Table of Contents

• Best Reads of the Week

• AI-native vs AI-enabled: Why not both?

  • The False Binary: No choice is also a choice.

  • A spectrum of choices

  • Why incremental AI compounds

  • The market is king: When incremental isn’t enough

  • From Incremental to transformational: A short play …

  • It's a Ladder, Not a Fork

Best Reads of the Week

• The Engineer’s Dilemma - Are you solution looking for a problem? A perfect illustration of being solution first. A strong narrative, a point of view, an enemy is as important as having a technically sound product.

• We Know We’re Being Manipulated by AI—Now What? Not only a grim obvservation but a method to “reclaim you feed, rewire your brain”

• The Barrier to Entry to Be Successful In Any Field Is Painfully Lower Than You Think Apparently I needed a little pick me up. Check this out if you want a zest of extra-motivation.

• A taxonomy for next-generation reasoning models It’s hard to find technical topics treated in a non-technical way (or am I delulu and is it too technical already?) but I enjoyed the read on what the focus will be on the next-generation reasoning models.

• Why AI startups are blowing past revenue milestones that old-school SaaS could only fantasize about - I imagine as you reading an AI Product newsletter, you would be interested by this one too.

AI-native vs AI-enabled: Why not both?

You have an existing product, a profitable business, and the current discourse is debating AI-native products versus AI-enabled approaches. You're stunned.

You're caught between existing customers, legacy systems, and a somewhat limited innovation budget. If you're C-level, you're feeling the heat from investors or the market through AI-native companies eager to disrupt your space.

You have to choose a side… or do you?

The False Binary: No choice is also a choice.

AI is a disruptive technology, this much shouldn’t surprise you.

Adding AI in your current workflows, chasing “incremental improvements”, feels safe but isn’t really defensible. Can you afford to fall behind AI-native competitors?

Creating an AI-native product feels visionary, but what about your existing revenue? Your current customers? Can you afford to alienate them with radical workflow changes?

While you’re debating, competitors are executing, some going native, others choosing enablement, all moving forward.

No choice is also a choice. But if you want to win long-term, you don’t have to pick the “right” approach, you can hedge your bets by sequencing both approaches strategically.

A spectrum of choices

If you are coming from a more technical background, you may think that AI-enablement vs AI-native is all about product features. It’s also about positioning:

Digits is clearly an AI-native accounting platform, reimagining accounting workflows to challenge industry giants. They’re all about being all-in on an end-to-end accounting platform for the AI era, while competing accounting programs “are simply throwing numbers into broad-based LLMs” to quote their CEO.

A company like Quickbooks focuseson adding AI to existing workflows, improving familiar processes and retain customers.

But the smartest established companies are using AI-enablement as market research: Which workflows should only be optimized? Which workflows do users actually want transformed?

Notion gradually transformed from the “all-in-one worspace for your notes” to “The AI workspace that works for you”

If you are a new-ish company, going AI-native will be easier. But established companies are leveraging existing moats to develop both defensive AND offensive capabilities; providing better experience in current workflows while capturing data and insights for future AI-native experiences.

Why incremental AI compounds

AI-native is flashy but if you can’t go back in time and start an AI-native company, you still have cards to play.

While it depends of you positioning, your market and blablabla, here are a few advantages of incremental AI:

• Risk mitigation: Early wins build stakeholder confidence for bigger bets

• Data flywheel: Enhanced workflows generate better training data for future native experiences

• Talent development: Build internal AI expertise without betting the company

• Market intelligence: Live feedback on which workflows users actually want transformed

• Competitive positioning: Retain customers while building next-generation capabilities

If you play your cards right, you’ll have enough defensive capabilities to start making more transformative bets. You might even be the one acquiring AI-native startups burning cash in their unwanted transformation?!

The market is king: When incremental isn’t enough

Successful AI strategy requires knowing when to shift from optimization to transformation. Move too early and your risk alienating existing customers; move too late and you are playing catch-up to AI-native competitors who have transformed the market.

This list of triggers is a good start if you are looking for signal pushing you to the more transformative road:

• Behavior shifts: Users start working around your AI-enabled features

• Competitive pressure: AI-native competitor launch something 10x better, not just 10% better

• Market maturation: Your AI-enabled improvements hit diminishing returns while customer expectations continue rising

• Business model stress: Current architecture can't support the pricing or value proposition your market demands

• Internal radiness: You've built sufficient AI expertise and user insights to de-risk a native approach

Also a few leading indicators to track:

• Feature adoption flattening despite continued investment

• Customer churn to AI-native alternatives accelerating

• Sales cycles lengthening as prospects compare you to AI-native alternatives

• Internal teams requesting capabilities your current architecture can't support

From Incremental to transformational: A short playbook

Phase 1: Deploy AI-enabled features as market research

Focus on clear ROI wins: better recommendations, speed improvements, intelligent defaults. Small moves that advance the ball. Treat every experiment as market research for future transformation

Key Metrics: Feature adoption, efficiency gains, and user satisfaction scores

Phase 2: Identify and validate transformative opportunities

Your incremental AI investments give you advantages in data, customer insights, and the skillset needed for ambitious features. Use them.

Key metrics: User willingness to pay for AI-native prototypes

Phase 3: Build and launch your first AI-native experience

Your incremental AI investments give you advantages in data, customer insights, and the skillset needed for ambitious features. Use them.

Key metrics: New customer acquisition, expansion revenue, and competitive win rates

It's a Ladder, Not a Fork

If you have existing customers, cash-flow, and a validated market, you have the resources to de-risk AI transformation through incremental progression.

The existential threat isn't choosing the wrong path, it's either sticking purely to AI-enabled features without daring to dive into AI-native territory, or jumping straight into transformation without the talent and data to back up your ambitions.

One question to ponder: What's your first step? What AI-enabled feature could generate immediate ROI while collecting insights on which workflows your customers actually want transformed?

It’s a big question, I know. But it’s the right one.

More to explore:

• The Old Playbooks Are Dead — Time to Rewrite the Rules

• The Only Framework That Hasn’t Lied to You Yet

• Learning to Listen: The Art of Uncovering Demand for AI Solutions

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