The Aha! Framework for software built by product teams

Q: Is The Aha! Framework suitable for small teams or only large organizations?

The Aha! Framework for software built by product teams may be the approach our own team uses to build lovable products — but it is also the result of decades of working with many different types of product teams. With this background, it touches on every aspect of product development and is certainly flexible enough to suit your team's workflows. (For example, teams may personalize how often they carry out certain activities within the framework — we just provide the suggested timing.) We are happy to chat about how The Aha! Framework could enhance your team's product development process. Contact us or join a demo to get some personalized inspiration. We have also mapped out The Aha! Framework on a whiteboard template in Aha! software , which you can access free for 30 days when you sign up for a trial .

Q: How do I adopt The Aha! Framework for my existing team workflows?

We have an entire guide covering how to adopt The Aha! Framework, which includes step-by-step pointers on how to incorporate it into your team's workflows. If you follow those steps, you will begin by honestly evaluating how you build products today and end with a personalized framework for building truly lovable products (with plans in place for continuous improvement and iteration). Our Customer Success team — made up entirely of former PMs — is also ready to help out. Contact us or schedule a demo to get some advice on how to tailor The Aha! Framework to your team's specific needs.

Q: Where can I find additional resources or training to learn more about The Aha! Framework?

If you are looking for more general information on The Aha! Framework, we recommend looking at the other guides we have published in this category. These cover the framework's activities in more depth, a comparison between The Aha! Framework and other methodologies, a product manager's role in managing it, and a guide on adopting The Aha! Framework at your own org. We also have a whiteboard template that maps The Aha! Framework out in detail, offering a starting point to define how your team might use it. Once you are completely ready to adopt it, we have a dedicated space in Aha! software where you can map everything out. Every step includes best practices and helpful how-to articles.

A complete process for established product, UX, and engineering groups that defines discovery to delivery

Updated: August 2026

The Aha! Framework is a flexible product development process. It has multiple versions depending on the type of team you work with and what you are trying to build. Traditional teams: Use the approach described below if you are working with a traditional team to update an application or create a new one. Independent product manager and emerging teams: If you are a solo product manager or working independently to create a prototype, internal application, or even exploring a new product offering, you should use this lighter-weight version of the framework. You should also know that we built a separate product for exactly that. Aha! Builder is purpose-built for creating robust prototypes and business tools using AI — without the overhead of a traditional development cycle. If that is the work you are trying to do, start there.

Editor's note: The contents of this guide refer to The Aha! Framework for software built by product teams. We also have a more lightweight approach to product development suited to delivering AI-driven prototypes and applications — read about it here.

The Aha! Framework for software built by product teams promotes a strategic approach to agile work. It is a flexible model that product organizations can leverage to improve efficiency and increase value delivered to customers.

It was pioneered by Aha! and gives product teams a way to set strategy, prioritize work, and achieve quickly — balancing long-term planning with agile delivery. This balance is especially critical in the new era of product development. As AI integrates into every stage of work, product managers are increasingly becoming product builders themselves. But moving faster only matters if you are building the right things. Use this guide to explore the complete framework and see how AI integrates into all of your activities.

The Aha! Framework ensures prescribed biannual strategic alignment while supporting ongoing sprints with continuous deployment. That is what makes it unique. We developed and have used the framework for more than a decade — it continues to power our product success.

Our goal when we founded Aha! in 2013 was to help organizations set clear strategy and connect it to the work of building products that customers love. We pioneered a new category with our first and now flagship product, Aha! Roadmaps. And our software currently serves more than 1 million product builders at many of the world’s most innovative and well-known companies. (If you have not yet tried Aha! software, you can start a free 30-day trial.)

But we did not explicitly share our own product development methodology for many years. It is time to change that. The content below is organized logically for folks who are new to the method. If you know what you are most interested in, you can also jump ahead to any section:

Why standardize work with a product development methodology?

Before we get into what we believe is the best way to develop products, it is worth reviewing why you need a way to do so at all. Remember that breakthroughs are rare. And despite the mythologies that exist about flashes of insight or “eureka” discoveries, real innovation is almost always the result of careful research and planning.

Product development is inherently complex. Setting strategy, discovering what customers want, brainstorming, planning, building, and measuring success requires close collaboration and tight coordination.

Over the past few years, AI has added a new dynamic to this complexity by fundamentally shifting how product teams work.

Product roles are consolidating. PMs are increasingly acting as "full-stack" builders — synthesizing massive bodies of user research, creating wireframes and prototypes, and even building the applications they need themselves. AI speeds up discovery and compresses planning cycles, pushing the pace of work even faster.

It is tempting to think that because technology allows you to move so fast, you can abandon structured processes and just start building. But the opposite is true. When you have the power to build instantly, a lack of shared methodology quickly leads to chaos.

You choose a product development methodology because it gives the team a process for achieving repeatable success.

Adopting a new way of working is a major investment. Very few companies adopt a methodology before building anything, so you have the added challenge of integrating it with entrenched habits. But with expectations mounting and PMs taking on fresh responsibilities, having a strategic framework is what ensures your speed actually results in meaningful value. The discipline required to build what matters does not change — it just becomes more urgent.

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Why a strategic approach to product development is better for most teams

Almost everyone today dismisses old-school waterfall approaches as too outdated and rigid for software development. Agile methods — like scrum, kanban, and the Scaled Agile Framework® (SAFe®) — are celebrated instead because they promise total flexibility, giving more control to the people doing the work.

But in practice, teams often get lost in agile because there are no clear strategic goals to guide that work. Some end up with painstakingly complex methodologies where the process becomes the work. Others cobble together a homegrown approach.

After working with hundreds of product development teams, we can confidently let you in on a secret — no one is strictly adhering to any of these legacy methodologies anyway.

Instead, folks try to force-fit agile practices onto legacy workflows. And when they do, we hear the exact same complaints:

There is no strategic alignment.
It is difficult to uncover and implement customer insights.
Processes are not clearly documented.
The team does not fully embrace the process.
Different groups work differently.
There is too much bureaucracy.
Too much time is spent in meetings talking about work vs. just doing it.
Transparency into team efficiency and value created is impossible.

The reality is that most folks benefit from an agile-ish approach that is grounded in strategy. You need defined goals to work toward, clear processes and work ownership, the flexibility to improve over time, and the responsibility to measure the value of what you deliver.

Why faster product development requires stronger product decisions

For years, engineering effort forced teams to be selective. Many ideas dropped away because they were simply too expensive to pursue. AI makes more of those ideas viable and gives PMs more room to test them before committing.

The pressure has shifted toward choosing well and choosing often. This has practical implications for the methodology you use:

Start with strategy: Many product frameworks focus on rituals — sprints, backlogs, and ceremonies — but say little about how teams actually decide what to build. A larger set of feasible ideas makes strategy more valuable as a filter. Clear goals and initiatives give PMs a basis for deciding which customer problems deserve further discovery and which proposed features should stay out of the plan.
Keep discovery continuous: Most methodologies treat discovery as a distinct phase that happens before delivery. That cadence is increasingly hard to sustain. If engineering finishes the work sooner than expected, the PM should already have current customer evidence to draw on for the next set of decisions. Regular conversations and ongoing feedback analysis keep that evidence close at hand.
Resolve product decisions before development: AI gives PMs a much quicker way to test an idea before engineering starts. Build a prototype with AI and click through the proposed experience. Look for missing states, unclear behavior, or decisions you have not made yet. Use the prototype to answer those questions before development begins.

Prototyping gives PMs a concrete way to examine an idea in more detail before committing to it.

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What is The Aha! Framework for software built by product teams?

The Aha! Framework has powered our product development for more than a decade. We built it because we needed something that balanced long-term strategic planning with the pace of agile delivery — nothing we found did both well.

The philosophy starts with a concept we introduced in 2013: the Minimum Lovable Product (MLP). The MLP is a direct challenge to the minimum viable product. Where the MVP asks what customers will tolerate, the MLP asks what will make them love what you built. That distinction drives every prioritization decision we make.

A table showing the differences between a Minimum Lovable Product versus a Minimum Viable Product

A year after introducing MLP, we codified a broader framework for how we work: The Responsive Method (TRM), centered on the belief that urgency in every interaction — with customers, colleagues, and the work itself — is what propels organizations forward. Brian de Haaff expanded on this in his bestselling book, Lovability.

Company leaders frequently come to us asking how we approach product development. They are often running a methodology designed for predictability that is not producing the value they hoped for. This guide is our answer. The framework is also built directly into Aha! Roadmaps, so teams can define how they work and connect it to everything they are building — across the entire Aha! suite: Aha! Roadmaps, Aha! Discovery, Aha! Ideas, Aha! Whiteboards, Aha! Builder, Aha! Develop, Aha! Teamwork, and Aha! Knowledge.

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What are the activities in The Aha! Framework for software built by product teams?

The Aha! Framework is organized around what we have defined as the 10 core stages of product development. Within each stage, there are discrete activities that support positive progress throughout the product development process. It starts with biannual strategic alignment and includes ongoing delivery.

The different stages of product development include strategize, discover, capture, explore, plan, showcase, build, document, launch, and analyze.

The activities in The Aha! Framework are presented sequentially, but many run concurrently in practice. While the engineering team is delivering features, a PM is conducting customer interviews, capturing new ideas, and planning the next release. The framework is not a handoff chain — it is a set of ongoing practices that operate in parallel.

If you use Aha! Roadmaps, you can view the entire framework, define your own best practices, and link to where the work should be completed with our Frameworks functionality. Or, if you simply want to explore this methodology on a whiteboard, you can try The Aha! Framework template.

Start using this template now

A note on the power of AI throughout the stages: AI is woven into how we run every stage of this framework. Throughout the activities below, we call out where we use it — both where it speeds up existing work and where it creates possibilities for something new (such as synthesizing dozens of customer interviews at once or generating a prototype from a feature description). We do this using Elle, the AI assistant built into Aha! software. Elle is accessed using a chat interface and has full awareness of all the product data within the Aha! account — goals, customer feedback, features in progress, and more. This makes it possible to put Elle to work on real product questions the moment we open a conversation. We also have an AI prompt library for product work, and many of the prompts in it are referenced below. Whether or not you use Aha! software, you will find these callouts useful as a guide to where AI might fit in your own product development process.

Ten stages and many activities might look like a lot on paper. In practice, most of this work happens on your team already — and several stages run at the same time. The framework simply gives it structure.

Stage 1: Strategize Every product decision you make for the next six months flows from what you set here. This stage is where you establish what you are building toward, why it is worth building, and what success looks like.
Define product vision	Your vision is the single most important thing you will write for your product. It tells everyone — your team, stakeholders, and customers — what you are building toward and why it is worth building. This is also a good time to get clear on your product positioning and the unique value your offering provides. We have a dedicated AI request in Aha! Roadmaps to help draft vision statements: AI request: Product vision	Frequency: Semiannually
Review market conditions	Before building a strategy, you need to know what has changed. Markets move. Competitors shift their positioning. Customer expectations are reset by products in adjacent categories. Create or update your business model, personas, and competitor profiles as needed, and use this research to inform your product strategy. Elle can help run these reviews: AI request: Market assessment AI request: Competitive analysis	Frequency: Semiannually
Draft product goals	Product goals connect your vision to the work your team will actually do. They should be ambitious enough to require actual prioritization decisions and specific enough that you will know at the end of the period whether you achieved them. We use Elle to generate a first draft of goals and refine from there: AI request: Strategic goals	Frequency: Semiannually
Identify key product initiatives	Initiatives are the major themes of work you will invest in. They sit between your goals — which describe outcomes — and your features, which describe specific work. Defining your initiatives forces you to make explicit choices about where to concentrate effort. We use the following request to analyze customer feedback and propose initiatives based on what customers want: AI request: Strategic initiative suggestions On the flip side, this request reviews initiatives and tracks down related customer ideas: AI request: Initiative related ideas	Frequency: Semiannually
Gather feedback	Share the draft of your goals and initiatives and gather live feedback from leadership and other key stakeholders. This ensures everyone is aligned on the "why" behind your roadmap before you get deeper into planning. We ask Elle to summarize key takeaways from the discussion: AI request: Meeting summary	Frequency: Semiannually
Set product strategy	Now, it is time to give the team a true north to follow. The strategy you document shows how your goals and initiatives connect — and why that combination is the right way to achieve what you set out to do. Once product strategy is solidified, everyone on the team should be able to make prioritization decisions that reflect the direction you set.	Frequency: Semiannually
Stage 2: Discover Product discovery is not a phase you complete before building. It is ongoing work that keeps your decisions anchored in what customers experience. Every team we have talked to that stopped doing regular discovery eventually found themselves building based on internal assumptions — and shipping things customers did not need.
Define research goals	Before planning research or scheduling a customer interview, write down what you are trying to learn. Research without a clear objective produces interesting observations, but rarely useful decisions. Your research goals should be tied directly to product questions you cannot currently answer from existing data.	Frequency: Weekly
Identify your audience	Choosing who to talk to is as important as what you ask. A sample that skews toward your most vocal customers, your largest accounts, or whoever is easiest to reach will produce a skewed picture. Be deliberate about which segments, use cases, and experience levels you need represented in your research.	Frequency: Weekly
Schedule customer interviews	Define outreach emails and invite research participants to meet with you. Draft discussion topics and script a set of questions that will help secure the answers you need. These can generate interview scripts in advance: AI request: Interview script generation AI request: Interview script format	Frequency: Weekly
Conduct customer interviews	Engage in discussions with customers to understand their challenges, needs, and aspirations. Follow the thread of what surprises you. The most useful data often comes from the moment a customer describes something you did not expect.	Frequency: Weekly
Analyze transcripts	This is where discovery work used to stall. Reviewing hours of recordings, pulling quotes, finding patterns across 10 or 15 conversations — done manually, it was slow enough that teams would skip the synthesis. AI changes the scale of what is possible here. In Aha! Discovery, Elle analyzes transcripts across an entire study, identifies recurring themes, and pulls the most relevant quotes. It also gives you a way to investigate a body of research in more depth: Compare findings, look for evidence related to a specific question, or trace a theme back to the customers who discussed it. The PM still decides which findings deserve product investment. AI request: Customer interview analysis AI request: Study insights	Frequency: Weekly
Connect insights to work	Research that does not connect to actual product decisions is just documentation. As you complete each study, link the insights you gathered directly to the initiatives, features, and ideas they inform. This is what keeps discovery from being a separate track that runs alongside the roadmap without ever influencing it.	Frequency: Weekly
Stage 3: Capture Customers and colleagues are constantly telling you what is and is not working — through support tickets, sales conversations, and direct feedback submissions. This stage is about making sure that input actually informs product decisions.
Review customer feedback	Your ideas portal and support channels provide a continuous stream of customer feedback. The challenge is that volume alone does not tell you what is important. You are looking for patterns — the same underlying problem showing up in different forms across different customers and contexts. In Aha! Ideas, Elle helps surface those patterns across large volumes of submissions so we can spend our time on analysis rather than sorting. AI request: Top current product ideas AI request: Idea themes AI request: Interview related ideas	Frequency: Weekly
Review colleague feedback	Gather different perspectives. Ask internal teams — such as marketing, sales, and support — to share their ideas on how to best serve your customers' needs. This gives everyone a chance to weigh in and broadens your thinking.	Frequency: Weekly
Score ideas	A scoring model based on measures like customer value, strategic fit, and implementation effort gives you a defensible starting point — and makes the conversation with stakeholders about what to build next much more concrete. We use AI in Aha! Ideas to generate initial scores and summaries across a backlog of ideas, then adjust based on context: AI request: Feedback prioritization	Frequency: Weekly
Promote ideas to features	Not every highly scored idea belongs on the roadmap. Before promoting an idea to a feature, review it with stakeholders who have visibility into revenue potential, customer relationships, and delivery capacity. The goal is to get the most impactful ideas into planning with enough context that engineering can begin scoping them properly.	Frequency: Weekly
Stage 4: Explore Exploration is where you figure out how to solve the problems surfaced during discovery. AI makes it practical to explore more possible solutions and test them at greater fidelity before committing engineering time.
Whiteboard new user experiences	Explore possibilities on a whiteboard. Map out a user flow, identify where it breaks down today, and generate multiple approaches before committing to any of them. In Aha! Whiteboards, we use these requests: AI request: Solution brainstorming AI request: Idea user flow	Frequency: Weekly
Create wireframes and lightweight prototypes	Rough wireframes and low-fidelity prototypes make abstract solutions concrete enough for useful feedback. This gets everyone thinking about whether the solution makes sense and if the team was imagining the same thing. In Aha! Whiteboards, Elle can generate a wireframe or lightweight prototype from a text description of what you want to explore. For teams without dedicated design resources, this closes a gap that used to require waiting on design capacity. AI request: Popular idea prototype For more sophisticated prototyping, try Aha! Builder.	Frequency: Weekly
Gather feedback	Share your early concepts with stakeholders to gather different perspectives on the best approach.	Frequency: Weekly
Update wireframes and prototypes	Refine your ideas based on stakeholder feedback and new learnings. These wireframes and prototypes will be used to inform more detailed designs once the actual work is about to start.	Frequency: Weekly
Stage 5: Plan This is where strategy becomes a concrete set of commitments. You are sequencing work, scoping features, and producing a roadmap that makes your priorities visible to everyone who needs to understand them. At a faster delivery pace, planning also needs enough depth beyond the feature currently in development. PMs should have several high-value possibilities progressing through discovery and exploration — so the next decision does not begin after engineering is already ready.
Draft roadmap	Map out a high-level plan for what you want to deliver and when. Create a prioritized list of features and lay them out on a visual timeline to think through sequencing and target delivery dates. We use Elle in Aha! Roadmaps to suggest epics for related features and build a plan for what features to release and when: AI request: Feature themes AI request: Release plan	Frequency: Quarterly
Scope features	Capture key details about each feature based on what customers need to accomplish. Collaborate with engineering to identify any major technical requirements and build an initial estimate. Bring in the customer evidence and exploration already associated with the feature so the initial scope reflects decisions the team has already made. AI request: Effort estimation	Frequency: Weekly
Score features	As features get scoped, their product value scores should be updated to reflect what you now know about the effort required. A feature that looked high-value at the idea stage might look different once engineering has weighed in on complexity. AI request: Feature scoring AI request: Feature prioritization	Frequency: Weekly
Update draft roadmap	Refine your initial roadmap. Adjust the sequencing, duration, and target delivery dates of your prioritized features. Define releases to explain at a high level when new functionality might be delivered. Of course, you will need to continually adapt your plans based on new customer insights, learnings, and progress.	Frequency: Weekly
Stage 6: Showcase This stage is about giving stakeholders the reasoning behind what you have planned — what you prioritized, what you did not, and why — so they can support the work.
Share roadmap	Share your roadmap with stakeholders. It is important to give strategic context about what you want to accomplish. Explain the trade-off decisions you had to make and why to get everyone on board before you finalize your plan. Elle can help build a roadmap presentation: AI request: Presentation preparation	Frequency: Monthly
Gather feedback	Build time in the review process for discussion. Give people the opportunity to share their perspectives on what you have planned or what might be missing.	Frequency: Monthly
Complete roadmap	Make any final adjustments. Then, share the updated version to close the feedback loop. This does not mean you are done, though — keep the team informed as plans evolve and progress happens.	Frequency: Monthly
Stage 7: Deliver The activities here are about making sure what gets built is what customers actually need — and that the team has everything required to build it without unnecessary stops.
Focus on high-priority features	Start each delivery cycle by confirming that your highest-scored features are defined before development begins. Defining the MLP captures what is needed for customers to genuinely value the feature. Elle helps define features, generate user stories, establish what success looks like, and more. AI request: Feature definition AI request: User stories AI request: Acceptance criteria	Frequency: Weekly
Complete designs	By this stage, you have committed to what you are building. The visual work here is about finalizing exactly how it will work — specific enough that engineering can build from it and detailed enough that you can confirm the solution addresses what customers need. Elle can generate an interactive prototype directly from a feature description, producing something that behaves like the finished feature. This ensures stakeholders and engineers can react to it concretely. AI request: Feature prototype AI request: Approaches to consider To create even more sophisticated prototypes, use Aha! Builder.	Frequency: Weekly
Gather feedback	Before requirements are finalized, get input from the people who will support what you are shipping: IT, operations, and if possible, a customer or two who will use it. The questions that come back from this step are almost always worth the time it takes to ask them.	Frequency: Weekly
Update MLP requirements	Finalize requirements based on everything you learned in the design review. What goes to engineering at this point should be complete — not a starting point for further discovery during development. We use AI requests to assemble the PRD and test plan from the feature definition and design outputs. AI request: Product requirements document AI request: Test plan	Frequency: Weekly
Organize sprint	Use sprints as a way to group work for implementation. Sprints should be in your development tool and contained within a release. If you prefer a kanban approach, that can work too. Remind yourself at regular intervals to confirm that your list of priorities is still aligned with the overall product goals. AI request: Release backlog refinement	Frequency: Weekly (or biweekly)
Develop functionality	Partner closely with the development team to build the MLP version of the feature. AI-assisted coding helps engineers complete this work much sooner. Keep discovery, exploration, and feature definition active for the priorities that follow so engineering has valuable work ready at the next opening. Aha! Builder is also a great option for PMs who want to build themselves.	Frequency: Weekly
Confirm built as designed	Ensure that you are delivering what customers will love. Engage the core product team — product management, UX, and engineering — to give the final sign-off.	Frequency: Weekly
Ship functionality	Prepare for launch. Deliver the functionality to production for all customers or behind a feature flag.	Frequency: Weekly
Communicate progress	Keep stakeholders informed of progress as you deliver against your roadmap. Be transparent about priority changes and delivery risks that might impact dates — so teams such as operations, IT, and marketing can adjust their rollout plans. This ensures that there are no unexpected surprises and keeps everyone in sync.	Frequency: Weekly
Stage 8: Document What you ship is only as useful as people's ability to understand and use it. This stage ensures that customers and internal teams have accurate, accessible information from the moment something goes live.
Update knowledge base	Ensure customers and cross-functional teammates get the most benefit from what you built. Publish user guides, FAQs, and best practices on how to successfully configure and use the new functionality.	Frequency: Weekly
Stage 9: Launch This stage coordinates the work across marketing, sales, and support — so what you built reaches customers in a way they notice and can act on.
Set go-to-market activities	Prepare the cross-functional team for launch. Set up a kickoff call to align on a launch date. Define positioning to help marketing effectively communicate the value of the new functionality. AI request: Launch checklist AI request: Change log	Frequency: Weekly
Announce new functionality	Share the exciting new functionality with the world. Communicate the news to prospective and existing customers via different channels to drive awareness and adoption. AI request: Release notes AI request: External communication style guide	Frequency: Weekly
Stage 10: Analyze This stage holds the strategy accountable — comparing what you projected to what actually happened and using that to inform the next planning cycle.
Assess product value	Measure the actual value created against what you projected early on in the planning process. Track key product and feature adoption metrics to understand whether you achieved your goals. Build a dashboard to show what you delivered and why it matters. AI request: Goal progress AI request: Initiative progress AI request: Release progress AI request: Report progress summary AI request: Dashboard executive summary	Frequency: Weekly
Improve overall process	Keep raising the standard. Take time with the team to look back and identify what went well and what could have been improved. Include the pace of product decisions in that review. Did customer discovery provide enough evidence for upcoming priorities? Did prototypes expose important decisions before development? Was the next high-value feature sufficiently defined when engineering was ready for it? Use what you learned to make adjustments so you can maximize productivity for the next iteration.	Frequency: Weekly

We have a library of tutorials and videos showing how product teams can use AI at each stage of the framework. You can find them (and many others) in the video resource center.

How does the framework work for multiple products? The Aha! Framework works for a single product and scales to a full portfolio. Managing multiple products requires modest adjustments — primarily setting strategy at the portfolio level, consolidating customer research into a single research center while keeping segments separate, and coordinating sprints and launches across products so releases stay synchronized. The core framework stays the same.

A shared framework frees your team from relitigating the same process questions every cycle — putting that energy back into the actual work. With AI now embedded across every stage, more of that work is within reach of a single PM than it has ever been.

If you want to see how The Aha! Framework runs inside the software we use to build our own products, start a free 30-day trial of Aha! Roadmaps or join a live demo. Our Customer Success team (made up entirely of former product managers) is available to help you tailor it to how your team works. And if some of what you are building calls for sophisticated prototypes or internal tools rather than a full product cycle, Aha! Builder is where to start.