models—specialized programs capable of learning patterns from data during a process called training.
During this process, these models try to find a general relationship between inputs and outputs. For example, Large Language Models (LLM) that support ChatGPT or Claude have learned the relationship between words in a sentence. So if you give them a text, they can “complete” it based on what they have previously learned from the massive amount of data they have been trained on.
Developing advanced AI models often requires significant resources. However, businesses can use foundational models such as ChatGPT, Claude, or DALL-E for complicated tasks (such as content creation) and build in-house models for less complex tasks (such as sales estimation, performance prediction, etc). These are models that are trained by 3rd party companies on massive amount of data, and are capable of performing a large number of generic tasks.
Building an AI product on top of these models, whether the model is built in-house or not, follows a certain lifecycle. It is important for all stakeholders in an AI product to understand this life cycle.
Before introducing the AI product development lifecycle, it’s worth noting a similar-sounding and widely used term: the software development lifecycle (SDLC).
The SDLC is typically a linear, rule-based process where teams define requirements, design the system, write code, test it, and deploy a predictable product. In contrast, the AI product development cycle is iterative and data-driven: instead of coding behavior directly, teams train models to learn from data, evaluate their performance, and refine them over time. Success in AI hinges less on writing perfect logic and more on collecting the right data and designing a solid evaluation strategy—making AI development a more experimental and evolving process than traditional software engineering.
Building an AI product involves several stages, each of which can be guided and influenced by technical and non-technical stakeholders. To understand how these pieces come together, let’s walk through the AI product development lifecycle—from initial idea to ongoing improvement—highlighted in the diagram below.
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Here’s an overview of the stages that comprise the AI Product Development Lifecycle:
Idea Generation
This stage starts with identifying a real-world problem where AI could create value. The idea should be grounded in a clear use case—like helping self-publishers draft a manuscript faster or enabling sellers to estimate monthly sales based on Amazon’s Best Seller Rank (BSR). Good ideas often emerge from domain experts who know the pain points and inefficiencies in existing workflows.
Defining Success
Once a promising idea is identified, it’s important to define what success looks like. This means setting measurable, realistic goals—such as improving customer satisfaction, achieving a certain level of coherence in AI-generated manuscripts, or minimizing the error in monthly sales predictions. These metrics will shape how you evaluate performance later and help keep development focused on outcomes that matter.
Data Collection
AI models learn from examples, so this phase is foundational. Teams need to gather relevant data, ensure it's high quality, and clean or label it appropriately. For a manuscript draft builder tool, this might involve collecting a set of well-written, human-authored manuscripts to use as reference material. For sales estimation, it might involve pulling historical sales data and BSR numbers from a Kindle Direct Publishing (KDP) dashboard. Without good data, even the best models won’t perform well.