I’ve been thinking lately about a conversation that’s happening everywhere in knowledge work: how do we actually get good at using AI? The answer, surprisingly, came to me from a construction site I worked on years ago, watching a veteran carpenter choose his hammer.

Lessons from the job site

Right after high school and during part of college, I spent time working construction projects. I started as a helper with various trades, working alongside plumbers and sheet metal workers, essentially doing whatever needed to be done around the shop. Later, I moved into framing and learned house construction, working on both commercial and retail projects.

What struck me most about working with these experienced tradesmen wasn’t just their skill, it was their relationship with their tools. These weren’t people who had read about carpentry or plumbing in a book and then shown up to work. They had learned by doing, which is exactly why apprentice programs exist. You simply cannot read your way into this kind of expertise.

But here’s what really caught my attention: every skilled worker had their own hammer. Not just any hammer, but a specific weight, style, and claw type that worked for them. They would invest in these tools, take care of them, and genuinely cherish them. When a tool broke or went missing, work stopped until they could get a replacement.

These workers also had a no-nonsense approach to getting things done. Sure, there was joking around and camaraderie, but the underlying philosophy was clear: work before play. Get the job done, and get it done right.

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The knowledge work parallel

Fast forward to today, and I see the exact same dynamic playing out with AI tools in knowledge work. These tools are fundamentally enabling technologies that help us achieve outcomes and deliver results. The frontier models are advancing seemingly every week, and the capabilities are genuinely amazing.

But here’s where the construction analogy becomes powerful: it’s not about having the shiniest, most expensive hammer. It’s about how you wield the tool and, crucially, understanding the craft you’re trying to practice.

The most effective knowledge workers I know who use AI aren’t the ones obsessing over which model has the highest benchmark scores or the latest features. They’re the ones who have developed fundamental knowledge through actually doing the work. They understand their craft deeply enough to know when the AI is helping and when it might be leading them astray.

The craft still matters

This reminds me of learning math in school. Teachers would always say “show your work,” even when you could get to the answer faster without it. At the time, it felt tedious and unnecessary. But showing your work meant you actually understood the underlying principles, not just the shortcuts.

The same principle applies to knowledge work with AI. I’m using technology right now to help me format and refine this very article. But that doesn’t mean I don’t know how to write without it. The underlying craft knowledge, the ability to structure thoughts, develop arguments, and communicate clearly, those skills remain essential.

When you have that foundation, AI becomes like that perfectly weighted hammer in the hands of an experienced carpenter. It amplifies your existing capabilities rather than replacing your need to understand what you’re building.

Beyond efficiency: the joy of craft

There’s something else I learned from those construction projects that applies here: there’s genuine joy and artistry in manual craft work. The satisfaction of framing a wall square, or seeing how an experienced plumber solves a tricky routing problem, that’s not just about efficiency. It’s about taking pride in work well done.

I think we risk losing something important if we view AI purely as a way to shortcut or eliminate the learning process. Yes, these tools can handle much of the heavy lifting, and they’re getting easier to use all the time. But there’s still value in understanding your craft deeply enough to do it by hand when needed.

The best knowledge workers I know treat AI like those skilled tradesmen treated their tools: as carefully chosen instruments that amplify their existing expertise, not as replacements for developing that expertise in the first place.

Choosing your hammer

So what does this mean practically? Instead of chasing the latest AI tool or trying to find the “best” option, focus on developing your underlying craft. Learn to write well, think clearly, solve problems methodically. Understand your domain deeply enough that you can recognize when an AI suggestion makes sense and when it doesn’t.

Then, like those experienced carpenters, find the tools that work best for how you think and work. Invest time in learning them properly. Take care of them. But remember that the tool is only as good as the craftsperson wielding it.

What’s your hammer? And more importantly, how are you developing the expertise to use it masterfully?

What parallels do you see between traditional craftsmanship and knowledge work in your field? I’d love to hear about the tools and practices that have become essential to your work.

We’re living through one of those rare moments when the fundamental rules of how things get built are changing. I recently dove into an a16z Enterprise piece about greenfield strategies for AI startups and their thinking on distribution versus innovation, and it got me thinking about something bigger: we’re not just watching AI disrupt existing markets, we’re witnessing the birth of an entirely new category of problem-solving.

From filing cabinets to AI artifacts

Think about the journey we’ve been on. First, everything was physical: filing cabinets, paper records, manual processes that required human hands to touch every step. Then came digitization, where we scanned those papers with OCR, stored everything in databases, and suddenly could search through archives that would have taken weeks to manually review.

Now we’re in the AI phase, and it’s fundamentally different. AI doesn’t just store and retrieve our digitized information, it reads, understands, and connects dots across all that data to solve actual workflows. It’s not just finding the needle in the haystack; it’s weaving the needle into exactly the thread you need for the specific problem you’re trying to solve.

What fascinates me about this progression is how each stage has democratized access to capabilities that were previously exclusive. Physical filing systems require dedicated staff and physical space. Digital systems need IT departments and database administrators. But AI-powered solutions? They’re starting to respond to natural language descriptions of problems.

The greenfield opportunity hiding in plain sight

The a16z Enterprise article by and talks about startups using greenfield strategies to capture customers at transition moments, when they’re moving from small startup to large enterprise, for example. These transition points create new needs that existing solutions weren’t designed to handle, opening opportunities for new players to step in.

But here’s what I think there may be an even larger opportunity: the biggest greenfield opportunity isn’t just about startups unseating incumbents. It’s about individuals and small teams creating solutions that didn’t exist before because they couldn’t exist before.

I keep coming back to this scenario: you’re a subject matter expert in your field. You understand a problem deeply because you’ve lived it for years, maybe even that hard won 10,000 hours that creates real expertise. Previously, if you wanted to solve that problem with software, you had limited options: find an existing product that kind of fits or invest significant time and resources into a full development cycle. For many smaller problems or highly specific workflows, the juice wasn’t worth the squeeze.

Now there’s a third option: describe your problem to an AI interface and watch it build you an artifact that solves it.

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When everyone becomes a builder

This shift is profound because it changes who gets to be a creator of solutions. We all carry around incredibly specific knowledge in our heads, knowledge earned through experience and refined through repetition. Most of this knowledge falls into two categories: stuff everyone knows (like basic geography or common sense about the world around us) and stuff that requires real expertise.

The common knowledge is becoming commoditized by AI, but the specialized knowledge? That’s becoming more valuable than ever. The difference is that now you can take that specialized knowledge and rapidly prototype solutions for those edge cases and specific workflows that previously sat in the “too small to build” category.

I think about this in practical terms all the time. A marketing manager who deeply understands customer segmentation could describe their ideal attribution model and have an interface build them the exact dashboard they need. A product manager who has spent years thinking about feature prioritization could create a custom scoring system that reflects their hard-won insights about what matters.

These aren’t hypothetical future scenarios. They’re starting to happen now, and the pace is only accelerating.

The wisdom premium

Here’s what I find most interesting about this trend: as AI handles more of the routine problem-solving, human wisdom becomes the premium differentiator. There’s a difference between knowledge (which can be learned and increasingly replicated) and wisdom (which comes from lived experience and often can’t be easily articulated).

Some of the most valuable decisions we make aren’t based on logic trees we can trace step by step. They’re based on intuition built from years of seeing patterns, making mistakes, and developing that gut feeling about what works and what doesn’t. This kind of multifaceted, experienced-based insight is exactly what AI can’t replicate but can amplify when combined with the right tools.

Think about it: the ability to quickly spin up solutions means the value shifts to knowing which problems are worth solving and how to solve them in ways that matter to people. That requires the kind of contextual understanding that only comes from being embedded in real workflows and real pain points.

The self-service future

I keep imagining what this looks like when it’s fully developed. You open an interface, describe a problem you’re facing (like how you’d search Google), and after some quick refinement, you get back a working solution. Yes, this exists now, but I’m talking about the simplest bare bones interface you can imagine. Maybe it’s a custom workflow, maybe it’s a purpose-built interface, maybe it’s something we don’t even have words for yet.

The key insight is that this isn’t about replacing existing software. Most of the tools and platforms we use today will continue to be valuable because they solve common, well-understood problems at scale. This is about filling in the gaps, solving the edge cases, addressing the problems that are too specific or too niche for traditional product development cycles.

What excites me most is how this expands the universe of problems that can actually get solved. Right now, there are countless workflow frustrations and process inefficiencies that never get addressed because they’re not quite big enough to justify building a product around, but they’re real enough to cause daily friction for the people who experience them.

In a world where solutions can be created on-demand, those problems suddenly become solvable.

What this means for how we work

This transformation is already changing how I think about product development and business strategy. If your customers can increasingly build solutions themselves, what’s your role as a product company? If individuals can create custom workflows using AI interfaces, how do you compete with infinite customization?

I think the answer lies in understanding that expertise and wisdom become your core differentiators. The companies and individuals who thrive will be those who combine deep domain knowledge with the ability to leverage these new creative tools effectively.

We’re heading toward a world where the barrier to testing ideas and solving specific problems drops dramatically. We’re heading toward a world where everyone can be a creator of solutions, and the most successful creators will be those who combine technical possibility with deep understanding of real human problems.

What problems are you uniquely positioned to solve that you’ve been putting off because they seemed too complex or too niche to tackle? The tools to build those solutions might be closer than you think.

Planning still matters (even more than you think)

Here’s something that might surprise you: the more context you give AI tools, the better your results. Yes, you can throw a simple idea at ChatGPT or Claude and get something functional, but I’ve learned this approach often creates problems you don’t see until later.

Think of it like building on shaky foundations. Your UI might look perfect, but underneath the hood, there are fundamental architectural issues waiting to surface. By the time you discover them, you’re already deep into development, and fixing them requires significant rework.

I’m not saying you need a full product requirements document (though it wouldn’t hurt), but taking time upfront to plan and provide detailed context makes a meaningful difference. This isn’t unique to AI development, of course, but the principle becomes even more critical when you’re relying on tools that thrive on rich, detailed input.

The great model convergence

Here’s something interesting I’ve observed: frontier models are producing remarkably similar outcomes. Whether you’re using ChatGPT, Claude, or other leading models, the end results tend to converge despite all the benchmark wars and accuracy comparisons.

Don’t get me wrong, those benchmarks matter for people in the industry, but for builders just trying to turn ideas into reality? The model choice matters less than you’d expect. What’s more impressive is how these models are getting better at error reduction, writing cleaner code with less tech debt from the start.

This convergence means you can focus less on which AI tool to use and more on how you’re using them.

Infrastructure is the new battleground

AI development tools have realized they can’t just be about writing code anymore. As vibe coding becomes mainstream, people need the full stack: servers, databases, storage, all connected and working together.

The platforms that are winning this race are the ones that remove complexity while maintaining flexibility. They’re enabling non-technical people to build what previously required teams of engineers and infrastructure specialists. This is a massive shift in who can participate in software development.

But here’s the thing, this infrastructure piece can’t be an afterthought. It needs to be baked into the development process from day one, seamlessly integrated so builders don’t have to think about it until they need to.

The last 20% problem

I’ve noticed a consistent pattern: most people can handle the first 80% of development remarkably well. They can build prototypes with AI, create functional apps, even get close to production-ready products. But that final 20% often becomes a significant hurdle.

Sometimes it’s technical (security considerations, performance optimization), but often it’s psychological. There’s uncertainty about publishing to production, especially for business applications where the stakes feel higher. This hesitation is understandable and often justified, but it represents a clear opportunity.

For platforms building these tools, solving the “last mile” problem could be a major differentiator. For service providers (technical and non-technical alike), there’s a growing market of builders who need help bridging that final gap.

The executive prototype revolution

Not everyone will embrace vibe coding initially. I remember similar resistance to social media in its early days. But I’m already seeing something fascinating happen in forward-thinking companies.

Picture this: a CEO has an idea over the weekend. Instead of scheduling meetings and trying to explain it to their technical team, they jump into an internal tool, describe their concept in plain language, and create a working prototype. That prototype gets automatically routed to a product manager, analyzed against customer data, and scored for potential impact.

This removes a massive communication barrier. Subject matter experts can finally create solutions for the real problems they understand intimately. No more lost-in-translation moments between business vision and technical execution.

This shift creates both challenges and opportunities for existing solution providers. Some may see their products replicated internally, but smart companies will find ways to offer complementary services, integrations, and specialized expertise around these internal tools.

What this means for you

Whether you’re a product manager exploring AI tools or a founder considering vibe coding for your next project, these patterns suggest we’re still in the early stages of a fundamental shift. The barriers between having an idea and building a solution continue to collapse.

The key is approaching this evolution strategically. Plan more than you think you need to. Don’t get caught up in model wars. Focus on platforms that solve the infrastructure puzzle elegantly. Prepare for that last 20% challenge. And if you’re in a leadership position, consider how empowering your team with these tools might unlock ideas that would otherwise never see the light of day.

What’s your experience been with AI-assisted development? Are you seeing similar patterns in your organization, or have you encountered different challenges entirely?

If you found these insights valuable, I’d love to hear about your own AI development experiences. Reply and let me know what best practices you’ve discovered or what challenges you’re still working through.

By the time you’re done, you’re exhausted and the map is already outdated.

I’ve been there. So I built something different.

The Journey Mapping Problem

Here’s what usually happens when teams try to create journey maps:

You start with research notes scattered across different tools. Then you try to organize everything into a coherent flow. You debate whether "Awareness" comes before "Discovery" or if they’re the same thing. Someone suggests adding emotional states. Now you need different colors and icons.

Two hours later, you have a beautiful map that nobody will update because it’s too much work.

Most teams either skip journey mapping entirely or create maps that become digital dust collectors. The process is just too painful.

What if AI Could Do the Heavy Lifting?

I started wondering: what if you could just describe a customer journey in plain English and have AI create the visual map?

So I built an AI-powered Journey Mapper to test this idea.

You type something like "A customer discovers our eCommerce site through social media, browses products, adds items to cart, gets distracted and abandons it, receives an email reminder, and completes the purchase."

The AI generates a complete journey map with stages, emotional states, and even improvement suggestions.

Demo Time

In the video above, I walk through the entire tool. You’ll see how it works in real-time, including some of the editing features I built in.

What I found interesting is that the AI doesn’t just create a generic template. It actually analyzes your specific scenario and suggests relevant touchpoints and emotions. For the e-commerce example, it picked up on cart abandonment and suggested email remarketing strategies.

Pretty cool, right?

How I Built This

This was a fun technical challenge. The core is built around natural language processing that understands journey descriptions and maps them to visual components.

I used Replit’s agent capabilities for the AI integration, built in user authentication, and added a credit system to manage API costs. The whole thing is designed to be remixed and customized.

The best part? It’s open source. If you want to build on it or adapt it for your specific needs, you can.

Try It Yourself

Want to test it out?

Live Demo: Customer Journey Mapper

Just log in with your Replit account and you get 10 free credits to start. You can describe any customer journey and see what the AI creates.

Want to Customize It?

The template is available on Replit Gallery: Journey Mapper Template

You can remix it and add your own features. I’ve already seen a few people customize it for different industries, which is exactly what I was hoping for.

Why This Matters

This isn’t just about journey mapping. It’s about removing friction from important product work.

How many times have you skipped doing something valuable because the tooling was too complex? Customer interviews, competitive analysis, user research synthesis... there are so many areas where AI can handle the grunt work so we can focus on insights and strategy.

I think we’re just scratching the surface of what’s possible when you combine AI capabilities with product management workflows.

What’s Next?

I’m curious to see how people use this tool and what they build on top of it.

If you try it out, let me know what you think. What worked? What didn’t? What would you add?

And if you remix the template into something new, definitely share it. I love seeing how people adapt these experiments for their own use cases.

The goal is to keep experimenting with AI tools that solve real problems for product teams. This Journey Mapper is just one example of what becomes possible when we stop thinking about AI as magic and start thinking about it as a really powerful productivity tool.

What customer journey would you want to map first?

Thanks for reading Progress Forge! Subscribe for free to receive new posts and follow along as I continue exploring practical AI applications in product management.

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What you’ll learn

The video walks through a complete end-to-end example of creating a bakery ordering system prototype, inspired by my previous posts on product planning and prioritization frameworks.

I demonstrate:

• How to set up an AI project in Claude (or similar tools like ChatGPT) with proper context

• Converting product requirements into effective AI prompts

• Using Replit Agent to generate a working prototype from those prompts

• Comparing different approaches to prototyping (conceptual vs. functional)

• Practical applications in the product development lifecycle

The power of contextual prompting

One of the most fascinating aspects demonstrated is how providing the right context to AI tools fundamentally changes what they can produce. By loading a project with relevant documentation and clear requirements, the AI is able to transform a simple product brief into detailed specifications that Replit can turn into functioning code and a testable prototype.

Why this matters for product teams

This approach opens new possibilities in the product development lifecycle. Imagine being able to rapidly test concepts with customers before committing engineering resources or exploring multiple design directions in a fraction of the traditional time.

As I mention in the video, “Is this always the best way to go about doing it? Not necessarily. Is it another tool in our toolbox as a product manager? Absolutely.”

Key takeaways

• AI-powered prototyping bridges the gap between concept and implementation

• Focus on outcomes rather than specific implementations when crafting prompts

• Rapid prototyping can strengthen the discovery phase of product development

• These tools complement (not replace) the expertise of the product trio

• The most valuable use case is getting tangible feedback earlier in the product lifecycle

If you’re interested in seeing how these capabilities and tools might fit into your product development approach, the full video provides a practical demonstration that could spark ideas for your own processes.

Sometimes the change isn’t in the product lifecycle itself, rather it is in the initial priorities that were set at the beginning. While AI can help us move faster and work more efficiently, having clear priorities becomes more important than ever.

It makes sense that many frameworks exist to help guide product leaders and their cross-functional teams on the journey to producing quicker and better outcomes. Sometimes simple frameworks and priorities can be key to a team’s success.

It can be helpful to understand some widely used frameworks in product management today.

Common product prioritization frameworks

Common prioritization frameworks such as ICE, Value vs. Effort, and Cost of delay have their place. There are also larger frameworks that can go more in-depth such as Jobs to Be Done (JTBD).

These frameworks and others can help provide clarity when you need it.

For the times when you need clarity fast and the priorities aren’t completely clear, what is a lightweight litmus test to use?

Three simple questions to help drive product prioritization

When prioritizing a product decision, initiative, or feature, try asking the following three simple questions:

1. What problem are we solving?

2. Who are we solving it for?

3. Why now?

Let’s break down each of these a bit further.

1. What problem are we solving?

It is well-known that seasoned product managers deeply value understanding the problem they are trying to solve.

When trying to solve a problem, the solution usually equates to what needs to exist to solve the given problem. This can be accomplished in a variety of ways, however, what is the most optimal in the scenario?

2. Who are we solving it for?

This is the target audience who the problem is being solved for.

Answering this question helps get more laser focused on ensuring that what opportunity you are working on isn’t too broad and directly connects to your ideal customer or user.

3. Why now?

Here comes the beginning of answering the timing and prioritization part of this method.

When you ask yourself ‘why now’ - you can zero in on if now the time is to do this.

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Putting these three simple questions into action

When asking and answering these three questions, you end up with answers or artifacts that could look like:

• Problem statement - what problem are we solving?

• Target audience or market - who are we solving for?

• Expected or desired outcomes - why now?

These don’t have to be lengthy but do need to have the core problem to answer thought out more than surface level.

A practical example: Online ordering for bakeries

Let’s take a fictional yet could be real scenario of a new product feature for a CRM focused on bakeries. Bakers consistently share feedback that they want customers to be able to order baked goods directly through their website.

Let’s break down our three simple product questions for this situation:

What problem are we solving?

Bakers are losing potential sales because customers can only place orders during business hours via phone or in person. This creates unnecessary friction in the ordering process and often leads to errors in orders. The different systems for their website and order management add complication that takes time away from what bakers do best, creating amazing baked goods for their customers.

Who are we solving it for?

We’re focusing on small to medium sized bakeries with 1-3 locations that:

• Process 20 or more custom orders per week

• Currently manage orders through phone calls, email, or paper forms

• Have limited staffing resources for order processing

Why now?

The timing becomes clearer when we look at key market signals:

• Over 70% of bakery customers now prefer to order online

• Competing bakeries are gaining market share through ordering capabilities

• There has been a 40% increase in customer requests for this feature in the past quarter

By running this quick litmus test with our three questions we can get the clarity needed to move forward with confidence. The problem is real and does impact business outcomes, we have a clear target user in mind, and the market appears ready to support the timing.

This method can help drive better product decisions without getting slowed down in complex frameworks or unnecessary analysis. Sometimes simpler is better when it comes to product prioritization, especially when you need to move quickly while staying focused on what matters most.

Looking ahead: A framework that scales with change

I’ve seen how product management is evolving with AI. What I find fascinating is that even as our tools and capabilities progress, having a simple framework for prioritization becomes more valuable, not less.

Next time you are faced with a product decision consider giving the three simple product questions a test to see if it helps frame things up better for you and your team. You can also give this a test with an AI agent to run through different scenarios.

This question hit me recently while reading a blog post that made me pause and reflect. Chris Kiehl, observing software development over the last ten years, made a statement: ‘Most programming should be done long before a single line of code is written.’

That is a profound statement for many reasons.

When you build a house, do you start without a plan or idea?

Even if you build a simple shed, you start with a plan.

If you cook a recipe, you have some idea of what you want to make. Sure, you can throw ingredients together, but you have a basic recipe in mind, even if you are making it from scratch.

When you build a software product, you have a basic result in mind. Yes, even for a component.

This applies to engineering, design, marketing, product management and more.

With hand-written code, you start with an idea.

With AI-generated code, you start with an idea, and a prompt.

As humans, we are great at generating ideas.

AI is getting great at generating ideas, but there was still a spark of an idea from a human, at least currently, that prompted the seed of the idea.

Back to Chris’ statement around how long before a single line of code is typed/generated, the programming idea should have started well before.

I couldn’t agree more!

The question really is, what is the pre-work or idea generation that is adequate or needs to happen to drive this from A to B, and then C to D, and so on.

Where do product ideas originate?

Some ideas for products are intuitive and some don’t really present themselves until later as an iteration.

Some are visionary and might take years to come to fruition. Take Steve Jobs vision for the App Store in 1983.

Others can be realized in a day.

AI prototype example: From idea, to prompt, to prototype

Some ideas can go from thought to prototype in minutes. Companies and platforms such Replit are doing this. They just launched a service where you can suggest through a prompt to the Replit Agent what to build and the Agent builds your initial idea into a workable initial product prototype.

Let’s try this out… I will give Replit Agent a prompt with a spur of the moment idea.

Initial idea

It would be cool to have a product idea generator. Usually, product ideas come from existing customers/market and discovery about what opportunities exist, however, what if you have an initial idea and want to brainstorm a little more with AI assistance?

Initial prompt

Build a product idea generator. I can give you an OpenAI API key to add AI generation capabilities.

Initial prototype

Let’s head over to Replit and try out this initial prompt.

1. Sharing the initial prompt idea with Replit Agent

2. Optimizing the prompt idea with AI assistance

3. Replit Agent suggests the initial prototype to be built, along with other initial feature ideas to consider

4. The initial prototype is ready for testing

Quick demo video showing the finished prototype in action

Now that we have a working prototype that was built with AI agent assistance, we can move on to testing, and then further iteration if desired.

Here is a summary of what takes place in the following video demo of the ‘AI Product Idea Generator’.

First the product idea is typed into the form field and then the ‘Generate Idea’ button is clicked and with AI assistance a product idea is generated.

The output includes:

• Proposed product name

• Initial proposed product description and positioning

• Market Potential

• Target Audience

• Key Features

Debrief on this AI prototype example

How long did this AI prototyping process take overall? Less than 5 minutes to go from idea, to prompt, to prototype.

Let’s pretend this AI prototype was tested out in the market, got some positive feedback, and we wanted to take it on its way to a production build?

The great part of generating this initial prototype is now the initial code can be shared with those with engineering and design skillsets, and it can be taken to the next level.

Product ideas can originate from multiple sources

Typically, product ideas can come from a variety of sources.

Working within a product team those in the ‘product trio’1 are often those that are pulling product ideas from various sources. These could be from internal ideas (founders, executives, and teams), customer feedback, market demand/shifts, discovered opportunities from customer discovery, and more.

With a vision painted, the why considered, and the layers of the what and how becoming clearer, what do we do with these ideas?

Considering the agentic AI example above with Replit Agent driving much of the underlying idea to prototype work, how can we ensure this idea we originally had is something worth exploring more?

Prototype experiments

With the ability to spin up a prototype within a matter of minutes, as briefly touched on in the debrief above, we can go from prototype to experiment to production build - if an idea shows promise.

I’ve thought about the signal vs. noise of how easy it is to spin up a prototype and how the quality of the output compared to a well thought out, researched, discovered, and planned prototype could be.

Will the market be flooded with AI built prototypes and market demand show simple and quick prototypes aren’t as effective?

Or will these AI prototypes be a gateway into faster discovery and feedback loop cycles with product development teams and customers?

I think time will tell, and the end results will be shown by what goes from a simple product idea to a product with product market fit.

Wrapping it all up

Drawing inspiration from Chris’ original thought of thinking through an idea before generating code, whether traditionally or AI-driven, one can conclude that taking some time still to ponder, research, and validate an idea first, before jumping right into building, is still very valid.

At the same time, it behooves one also to experiment with fast prototypes and ideas to ensure speed to market, taking an intuitively innovative idea and building it faster than available resource streams, could benefit the end customer in the end while driving business outcomes.

As with anything in product and tech, experimenting with old and new ways of doing things and testing the ideas is always the best path forward.

The product manager role spans tech, engineering and development, design, strategy, marketing, sales, support and more.

I am going to insert the inevitable Venn diagram here just for kicks to highlight where it sits, at least now, in the business.

This isn’t a perfect Venn of product management - but it gets the idea across.

Why product management?

Insert all the product manager memes here.

In all seriousness, the product manager role, in my experience, helps drive meaningful business outcomes while ensuring customer experience is front and center and advocated for all the way.

I won’t belabor here all the philosophy behind product management as this is covered extremely well by the top thought leaders in product.

I will share a few resources here that I think are valuable for anyone interested in what I would consider ‘good’ product management. Sorry if your resource isn’t here, as there are too many to call out. In future newsletters and posts, I promise to link to more.

• • This newsletter helped me go from a newbie PM to one that now feels much more confident. Read Lenny’s Newsletter and listen to the podcast. It aids in removing impostor syndrome and helps you be better. Thank you .

• Marty Cagan’s books

  • Marty is known in the product world as a sage and the wisdom that he shares in his books and blog has helped elevate the understanding of this unique role.

• Teresa Torres’s Continuous Discovery Habits

  • Teresa makes customer discovery fun. When done right, in my opinion, and those that know me know I lean into this, customer discovery is one of the most important parts of product management. ‘Know thy customer’ is one of the product management commandments, didn’t you know? has evangelized customer discovery more than anyone else I’ve known about. Get the book and read the blog or daily newsletter, .

I’m going to leave it at three for now.

Product management as a philosophy

If you think about product management as a philosophy, it is this: help solve problems for someone.

Improving a product’s design, load time, and efficiency could give someone more valuable time back in their day so they can go spend that time doing other things in the world. Volunteering, spending time with others, and whatever else they value.

Even with all the AI buzz, humans still do business with other humans.

Where is this whole product management thing going now with AI?

I don’t think anyone really knows yet.

People have some guesses.

One person that has some widely published guesses that are well thought out is Claire Vo. Claire gave a talk and the topic was product management is dead. Here is a link to the video and a blog post with slides.

The overall premise of what Claire talks about is something I agree with 100%. The traditional way of doing things will be replaced in the future with a whole new way of doing things.

Yes, that is an oversimplification, however, if you think about it, AI as a copilot and productivity tool, is a very real thing and is possible right now.

Imagine what it is going to be like 2-5+ years from now.

Will the product manager's role shift to something else?

Perhaps, but perhaps not. There are going to be many positive changes such as an increase in capacity and bandwidth not only for product managers but also for most knowledge workers.

You can become an AI powered product manager now.

Part of why I re-launched Progress Forge is to talk about, share, and promote this inflection point that is happening now.

Am I being overly optimistic about the impact of AI on product management? I might be, however, since I’ve started using it over a year and a half ago in my role day to day, it has increased my capacity and allowed me to focus on the more fun aspect of product management, getting to ‘know thy customer’ and collaborate more deeply with my human counterparts.

Is now a good time to get into product management?

This brings me back to my original starting point in this post. I love product management. Primarily because there is so much depth to be uncovered in the various ‘problem space’ you can exist in as a product manager.

Approximately ninety percent of the iceberg is underwater. The top ten percent is what you see.

A quick study of the product development life cycle (PDLC) will indicate that in the preliminary stages of the PDLC is the ~10% of the iceberg while 90% is the middle and later stages.

With the changes in product management and adjacent teams inevitable, is now a good time to still get into product management?

I think so, yes.

For me, what drew me in originally to product management, was accidental to be honest.

Having worked in various roles throughout my career spanning the technical side and business side, I realized as I shared before in the earlier Venn diagram product management is a cross section of many of these pieces.

Realizing this, I jumped into the role and found it was indeed the cross-section it is purported to be. And the best part? It is the most fun part of all the various pieces of the puzzle.

With AI copilots, rapid MVP prototyping, and agents that can and will automate many mundane things, what impact will this have on the career of a product manager and similar roles?

No one knows for certain - but as shared earlier with Claire’s perspective, AI will create empowered product teams.

Businesses still need outcomes. Customers still need problems solved. Human interaction is still truly relevant and important, so yes, product management is still a good path to consider.

There are some great articles on the fundamentals of what makes a great product manager and how to get into product management. These definitions and the path will shift and change over time.

Where do we go from here?

The future of product management is exciting. There won’t be a dull moment in the next 2-5+ years.

While AI will augment and enhance our capabilities, the human elements of product management empathy, strategic thinking, and cross-functional leadership will become even more valuable.

Whether you are a seasoned PM or someone that is interested in the field, I believe based on all this that embracing and learning how to bridge the gap between AI capabilities and human-centricity will be key to success.

I’m curious to hear your perspectives on this topic. Where do you think this is all going? How do you see the role evolving?

How does AI tie into basic productivity?

Core productivity principles

One main objective of productivity is to have an underlying system or method of organizing actions and work into some kind of meaningful output.

Most people are driven toward being more productive to try to increase their output or just maintain sanity in demanding situations.

It is important to remember that productivity principles are simple:

• Getting items organized

• Establishing a simple system

• Periodic reviews of this system and way of organizing things

I won’t talk here about how to create these systems and get organized as much has been written on this topic and there are countless ways to accomplish this.

I will, however, share a couple of key ideas that might be helpful:

• Don’t overthink it - If you have something that works for you now, great. If not, start with the simplest way to accomplish your goal.

• Try new things - If listening to a certain type of music helps you while you work, do it. If not, and you prefer to work in silence, so be it.

• Find something that works for you - Do you have a go to productivity system? A few systems: Getting Things Done, working in Sprints, Shape Up, or Ivy Lee Method?

How does productivity work for product managers in an AI-enabled world?

Leveraging AI tools for productivity

Context is king when it comes to working effectively with AI. The underlying models that have been built over time are amazing and the vast information they understand is immense.

That being said - for you to get the maximum value out of working with AI technology, such as ChatGPT, Claude, Gemini, Microsoft Copilot, etc. - you need to share context about what you are trying to solve.

Quick thought on confidentiality

Be aware of confidentiality rules for your current employer or if you are a founder or business owner, be aware of the privacy policies of the AI tools you leverage.

Mainstream AI products have built in privacy tools and settings where you can adjust and opt-out of training on your content. Here is some information on ChatGPT’s opt-out as well as Claude’s opt-out, for example.

How to share context with AI

First, what do I mean by ‘context’? It might help to think of it this way, what do you need to share with your AI copilot to help you accomplish the result you are looking for?

Artificial intelligence and machine learning models can comprehend a vast amount of context quickly. That certainly is not a challenge. However, to give you precise feedback on what you are working on, it helps to understand the specific context in which you are chatting, or talking via voice, about.

Organize with Projects

In the last several months major AI tools ChatGPT and Claude have introduced ‘Projects’. More details on ChatGPT Projects and Claude Projects.

The Projects feature is available to paid subscribers for both ChatGPT and Claude. I imagine in the future, this could be something that they would open to free users, but for now, it is gated.

What are some of the advantages of using Projects to organize your work?

• Built in context - When you use the projects functionality built-in to these AI tools, chats you engage in within the project understand and refer to the information you’ve shared.

• Organization and structure - Without using the projects structure it can become challenging to manage and recall many chats.

Product management work typically spans over weeks, months, and even years. Being able to organize your AI chats within projects helps make it more streamlined to go back and refer to the information you might need to recall later.

What context should you share?

This is up to you, however, one fitting example that resonates with me, is to think of onboarding AI like a new team member on your team. This was mentioned by in a podcast episode recently with and .

Some ideas of things you can share:

• Key knowledge documents

• Product roadmap

• Product vision

• Visuals for prototyping

What if you don’t have access to ‘Projects’ since it is only for paid users currently?

You can still attach documents to individual chats with most AI providers. This can still allow you to ‘onboard’ your AI copilot to understand the context of what you are trying to solve for.

Integrating AI into existing workflows

As a product manager, you are working to drive specific business outcomes while ensuring customer-centricity stays at the center of what is delivered.

To do this, while balancing multiple priorities, engaging cross-functionally and communicating often, how do you balance this with the deep work you need to do?

Map out some of the common things you work on, some of these could be roadmaps, strategy, planning meetings, customer interviews, discussing design mockups, and reviewing data.

How can you integrate AI into these workflows?

It depends on your situation, however, here are a few ways to get started.

1. Brainstorming and ideation

2. Enhanced meetings (pre- and post-meeting)

3. Organization

Let’s break these down further.

1. Brainstorming and ideation

If you have shared enough context with the AI agent and copilot that you are collaborating with, you can quickly brainstorm and ideate on many topics.

This can range from how to handle a particular situation you are dealing with, such as how to communicate with a specific stakeholder on a topic. Also, you can work with your AI agent to act as a sounding board for some visionary ideas you might have for the product you are responsible for.

You would be surprised at some of the detailed input you can get.

2. Enhance meetings

Taking hand-written or typed-out notes during meetings is a great way to stay engaged. But do you really want to have to write down every single takeaway?

This is where AI copilots can give you a major advantage in helping you ensure that important actions and takeaways are captured from a virtual video meeting.

By sharing a transcript with an AI tool, you can quickly create a summary and key takeaways.

What if you are not in a virtual meeting? The notes you take, no matter how messy, can be cleaned up quickly with an AI agent’s assistance. You can share the notes and ask for a cleaned-up version.

This is perfect for situations where you want to share the notes with the audience but didn’t want to or have the time to clean the notes up yourself.

3. Organization

Do you feel like you have a million projects and things you are working on all at once? I feel your pain. Most people work more hours to get more done. This isn’t sustainable over the long term and could have an impact on your productivity and well-being.

By working with an AI agent, you can organize your projects, tasks, and to-dos into meaningful action plans and strategies.

How can you do this?

• Create separate ‘Projects’ or chats for different initiatives

• Prioritize projects by impact

• Let AI help you with the mind-numbing work

Voice integration is a powerful way to communicate with AI

The major AI tools have voice integration built in. This can be one of the most powerful ways to communicate a large amount of context in a short amount of time to your AI copilot.

You can absolutely type out all the details, but experiment with them, and try the voice integration feature.

The key to remember - the more context the better. Voice to text is one of the easiest ways to share a lot of contexts in a short amount of time.

Mac and Windows both have built in voice to text features that you can leverage as well. You can use those in any window that allows for text input, so it can also be used within AI if you prefer not to use the voice integration, or if for some reason, it doesn’t offer it yet.

Some use cases for voice integration with AI:

• Quick brainstorming session

• Meeting note dictation

• Writing and improving an email or message

• Task list creation

Some best practices and considerations

The possibilities of leveraging AI to boost your productivity as a product manager are exciting. There are some things you may want to consider as you dive deeper.

Recap of the data security implications

• Remember to review company AI usage policies.

• Understand data usage and retention policies for AI tools.

• Check the training opt-out settings.

• For sensitive topics, consider using generic examples instead of live data.

• Sanitize sensitive information. Don’t put sensitive data into an AI tool.

Set yourself up for success, with some simple steps

When working with an AI copilot, remember the principles:

• Share enough context to help the AI understand what you are trying to solve. The projects feature of AI tools can help organize this.

• Experiment with your own workflows and where it helps you best. While the tools and models of AI can work across many aspects of our lives, find your best flow.

• Never neglect or forget the important human aspect of doing business. Engaging with other team members cross-functionally is important. Human intuition is an amazing thing.

• Don’t over-rely on AI suggestions. You can rely on your own intuition. AI is an enabling technology to help increase your capacity.

Implementing AI: Putting it all together

One of the best parts about where we are leveraging AI tools in our roles in product management is that we are still early.

Yes, advancements happen weekly, and automation with AI is becoming increasingly better.

But at the same time, ChatGPT was only released publicly 2 years ago.

Imagine where we will be in another 2 years from now.

What are the next steps?

1. Set up your AI tools by sharing context on what you are working on

2. Create some basic templates you can use - leverage AI to help you build them

3. Work with your AI copilot as needed

4. Experiment with different ways of working with AI agents.

Integration AI tools into product management workflows is a great way to be more productive while still driving desired business outcomes for the business and driving a positive outcome for the customer of your product.

I’d love to hear more about how you leverage AI into your daily workflows. We can all learn from each other.

Why Now?

As I pondered how innovation and time in the product and technology world seems to be rapidly moving faster it became clear to me that there is an opportunity to share what I am observing as I and others are building and experimenting.

For example, you can quickly prototype an MVP product idea in minutes, not hours, days, or weeks.

When I wrote the original Progress Forge welcome post I shared the following quote: “You cannot dream yourself into a character; you must hammer and forge yourself one.” - James Anthony Froude

While this quote still stands true today, so much has changed since then. The pace at which we live has sped up and technology continues to evolve.

This is an exciting time to be alive. How do we apply these emerging technologies in our day to day lives? Are there things we can learn from the past, to consider for the future?

What to Expect

Progress Forge will publish insights focused on four key areas:

1. Product Management & Strategy - As business, product, and technology evolve, how do we evolve our strategies?

2. Technology & Innovation - We are past AI/ML being a buzz word. How do we leverage this enabling technology?

3. Leadership & Communication - How can we effectively lead teams and communicate better?

4. Business Strategy - In what ways can business and organizations approach the need for clear strategic direction?

The Journey Ahead

Re-launching Progress Forge, with this new focus and platform, opens more possibilities.

We can all learn from each other. There is so much quality content being published daily. I enjoy learning from others, and understand we are all up against competing priorities and focuses. What I can say is that I will try to share my unique perspective where I am able.

For the first several posts, the publishing cadence will be fluid. However, I will be striving to publish weekly at a minimum.

I look forward to engaging with the community on these topics and more.

A Note About the Archive

Some readers may remember the original Progress Forge (2008-2014). While that content remains available at archive.progressforge.com, this new chapter represents an evolution focused on today’s challenges. I’ll occasionally reference relevant historical pieces when they add valuable context to current discussions.

Let’s Connect

I want this to be more than just another newsletter in your inbox. Progress Forge aims to engage in and build a thoughtful community who are actively working to drive meaningful progress in their organizations.

If you’re interested in:

• Building better products

• Leveraging emerging technology effectively

• Leading teams through transformation

• Making better strategic decisions

...then I invite you to subscribe and join us on this journey.

Subscribe now

P.S. I’d love to hear what topics you’re most interested in exploring. Drop a comment below or reply to let me know.