The AI Roadmap: 4 Steps to a Market-Ready AI Product

Successful product development with AI consists of several steps. Fundamentally, this is an innovation initiative, meaning time and money must be invested upfront before generating revenue from the results afterward. Two questions are crucial for decision-makers. First: How long until the innovation generates initial revenue (time-to-market)? And second: How high are the time and financial investments up to that point?

In the following, get an overview of the 4 steps of AI product development to derive answers from them.

Step 1: Potential Analysis

At the beginning is the central question of which AI use case and underlying business model can create sustainable added value and what that value proposition would be. The goal of this phase is to develop a coherent target image for use case, business model, and value proposition to drive the initiative during a potential implementation phase. Concrete descriptions of objectives are important—ones that can be evaluated qualitatively or even quantitatively during development (hypothesis validation). There may well be multiple alternative ideas. What matters is not pursuing too many ideas simultaneously and identifying non-productive approaches as quickly as possible and discontinuing them.

In addition to business prospects, data availability must also be in place. Is all necessary data already available, or must technical prerequisites be established first? A good AI potential analysis provides clarity here and shows which measures need to be taken and at what cost.

Step 2: AI Demonstrator

If the potential uncovered in Step 1 seems large enough to pursue the defined objectives, it must be clarified as quickly as possible whether the initiative is technically feasible. This technical feasibility proof is provided using an AI demonstrator. The AI demonstrator does not aspire to be a market-ready product. It serves solely to verify whether the expectations placed on the use case in the target image are technically achievable. As a rule, this is an iterative process and not a one-way street. This means that technical hurdles encountered by the AI demonstrator in a test run lead to further development and testing in the next test run. Of course, there must be time and resource limits to prevent the process from running indefinitely. In most cases, the feasibility question is answered after a few iteration cycles. 

A "no" to feasibility does not necessarily mean the end of all AI aspirations. The experience gained through concrete testing often leads to other use cases and business cases being viewed with fresh eyes, and a new initiative is quickly launched.

Step 3: AI Product

Once feasibility is assured, Step 3 is about developing the AI demonstrator into an operational AI product. This means not only refining AI functionalities but also integrating and connecting user interaction capabilities. For software products, this typically means developing graphical user interfaces (GUI) and usage mechanisms (UX). In this phase, a final end product does not yet need to emerge immediately. Often it is faster and cheaper to keep final implementation decisions open and first gather feedback from initial test users on preliminary implementations (mockups). Once they start using your preliminary AI product, the AI can be continuously trained on newly generated data, and you gain valuable insights into actual user behavior. Through a few adjustment cycles, the first marketable AI product (Minimum Marketable Product - MMP) emerges.

On the business side, this phase gives you time to train your sales team and establish a help desk for customer support. Additionally, for future software product sales, legal questions may still need to be clarified, such as licensing terms or data security.

Step 4: Product Launch

In the final step, the new AI product is marketed and delivered to your customers. In an initial phase, these could potentially still be selected friendly customers. This customer group is characterized by their openness to innovation. They seek to gain a competitive advantage by being among the first to adopt a new technology. From past innovation projects, you likely already have relationships with customers who fit this description. Ultimately, though, this is only an optional intermediate step if final reservations need to be addressed. After all, you want to benefit from feedback from the broader market regarding value and willingness to pay. 

From this point on, the lifecycle of your AI product begins, where sales figures, customer feedback, additional data, and new ideas create a continuously evolving AI experience for your customers and for you.

Your AI Journey with aiXbrain

As an AI service provider for product innovation, the aiXbrain team uses the AI roadmap presented here as a blueprint. We structure the concrete process so that investment costs and time-to-market are as low as possible. Please feel free to contact us to learn how this works in practice.

Of course, you can also implement the blueprint with your own AI team. With the right combination of AI expertise and an AI roadmap, nothing stands in the way of your next AI product!