What is a Frankenstein Product: Overview

I’m not the first to have used the term “Frankenstein Product” which is often used to describe a product that is a mishmash of features hastily stitched together without a clear vision or purpose. These products often result from a lack of understanding of the customer’s needs or an overzealous attempt to incorporate every possible feature into a single product. The result is a confusing product, difficult to use, and often fails to meet the customer’s needs. It also becomes bloatware difficult to QA, manage and support. For buys, you can also tell these products if suppliers are unwilling to let you have a free trial or if the deployment is measured in months instead of days or weeks!

In the Artificial Intelligence (AI) age, the risk of creating Frankenstein Products is even greater. With the vast array of AI technologies available, it’s tempting for businesses to incorporate as many features as possible into their products. However, without a clear understanding of the customer’s needs and why the customer will use your to-do list over the competition’s; and a strategic approach to product development, these products can quickly become unwieldy and ineffective. I could name some examples, but maybe that is better for people to leave in the comments.

The term “Frankenstein Product” is derived from Mary Shelley’s novel Frankenstein, in which a scientist creates a monster by stitching together parts from various bodies. Just like the monster in the novel, Frankenstein Products are often seen as abominations, lacking in coherence and functionality.

Why AI will lead to an explosion of Frankenstein Products

AI’s emergence has revolutionized product development. It enables the analysis of massive data volumes, prediction of customer behavior, task automation, and personalization of experiences. For developers, AI enhances speed, bolsters QA procedures, and aids in code refactoring. However, this acceleration could lead to an overabundance of features. Without collaboration between a product manager and a product designer skilled in UX and CX, this risk escalates.

Speed does not equate to value if quality or usefulness is missing; a principle acknowledged in Agile, where building irrelevant or unused features quickly is the most significant form of waste, moves you further from the goal of product-market fit. Hence, the emphasis should be on outcomes rather than output when creating products that customers will love.

The problem lies in the fact that AI is not a one-size-fits-all solution. Each AI technology has its own strengths and weaknesses, and not all of them will be relevant or beneficial to every product. Furthermore, the integration of AI technologies into a product requires a deep understanding of the technology and its implications. Without this understanding, businesses run the risk of creating products that are confusing, difficult to use, and fail to deliver on their promises.

Moreover, the rapid pace of AI development means that new technologies are constantly emerging. This can lead to a constant cycle of adding and removing features, resulting in a product that is constantly changing and lacks stability.

How to Avoid Frankenstein’s Monster: Product Market Fit

To avoid creating a Frankenstein Product, businesses need to focus on achieving product-market fit. This means developing a product that meets the needs of specific customer segments and creates value for them which in turn creates value for the business building them at a price that the market is willing to pay.

Achieving product-market fit requires deeply understanding the customer’s needs and desires. This can be achieved through customer interviews, surveys, and market research. By understanding the customer’s pain points, businesses can develop a product that provides a solution to these problems. To do this good product managers and product designers need to spend a minimum of 20% of their time interviewing customers. Yes, that one whole day a week? Are you spending that much time a week interested in the comments below?

Know your customer’s Jobs-to-be-done.

The final guard against creating Frankenstein Products is deeply understanding the customer’s Jobs-to-be-done (JTBD). The JTBD framework is a way of looking at customer needs not in terms of products or services, but the jobs that customers need to get done. By understanding the customer’s JTBD, businesses can develop products that solve these jobs. This can help ensure the product is relevant and valuable to the customer. Mapping your product blue ocean strategy canvas can also help you avoid the monster.

The concept of Jobs-to-be-Done (JTBD) was popularized by Clayton M. Christensen, a Harvard Business School professor, in his book “The Innovator’s Solution: Creating and Sustaining Successful Growth,” which was published in 2003. The book was co-authored with Michael E. Raynor.

The JTBD theory is a framework for understanding what causes consumers to adopt new products or services. According to Christensen, customers don’t simply buy products or services; they “hire” them to perform a job that they need to get done. This idea has influenced a wide range of innovation and product development practices.

In conclusion, while the advent of AI has opened up a world of possibilities for product development, it has also increased the risk of creating Frankenstein Products. By focusing on achieving product-market fit, understanding the customer’s JTBD, and being strategic in using AI technologies, businesses can avoid this pitfall and create valuable, relevant, and loved products. Next time I will wring about Zombie products, why they exist and what to do about them. Tip: Traditional projects have a lot to answer for… 

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