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Product Management in the Age of AI and Machine Learning

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By Sagar Patidar, CEO, Primathon

Today, we see a world that is evolving and adjusting to the challenges brought on by new and changing technology. We’re all getting ready for the ‘golden age’ of AI and machine learning.
Artificial intelligence, deep learning, and machine learning are all used in product management to enhance, improve, create, and shape products.

In the tech industry, product development used to be a lengthy and difficult process. You went through a waterfall process, first doing research first, then writing a massive product requirements document over several months, then throwing it over the wall to engineering to build, only to get something completely different out the other end several months later, before starting the process all over again.

Until recently, product management was still a part of the marketing or engineering departments, reporting up through those hierarchies and naturally aligned more with one or the other, resulting in prioritisation and focus conflicts.

Considering product management is a relatively new field, it is still progressing. The birth of software technology had a significant impact on its development, and subsequently came agile approaches. Today, as artificial intelligence devours software, it is silently reshaping the function of product management. It is becoming more of a stand-alone job, with a place at the management table and direct reporting to the CEO. This is important because it aligns the product team with the company’s vision and goals, makes them internal and external champions for the vision, and allows them the autonomy to make difficult prioritisation decisions.

Effective product management is becoming a sustainable competitive advantage and is continuing to evolve in the age of AI and machine learning. It continues to integrate user experience aspects, separating user flows and experiences from aesthetic design. It promotes flexible working methods that adapt to the needs of the team, the product, and the market.

Within organisations, it is becoming more broadly recognised and owned. It’s evolving into a discipline in which you can be an engineer, a designer, a founder, or a product manager—all that matters is that you’re at the heart of the product and work to improve it for the benefit of the consumers. It emphasises the importance of product management as a craft.

The New Age
The world is shifting toward a model of product-enabled services. Whether it’s global technology giants or startups like, the most successful companies have developed products that address customers’ problems in a variety of ways. This tendency has accelerated as businesses increasingly adopt the subscription-based SaaS (Software-as-a-Service) model as a cost-effective way to pay for what they use.

In this context, organisations must continue to develop in the product space in order to remain competitive in the technological industry. However, not only does the product development process require frequent client feedback, but it also necessitates a strong marketing plan to generate demand. As a result, product management is the most important part of a product’s success.

According to a recent survey of global company leaders, 70% have begun AI activities. With the rise of AI in business, it’s easy to see how it may be applied to both B2C and B2B products and services: Google Search, Photos, and Translate, Alexa, Amazon Recommendations, and Stitch Fix, to name a few. All of these have one thing in common: machine learning (and data). Discovering the proper data and figuring out how to use it to develop an innovative product that thrills customers and keeps them coming back for more is the key to successful AI product management.

Opportunities for AI Product Managers (PM) are available due to the existing shortage of AI PMs and the rapid rise of AI-related development and technology. In addition to the usual product team and stakeholders, AI PMs collaborate with data scientists and data engineers. AI project managers must be able to properly deliver AI-powered specs to data science teams. In the face of AI, though, it’s critical for AI PMs to remember to keep the client in mind.

While AI’s potential is intriguing, an AI PM’s first purpose is still to solve a customer’s problem.

AI and machine learning are here to stay, and they will continue to change the way we interact with one another and the rest of the world. AI project managers must take a fresh approach to AI projects, beginning with data analysis to find and validate commercial prospects. For AI project managers to know how to ask the correct questions to clients,

They must also continue to collect data in order to fine-tune current AI programmes. AI ideas are brought to life through effective collaboration among cross-functional teams. AI PMs must use AI and machine learning to have a better understanding of their clients. Perhaps most crucially, AI project managers need to consider failure.

Uncertainty is unquestionably higher when it comes to AI projects. As artificial intelligence (AI) and machine learning (ML) continue to transform the world around us, they are also having a big impact on and reshaping software product management as we know it.

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