Applying Product Mindset in Cross Functional Teams

"Just write the code." If that's what you're telling your engineers, you're creating the wrong kind of development team. After building three successful software companies, here's what I know works to develop product thinking in engineering teams: Start with meetings. Not more meetings - better ones. Get your engineers in product planning sessions. Even if they just listen, they'll absorb crucial context about user needs and business goals. Break down the walls. Stop treating your engineering team like a code factory separated from the rest of the business. At Full Scale, we make sure our engineers understand the client's business, not just their technical requirements. Change your questions. Instead of asking "When will it be done?" Ask "What problem are we solving?" Ask "Why does this matter to users?" Ask "Is there a simpler way?" Create ownership. Give engineers responsibility for outcomes, not just output. Let them own the solution, not just the implementation. Most importantly: Kill the feature factory mindset. Your engineers shouldn't be waiting for tickets. They should be participating in problem-solving. I see this work every day at Full Scale. When engineers understand the business context, they make better technical decisions and build better solutions. This isn't just about building better products. It's about building better engineers. Because in today's market, engineers who can think about product are worth their weight in gold. What's one step you've taken to help your engineers think beyond code?

GTM Systems teams CANNOT be order-takers in the AI era. Innovation won’t come from requirements - it will come from experiments. And these teams must evolve into true Product orgs. I see this as the most overlooked challenge with adopting AI in GTM Systems. A successful strategy means you need to move FAST. Experiment. Prototype. Iterate. This is the default standard in Product culture. The Problem: this approach runs counter to Biz Tech culture. Salesforce & Internal Tools experts will hear this and say I’m crazy. “You need strict governance & careful planning to scale systems infrastructure.” And previously, I would completely agree. But the AI era is a different beast for a few reasons. 1) Teams don’t know what’s possible or what they want from AI. • Success is judged by behavior change, not completion of a backlog item. • The value of AI will emerges through usage and iteration • New features will not result from traditional requirements gathering. 2) AI has completely shifted the delivery timetable. • Historically, the goal is to craft a long-term GTM Systems roadmap. • Then, you break key initiatives into months long implementation cycles. • But AI innovation is moving too fast to only ship 1x in 3 months. • Companies need to adopt a rapid experimentation mindset. 3) You CAN move fast by investing in composability. • An API-first approach allows you to ship outside core infrastructure. • Previously, all new feature build happened in tools like Salesforce. • You’re constrained by technical debt, dependencies, and more. • Now, you can deploy AI solutions in isolation. • An app that communicates to other systems via API is relatively low risk. Realistically, this approach will make most Biz Tech teams uncomfortable. Rapid experimentation historically led directly to scalability issues. But this is the default way of operating for core Product teams. A few ways they get it right without leaving a wake of technical debt: 1) Use MVPs with clear scope • Ship measurable slices of value to learn, not solve a whole problem up front. 2) Invest in composability • Every test is built with future modularity in mind - winning ideas can scale. 3) Leverage Users for Research • Stakeholders & Users are a source of insights, not requests. • It’s the old Henry Ford quote: “If I asked people what they wanted, they would have said faster horses.” 4) Document Assumptions • Experiments have clear hypotheses - learn from every test, even if it fails. GTM Systems teams have the opportunity to lead innovation like never before. AI is delivering the much-needed attention and investment in this function. And for the first time, they are less constrained by stakeholder requests. These teams can finally DRIVE strategy, not just support it. But success will depend on their ability to embrace this new approach. __ #AI #GTM #CRM

Your engineers should annoy your PMs (I say this now as a product leader). As a senior product manager, I launched a new onboarding flow that boosted trial conversions by 25%. I was riding high on success…and then an engineer on my team suggested removing an extra step. Sure, it would further reduce cognitive load and drive even more conversions, but I was so in love with the design of the onboarding flow that I got annoyed. I pushed back—despite clear data! In the end, that engineer (rightfully) sidestepped me, ran the experiment, and proved me wrong. My self-indulgence almost cost our team another win. Now, as a product leader, I can see that customer- and data-centric engineers who help define product are a product manager’s gold mine. They enable PMs to scale out of the day-to-day and drive more impact across the organization. Leading tech companies and high-growth startups already encourage engineers to act like PMs. With AI making customer, competitor, and market insights more accessible, product-centric engineering will soon be standard everywhere. Product leaders, here’s how to embrace and empower these cross-functional teams: 1️⃣ Foster a culture where engineers, designers, and customer success teams don’t just share ideas, but actively shape product definitions. This allows PMs to act like air traffic controllers rather than pilots, guiding multiple “flights” simultaneously so more initiatives can land successfully. 2️⃣ Provide the right tools broadly—including direct access to customer feedback and data—so every role can make informed recommendations. 3️⃣ Encourage PMs to delegate and scale beyond their core responsibilities, taking on broader, more cross-functional work while peers step up with product insights. Your PMs and your organization as a whole will benefit. How have you encouraged your PMs to scale themselves? #productmanagement #productleadership