Product Development Blooms with Artificial Intelligence

Product Development Blooms

In the past decade, artificial intelligence has dramatically affected humankind, and experts say this is just the beginning. We may not realize it, but AI makes things easier and better in all facets of daily life, whether through innovation or consumption.

There is a reason why AI is called the fourth industrial revolution: AI startup funding worldwide grew a staggering 30 times, from $800 million in 2012 to $24 billion in 2019, according to Statista, and product management professionals are leveraging this technology to its full extent. Find that hard to believe?

Well, there are winning and inspiring stories of artificial intelligence in each phase of product management, from idea generation to prototyping/concept development, through product development and, finally, in commercialization. Read on to see examples of each.


Product managers can leverage AI to collect and analyze big data to learn more about the market.

For example, product managers at Spotify or Netflix start their days with data-enabled recommendations on what customers have been watching and listening to—and what they haven’t been watching or listening to. In turn, this reveals what customers will want next, informing decisions about what the services’ next offerings will be.

The Internet of Things (IoT) brings a whole new paradigm of product ideation. Consider smart cities, which are generating whole new ideas of product development.

For example, Idaho-based Solar Roadways uses solar panels to provide power to electric vehicles as well as digital driving assistance for drivers. These same panels can be used to defrost ice electronically, too. Imagine the same concept used across the United States: It could fulfill the demand for all electricity across the nation.

More simply, IoT has already demonstrated that your refrigerator can basically be used as an ordering management system by checking its inventory and sending an order to a grocery outlet (Instacart/Amazon delivery) for delivery and restock of your fridge. It can also integrate with your calendar and re-adjust your stock based on the next houseguest to arrive. From smart cities to smart homes, IoT brings new concepts of idea generation.


A basic premise of prototyping and concept development is to fail fast: If we’re going to fail, let’s fail fast and adapt and adjust based on feedback and learnings.

AI comes in handy here, as it helps product developers make the product lifecycle more transparent and nimble, which eventually makes it more valuable.

For example, a product is a cohesive bond of various subcomponents that can be developed in parallel, but without machine learning and AI, it is impossible to also manage the chaos of ingesting all subcomponent learning in parallel.

Also, AI in the form of tools like 3D printing, Computer Numerical Control (CNC), and rapid tooling help you assess how a conceptualized product is coming along.

You save both time and money by using AI this way, and your product ideas are validated. For example, 3D printing showed how to create hospital-grade personal protective equipment (PPE) when the world was facing a severe PPE shortage during the pandemic. In fact, at a time when the world is fighting to control COVID-19, almost all medical procedures depend on AI.


The pandemic brought the global scientific community together in a race to develop a vaccine—and this product development was heavily based on artificial intelligence. An AI model trained on a specific molecule offered a very cost-effective and rapid implementation method for drug (product) development.

It also offered various changes as the product moved from ideation to concept. Trained on enough data, AI was able to home in on specific biomolecules that helped design the vaccine in record time. What used to take years or decades was accomplished in months.

Another inspiring example comes from the online personal styling service Stitch Fix, a pioneer in AI-based product development.

Powered by data at every aspect of the business, this company helps consumers find the right clothing in minimal time. Data helps the company offer the right style and the right price—and it helps them scale.

By incorporating millions of customers’ feedback and precisely measuring every aspect of the clothes it offers, Stitch Fix offers highly personalized clothing at the right price. By churning big data and customer preferences, the company knows that the most popular attribute for clothing is fit, not price. Its customers are willing to spend more money for a great fit that looks good.


One of the most critical levers for all product managers is digital transformation, another core piece of machine learning and AI. Billions of data points are processed to outline consumer behavior about what they have previously purchased.

Perhaps more important, though, is data about what consumers are likely to buy, their preferred purchase channel, and the timing of when they are likely to buy. AI builds a complete customer journey that is further strengthened by product managers through the right go-to-market strategy.

For example, one customer has many faces across a variety of social media platforms. AI helps product managers demystify these complex data points and provide a 360-degree view of the consumer. Tools like Datorama, SprigHub, and Segment process billions of data points and provide harmonized insights about the market. They also define the next best course of action for any given product.

Still using the social media example, AI helps product managers control their go-to-market strategies by influencing what’s being discussed in any given platform. Big data from social script can be brought into an organizational data lake and, with the use of a machine-learning algorithm, can identify which topics are being discussed, and their sentiment and net promoter scores.

Amazon is the best example of product commercialization using artificial intelligence. The leader in tapping big data, Amazon’s product managers track not just campaign ROI but also the upsell/cross-sell opportunities that exist within their products.

They track what customers really like or really dislike, and that feedback goes straight to product improvements.


Successful new product introductions are critical for all product managers. Yet, launching products is one of the most ambiguous processes, as it deals with a great degree of uncertainty.

Taking a machine learning/AI-driven approach leverages a neural network approach to predict the chance of success in new products—and keep existing products thriving. It’s all a matter of using the information that’s all around you.

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  • Head of business planning and analytics for the consumer business unit at Western Digital Corporation. He can be reached at Follow him on Twitter @kiran_shashi or connect with him on LinkedIn at

Shashi Kiran

Shashi Kiran

Head of business planning and analytics for the consumer business unit at Western Digital Corporation. He can be reached at Follow him on Twitter @kiran_shashi or connect with him on LinkedIn at

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