In the previous post, we’ve looked into characteristics of digital products. Now, let’s focus on techniques for a modern digital product development. More importantly, how the AI affects the digital product development process today. Due to the rapid growth of AI, the companies are facing more challenges than ever in addressing the users’ needs. This phenomenon implies a substantial shift in terms of selecting suitable techniques and flexibility in defining a digital product as MVP.
Traditional UX techniques vs. lean UX technique
In a traditional UX, we build the project upon requirements and deliverables. The objective is to ensure that deliverables are as detailed as possible. More precisely, they need to respond adequately to the requirements that are laid down at the start of the project.
Lean UX in agile development is slightly different. In general, the nature of agile development is to work in rapid, iterative cycles and lean UX mimics those to ensure that data generated can be used in each iteration.
Lean UX focuses on the experience under design and is less focused on deliverables than the traditional UX. It requires a greater level of collaboration with the entire team. The core objective is to build MVP as quickly as possible. Then, focus on obtaining feedback so you can use it to make quick decisions.
When to deploy an MVP in a digital product development process
A minimum viable product (MVP) is a concept from the Lean Startup that stresses the impact of learning in new product development. Eric Ries, the author of the Lean Startup, defined an MVP as:
That version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort.
This validated learning comes in the form of whether your customers will actually purchase your product. Nevertheless, with a single definition of an MVP has become too fixed. As a consequence, the customer is compromised with the ‘agreed’ rather than what is really required.
Ambiguities about MVP
However, MVP is one of the terms that seems to generate a lot of traction during an agile transformation. Speaking generally, people are happy about trying to do ‘just enough’ to accomplish a certain goal. But, for the companies with a fixed mindset the term can quickly get misunderstood. For those companies, the focus is typically on delivering ‘what was promised’ rather than building up a deeper understanding of how to deal with modern projects.
The definition of an MVP should really be specified as it is designed to achieve a certain goal. For instance, we might create an MVP to see if our product solves a specific issue for a specific customer. Or, we might create the smallest possible product to launch a global phenomenon. The two are completely different and require very different strategies to achieve the goal – and probably a different management and governance approach. So. we definitely can’t claim that a general MVP framework can fix all those issues.
And here the lean AI comes into play
It is an undeniable fact that machines are better than humans at processing large volumes of data within a short amount of time. AI-powered machines can learn to make better decisions based on the successes or failures of their previous tasks. Consequently, they over time produce better results to hit the success metrics like customer acquisition cost, customer lifetime value etc.
Lean AI is actually an upgraded workflow of lean UX. It allows for innovative startups to use artificial intelligence and machine learning with automation to scale up growth using a lean startup team. The lean AI approach offers companies the ability to conduct far more experiments simultaneously.
AI-powered vs. human research skills
Conducting experiments at scale improves the likelihood of finding successful experiments. Some of them you’d have had time to test in a world before AI and machine learning supremacy.
It goes without saying that AI is much better than humans for replacing menial repetitive tasks. In addition, when those tasks are done manually, they are prone to errors which can be costly. Capability is different when it comes to strategic thinking, or any task that exceeds the platform’s capacity for learning or for a statistical analysis. AI and any form of machine learning in their current state are lagging behind the human reason.
The difference is obvious when a digital product development scales
However, as the project grows and matures it can become increasingly challenging to work iteratively. Iterations imply pauses between cycles. Projects built with low standards will also be projects where it is very hard to resume work. Especially after pausing for a few months of acquiring customer feedback.
Therefore high and clear standards (e.g. software engineering standards), methodologies and processes (e.g. experiment management systems, data collection and model development processes) facilitate agility. These significantly reduce the iteration time needed. Obviously, as the product becomes more mature — adopting and implementing standards for development processes, software engineering, etc. — it becomes increasingly important.
As you could see, AI implies changes in digital product development. To what extent, largely depends on the complexity and scope of the project. We will take a closer look at the impact of AI at individual stages of product development in the next post.
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