Join thousands of product people at Insight Out Conf on April 11. Register free.

Try for free
GuidesProduct developmentHow AI can transform product management

How AI can transform product management

Last updated

10 August 2023

Author

Dovetail Editorial Team

Reviewed by

Jean Kaluza

We know what you’re thinking: “Oh no, not another blog about artificial intelligence!”

But even if you’re tired of hearing about this already notorious technology, it’s probably here to stay. We’re only scratching the surface of its potential impact.

One of the perks of the rise of AI systems is their use in product management.

According to an IBM survey of 7,502 companies worldwide, product managers are one of the top 10 user groups of AI in organizations today. Furthermore, 21% of product managers use AI every day.

AI in product management is quickly gathering momentum. It’s only a matter of time before this new wave knocks on your organization’s door. The modern product manager can’t afford to sweep AI under the rug. Used well, it can help product managers succeed while enabling companies to maintain a competitive edge.

Let’s dig deeper into the topic and see how you can use AI to your advantage in product management.

What is AI product management?

AI product management is all about incorporating machine learning (ML), deep learning, and artificial intelligence into product-related decisions and processes.

AI’s key role in product management is adding value to different stages of the product life cycle. The technology can create superior products faster by helping shape decisions to better reflect customer requirements. It can also help write documentation and user stories so that product managers are more effective.

While AI isn’t difficult to adopt, applying it on a whim yields mixed results. The key to successful AI product management is discovering the proper data and figuring out how to use it to develop an innovative product that thrills customers and keeps them returning.

What is an AI product manager?

At its simplest, an AI product manager utilizes artificial intelligence capabilities to oversee and manage a product throughout its lifecycle. They are highly adept at using AI tools to elevate product management processes.

A typical software product manager is not expected to know the intricacies of AI. Most have a development, management, or user experience (UX) design background.

In contrast to a more traditional product manager, an AI product manager will also have a statistics or data processing background. They need every ounce of that knowledge to get the most out of AI.

The role of an AI product manager

While traditional and AI product management share common responsibilities and skill sets, the AI role is quite comprehensive. It encompasses the following aspects:

Data literacy

Having enough customer data is one thing; knowing how to analyze it is something else.

An AI product manager needs a solid grasp of data and models to use the right data, at the right time, for the right use case.

Communication

Communication is a key responsibility for AI product managers. They are expected to communicate product strategies to shareholders and orchestrate development processes across multiple teams.

On top of that, they are responsible for actively considering user feedback to continuously enhance the product even after its release.

AI specialization

An AI product manager should have a strong understanding of various AI and ML algorithms. They will need this knowledge to determine the most suitable algorithms for internal use or product implementation.

Acceptance criteria

AI product managers must understand how the accuracy of AI implementations will improve or diminish their products.

Before diving into development, they establish clear pass/fail criteria for their product or service. They will continuously evaluate the product against these criteria during development to ensure it hasn’t veered from its original goal.

Product evangelism

It’s not enough to simply present on product growth and updates. AI product managers must also establish the best way to utilize AI internally and train teams to utilize it consistently. That’s the best way for companies to stay competitive and relevant now that AI is here.

Ethics

Customer privacy, radical sentiments, and possible errors in AI results are just some of the challenges of utilizing AI in the product management space.

AI has the incredible ability to make vital decisions quickly and accurately. For this reason, AI product managers must keep a constant eye on the technology to ensure it doesn’t hurt the company or its clients in any way.

How AI is changing software product management

Not too long ago, software product management was fully dependent on human abilities and input.

Even when agile approaches came about, software product managers still struggled to get projects validated and aligned with requirements while balancing feasibility. They had to develop questions based on difficult-to-acquire data and rely on time-consuming and expensive research to make decisions with confidence. Naturally, this was challenging for deadlines and budgets.

The advent of AI and ML comes as a much-needed reprieve for product managers. Expanding your knowledge of these two technologies could be a game-changer for you. Here’s why:

AI transforms the user feedback process

You can think of AI as a product management assistant that’s here to liberate you from mundane, monotonous, and resource-intensive tasks.

Product managers can get bogged down by lots of repetitive and manual feedback work. Sifting through huge stacks of support tickets or feature requests to identify urgent pain points is a necessary but low-value task. It can be tedious, even with a UX researcher’s support. Once you have the findings, you’ll need to validate them by conducting surveys to understand statistical relevance across a market space.

Alternatively, you can collect an aggregate of all requests and let AI analyze them—perhaps against customer service requests—to properly assess urgency and establish priorities.

Using ML algorithms, AI can quickly categorize and cluster feedback items by automatically labeling them, identifying key themes and sentiments, and providing you with actionable insights. This saves time and improves the accuracy and consistency of feedback analysis.

AI enhances your meetings

When you’re in an unproductive meeting, it can feel like your time is being sucked into a black hole. You might be better off doing something else. With AI’s help, you can avoid this and ensure your meetings are valuable for everyone.

For starters, you can plug AI into your Scrum meetings. Besides analyzing past data and suggesting relevant topics for upcoming meetings, it can suggest attendees based on topics. AI can also format the agenda automatically, ensuring it’s well-organized and easy to read.

Some AI tools can record your meetings, write notes, capture slides and screen sharing, and generate summaries. Others provide real-time updates about your Scrum team so you can tackle any pending query or task without having to wait for the next meeting.

AI improves QA testing

Quality assurance testing is one of the most taxing tasks in product development. Yet, for an end product to be successful, it’s absolutely necessary.

Software that doesn’t perform as expected won’t last. Thankfully, this is another area that can greatly benefit from AI.

Utilizing AI for QA testing means software testers don’t have to spend hours on repetitive tests such as regression and sanity tests. They can complete them in minutes. What’s more, AI tools don’t get tired. Their testing output is consistent from the first minute to the last. This means testers can become more agile and efficient at their jobs. 

Similarly, AI QA automation can help testers analyze an app by crawling through every screen while simultaneously generating and executing test case scenarios. Not only does this save planning time, but it also improves the quality of test cases for automation testing. AI can offer real test cases that are quick to operate and easy to regulate.

AI helps you write more detailed product specifications

Product specifications are, in many ways, the heart and soul of any product development process. Without them, you wouldn’t know what the end product will look like or whether it will fulfill the necessary specifications and requirements.

Crafting product specifications can be a tedious task. Some product managers write brief, underdeveloped specs that don’t help developers or clients.

AI tools learn on the go. All you need to do is constantly feed them descriptions of your product specs to position them to write high-quality, well-developed product specifications. Three inputs are the minimum you need to trigger high-quality outputs.

Additionally, you can use AI-powered writing assistants to generate ideas, organize your thoughts, and improve the overall quality of your writing. Tools like Frase use ML algorithms to suggest alternative phrases. They also offer grammar and spelling corrections.

AI-powered templates can help you generate robust product specs with only half the effort. These templates use ML algorithms to suggest the most relevant sections to include in your document—all in a very short timeframe.

AI enhances your market intelligence

Integrating AI into your research and development endeavors will make your job simpler. It will save you the numerous hours you or your team might spend manually creating reports for your market research.

With users’ needs and wants constantly changing, you don’t want to get your market intelligence wrong or get it late. Leveraging the power of natural language processing and sentiment analysis tools enables you to surface up-to-date insights on sentiment, themes, and trends in your existing audience. No guesswork. Just timely reports and actionable data.

AI gives you a competitive advantage

In the ever-evolving business landscape, AI can help organizations attain operational excellence. With robust data analysis and predictive modeling capabilities, AI has become a secret weapon for unlocking competitive advantage.

Here’s how AI and ML can make your business stand head and shoulders above your competition:

Better decision-making

Why make product decisions based on intuition when you can rely on real data?

Compared to humans, AI is better at crunching numbers, identifying patterns, and making fast, data-driven decisions.

With the ability to process large amounts of data and produce trend directions and actionable advice, AI can be a vital tool for any product manager looking for quantitative support in their decision-making.

Task automation

A typical product manager’s day is made up of countless manual tasks. Jobs like data analysis and data entry can really eat into your time, energy, and productivity. By infusing AI into these processes, you can free up you and your colleagues to tackle high-level jobs that have the most impact on your bottom line.

Opportunity for personalization

Today’s consumers rarely settle for the bare minimum. They crave software products that meet their unique requirements and needs precisely. This is where AI in product management truly shines.

By analyzing data about your target users and clients, you can customize aspects of the user experience to cater to their real-time preferences. The final outcome is a product they can’t help but fall head over heels in love with.

Tips for AI product managers

Companies are investing in AI at a fast rate. The time is ripe for product managers to jump on the bandwagon and capitalize on this growing opportunity.

You might have more luck landing a job in product management if you have additional AI experience. To lock in the best role and make a positive impact, you need to put in the work and avoid taking needless shortcuts.

Leverage your foundational product management skills and keep these five tips in mind as you build your career in AI:

1. Be open to continuous learning and self-improvement

Don’t sit still and do nothing. Evolve along with AI.

To become a highly competent AI product manager, spend time reading research papers and articles. Attend training programs, conferences, and relevant workshops to stay up to date with the latest product management best practices and trends.

2. Build an irresistible AI portfolio

Taking part in academic projects, such as minor AI implementations in hackathons or online classes, is a great way to gain experience.

If you have prior experience working in a specific industry, seek out AI opportunities related to that background.

3. Get well-acquainted with AI for interviews

Just because you have learned the ins and outs of product management-oriented interviews, paying extra attention to the AI aspect could help you.

For starters, you might want to acquaint yourself with common AI interview questions. If you’re applying for a product manager role for a highly technical product or space, consider preparing for in-depth technical discussions with engineers and data scientists.

You never know which path the interviewing team might take. Doing your due diligence from the get-go greatly increases your chance of landing a suitable AI role.

4. Once hired, train your team on how to use AI

If you’re already an AI product manager with your own team, you can teach them about appropriate AI use cases in the development lifecycle. Be sure to choose the right training sessions that fit the needs of the company and its products.

It can be costly to train team members on AI usage and capabilities from the get-go, but it will make perfect sense in the long haul. A well-oiled, highly experienced team is less likely to commit errors that impact your project. Beyond that, the overall project costs may decrease because the team knows how to get the most out of their resources.

5. Soft skills matter, too

Without a doubt, AI in product management requires technical skills. However, communication needs to be part of your repertoire if you’re to succeed in the role.

Skills such as emotional intelligence, critical thinking, and creativity are also worth developing. The time will come when you need to showcase them.

Product managers are often asked to question their decisions and present findings, analytics, and product releases during all-hands meetings. Prepare to be called upon to come up with solid decisions fast and justify them with confidence.

FAQs

How does AI help in product management?

AI is fast emerging as an important piece of the product management jigsaw. Not only does it enable product managers to make more informed decisions and streamline workflows, but it also removes time- and resource-intensive tasks from the equation.

The end result is better, well-crafted, customer-centric products that make sense from an economical and practical standpoint.

In the long run, thanks to the ever-evolving capabilities of artificial intelligence, AI product managers can achieve a considerable competitive edge in their roles and for the companies they work for.

How is AI involved in the production of a product?

AI can improve the entire product development process from start to finish. It helps technical teams adopt an efficient approach at each stage, from planning, designing, and development to testing, deployment, and marketing.

How will AI change product management?

The short-term possibilities of AI in product management are still being explored. AI currently has the potential to change how companies develop, test, and launch products. It automates repetitive tasks, improves decision-making processes, supercharges product development, and elevates the user experience.

In the future, AI will excel more and more in the strategic aspects of product management—particularly in market analysis and business requirement capture.

AI can easily generate a comprehensive that identifies your most profitable market segments both in the short and long term. Further, it can help identify the products you should emphasize to existing and potential customers.

Get started today

Go from raw data to valuable insights with a flexible research platform

Try for freeContact sales

Editor’s picks

Sunk costs: why they matter and how to avoid them

Last updated: 13 April 2023

What is a product mix?

Last updated: 10 June 2023

What is a use case?

Last updated: 10 February 2024

What is a chief product officer (CPO)?

Last updated: 29 March 2023

What is a similarity matrix?

Last updated: 11 May 2023

What is a staging environment?

Last updated: 11 January 2024

A guide to feature-driven development (FDD)

Last updated: 11 January 2024

Stakeholder interview template

Last updated: 26 May 2023

Latest articles

Related topics

Product developmentPatient experienceResearch methodsEmployee experienceSurveysMarket researchCustomer researchUser experience (UX)

Your customer insights hub

Turn data into actionable insights. Bring your customer into every decision.

Try for free

Product

InsightsAnalysisAutomationIntegrationsEnterprisePricingLog in

Company

About us
Careers8
Legal

© Dovetail Research Pty. Ltd.
TermsPrivacy Policy

Log in or sign up

Get started with a free trial


or


By clicking “Continue with Google / Email” you agree to our User Terms of Service and Privacy Policy