Product-Led Growth in the AI Era

The formula for product-led growth in the B2B world seemed straightforward: build a great product, let users discover value through self-service trials, and use clever nudges and upgrade flows to convert them into paying customers. You are right - it's more complex than that, but the foundation boils down to this. But as we enter the AI era, I'm seeing that this playbook is not sufficient. It has got a whole new dimension - value addition.

The traditional PLG toolkit—onboarding flows, usage-based triggers, in-app notifications, and friction-reducing upgrade paths—remains important. But it's no longer sufficient. In the AI-first world and especially for the AI-native companies, the real growth levers are shifting towards growth through net new adjacent value-addition.

The Value-Add Imperative

AI has fundamentally lowered the barrier to building adjacent capabilities. What used to require months of development and deep product teams can now be prototyped and shipped in weeks. This creates both an opportunity and an expectation problem.

The opportunity is obvious—you can quickly expand your product's surface area and create more touchpoints for value delivery. The expectation problem is more nuanced: customers increasingly expect AI products to solve not just their immediate problem, but the entire workflow around it.

Take foundational model companies as an example. It's not enough anymore to provide a great language model through an API. Customers evaluating your offering are asking: "Where's the datawarehouse integration? What about the analytics dashboard? How does this connect to our existing tools? Can you show me ROI metrics specific to my vertical?"

This forces these companies to rapidly build vertical-specific applications, complete with analytics layers, integration frameworks, and domain-specific solutions. They're not just selling compute anymore—they're selling speed, cost reduction, and execution efficiency through a complete stack.

The Adjacent Workflow Strategy

For AI applications and agents, the dynamic is even more pronounced. Users don't just want a chatbot that can answer questions—they want it to seamlessly integrate into their existing workflows and ideally eliminate adjacent friction points entirely.

Consider an AI-powered customer service agent. The traditional PLG approach would focus on demonstrating superior response accuracy and maybe some usage analytics. But in practice, customers need that agent to connect to their CRM, integrate with their ticketing system, provide sentiment analysis, generate summary reports, and ideally offer predictive insights about customer behavior. Those are all real value adds.

Each of these adjacent capabilities becomes a retention lever. More importantly, they become expansion opportunities that feel natural rather than forced. When your AI tool saves someone 2 hours per day on customer service, and then offers to save them another hour on reporting, the upgrade conversation writes itself.

Execution Implications for PLG Teams

So what does this mean practically for teams executing product-led growth in AI companies?

For starters, PLG teams and leaders will need to go beyond foundational PLG capabilities. They will need to identify adjacent verticals, customer workflows, and build those value gaps to grow the adoption and user base.

Activation metrics need to evolve. Traditional SaaS might measure time-to-first-value or feature adoption. AI products should measure time-to-workflow-integration. How quickly can users incorporate your tool into their actual work processes, not just see a demo result?

Expansion strategy becomes about capability bridging rather than usage scaling. Instead of "use our tool more," the conversation becomes "let our tool handle more of your adjacent workflows." This requires deeper customer research and more sophisticated product positioning.

Third, your competitive differentiation shifts from feature superiority to ecosystem comprehensiveness. Users will tolerate a slightly less accurate model if it comes with better integrations, clearer analytics, and obvious cost savings.

The companies winning at PLG in the AI era are making a clear strategic bet: they're choosing comprehensive value delivery. They're building solutions that solve broader business problems, not just individual tasks.

This doesn't mean shipping half-baked features or losing focus on core capabilities. It means recognizing that in the AI world, sustainable growth comes from expanding the scope of value you deliver. Your customers' willingness to pay and stay depends on how many business problems your AI solves, not just how well it performs its primary function.

The traditional PLG metrics still matter—conversion rates, expansion revenue, net revenue retention. But the levers you pull to influence those metrics are fundamentally different when your product can rapidly expand to solve adjacent problems and when customer expectations include comprehensive business value.

It's just raised the bar for what "led by the product" actually means. In a world where building new capabilities is easier than ever, the product that leads is the one that delivers complete value across multiple dimensions, not just core functionality.