Leading Products in the AI Era: Staying Ahead When the Ground Shifts Every 60 Days

Leading a product in the age of AI means operating in constant uncertainty. Your job is no longer just to build great features—it’s to see around corners, separate signal from noise, and rally your team to move at the speed of AI’s evolution. Every product decision is now a bet: will AI make this capability obsolete? Is this capability defensible, or will open-source models commoditize it? Are we leveraging AI to create real customer value, or just adding complexity?

I feel this shift firsthand every day. I’m constantly testing out new AI tools, spinning up prototypes with Bolt to see what’s actually possible, and experiencing the latest AI-powered products on Product Hunt. I read everything I can—technical blogs, research papers, deep dives from builders who are pushing boundaries. Because it’s not just about keeping up; it’s about developing an instinct for where things are headed before they arrive.

Some will lead. Some will follow. The difference comes down to how well you adapt to an environment where the ground shifts every 60 days.

Seeing Around the Corner

AI doesn’t evolve gradually—it leaps forward in unpredictable bursts. A roadmap built six months ago is already outdated if it didn’t anticipate the latest breakthroughs. The only way to stay ahead is to build optionality into your plans. Shorter cycles. Running two roadmaps at once—one for what's possible today, another for what AI will make possible soon.

If AI can already do what you planned to build, your roadmap needs a reset. If you’re waiting for things to stabilize, you’re already behind.

AI Gimmicks vs. Real Customer Value

The fastest way to burn engineering time? Shipping AI features that look impressive but don’t solve a real problem. AI-generated content, chatbots, predictive analytics—none of it matters unless it drives revenue, retention, or operational efficiency. A simple test: if you removed the AI label, would customers still pay for it? If not, it’s a gimmick.

AI should make workflows disappear, not create new ones. It should automate real pain points, reduce friction, and help customers make better decisions, faster. If your AI feature requires a user manual, you’re doing it wrong. A lesson learnt hard way - the expectation to change user behavior now that you have AI is a huge mistake, don’t make things hard for them to discover , ensuring intuition led experiences is critical

The New Reality of Product Roadmaps

AI doesn’t just change what gets built—it changes how product teams operate. The old model of stable problem spaces and long-term planning no longer works. What seemed impossible to automate last year is now trivial. What was cutting-edge six months ago is now a commodity.

The best product teams don’t just plan for AI advances; they expect them. Shorter planning cycles. Multiple tracks running in parallel—one for what’s possible today, another for what’s likely to emerge soon. A ruthless willingness to kill projects that don’t move the needle because AI has already made them obsolete. The roadmap isn’t a fixed path—it’s a system that evolves as fast as AI itself.

First Principles Thinking Over AI Hype

AI can hallucinate, misfire, and introduce unpredictability. That’s why product leaders can’t rely on models to do their thinking for them. The foundation must be first principles. What problem are we solving? What’s the simplest, most reliable way to deliver value? AI is a means to an end, not the starting point.

Just because AI can do something doesn’t mean it should. Do users actually want full automation, or do they need a better assistive workflow? Does AI improve decision-making, or does it introduce unnecessary complexity? The best product teams ask these questions before a single line of code is written.

AI Isn’t a Feature—It’s an Organizational Capability

AI is not a product add-on. It’s an entire capability that needs to be built into how a company operates. That means data pipelines that aren’t a mess, feedback loops that continuously improve models, and engineering teams that understand how to balance AI complexity with usability.

It also means understanding AI’s cost structure. Every AI-powered feature has a real-time operational cost. Compute, model training, API calls—it all adds up. If your AI isn’t delivering more value than it costs to run, it’s a liability. The smartest companies bake AI economics into product decisions early, not after launch.

Trust and AI UX

AI adoption isn’t just about functionality—it’s about trust. Users don’t care about model architectures or neural networks. They care about whether AI helps them without failing unpredictably. One hallucination, one bad decision, one broken experience—and trust evaporates.

That’s why AI UX must be built around predictability, control, and explainability. Show users why AI made a decision. Give them the ability to override it. Make it assistive, not intrusive. AI that confuses or frustrates users isn’t a feature—it’s a risk.

Monetizing AI Without Giving It Away

AI shifts the way software is priced. Unlike traditional SaaS, AI-powered features consume resources every time they’re used. That changes the economics of pricing. Some companies will bundle AI into existing plans, others will charge for premium capabilities, and some will move toward usage-based pricing, and some towards Outcome based.I’ve been tracking companies that have successfully charged on outcomes, not just usage—because that’s where the real AI pricing frontier is.

If AI helps customers sell more, close deals faster, or automate work, there’s a price tag for that.

Measuring AI Success Beyond Adoption

Traditional product metrics don’t work for AI. Adoption alone doesn’t tell you if an AI feature is useful. Instead, track real impact. How often do users correct AI decisions? Is it saving time or adding friction? Are outcomes improving?

A good AI feature isn’t just used—it’s trusted. If you’re not tracking how often users override AI suggestions, how much time it actually saves, or whether it drives better decisions, you’re flying blind. AI is never finished. If you’re not constantly iterating, it’s getting worse.

The AI race is unforgiving. Every breakthrough compresses competitive timelines. Companies that integrate AI quickly will dominate. Companies that hesitate will struggle to catch up. Speed matters more than ever—not just speed of execution, but speed of learning, adapting, and iterating.

The best AI-first companies don’t just react to change; they force it. They run constant experiments. They launch before the tech is perfect, then refine in real-time. They create new AI-driven moats before competitors even see the opportunity. AI isn’t just a battleground, it’s an arms race.

Good product leadership has always meant anticipating trends, separating hype from real value, and rallying teams around the right bets. But AI changes the stakes. The ones who get it right will define the next generation of SaaS. The ones who don’t will spend the next decade reacting (if they get to stay around)