Envision a future where using software is intuitive, seamless, and genuinely delightful. Where you can learn everything you need to know about a product yourself, at your own pace. Where getting help is instant and personalized, and the product itself anticipates your needs.

This isn't a futuristic vision: it's the goal of product-led growth! PLG, at its core, is about putting the user first. It's about building products that are so valuable and easy to use that they become their own best advocates. It's also the smartest path to sustainable growth for businesses looking to thrive in the years ahead.

We're at an inflection point in 2025. We've learned real-world lessons from implementing product-led growth over the last few years. We're also seeing a shifting landscape ahead. Meanwhile, a new toolbox of solutions with Artificial Intelligence has emerged and become more accessible than ever.

In this blog post, I'll highlight three of the biggest challenges ahead, and how AI can help. As a lead for Product Developer Relations, I'll share my own personal experiences and best practices from the field with Google Cloud.

Maintaining product excellence in an era of accelerating change

An exceptional product is the cornerstone of PLG, and the bar is constantly rising. Staying exceptional isn't a one-time achievement. It's a constant race to keep pace with accelerating market change. Getting strategy, quality, and usability right is becoming exponentially harder.

Today, user expectations are shaped by a constant stream of social media trends and influencer opinions. Sprout Social's 2024 Influencer Marketing Report showed that "49% of all consumers make daily, weekly, or monthly purchases because of influencer posts, with 30% trusting influencers more today than they did just six months ago."

To address rapidly changing consumer preferences, executive strategies are increasingly focused on offering the right range of products rather than simply trying to maximize sales volume – demanding a more nuanced and agile product approach.

Let's now look at the tools AI can offer to not just keep up, but to get ahead.

LLM-assisted research agents

AI agents are intelligent systems that can perform tasks on your behalf. For example, consider a strategy agent that can research market trends and user sentiment from platforms like Reddit, to provide a consistently updated SWOT analysis.

Agents go beyond gathering data to drive strategic insights to ambiguous questions. They start by developing a plan to solve a problem. Then, they can incorporate a wide array of tools for research, data analysis, validation, and more to derive these insights.

AI-Assisted SWOT Agent providing community and competitive insights

AI-powered friction programs

Customer journey maps are a great tool, but they often fall out of sync with an evolving product. How can AI help give us a dynamic view that keeps pace with rapid product iterations?

It all starts with identifying critical user journeys or CUJs. Through a combination of SME expertise and data analytics, your team can focus on the end-to-end paths that will matter most.

To assess the CUJ experience, frictions logs are often used by our team of developer advocates at Google Cloud. They put friction in context, showing why feature requests and bugs should be addressed.

Friction log template instructions

Individual friction logs can be extremely helpful to our product teams. However, it can be challenging to get a big-picture view of our progress with friction logs spanning many products at different lifecycle stages. Generative AI can help summarize and quantify findings. Gemini helps us translate our friction logs to a Developer Experience Score (1-5) to visualize progress.

Example visualization of a Developer Experience Score after repeated CUJ review

Moving from transactional funnels to continuous user lifecycle engagement

The traditional marketing funnel is rapidly evolving. Omnichannel user journeys are the norm, where users enter and exit at various points on non-linear paths. Meanwhile, expectations for personalized experiences are skyrocketing. McKinsey research shows that 71% of users expect personalization, and 76% will be frustrated if they don't get it. Generic approaches just don't cut it anymore.

This changing landscape requires a shift in mindset toward continuous engagement across the entire user lifecycle. The thing is, it's hard to implement in practice. That's where AI can help. Instead of focusing solely on conversion at each stage of a funnel, the goal becomes nurturing a continuous dialogue and providing value at every interaction.

Conversational AI

Being available to users 24/7 with instant, personalized help is now a reality thanks to Conversational AI. Users can ask natural language questions and receive in-depth explanations.

The more integrated conversational AI is into supporting user journeys, the better. Can it incorporate contextual information and suggest actions? At Google Cloud, our Gemini Cloud Assist in our UI console helps to configure, troubleshoot, and optimize the Cloud experience.

Gemini Cloud Assist providing contextual troubleshooting

Intelligent search

Beyond conversational assistance, intelligent search is a key component of exceptional product experiences. A powerful, intuitive search experience within your product can be the difference between a frustrated user reaching out for support (or churning), and a delighted user who quickly finds the answers they need and continues their journey.

Traditional keyword-based search is often insufficient. Users don't always know the precise terminology and their queries might be nuanced or expressed in natural language.

Picture a search experience that understands the intent behind user queries, not just the keywords. Search that can sift through documentation, help articles, community forums, and tutorials to surface the most relevant information. Retrieval-augmented generation is the underlying technology that grounds a model's response in a company's own data.

Fortunately, this level of intelligent search is available to businesses of all sizes, with off-the-shelf solutions like Vertex AI Search. They can integrate sophisticated search capabilities directly into their products and empower their users like never before. In the Google Cloud documentation, we've enabled AI Powered Search, currently available to users in US/Canada with a Developer Profile with the feature enabled.

AI-Powered search in the Google Cloud documentation

Scaling PLG operations effectively with analytics

Data is the foundation of product-led growth: from understanding usage patterns to enabling experimentation to tracking progress. As companies scale, two key challenges emerge. One is a lack of actionable data as silos and process blockers emerge. Ironically, the opposite scenario can also be a problem: a deluge of raw feedback that's impossible to process manually. Making fast, data-informed decisions, consistently across your entire product portfolio, becomes a Herculean task.

AI for content intelligence

Enabling users to self-educate is a critical part of PLG. How do enterprises know if their content addresses the right market segments? Are they focused on the right mix of awareness or adoption content? How is that picture changing over time?

Traditionally, content marketers would plan for these questions in advance. That would mean asking content contributors to enter metadata into a content management system, such as product covered, expertise level, and so on. Not only can this be tedious, it's prone to data quality issues.

Generative AI drives insights without knowing the questions in advance—and without creating friction in the content creation process. On our Google Cloud advocacy team, we are using generative AI to derive content metadata, such as persona and expertise level, directly from our content.

AI Content Intelligence process used by Google Cloud advocacy team (hypothetical data)

AI for feedback analysis

Marketing research from Voorhees et al (2014) shows that receiving feedback provides a "moment of truth" for a firm that impacts customer loyalty. Even just acknowledging positive feedback has an effect on relationship building. AI can play a crucial role in optimizing relationship management by triaging feedback that spans product portfolios and a diverse customer base.

Sentiment analysis identifies the emotional tone (positive, negative, neutral) and classification groups feedback by topic. Together, these techniques can identify that a surge of negative sentiment in user reviews is related to a specific new feature, and categorize the feedback into themes like "usability," "performance," or "bugs."

The next step is to prioritize that feedback. Again, AI algorithms can help assess factors like sentiment severity, frequency of occurrence, and potential user impact. From there, feedback can be automatically routed to relevant product teams.

Predictive analytics enable forecasting emerging issues before they become widespread problems. A subtle increase in negative feedback around a specific workflow, for example, could signal a developing usability problem. The next level of user satisfaction can be achieved and maintained with this proactive approach.

With so many moving parts to the process, a feedback dashboard can help bring all of this information together. The feedback dashboard can display real-time sentiment scores across key product areas and highlight trending topics in user feedback.

The future is intelligent PLG

Big opportunities lie ahead for Product-Led Growth in 2025, and the transformative power of AI is paving the way. Customer expectations are reaching new heights, and the user journey is increasingly complex. Artificial Intelligence is here to empower PLG practitioners to achieve their outcomes at scale in this environment. Indeed, PLG's future is not just product-led, but intelligently driven.

Wondering how top product managers are making informed decisions that shape the success of their products in 2024? The State of Product Analytics report is here to answer that and more.

The future of PLG: Navigating 3 key challenges with AI