1. Can you describe your background and how the software development landscape has evolved since you got started in this industry?

I've been in software development professionally for about 25 years. The first program I ever wrote in BASIC when I was 8 years old was to get a computer to have a conversation with me. That's all I ever wanted was to be able to converse with my computer. I've always been very interested in AI, and it's been a driving force throughout my career.

When I started, the landscape was completely different. Programming was at a very low level and it was very difficult to get anything done. There was no agile methodology, no object-oriented programming, and the internet was barely accessible to the public.

Over the years, layers of abstraction have made it easier and more intuitive to create, interact, and program. With generative AI, we can finally give high-level commands, and the system figures out the details. This shift from low-level programming to high-level abstraction is the most significant change I've witnessed.

2. How has the inclusion of AI in products changed the way product teams operate?

To answer that question is hard because you're asking it as if it's already happened. Right now we're really in the middle of it. But there are some specific changes in progress that I see going on right now.

Creating products that include AI in the center means taking a different approach altogether. For example, traditionally when building software we build the smallest working version of the product first and then we iterate on it, adding features and capabilities. Somewhere down the line we create dashboards that administrators can use to manage the system. AI-based software flips this on its head. 

We have to create dashboards right up front that allow us to give rapid feedback on how the LLM or diffusion model is interpreting what we ask of it. As an LLM can take any input in multiple modalities, that means a lot of variation from one project to the next. So a software team has to build testing frameworks and dashboards first. 

Another significant change is that balanced teams used to consist of design, product and engineering. AI-based applications have added a fourth leg to that stool: AI. AI affects how the business approaches the problem, how the UI is designed and built and the architecture of the app. Not having a person in the role to advocate for AI, the way product advocates for business, designers represent users and engineers champion systems, is a sure path to a half-baked AI effort. 

There are also a lot of examples of how AI is changing how product teams operate whether they are building AI-products or something more traditional. Lots of other examples exist such as, AI helping product teams by handling time-consuming tasks like data analysis and summarization, allowing product managers to focus more on strategy and user engagement. So what happens here is the AI helps by augmenting the human intelligence, and then the humans use that and augment the AI. They're both collaborating to get to a shared goal and a shared vision. 

The AI tool that we built at Artium called APEX can actually take an initial idea for a new product, expand it into a comprehensive plan, and then distill it back into actionable user stories for engineers to build. This process has streamlined our workflow and made product management way more efficient.

3. What tools do you believe are crucial for product managers to be successful in working with AI-driven products?

It depends on if the PMs are technical or not. For technical PMs, getting familiar with data handling tools can be very useful in better understanding the users needs. So setting up some quick agents in frameworks like Llama index help with data organization and transformation. There are also a number of low/no code tools like Retool that can help a PM quickly mock up ideas to discuss with stakeholders. As AI is interactive, it’s not like the old days of getting a screen mockup to share, these things need interactivity. 

Of course, traditional product management tools, when augmented with AI capabilities, can become even more powerful. In my daily workflow I rely on AI tools like ChatGPT, Claude, Perplexity, and our own tool, APEX. A lot of PMs use tools like FigJam and Miro to create maps of complex ideas and consolidate feedback. These tools, augmented with AI, provide a great path for getting from scattered info to a honed message and strong direction. 

LLM tools assist with things like generating user stories, analyzing data, and providing actionable insights. It’s like I have a buddy now where I can just write a line that says, “Hey, I want to do this” and the agent will ask me 3 or 4 questions, and then spit out an entire user story. 

The killer tools are yet to come.

4. How do you stay updated with the latest advancements in AI and ensure that your team is equipped with cutting-edge knowledge?

I have a bunch of AI agents that I've created that can distill down the firehose of information that comes in around AI. And so what I do is I look for new research papers that have come out and pass it through an AI, and just share the summary with the link to the paper. So I am up-to-date with any significant update and changes that are going on and always share those with my team.

There are also a few popular Youtube channels and newsletters that I subscribe to that bring me weekly news or summaries. Linkedin is also a great resource. Also I never stop taking courses online from reliable sources like deeplearing.ai - learn by doing is great, but even better mixed with study.

5. How do you foresee the role of AI in product development evolving in the next 5-10 years?

There's a lot that can happen in 5 years. But I think that it's like most jobs, it's going to continue to be redefined. When you're collaborating with an AI it will become smarter and will get to know you better, and with that it will be able to do more and more of the work. 

Then the question is… What do we want to do with the time that AI can help free up? And I think that differs from person to person. You can put that effort into research and looking into the future and seeing what that product will become, or you can put that effort into being more strategic with your stakeholders and so on. So I think overall what AI is going to do is free up a lot of time for product people to be more strategic and level up their careers.