Product Management

7 min read

Harnessing AI and Customer-Centric Strategies to Elevate Product Management in 2024


Product management is an ever-evolving field, and the pace of change has only accelerated in recent years. Driven by cost-conscious growth, reduced capital availability, and more scrutinizing stakeholders, product teams face increasing pressure to focus on customer needs and deliver outcome-focused roadmaps. This shift pushes product teams to adopt customer-centric product strategies, ensuring they maximize value for customers while hitting key business goals.

Adding fuel to this transformation is the proliferation of AI in product management, making advanced capabilities more accessible both within products and for teams. AI is helping drive the focus on customer value to deliver against KPIs, as well as streamlining the actual execution of production. Teams that lag in adopting customer-centric strategies and leveraging AI to scale them are quickly being left behind.

At UserVoice, we’ve had a front-row seat to this evolution. For many years, our customers have been enriching user data to profile and prioritize the value of their product feedback relative to their strategic goals. Now, AI-driven product management is enabling these teams to conduct deeper, more effective, and more automated research into their feedback, while also allowing these teams to accelerate their product strategies. In this blog, we’ll unpack these parallel but deeply interdependent evolutions and share best practices from some of the world’s foremost product teams.

The Role of AI in Modern Product Management

Automating Routine Tasks

AI is revolutionizing product management by automating routine tasks, allowing product managers to focus on strategic initiatives. At UserVoice, we've seen firsthand how AI can transform workflows. For example, AI-driven tools handle extensive data analysis, summarizing customer feedback, and drafting Product Requirement Documents (PRDs) with remarkable efficiency. This not only speeds up processes but also ensures that product managers can dedicate their time to high-impact activities.

Consider the task of data analysis. AI algorithms can process massive datasets, identifying patterns and generating actionable insights far quicker than manual analysis. This capability accelerates decision-making and enhances its accuracy. Similarly, AI tools at UserVoice analyze customer feedback from various channels, providing concise summaries that highlight key issues and trends. This allows product managers to respond promptly and effectively to user needs. Drafting PRDs is another area where AI excels, using natural language processing to generate initial drafts based on predefined templates and user inputs, significantly reducing the workload on product teams.

Enhancing Decision-Making

AI's impact extends beyond automation to significantly enhance decision-making in product management. Leveraging AI to gather insights from large datasets enables product managers to make more informed decisions. Predictive analytics in product management, for instance, allows teams to forecast trends and user behavior with a high degree of accuracy. This proactive approach is crucial for developing strategies that keep product teams ahead of the curve.

Product teams are now taking advantage of AI to analyze user behavior data, allowing them to predict which features will be most popular. This significantly aids product managers in prioritizing development efforts, not only optimizing their product roadmap but also ensuring that resources are invested in areas with the highest potential for return on investment.

Personalizing User Experiences

AI is also redefining how we personalize user experiences. By analyzing user behavior and preferences, AI-driven personalization tailors product experiences to individual users, enhancing satisfaction and engagement. This leads to higher retention rates and customer loyalty.

Real-world examples of successful AI implementations in personalization are plentiful. Streaming services like Netflix use AI to recommend content based on viewing history and preferences, while e-commerce platforms like Amazon suggest products users are likely to be interested in based on their past purchases and browsing behavior. In the SaaS realm, AI can personalize dashboards and features based on user roles and usage patterns, ensuring each user receives the most relevant and valuable experience. UserVoice leverages similar AI capabilities to deliver personalized user experiences, driving higher engagement and satisfaction.

Customer-Centric Strategies

Importance of Customer Feedback

Customer feedback has never been more critical in shaping successful products. In today's fast-paced market, product managers must stay closely attuned to their users' needs and preferences. At UserVoice, we’ve long recognized the invaluable insights that come from direct user feedback. This feedback is not just data; it's the voice of your market, offering a roadmap to better product development and customer satisfaction.

Implementing customer-centric strategies involves several key practices:

  1. Customer Advisory Boards (CABs): Customer Advisory Boards (CABs) play a pivotal role in strategic planning. These boards consist of key customers who provide regular, structured feedback and insights. They help ensure that product strategies align with real user needs and market demands. At UserVoice, we’ve seen how effective CABs can be in guiding product decisions, fostering stronger customer relationships, and driving innovation.
    Example: According to Gartner, successful CABs engage customers in meaningful ways, providing valuable insights that can shape product roadmaps and enhance customer loyalty. This approach not only improves product alignment with customer needs but also helps in identifying new market opportunities
  2. Voice of the Customer (VoC) Programs: VoC programs systematically gather customer feedback across various touchpoints, helping companies understand customer expectations and experiences. This feedback is then analyzed to inform product improvements and strategic decisions.
    Example: Adobe utilizes VoC programs to capture feedback through surveys, social media, and direct customer interactions. This comprehensive approach enables Adobe to refine its products continuously and stay aligned with customer needs .
  3. Net Promoter Score (NPS): Measuring NPS helps companies gauge customer loyalty and satisfaction. By asking customers how likely they are to recommend the product, companies can identify promoters and detractors and take targeted actions to improve the user experience.
    Example: Apple consistently uses NPS to collect feedback from its customers, which informs product development and service enhancements. This focus on customer satisfaction has been a key driver of Apple’s success and high customer retention rates .
  4. Customer Journey Mapping: Mapping the customer journey involves tracking the end-to-end experience of a customer with the product, identifying pain points and opportunities for improvement. This holistic view helps product managers create more user-centric products.
    Example: IBM uses customer journey mapping to understand the interactions customers have with their products and services. This detailed analysis allows IBM to address specific customer pain points and enhance the overall user experience .
  5. Feedback Integration into Product Development: Integrating customer feedback directly into the product development cycle ensures that new features and improvements are aligned with user needs. This approach fosters continuous innovation and enhances product relevance.
    Example: Slack actively solicits feedback from its users and incorporates it into product updates. By doing so, Slack ensures that its platform evolves in ways that genuinely benefit its user base, leading to higher engagement and satisfaction .

By leveraging these customer-centric strategies, product teams can stay ahead of market trends, meet user expectations more effectively, and continuously improve their offerings. As we move further into 2024, adopting customer-centric strategies supported by advanced technologies like AI will be crucial for any product team aiming to lead in their industry.

Integrating Feedback with AI

The integration of AI with customer feedback processes is a game-changer in product management. AI can analyze and summarize vast amounts of feedback efficiently, turning raw data into actionable insights. For instance, UserVoice utilizes AI to parse through feedback from various channels, identifying common themes and urgent issues. This not only speeds up the analysis but also ensures that no critical feedback is overlooked.

Processing Large Datasets

One significant advantage of AI-driven product management is its ability to handle large volumes of data quickly and accurately. By leveraging machine learning algorithms, AI can detect patterns and trends in customer feedback that might be missed through manual analysis. According to an article by Forbes, AI's ability to analyze customer feedback helps companies respond more effectively to user needs and improve their products continuously . For example, a leading e-commerce platform used AI to analyze customer feedback, which revealed a frequent complaint about the checkout process. By addressing this issue, the company saw a notable increase in conversion rates and customer satisfaction.

Prioritization and Confidence Levels

Additionally, AI can help prioritize feedback by categorizing it based on urgency and relevance. As highlighted in a piece by Harvard Business Review, AI tools can filter out noise and focus on the most critical feedback, enabling product managers to make data-driven decisions more efficiently while reducing stress around those decisions. At UserVoice, one of the biggest concerns we hear from product managers is their confidence that they are prioritizing the right features for their users. While product prioritization is still a skill that requires the oversight of an experienced product team, AI’s ability to process data based on prioritization criteria and express urgency has lead many teams to start feeling more confident in the decisions they are making.

By prioritizing customer feedback and leveraging AI, product teams can stay ahead of market trends, meet user expectations more effectively, and continuously improve their offerings. As we move further into 2024, adopting customer-centric strategies supported by advanced technologies like AI will be crucial for any product team aiming to lead in their industry.

Future Trends and Predictions

While we won’t pretend to have a crystal ball, our interactions with our customer partners have given our team some unique insights as to where things are headed. The most immediate evolution, we believe, is going to be tied to understanding trends in customer satisfaction and engagement at scale. Most teams that have shifted to a customer-centric approach have been able to establish a robust engagement model; now, the focus will shift to scaling that model across an ever-increasing number of feedback channels and a growing customer base.

AI is uniquely positioned to accomplish this due to its ability to process large swaths of variable data to identify thematic elements across user feedback, determine the sentiment of that feedback, and map the trends of those themes. We’ve been able to validate this need by testing these protocols within our own product; furthermore, there’s clear anecdotal evidence, as this is the most common area of interest across our customer groups.

Beyond surface-level customer needs and feature enhancements, product teams are now seeking to more deeply understand the jobs to be done and how those jobs are changing over time. Again, this has historically been a key component of a customer-centric strategy, but was often difficult to do as it required a multitude of customer interviews, followed by consuming qualitative analysis. However, the findings of those interviews are critical in root-cause analysis, particularly with the proliferation of growth loops modeled from user behavior and jobs to be done.

Now, as AI models begin to learn about product teams’ customers and use cases, they can more easily suggest both the use case and provide recommendations on a product solution. This has made it easier to combine data points like usage data with customer feedback and interviews, allowing product teams to more quickly and consistently build to optimize for user behavior. At UserVoice, the team has started to validate this by building product cases automatically from open-sourced feedback using AI tools, leveraging user feedback to articulate a use case, recommended solutions, and potential roadblocks. This alignment of high-value research and technical scalability becomes increasingly critical as more and more product teams are asked to build a roadmap that is results-driven.

Another key evolution, and one that the UserVoice team has been particularly focused on, is the increased personalization, and scalability of that personalization, in the interactions with your customer base. To encourage deep customer engagement, it’s critical to have personalized, authentic messaging to your customers. The challenge has often been that this is difficult to do in programs where interactions with the customer are frequent.

Insert AI, which is now helping teams automatically shape and send their messages to customers. At UserVoice, we’ve been experimenting with this by using AI to digest large groups of feedback, understand the different user profiles, and auto-create relevant summaries and personalized responses for each customer.

From a product team perspective, what is this time-saving technology in service to? In the current economic environment, we know that teams are being asked to do more with less. Cost-conscious growth is at the top of everyone’s mind, and the responsibilities of each individual role have grown exponentially. Teams in these scenarios are demanding more out of their limited resources, and those that don’t will most likely remain inefficient and, more importantly, ineffectual.

Historically, customer-centric strategies have failed to garner broader adoption due to the heavy resource demand they require, even though they can be incredibly effective at producing targeted outcomes. However, with the introduction of AI and the subsequent efficiencies, teams can now more easily target customer needs and experiment without a draw on valuable resources. This enables product teams to hit goals with a fraction of the resources they needed two or three years ago.

Our team also believes that product managers have a deep desire to return to the work they’re great at: thinking and building creative solutions to interesting problems. The resource constraints and evolution of responsibility for outcomes have pushed product managers into more tactical aspects of the job. While this type of work is important, our perspective is that it’s NOT the most valuable use of a product manager's time.

This has been proven time and again in our own tools, where the introduction of features that allow product teams to automatically deduplicate and triage feedback, update statuses, and detect user sentiment have increasingly outpaced other product features in terms of adoption and continued product usage. Our perspective is that product managers will increasingly push for these capabilities across their tech stack, creating a shift in tooling for these teams.


It’s clear to see that both AI and customer-centric strategies aren’t just passing fads; they're game-changers that can elevate our work to new heights. The most innovative product teams are asking themselves this question: How are we planning to integrate AI and customer-centric approaches into your product management processes this year? 

If you found value in this blog, why keep it to yourself? Share it with your colleagues and fellow product managers. Let’s spread the knowledge and help each other grow. Drop your thoughts on LinkedIn  and share this post/tag us on social media. We look forward to hearing your feedback!