AI Native Product Teams: How They Will Think, Work, and Build Differently

Every decade, a technology shift rewrites how we build and grow products. We are in the midst of one with AI but are underestimating (not overestimating) where AI will impact product teams.

We Are Underestimating The AI Shift, Not Overestimating

Every decade or so, a technological shift comes along that doesn't just change how we build products—it rewrites the entire playbook. The shift from on-premise to cloud wasn't merely a hosting change; it revolutionized every aspect of how we build, ship, and grow software products. AI represents a similar inflection point.

Our belief at Reforge is that the technology shift to AI will completely redefine product teams and in a lot of ways I think the ecosystem is underestimating not overestimating the impact it will have over the next decade.

To understand AI's transformative potential, let's examine how the cloud revolution reshaped product development. This isn't just a history lesson—it's a blueprint for understanding the scale of change ahead:

How On-Premise To Cloud Redefined Product Teams

🔄 Development Methodology

The dominant development methodology pre-cloud was Waterfall. Product teams operated like factory assembly lines: Large rigid six-month release cycles, exhaustive requirements documents, and testing phases that lasted longer than development itself. The shift to cloud broke these constraints. Teams could now ship code continuously, in smaller more iterative releases, lightning the need for exhaustive testing and requirements. It wasn’t just faster development, it was a completely different way to develop.

🧰 Tools

Cloud computing didn't just add new tools to our stack—it catalyzed an entirely new ecosystem of product development technology. Version control and collaboration tools like GitHub and GitLab, continuous integration and deployment tools, cloud development environments, containerization platforms, cloud-native security and compliance, and feature flags just to name a few for engineering.

For product managers, tools like JIRA were used to manage iterative development cycles. Product Analytics was fueled now that we could get real-time usage data. A/B testing and experimentation platforms were now possible.

💰Monetization Models

Pre-cloud the dominant monetization model was transactional. Massive upfront licenses where the customer would hopefully upgrade on some multi-year cycle. The shift to cloud the enabled subscription to become the dominant monetization model. This enabled lower barriers to entry, free trial models, and different dimensions of charging for features and usage. This wasn't merely a pricing change—it altered the relationship between software companies and their customers, creating incentives for continuous improvement and customer success.

📈 Growth Models and Channels

The shift to cloud also fundamentally changed how technology products could find and acquire customers. It enabled product led growth, freemium models, viral expansion and bottoms-up adoption within organizations. It also enabled leveraging new online growth channels like search engine optimization, paid search, and paid social.

🏆 Measures Of Success

Cloud transformed what we measured and how we thought about success. On-premise teams tracked license revenue and maintenance renewals. Cloud teams moved to metrics that were previously impossible to capture: daily active users, time-to-value, feature adoption rates, and real-time usage patterns. This shift from lagging to leading indicators enabled product teams to build and grow in a different way.

🛡️ Defensibility

Cloud changed how technology companies built competitive moats. In the on-premise world, complex installation requirements and high switching costs created artificial lock-in. Cloud enabled—and required—new forms of defensibility: network effects that grow stronger with each new user, data advantages that improved product value, and platform ecosystems that increase in value as more developers build on top.

🧠 Skillsets & Roles

The cloud transformation didn't just change what product teams built—it redefined who we needed to build it. Pre-cloud product managers were primarily requirement gatherers and project coordinators. Cloud demanded a new breed of PM: data-driven decision makers who could analyze user behavior, run A/B tests, and optimize based on real-time metrics. Additionally, specializations emerged like the Growth Product Manager as new growth models became viable. The shift wasn't just about adding new skills—it changed how we evaluated product talent.

👏 Team + Org Design

Pre-cloud organizations were built like assembly lines: development threw code over the wall to QA, who threw it over to operations, who somehow had to keep it running. Large specialized teams organized by function (Development, QA, Operations) where each phase of project was handed off sequentially.

The cloud era introduced cross-functional pods. Smaller, more autonomous units comprising product, design, and engineering working together toward shared outcomes. The impact rippled up to executive leadership. Traditional IT organizations had CIOs who managed infrastructure and systems. Cloud demanded technology leaders who understood product strategy, customer experience, and business models. Chief Product Officer roles and product management grew substantially and expanded scope to include growth, data, and other domains.

AI Native Product Teams

New Constraints, New Possibilities

Just as the shift to cloud was much more than new technology, it’s easier to see that AI will not be an incremental change but a fundamental reinvention of how we build and grow products. AI is shifting the constraints and possibilities around:

• What products and features we build

• How we build those products

• How we grow products

• The roles, teams, and org structures

The next generation of product teams will be trained as AI-native from day one. They will think, work, and build differently. They will contain new roles, org structures, and processes. It is impossible to predict exactly what this looks like 5+ years from now. But here are some of the things that AI is enabling in the short term.

The First Wave: How AI is Already Reshaping Product Teams

1. From Exploring A Couple Paths To Exploring Many Paths

2. From Idea → Doc to Idea → Prototype

3. Problem Expansion, Prioritization Reshuffle, Solution Reinvention

4. Redefinition Of Product Team Roles

5. Growth Model and Channel Changes

6. A Major Retooling Of Fragmented Product Stacks

7. Realignment In How Products Create and Capture Customer Value

From Exploring A Couple Paths To Exploring Many Paths

In current product development, the pressure to ship often forces teams into premature convergence. Most teams can only afford to seriously explore one or two solution paths before committing—usually the ones championed by the loudest voices in the room. This linear exploration creates three critical problems:

1. High-stakes decisions based on limited data

2. Solutions optimized for internal consensus rather than customer value

3. Innovative approaches killed by time constraints before they can prove themselves

But as Scott Belsky, CPO of Adobe points out AI not only helps accelerate product cycles but also give us more exploration cycles:

“what makes this technology truly distinctive from other advances is its reasoning and imaginative capabilities (not taste-based imagination, but boundless directed exploration). What this technology really gives us is MORE CYCLES - more cycles to explore”

This expanded exploration capacity transforms every aspect of product development. Teams can simultaneously test multiple interface designs, generate dozens of copy variations, prototype competing technical approaches, and validate different go-to-market strategies—all while maintaining the quality of each exploration.

What historically took months of sequential iteration can now happen in parallel, dramatically increasing both the quantity and quality of product decisions. AI isn't just accelerating our existing processes; it's fundamentally changing how we discover and validate product opportunities.

From Idea → Doc to Idea → Prototype

The Document Death Spiral

The traditional product development playbook starts with documentation. Whether it's a PRD, an Amazon-style press release, or a detailed user story, product teams spend countless hours crafting documents that attempt to capture our product vision. These documents then enter the document death spiral- endless cycle of reviews, debates, and revisions—often becoming more about internal alignment than customer value.

The Understanding Gap

The fundamental flaw in this approach isn't the documentation itself—it's the gap between written description and shared understanding. When a product manager writes "intuitive user experience" or "seamless integration," each stakeholder envisions something different. This misalignment creates what I call "work around the work": endless meetings, revision cycles, and debates that consume energy without moving the product forward.

Clarity With Prototypes

Prototypes cut through this ambiguity. A working prototype—even a rough one—creates clarity and alignment that no document can match. It transforms abstract discussions into concrete decisions, replacing "I think" with "I see." But until now, prototyping has been gated by technical constraints: either you waited for engineering resources or settled for limited mock-ups.

AI is changing this constraint. It is on a path to enable engineers to build functional prototypes in hours instead of weeks, while empowering non-technical team members to create interactive demonstrations without writing code. This shift from "documentation-first" to "prototype-first" development won’t just save time—it fundamentally improves product decisions by grounding them in tangible experiences rather than theoretical discussions.

Problem Expansion, Prioritization Reshuffle, Solution Reinvention

At it’s core, a product team’s job boils down to three essential responsibilities:

• Understand the Customer’s Problems

• Prioritize the Customer Problems To Solve

• Facilitate the Solutions to Those Problems

AI has a large impact on all three of these.

Problem Expansion

AI doesn't just help solve existing problems— it expands the realm of what problems are solvable. This means your problem space is no longer constrained by old limitations. More specifically, as Ravi Mehta, creator of our upcoming AI Strategy course and former product leader at Tinder, Facebook, and TripAdvisor states we move from being able to solve computation problems to learning problems.

*“In the Learning Era, we are no longer limited by what we can express in code. Given a sufficient amount of data — for example photos tagged with whether or not they contain a bird — an AI model can learn how to detect birds in nearly any photo.

Even more importantly, AI models can be stacked on top of each other and combined with computational approaches to make extraordinary products that can work in magical ways. The capability to detect a bird — or any other object — has led to image and video generation. Quickly, machine learning evolved from understanding images to making them.”*

Discovering and understanding the learning problems will take a mindset and methodology shift.

Prioritization Reshuffle

While there are many product prioritization frameworks out there, they almost all consider the four dimensions of feasibility, impact, risk, cost. AI introduces new factors into each element of prioritization.

• Feasibility - Solutions that were once too complex or technically impossible become attainable.

• Impact - The ability to personalize at scale means certain problems—like creating hyper-targeted user experiences—become more impactful than ever before.

• Risk - AI introduces new risk considerations, such as the potential for hallucination, bias, or misinformation.

• Cost - AI can both increase and decrease costs, depending on model usage, scale, and complexity.

Solution Reinvention

AI also enables entirely new solution spaces for problems we identify and prioritize. Just a few dimensions we are seeing this manifest:

• Adaptive vs. Static Solutions - AI enables solutions that evolve and improve through usage. Instead of building fixed features, we're creating learning systems that adapt to user behavior, context, and emerging patterns. A recommendation engine doesn't just follow rules—it discovers new patterns of user preference we never anticipated.

• Do The Work vs Enable The Work - Many software products today are tools that enable a user to create content and experiences. Canva, Notion, Google Docs, Gmail, etc. But AI is enabling just doing the work for the customer vs enabling it.

• Scalable Personalization & Dynamic Interfaces - Previously, personalization for most products meant simple if-then rules or basic user segments. AI enables hyper-personalization that scales: interfaces that adapt to individual working styles, content that reshapes itself based on comprehension levels, and workflows that optimize for each user's unique patterns. What was once a choice between standardization and customization becomes both simultaneously.

Redefinition Of Product Team Roles

Blurring Roles

AI is enabling product managers to write code, engineers to take on product management work, marketers to code landing pages, etc. The boundaries of roles is starting to blur. The expectations are fundamentally changing. This will eventually impact how we organize product teams.

The Magic Of Early Stage Teams

The impact of this is greater than some different role definitions. In early stage startups, there is something special that happens that feels like magic. Teams tend to ship a lot more with a lot less people. As a company grows you try to maintain that magic but it eventually disappears. AI can potentially maintain the magic of early stage startups you lose over time.

What feels like magic, isn’t magic at all. Early stage startups create the constraints and conditions for:

• Super tight feedback loops between the builders and the customers.

• These super tight feedback loops create “founder intuition.”

• Everyone on the team does a little bit of everything because they have to.

But over time all those things get extracted away from the builders.

• Tons of specialized roles (user researchers, data analysts, sales, success, etc) get introduced creating layers between the customers and the builders.

• This slows down the feedback loops between the builders and customers which slows down the team building intuition.

• Intuition is replaced by documentation, product reviews, sync meetings in order to keep things “aligned” and headed in the right direction.

• These things replace building time with time spent on the work behind the work.

The list goes on. But when I look at some of the changes that AI can have on how we build products and the boundaries of roles, a lot of it feels like returning or at least maintaining how great early stage teams operate.

Growth Model and Channel Changes

The cloud revolution gave birth to product-led growth, freemium models, and viral expansion. Now, AI is poised to trigger an equally dramatic transformation in how products find and engage customers. Early signals suggest we're entering an era where traditional growth playbooks may become obsolete.

Traditional Channels Show Cracks

We're witnessing the first tremors of change in established acquisition channels. SEO strategies that worked for decades are being disrupted by AI-powered search engines that bypass traditional content. Email marketing effectiveness is declining as AI assistants filter and prioritize messages. Even paid acquisition channels are showing vulnerability as users adopt AI interfaces that reshape how they discover and evaluate products.

The Rise of AI-First Distribution

The next wave of growth channels will likely be AI-native. As Aravind Srinivas, CEO of Perplexity AI, provocatively suggests, maybe we move to a world where AI agents—not humans—become the primary audience for product promotion. Imagine pitching your product not to end users, but to the AI assistants that increasingly guide their purchasing decisions.

This shift has profound implications:

• Discovery Mechanisms: Products will need to be discoverable and evaluatable by AI systems, requiring new approaches to product metadata and integration points

• Value Demonstration: Instead of emotional appeals and brand messaging, products may need to demonstrate value through quantifiable metrics that AI can assess

• Integration Points: Success may depend less on traditional marketing and more on building the right API hooks and AI-readable documentation

Product Development Implications

As we've long observed in Product Channel Fit theory, products must mold to their distribution channels—not vice versa. When AI becomes a primary distribution channel, it will force changes in:

• How we structure and expose product capabilities

• How we communicate product value propositions

• How we design onboarding and integration experiences

• How we measure and optimize for adoption

The winners in this new era won't be those who simply adapt their marketing to AI channels, but those who rebuild their products with AI distribution in mind from the ground up.

A Major Retooling Of Fragmented Product Stacks

Product Stack Fragmentation

Product teams have gone through a chaotic accumulation of purpose built tools As a result, today’s product stacks are layers of tools accumulated over time, each solving a specific need but never truly integrated. Feature flags in one tool, analytics in another, customer feedback in a third, and the list goes on. While this fragmentation was manageable in traditional development, it becomes a critical vulnerability in the AI era.

The Compounding Error Problem AI systems don't just struggle with fragmented tools—they fail exponentially because of them. As Dharmesh Shah, founder of HubSpot, explains:

"Let's say an agent needs to invoke the LLM a dozen times to accomplish a goal...Now mathematically, if each invocation has just a 95% success rate — or a 5% error rate, the success rate of the final result is 0.95 to the 12th power which is about 54%. So basically a coin toss. Half the time you'll get something right-ish and the other half you'd get something wrong-ish."

Error rate increases when working across fragmented systems. Each boundary between tools becomes another opportunity for errors to compound.

AI-Native Product Stack

AI-native product stacks will require a fundamental rethinking. The teams that solve this integration challenge first will gain a significant competitive advantage—not just in efficiency, but in their ability to leverage AI's full potential.

Realignment In How Products Create and Capture Customer Value

Just as cloud computing transformed software pricing from transactional licenses to per seat subscriptions, AI is catalyzing the next evolution in product monetization. This shift goes beyond simple pricing changes—it's changing how products create and capture value. Two key monetization models are already emerging in the AI era:

• Usage-Based Pricing at a New Scale - While usage-based pricing isn't new, AI is redefining what "usage" means. Companies are pricing based on intelligence consumption: queries processed, insights generated, or decisions automated.

• Outcome-Based Monetization - AI enables products to shift from charging for features to charging for results. Rather than paying for access to capabilities, customers pay for verified outcomes: successful customer service resolutions, qualified sales leads generated, or processing time saved.

As monetization models change, they will demand different success metrics. Traditional SaaS metrics like ARR will be supplemented by AI-specific indicators. Product teams must now optimize not just for user engagement, but for the efficiency and effectiveness of their AI systems.

The Second and Third Waves Are Yet To Come

The above is just the first wave of where AI is starting to have an impact. But just as in the shift from on-premise to cloud, there will be ripple effects that we don’t see yet. The second and third order effects stand to be even bigger then the first wave.

The challenge right now is that there is a large gap between AI promise and reality of implementing all this change in product teams. This something I will go deeper on in another blog post.

Redefine, Reinvent, Rebuild, Reforge

I started and named Reforge for moments like these. The word “reforge” is about breaking something down to it’s foundational elements, mixing with some new ingredients, and putting back together to make it stronger, faster, better. That is exactly what product teams, including Reforge, will need to do over the next couple of years. This blog post is a small beginning.

We are building tools for AI native product teams such as Reforge Insights. In addition, continue to build out in-depth expert led courses to help you make the transition like: AI Foundations, AI Strategy, AI Growth (coming soon), and AI Leadership (coming soon).