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Third, AI can change how to collect customer feedback. A minimum viable product is nothing more than a good start and feedback loop with the target audience is essential to taking it to completion. Until recently, product analytics has been largely restricted to structured or numerical data. Notable and eminent AI experts argue that this is merely 20% of the data and that companies have the remaining as unstructured and in the form of documents, emails, and social media chatter. AI is incredibly good with analyzing large amounts of data and even benefits from being tuned with more training data. Compare this with focus groups that are not always accurate representations of customer sentiment, and this leaves the product team vulnerable to potentially creating a product that does not serve its customers well. These same experts also make a case for the generative AI to help convert customer feedback into data for business.

Fourth, AI can help with redefining the ways teams develop products. It involves how engineers and product managers interact with the software. In the past, professionals were trained in the use of software-products-suite to the point where they were designated experts who understood how each piece worked and imparted the same via training to others. With AI, new team members can be onboarded rapidly by letting the AI generate the necessary boilerplates or prefabricated units or provide a more interactive way of getting help on software and hardware tools.  What used to be wire diagrams and prototyping can now be replaced with design examples with constraints provided to chatbots. The interface seems just as human as a chat interface, so nothing about the internals of machine learning needs to be known to those wishing to use the interface.

Finally, AI helps with creativity as well. Machine learning algorithms are already used to learn patterns of transforming inputs to outputs and then apply that pattern to unseen data. The new generative models can even take this process a step further by encoding state between the constant stream of inputs which not only helps to get a better understanding of such things as sentiments but also generate suitable output without necessarily understanding or interpreting each input unit of information. This is at the core of capturing how a software engineer creates software, a designer creates a design, or an artist creates an art.

By participating in the thinking behind the creation, AI is poised to extend the abilities of humans past their current restrictions. Terms like co-pilots are beginning to be used to describe this co-operative behavior  and come to the aid of product managers, software engineers, and designers.

The ways in which AI and humans can improve each other towards the development of a product is a horizon filled with possibilities and some trends are already being embraced in the industry. Customer experience is shifting in favor of self-service with near human like experience via interactive chats and industrial applications that leveraged machine learning models are actively replacing their v1.0 models with generative v2.0 models. More interactive and engaging experiences in the form of recommendations, or spanning across content, products or frameworks are certainly being envisioned. By virtue of both the data and the analysis models, AI can not only improve but redefine the product development process.

Experimentation at various scopes and levels is one way to increase our understanding of the role AI can play and this is getting a lot easier to get started. It is even possible to delegate the knowledge of machine learning to tools that can work across programmatic interfaces regardless of the purpose or domain of the applications. Just as prioritizing the use cases were a way to improve the return on investment for a product, AI initiatives must also be deliberated to determine the high-value engagements. In similar fashion, leadership and stakeholder buy-ins are necessary to articulate the value addition in the bigger picture as well as to take questions to cast away any rumored concerns such as privacy and data leakages. When convincing the leadership for investments,  the limitation of the role of AI to a trusted co-pilot is required. Lastly, the risks of not investing in AI could also be called out.