The typical product management team has a plate full of tasks meant to ensure a product’s integrity – research, strategy development, communication plans, etc.
Out of these tasks, communication and planning take far more time than they should. Team members spend significant time scheduling meetings, communicating asynchronously, analyzing data, and following up on tasks.
These workstreams are mundane, repetitive, and tiring. For product managers seeking to elevate the way they work, AI can help your team take on a variety of tasks, so your team can focus on excellence. Here’s 9 ways AI streamlines product management:
1. Facilitating Collaboration and Group Communication
AI-powered assistants can help product managers to stay in sync with their teams. For example, a project scheduling assistant can do more than just schedule. They can scan your team members’ calendars and find favorable times that work for everyone.
If you have a hybrid team with some workers abroad, these assistants consider time zone differences to set up a comfortable time for overseas workers without requiring anyone to wake up in the wee hours of the night.
Beyond just scheduling, a true AI Chief of Staff like Xena can also handle coordination across meetings and notes. This can significantly reduce the back-and-forth communication that slows teams down, so they can focus on the work that matters.
2. Understanding Product Usage
Savvy product managers can leverage AI to handle the frontlines of product usage analytics. To name just a few, the benefits of understanding product usage include:
- Boosting customer satisfaction by increasing or decreasing visibility for certain products or features. This could mean doubling down on great feature sets or removing poor performing ones.
- Boosting customer satisfaction by identifying the right kinds of customers to target (commonly known as ‘Ideal Customer Profiles’).
- Improving customer experience through identifying poor user experiences. In partnership with a strong design team, this can help reduce churn.
- Increasing conversion rates from trial to user or free to paid by acting on data such as inactive customers, super-users, and popular product features to create custom experiences and user journeys.
Product managers can also use the data to gauge whether their product has sufficient product market fit and decide what to do if it does not. All of these types of activities currently require human input to be done at the highest level. However, AI can supercharge this process by helping product managers quickly identify the data and trends that really matter.
3. Write More Detailed Product Specs
Any product’s strength is built on how easy it is to use, how well it meets user needs, and providing an engaging user experience.
Incorporating these aspects into a product is easier said than done. And while product specs are supposed to help developers to achieve that goal, writing a product spec takes considerable time. Introducing AI techniques in the initial stages of product development can help write helpful, detailed specs, including usability. Tools like ChatGPT are revolutionizing the way copy is written, and it’s only a matter of time before AI-assisted specs go mainstream.
Also, AI learns continuously. When you repeatedly enter parts of your product specifications or user persona into AI tools, it can generate a comprehensive report about your product specs and make informed decisions based on the totality of the data.
Beyond product specs, you can also use a product description generator to train the AI system to generate product descriptions in your brand’s tone of voice and style. This line of product marketing work is sometimes handled by product managers themselves.
4. Speeding up Product Development
Leveraging AI speeds up many of the processes involved in product development. For example, it can spot and help address any gaps in a project, such as informing product managers they need more employees or there’s a skills shortage.
AI also helps identify tasks that’ll take longer than others and can help avoid delay by recommending tasks that can be automated. With AI coding assistance, developers and engineers can also do more with less and focus their time on strategic ideation.
Considering all things together, rapid product development should feed a faster learning cycle that can enhance the quality of products. Agile methodologies helped transform how products get built and lowered the cost of failure. AI will similarly increase the natural rapid flywheel of product creation, user testing, learnings, and product improvement.
5. Automating Tasks
Beyond providing stellar connectivity between teams, AI can also automate certain tasks such as data entry, creating timelines, monitoring, reporting, etc. This can help reduce budgets and increase the scope and speed at which product managers can work.
While each individual one-off task may not seem to add much to the workday, the inevitable overload that comes from a multitude of small tasks can quickly overwhelm even the most talented of product managers.
Leveraging AI in a way that supports the everyday needs of product managers is one way to get more work done while also caring for the needs of the people themselves. In the long-run, this kind of support can also reduce burnout and increase employee retention.
6. Enhancing Meetings
A typical product manager’s day involves time-consuming back-and-forth communication between the engineering and testing departments. AI helps by saving time by summarizing FAQs and recommending answers based on historical interactions.
Additionally, you don’t have to subject your team to reading meeting transcripts. AI scans conversations and creates easy-to-share and skim notes from the most important topics. AI then shares these notes to everyone involved. Most AI note taking software like Xembly will also keep a full recording just in case you do need to reference a particular moment within your meeting.
Xembly and some other tools can also seamlessly integrate your action items from your meetings into your everyday task management workflows. This helps ensure continuity between what gets talked about and what gets done. It can also help foster a culture of accountability where what gets measured actually gets managed.
7. Planning and Forecasting
Data collection to estimate or determine how a product has or will perform can be quite demanding. You can choose pragmatic approaches to forecasting, such as consulting channel partners, consulting sales teams, and other experts, but they are often not accurate enough. AI presents an enhanced solution in several ways.
AI can support these endeavors by reading historical data and predicting timelines, budgets, and outputs for products and projects. This type of data-intensive probabilistic matching and curation can be used to support decision-making but should never replace the need for a human to assess all the inputs, outputs, and risks to make the final decision.
8. Risk Management
By applying deep learning, AI can gather and analyze real-time project data to gauge whether projects can meet their deadline and stay within budget. Data analysis can also be used to inform strategies that protect software products from being used as avenues to steal data or introduce malware in a company’s systems.
Moreover, AI can provide vital insights when incorporating new technology in a product. For example, it can reveal blind spots that could cause late development stage complications that could delay or stall production. This information provides project visibility that’s otherwise impossible to have. The executive can identify potential risks and mitigate them before they affect a project’s outcome.
AI for the Product Manager of Tomorrow
AI was once a preserve of big tech organizations. Today’s work environment brings its benefits closer to everyone, including startups with little to spend.
For product managers, it means less need to micromanage teams and more time to focus on a perfect balance between the product and the target market. Because AI will continue to expand in its capacity to support teams, it’s prudent for product managers, engineers, and everyone involved to invest in the skills they need to partner with AI.
Instead of being bogged down with mundane tasks, product managers and teams need to turn to the right resources that can support their tech stack, and more importantly, their working rhythms.
If you’re interested in testing automated meeting note taking, AI-assisted scheduling for meetings and work blocks, and everyday task management support, sign up for Xembly today.