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The playbook for AI-enhanced product management

Introduction

As creatives who build with an eye towards strengthening the business, product managers are always looking for ways to do more, better. But with new products geared toward them launching by the week, each one promising to fundamentally transform their space, it’s easy for PMs to get cynical about how much technology will actually impact their work. Most of the apps and tools that generate buzz each month end up being just that—a flash in the pan, a hype machine, nothing more.

When AI exploded onto the business scene, however, something was different. The launch of ChatGPT and other large language model (LLM) platforms didn’t feel like empty hype, and there was a reason for this. According to a recent McKinsey report, the broad utility of generative AI can create value across a wide array of business use cases. In macro, they estimate GenAI will add between $2.6 and $4.4 trillion per year to the global economy. Every business function will feel the impact in unique ways. That includes product managers, for whom this transformative tech will be particularly empowering.

A role transformed

Even before AI took the business world by storm, the role of a product manager (PM) was changing. What was once a niche, engineering-adjacent position limited to shipping product and feature updates has evolved into a holistic function critical to business health. In the face of an economic downturn, business leaders are turning to PMs and the tools they use to drive better outcomes—including higher growth, lower churn, and reined-in costs.

“Product management is to be like an evolving sport. It is getting transformed yet again in the wake of AI,” notes Mayukh Bhaowal, director of product management at Salesforce Einstein. Product teams are now wondering how their lives and work will evolve in the age of artificial intelligence. With the impact of this transformative technology only beginning to be felt across the business world, no part of the product management voyage will be left untouched. Throughout each stage of product development, PMs will be able to embrace AI as a partner and execute like never before.

What will this transformation look like? In this guide, we’ll consider that question through the lens of the product-management lifecycle, a framework for conceptualizing, building, launching, and iterating on software products in order to achieve a desired strategic or business outcome. While the specifics of the AI tools in question continue to evolve by the day, we’re beginning to understand the general impact they’ll have on the increasingly business-critical work PMs do.

Phase 1: Discover

For PMs, a successful discovery process comes down to understanding pain points—both of users and of the greater market. This means asking the right questions related to the business outcome you want to achieve. If your goal is to reduce customer churn by X%, for example, you might consider questions such as:

  • What are the general trends in user feedback? Is that feedback getting acted upon?
  • What do typical user workflows look like?
  • Is there a workflow or point within one where users who churn tend to get stuck or drop off?
  • What’s stopping users who churn from achieving their goals?
  • Do highly satisfied customers or “superusers” tend to engage in certain behaviors? If so, what are they?

In the past, it would fall on PMs to manually synthesize and analyze the data to get the right answers. But with the right AI-powered tools, they’ll be able to get concrete answers, spot signals in the noise, and make recommendations on how to move forward on a scale unprecedented until now.

How AI will change the game

PMs will be better able to synthesize data from customer support, user interviews, NPS, feedback, sales and support calls, product usage, and a host of other sources. AI tools will save PMs precious time by identifying patterns across multiple data sources.

The analysis these tools will produce will come with actionable recommendations and evidence to justify the proposed investment(s). This will make the discovery phase exponentially quicker, and the PM coming out of it more confident in their findings.

Ai enhanced product management lifecycle

Phase 2: Validate

There will always be multiple workable solutions to the problems or pain points you identify in the discovery phase. But which is the right solution—the one most likely to hit that sweet spot of maximizing both customer satisfaction and ROI for the business? This is where the validate phase comes in, and with the power of AI, PMs will be able to confidently decide what to build like never before.

Traditionally, the validate phase has proven time-consuming and unwieldy, tempting many PMs to skip or shorten it at the risk of building the wrong thing. This is because in the past, validation has involved setting up manual interviews with customers, walking them through proposed solutions to the problem, and getting their feedback and reactions. More recently, PMs have begun factoring in validation data from other sources, including product usage data, in-app polls and surveys, and support ticket requests. But here too the challenge has been to find ways to quickly analyze and take action on what can be massive data sets.

How AI will change the game

To start, AI-powered tools will allow for quick analysis of validation data points across mediums, with recommendations based on the findings to boot. From there, AI tools will make building potential solutions and product prototypes faster and easier than ever before. By providing prompts informed by customer and other data to an AI tool, PMs can quickly generate a prototype ready to validate. (You can ask an LLM such as GPT-4, for example, to write you better code with less explicit instructions.)

What’s more, AI will enable PMs to test many such prototypes simultaneously. This will give teams more time to work on building the right solution and increasing confidence before engineers proceed to build.

Phase 3: Build

Once the time comes to begin building a product or feature, it usually falls on the product manager to help put together and manage the roadmap. Remember, a product manager sits at the intersection of engineering, customer success, marketing, and increasingly finance and sales, so there is no one in a better position to lay out the scope, work required, and end goals in order for all of the above to begin executing.

In order to stave off the possibility of being blindsided and slowed down by last-minute requests for feature additions or changes from various parts of the business, PMs are more and more actively providing visibility into progress and learnings (for example, via dashboards) with a nimble roadmap that can quickly incorporate changes. The roadmap can then be shared as a single source of truth for a given product area.

How AI will change the game

The primary value add AI brings to the build phase comes in the form of product testing. PMs can now incorporate testing of the product into their roadmap earlier. AI tools will be able to map their product’s codebase and quickly suggest how feature changes impact the overall product, saving precious time. This innovation will dramatically shorten the QA process, allowing the volume of releases to increase and roadmap execution to happen faster.

With an assist from AI, PMs will also be able to speed up other work tied to the build phase. For example, rather than have to draft entire user stories (easy to understand explanations of a feature or functionality from user’s point of view) and acceptance criteria (the conditions a product for feature needs to fulfill so a user performing a given task will accept it) from scratch, they can submit short descriptions to an LLM to generate the descriptions instead, and edit them from there.

Phase 4: Launch

When a product is finally ready for release, it’s up to the PM to align sales and marketing efforts in order to maximize reach to the target customers and prospects. Working hand in hand with product marketing managers (PMMs), product managers normally provide crucial guidance on the timing of a launch, and how a product or feature should be positioned when it goes live. They also typically help decide which features to make paid vs. which should remain free, or what the paid cutoff for a feature should be in order to maximize conversion and retention.

Product managers are understandably eager to ensure users both know about and are adopting what they’ve built, and many now leverage in-app guides and walkthroughs as an ideal channel for communicating releases (and guidance on how to use them) to users where and when it matters most to them. Like these guides, AI tools will help further put the user at the center of a launch, and tailor the release to their wants and needs.

How AI will change the game

With an assist from emerging AI tech, PMs will no longer manually define the timing of a release. Instead, products will undergo “smart” releases, with a controlled rollout of both the product/feature and the marketing promotional content tied to them based on usage and feedback from users. With this data-driven launch process, PMs will be able to monitor goals with auto-created dashboards and reports to track adoption and the impact on business outcomes (gained revenue, impact on churn, etc.)

AI will also enable product-led growth mechanisms for any product launch. AI-powered tools will be able to identify what new products or features make sense to highlight to individual users in their journey. It can then guide them towards the next step in their adoption path and ensure they convert on the right paid products at the right time. The result will be increased conversion rates and greater product-led revenue.

The most powerful ways PMs can leverage AI

  • Get product discovery insights faster: Analyze, sort, and spot trends in data to quickly arrive at customer insights that empower you to build with confidence.
  • Make better product decisions: Quickly analyze large batches of validation data from multiple sources and test prototypes with unprecedented speed to arrive at the best path forward.
  • Build the right thing, faster: Dramatically shorten the testing and QA process to execute on and release products and features at an unprecedented pace.
  • Personalize from day one: Automate the creation of customized support and product content delivered to customers at the right time, from launch day onwards.
  • Fuel continued success: Drive better retention, engagement, and conversions by leveraging auto-determined notifications and recommendations tied to what is and is not working and what needs iteration.

Phase 5: Evaluate

A new product or feature rollout doesn’t end with a “go live.” In order to ensure continued success, PMs need to evaluate what’s resonating and working about a release vs. what isn’t. This requires analyzing product usage data, going through user feedback, and ascertaining whether support tickets are coming in tied to the release and if so, what’s generating them.

Here, zooming in on both product usage data and customer feedback is key to getting insights into user behavior, where people may be getting stuck, or what actions users are or aren’t taking. In a similar way, analyzing feedback and NPS data can give PMs a sense of whether they solved the problems identified in the discovery phase.

How AI will change the game

AI will radically optimize the evaluate phase by auto-determining what is and is not working about a new product and giving PMs recommendations on what to do next. It will do this through analyzing all the usage and feedback data mentioned above on a scale and at a speed that it would not be possible for humans to do unassisted. From there, the right AI-powered tools will create dashboards for PMs to monitor and match up release performance with business outcomes and goals.

Phase 6: Iterate

Once they’ve evaluated a new product or feature launch, PMs will come back to the question of whether it generated the desired business outcome. Remember, PMs are not just tracking the micro of user adoption, engagement, and sentiment, but the macro question of how well the launch is serving the business and its goal(s). If it didn’t (and even if it did), chances are they will be iterating to improve the product and achieve even better business results, starting the product management life cycle all over again. As they iterate and figure out what improvements to prioritize, AI will once again prove transformative, in all the ways mentioned above.

Conclusion

Not replacing the PM role, but augmenting it

The AI revolution in business will accelerate a pre-existing trend in product teams: More and more, the PM of the future will be measured by business outcomes achieved rather than mere features shipped.

AI will accelerate the product management process from discovery to iteration, and make product success increasingly synonymous with business success as a whole. And it will do this not by replacing PMs, but by augmenting their capabilities for analyzing data, forming recommendations, and taking the right actions. “As product managers, our unique blend of strategic thinking, empathy, adaptability, and real-world understanding still sets us apart in this AI-driven landscape,” argues Jing Hu, senior global product manager at Just Eat. This powerful tool will usher in a new chapter in product management, where PMs are freed up to be more creative, build more and better, and see their creations through to a business impact never before possible.

Interested in learning more about AI and product management? Explore AI thought leadership from Pendo and Mind the Product and see even more ways this technology will transform the product space.