Why most AI strategies fail and how to build one that wins
Every company claims to have an AI strategy now.
But let’s be honest — most of them are terrible.
Behind the flashy announcements and “we’re all in on AI” slogans, 90% of companies are either copying others blindly, building tech for tech’s sake, or launching pilots that never drive real value.
I’ve been fortunate to learn from some of the best — working alongside Miqdad Jaffer, whose frameworks shaped AI initiatives at Shopify, OpenAI, and Apollo. Together, we built (and broke) enough things to realize something important:
AI strategy is not just product strategy with a new coat of paint.
It’s fundamentally different. It demands a new kind of thinking — one that respects how AI systems grow, how trust is built with users, and how value compounds over time.
Here’s the battle-tested, field-proven framework we use.
Set Clear Objectives: Business Impact or Bust
At Shopify, Miqdad killed dozens of technically impressive AI projects.
Cool demos. Fancy models. Gone.
Instead, he doubled down on something seemingly “boring” — inventory management.
Why? Because that’s where merchants were bleeding money.
No business impact = No AI initiative.
Simple as that.
Lesson:
Start by identifying real pain points where human processes consistently break down — where mistakes hurt revenue, customer trust, or growth.
AI should solve meaningful problems, not just exist for headline value.
Understand Your AI Users: Trust Is Earned, Not Given
Building AI is not like adding a new button to your app.
Users don’t just use AI. They test it. Doubt it. Challenge it.
And only after repeated positive experiences do they trust it enough to rely on it.
Your job is to design for that trust journey.
Don’t assume instant adoption.
Think about how the system earns credibility, proves itself, and empowers users over time.
Empowerment, not replacement. That’s the mindset.
Identify Your AI Superpowers: Your True Moat Isn’t the Model
Not everyone has the same data signals, user context, or behavioral feedback loops.
That’s your moat.
The unique data and proprietary insights you can gather — and no one else can — are your advantage.
It’s not the model you fine-tuned.
It’s not the “agentic workflows” you stitched together.
It’s the compounding intelligence you own.
Build your product strategy around that, not the flavor-of-the-month AI architecture.
Build Your AI Capability Stack: Speed Beats Pride
In AI, slow perfection kills.
Imagine one team spends nine months painstakingly building their own LLM infrastructure.
Meanwhile, a scrappier competitor integrates OpenAI, ships a product, and captures the market.
Outcome?
The faster team wins, learns, iterates — and locks down users before you even launch.
Great AI product leadership isn’t about doing everything yourself.
It’s knowing when to build vs. when to leverage.
Pride doesn’t pay. Speed does.
Visualize Your AI Vision: Make the “After” Impossible to Ignore
Airbnb once worked with Pixar animators to storyboard their ideal user experience.
You don’t need a Hollywood budget today. Tools like Bolt, v0.dev, and Replit make it ridiculously easy to prototype “visiontypes” — visual, tangible versions of the AI future you’re creating.
When you visualize your AI strategy, focus on:
- Before vs. After: Make the “after” experience feel magical — something humans simply can’t replicate manually.
- Progressive Learning: Show how the AI system gets smarter over time with use.
- Human + AI Collaboration: Design workflows where AI enhances, not replaces, human effort.
If you can’t paint that picture, you don’t have a real AI vision yet.
Define Your AI Pillars: Bet Smart, Label Boldly
Your strategy should be a portfolio — a balance between quick wins and ambitious bets.
Structure it like this:
- Quick wins (1–3 months): Prove early value.
- Strategic differentiators (3–12 months): Strengthen your moat.
- Exploratory options (R&D): Build long-term leverage.
Label every initiative:
- Offensive: Creates new value.
- Defensive: Protects from disruption.
- Foundational: Unlocks future bets.
Without this clarity, your AI roadmap turns into a chaotic wishlist.
Quantify AI Impact: Model for Compounding Gains
If your AI financial models assume linear returns, you’re already wrong.
AI compounds.
Every user interaction trains the system, improves relevance, builds trust — and accelerates the value curve.
Even Sam Altman shared that adding something as simple as a “thank you” feature in OpenAI increased operational costs by millions.
Why? Because AI doesn’t stay static — it grows with usage.
Model that flywheel effect in your strategy.
Otherwise, you’ll consistently underestimate both the upside and the risks.
8. Establish Ethical Guardrails: Trust is Fragile
One biased result. One hallucination. One misuse.
That’s all it takes to destroy user trust — sometimes permanently.
AI needs strong ethical scaffolding:
- Bias mitigation during training.
- Guardrails during inference.
- Transparent fallback strategies.
Build safety into the system at every stage.
Trust isn’t a feature. It’s a precondition for survival.
Final Reflection: A Better AI Strategy Is Still Hard — But Possible
There’s no magic checklist for AI success.
The tech moves fast. The landscape shifts weekly.
But this much is true:
Building a great AI strategy demands discipline, humility, and ruthless clarity.
- Solve real problems.
- Move fast, but safely.
- Design for trust, not just adoption.
- Build around your data advantages.
- Visualize the future you want to create — and make it irresistible.
Most companies will stumble.
You don’t have to.
The question is: Will you build boldly or blend into the noise?
