The intersection of product management and AI technology together are reshaping the business strategy and companies’ product offering to customers. Product management comprises of three key areas which involves product strategy, product execution, and product operations. AI is reshaping every aspect of how the future products are going to look like and every phase of product life cycle.

With the advancement in the field of cloud technology and infrastructure, it has allowed now for the multinational organizations and companies to store the large amount of data. The large language models (LLMs) powered by improved hardware processing capabilities has resulted in the sharp growth in the space of AI models. This growth in AI space is now proven to be stepping stone for organizations to revamp their business strategy and entire product lifecycle including product strategy, product execution, and product operations.

Now, throwing light on each step of the product lifecycle starting with product strategy. Product strategy is around building high level roadmap of what and how organization plan for its product portfolio, what’s its overall product vision and its alignment with the corporate strategy. With AI picking up in recent years, every organization especially, financial institutions, ecommerce, and large retail businesses are already leveraging AI models to help shape their product strategy. If we talk of successful product strategies of the companies in the last couple of decades, the product that comes to our mind are from Google, Microsoft, Apple, Tiktok, Uber, Meta and the list continues. The reason for their successful product portfolio is their focus on utilizing data to make informed product strategy decisions. The intelligent data driven insights via AI models has helped organizations develop their product portfolio strategy optimizing the cost limitations, resource availability, quality customer service, delivery strategies, execution timeline etc. being the key constraints to manage. Further, these companies continue to build their AI capabilities and leverage smart insights via AI models for decision making for their product strategy.

Now, let us shift our gears towards product execution phase. This phase is about defining specific steps to deliver the product strategy factoring the resourcing and timeline. Product execution involves engaging right cross-functional teams and aligning resources towards delivering the product vision. With emergence of AI technology, the product execution phase is going to be impacted as well. For example - the AI based project management tools can help with its automation prowess. AI supported project management tool can handle continuous monitoring of complex product execution plan and intelligently come up with real-time insights so that product managers or product owners can make informed, rapid, and precise decisions for successful product delivery. These AI powered tools can also highlight patterns and trends which are difficult to identify ahead of the time during product execution phase. Further, as per PMI USA, around 70 % of the project fails or faces serious challenges or difficulties during execution. It is critical that such product execution level insights are observed faster and with accuracy to change the course of product development phase. While product execution will be impacted by AI, but it is very important across the organization for resources to have necessary AI skills to decide which available AI powered tools are best suited for their specific product execution use case. All future product solution development will be managed by smart analytics tools powered with AI models.

Further, talking about the product operations phase of product management activity, this will largely be impacted. The product operation involves setting up required resources and business process once the product is shipped to customers to allow for more detailed monitoring of product operational performance via keys metrics leveraging data generated via system, operations & customer activities. Also, ensuring necessary operational support is available once customer starts to use the product to allow the effective and efficient issue resolution. Product operations will be impacted by AI models intelligently reading the customer data signals or various product operations key metrics (including north start metrics) and providing insightful details for necessary actions by product team. AI based monitoring of key metrics can help with intelligent insights in terms of forecast and actual behavior. Differences in these key metrics (actual vs. forecast) can serve as early warnings for the product team to seriously start the investigation and provide resolution to future product issues ahead of time.

In summary, the intersection of product management activities and AI in going to impact the overall product life cycle across organization in different sectors. While the product strategy, execution and operations are going to be impact as mentioned above, the actual product solution design will be molded to leverage the analytical capabilities of AI. Especially, Gen AI which has the potential to generate text, audio, images, video for the given prompt by the users. The Gen AI technology has got a lot of potential to reshape the future product design and discovery. Further, as the advancement in the AI continues to progress – data is going to become a new oil for the organizations to develop their products around. Data management is going to become critical and essential activity for product managers to manage on a day-today basis creating the whole new world of Data Product Managers in the organization. These new product roles and future product manager will be expected to support their organization with AI adoption journeys. Also, every future product solution is bound to have analytical solution component that give key insights to customers, suppliers, vendors, and other key stakeholders for required business decisioning. Sooner the product managers realize the AI impact on their role and the overall product life cycle, better it is for them. In essence, the future product management talent would play a crucial role in leading and supporting organization in their AI transformation journey. 

Mostly based on real world experience and referring few inputs from PMI