Hirishkesh Paranjape, who has worked in product management at major companies, writes that companies can develop new products and services that delight their customers by using technologies such as AI, machine learning, IoT, and data analytics.

Advanced technologies like artificial intelligence (AI), machine learning (ML), advanced optimization, the Internet of Things (IoT), and data analytics make it possible for product managers to delve deeper into high-intensity user pain points and expand the possibilities in shaping delightful user journeys. Unfortunately, if product managers continue to look for incremental improvement opportunities to existing products based on legacy technologies instead of working backward from users each time and identifying the right solutions, they are likely to miss out on opportunities unlocked by newer technologies.

OYAK Cement used an AI-based solution to lower fuel and other delivery costs by $39 million annually.

Without constant learning and adoption, existing biases can prevent product managers from identifying the most persistent and intense challenges for clearly defined user personas that other products in the market have not addressed. As a result, many product managers build for the “average user” since this approach increases the total addressable opportunity during early assessment. This approach results in products that lack differentiation and scalability that create a durable competitive advantage over a period of time and are easily replaceable.

In today’s market, the most successful company leaders and product managers understand that product development starts with a deep understanding of previously unarticulated customer needs captured by closely observing customers in their workflow. An essential product competency is familiarity with new technologies like generative AI when approaching customer discovery, including at the earliest stages when diving deep into targeted user personas and identifying potential opportunities to address specific pain points.

The value of using extensive amounts of data from various user settings during discovery becomes magnified when the same datasets funnel into the development of personalized solutions. One powerful example of AI revolutionizing product development is the last-mile delivery systems companies develop and utilize for organizations’ delivery fleets, which they even sell as delivery-as-a-service white-label solutions. Fueled by massive growth in online shopping, the last-mile delivery market is expected to grow to more than $200 billion by 2027.

Using AI to make last-mile delivery more efficient

Not long ago, using generic routing algorithms and mapping technology and identifying the fastest, most efficient delivery routes with a simple “traveling salesperson problem” was considered sufficient in delivery systems.

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Now, machine learning, advanced optimization techniques, and AI have enabled product managers to go deeper into each part of the driver’s delivery journey and create last-mile delivery products using the most optimal routing constructs for dynamic factors that influence driver’s on-road decisions, like the predictability of parking in dense metro areas, weather conditions, a customer promise to deliver within certain hours, street-crossing safety, and more. These enriched, real-world datasets combined with the ability to “productionize” them to optimize each delivery or pickup make last-mile deliveries more reliable, efficient, and much safer.

For instance, with today’s routing algorithms and advanced optimization techniques, it is possible to determine whether it is more efficient for each driver to park in a central location and walk to multiple delivery addresses or drive to each address. With historical datasets and generative AI, drivers can also access specific information about where they are making deliveries, including access codes, business hours, customer notes, and even photos of buildings.

With AI and other advanced technologies incorporated into delivery workflows, drivers can receive details on connected devices from past successful deliveries, along with important information such as guidance on the presence of lockers and directions for alternative delivery locations, which could be spread across multiple floors. Because of these advancements, uncertainty and inefficiency are replaced by streamlined operations that ensure packages are received optimally based on up-to-the-minute delivery conditions.

Drivers will consider various delivery applications and unit economics when delivering in various marketplaces. If all factors are equal, they are more likely to choose the companies that make delivering packages as convenient and reliable as possible. McKinsey & Co. reports that AI-based delivery solutions can save companies up to 15% in logistics costs, 35% in inventory levels, and 65% in service expenses. In one recent example, OYAK Cement used an AI-based solution to lower fuel and other delivery costs by $39 million annually.

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Generic solutions for average users are no longer enough

The same degree of product specificity occurring in the delivery market is also happening in other areas of product management. As AI advances rapidly, the key to success for product managers today is “going deeper,” or using advanced technology to identify actionable insights from detailed datasets about users in their workflow.

Product managers leverage that information to create expansive lists of pain points organized by themes, test and validate hypotheses, and analyze results from surveys and focus groups to ultimately work backward from the user when envisioning product prototypes that achieve a higher level of differentiation.

Effective product managers understand that the most critical skill they bring to software development teams is acting as the user’s voice in the room. In this role, they prioritize product ideas likely to make the most significant difference to the user journey and ensure the development team designs features and solutions using the most advanced technologies that resonate deeply with consumers.

One example of this is a recent agreement between Mars, which creates a variety of food and pet care products, and PIPA LLC, a company that accelerates nutrition science and innovation by embedding AI in R&D, manufacturing, and commercial businesses. The partnership allows Mars to utilize PIPA’s advanced AI technology to design new products that meet the health benefits demanded by customers. The technology taps into clinical trial data, biomedical databases, scientific publications, and additional sources to identify market trends. The partnership and AI technology access have already led Mars to create a state-of-the-art diagnostic tool that predicts cat kidney disease.

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The most effective product managers leverage AI, ML, and deep learning techniques to build specific customer personas and craft innovative solutions that meet their needs. On the other hand, product managers who stubbornly continue to focus on the average customer will likely see suboptimal user growth and retention by delivering products that fail to stand out and satisfy specific user needs.

Designing for the average customer is a mistake in most industries because increased competition has divided demand among many players and made differentiation necessary for product-driven growth. AI and other advanced technologies complement existing techniques that product managers can leverage to ultimately become faster and more effective in their jobs.

With the tools now widely available, differentiation has become more important than ever for product managers. In a recent Fictiv survey, 97% of senior decision-makers said AI will impact their organization’s future product development and manufacturing processes. In that same survey, 78% of respondents said they are currently evaluating technology tools to develop new products more efficiently.

AI is quickly changing the way product managers work

Advanced technologies are transforming the entire product development process. AI empowers product managers to make better, more informed decisions and design highly personalized products for users. There is almost no limit to how granular product managers can understand user needs as they strive to improve and grow their products.

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In last-mile delivery, for example, product managers use enhanced map datasets driven by ML models to optimize delivery based on traffic lights, predicted street traffic, and parking availability instead of just determining the shortest map route for deliveries — a capability that is easily available in consumer map products. These capabilities are why 83% of CEOs say AI and advanced technology are critical to the success of their companies.

Paul Daugherty, chief technology and innovation officer at global information technology consulting firm Accenture, recently said, “The playing field is poised to become a lot more competitive, and businesses that don’t deploy AI and data to help them innovate in everything they do will be at a disadvantage.”

About the author:

Hrishikesh Paranjape is a senior product manager with experience across ecommerce, real estate, customer service and financial industries.

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