They say a carpenter is only as good as their tools, and while that’s true, it is on the carpenter to make sure that they are staying up to date on the best tools for the job. For product managers, one of the most exciting new tools in years is the rise of artificial intelligence (AI).
Particularly notable is the emergence of "generative AI," which refers to algorithms designed to generate content, whether it be images, text, or even music, from scratch. For product managers, the implications of AI are immense. By using AI, they can now gain a sharper insight into product strategy, hone their decision-making skills, and refine their prioritization process.
Product strategy is the lifeblood of a successful product. It informs the roadmap, the discovery process, the internal/external communications and marketing strategies, and more. An effective product strategy ensures that the product remains aligned with the company’s goals, competitive in the marketplace, and responsive to customer needs.
AI can analyze vast amounts of data regarding customer behavior. Through machine learning models, AI can identify patterns and trends that might be invisible to the human eye. For instance, if a segment of users consistently drops off from using a feature after a specific point, AI can highlight this pattern, enabling the product team to investigate further.
Product managers can use AI to predict future trends based on past data. Generative AI can generate extremely sophisticated regression analyses in minutes, using the data you (hopefully) already possess as part of your analytics stack. This foresight can guide the product strategy, ensuring that it remains forward-looking and aligned with anticipated market needs.
AI-driven analytics can divide users into specific segments based on their behavior. This granular view can then inform a tailored product strategy, catering to each segment's unique needs.
For a product manager, you make decisions roughly every 15 seconds - or at least it feels that way. What feature to release next? Which bug fix is a priority? Where should resources be allocated? When does the new hire start? Is my documentation finished? Do I really have to go to that meeting? The stakes are high, and mistakes can be costly. This is where AI steps in as a vital ally.
Modern AI can power decision support systems, providing product managers with data-driven insights to back their choices. Instead of relying solely on intuition, managers can make decisions armed with relevant, timely, and accurate data that has already been analyzed. Being “data-driven” only works if you know what the data means, have the time and talent to pull out the best conclusions, and have the internal influence to socialize your results and have your analysis taken seriously. While AI can’t help your ability to secure cross-functional alignment, it certainly can help generating the analysis of the data to help you facilitate the conversations.
One of the toughest challenges for product managers is prioritizing features, fixes, and requests. AI can help by analyzing factors like projected ROI, user demand, and technical feasibility, subsequently ranking items in order of importance. Cut through the mountains of prioritization frameworks out there to customize one that makes sense for your product and your business. AI can get you there.
AI models can be trained to forecast potential risks associated with various decisions. By analyzing past product releases, user feedback, and market reactions, AI can help product managers anticipate and mitigate potential pitfalls, ensuring smoother product rollouts.
Remember, Generative AI is a subset of artificial intelligence focused on creating new data instances that resemble a given set of data. Unlike other AI models that analyze and make predictions from existing data, generative AI synthesizes new data, such as writing new sentences, composing music, or generating images.
That means that once all of the above use cases have been handled, the product manager, instead of needing to write documents, create compelling visuals for slides, and the myriad other tasks that come with actually communicating the work done by the AI tools, can leverage the “generative” part of these new AI models to handle the synthesis of all the analyses into a coherent set of findings. As any product manager will tell you, writing and communicating are two sides of the most valuable coin we possess in our function.
Like any system that uses data, AI tools will produce garbage if their source material is garbage. After all, it’s highly unlikely you’ll build an energy-efficient house if you try to use shredded newspaper as insulation.
In order for AI tools to be able to produce the high-quality communications you would need to secure alignment on your product strategy and your list of prioritized work, you’ll need to make sure that whatever data sources you are using to feed the AI models are relevant, feature-rich, and accurate (among many other things!).
The current crop of generative AI tools are not great at cleaning data, so you’ll still need to work with your internal analytics teams and/or data scientists to make sure your data is ready for showtime.
Of course, there are other dangers to using AI - proprietary information leakage, misinformation, phony sources, hidden biases - but those have been extremely well-documented elsewhere!
The fusion of AI and product management heralds a new era of innovation and precision. By leveraging AI's data-processing prowess, product managers can craft strategies that are more aligned with customer behavior, make decisions that are data-backed, and prioritize tasks with unprecedented efficiency.
As AI technology continues to evolve and become even more integrated into the world of product management, the symbiotic relationship between AI and product strategy will only deepen, leading to products that are not just innovative but also profoundly attuned to the needs and desires of their users.