One Must-Read Article

AI as Your Revenue Engine

Moving Beyond the Cost-Cutting Trap

In Andreessen Horowitz’s Big Ideas 2026 report, one message is easy to miss: the biggest AI gains won’t come from working faster—they’ll come from working differently.

Too many companies are focused on automation. The companies pulling ahead are looking at their business differently—using AI to reinforce the model itself, not just speed up the work inside it.

Alongside the other Big Ideas (in my previous newsletters) —data quality as a growth constraint and operational discipline as a competitive edge—this shift re-frames how leaders should see AI right now.

Most companies ask the wrong question about AI. “How can this help us cut costs?”

The better question is simpler—and far more powerful: “How can this help us make more money?”

The best AI implementations don’t just automate tasks. They strengthen the economics of the business itself. That distinction is quickly becoming the line between companies that scale and companies that stall.

The Cost-Cutting Trap

Here’s a familiar scenario.

Consider a $5M services business. Someone pitches an AI tool that automates 30% of their administrative work. The math looks good: $150K in potential savings, $30K in software costs, net $120K benefit. It’s implemented.

A year later, they’ve saved time—but not actual headcount. The people who used to do admin work now do different admin work. Margins improve by a point or two.

Meanwhile, a competitor who went a different direction is pulling ahead.

They optimized for efficiency instead of effectiveness. They made the model slightly better instead of fundamentally stronger.

What “Strengthening the Business Model” Means

Consider a contingency-based law firm—paid only when it wins.

AI is used to predict case success before resources are committed. The firm selects better cases upfront. The lawyers aren’t working faster. The hit rate of the business improves.

They win more cases, avoid wasting effort on weak ones, serve more clients with the same team, and generate more revenue per lawyer. That’s not cost reduction – it’s business model reinforcement.

Four Ways AI Strengthens the Business Model

1. Improving Selection

A Midwest consulting firm struggled with inconsistent profitability. Some clients were great. Others were disasters.

They trained AI on project history—financials, email sentiment, change orders, timeline adherence, and team feedback.

The system identified client characteristics that reliably produced higher margins. During sales, prospects were scored before proposals were finalized. High-risk work was declined or repriced.

Result: A 40% improvement in project profitability—without changing how the work was delivered. They didn’t work differently. They picked better.

2. Optimizing Allocation

A $12M fabrication shop had a chronic quoting problem. Simple jobs were overpriced and lost. Complex jobs were under-priced and unprofitable. Only two people could estimate accurately.

They trained AI on ten years of job data—pricing, costs, complexity, photos, and outcomes. Junior estimators could now produce senior-level quotes.

Result:
• 25% more quote volume
• 15% higher win rate on profitable work
• Faster response times became a competitive advantage

The constraint wasn’t labor. It was judgment—and AI helped scale it.

3. Personalizing at Scale

A corporate training firm delivered the same curriculum to every client with light customization. Solid, but undifferentiated.

They used AI to analyze each client’s industry, challenges, learning preferences, and desired outcomes. Customized curricula, examples, and exercises were generated for each engagement. Delivery stayed human. Preparation became AI-assisted.

Result: Prices increased by 30% while satisfaction improved. The business shifted from volume to value—without increasing delivery costs.

4. Expanding the Addressable Market

A design firm did excellent work but could only serve larger clients. Early-stage work required too many hours to be profitable at lower price points.

AI was used for initial concepts and iteration. Designers curated and refined instead of starting from scratch, cutting early-stage work by 60%.

Result: A $15K service tier was launched. Within 18 months, it represented 40% of revenue—almost entirely incremental growth.

The Pattern: Compound Advantages

Across every example, AI doesn’t just save time. It creates compounding advantages.

Better selection improves future outcomes.
Better pricing improves margins and win rates.
Better execution attracts better clients.

Cost savings are one-time improvements. These are flywheels.

The Leadership Pattern

This isn’t an IT initiative. It’s a leadership sequencing problem.

1. Identify the value constraint
   What limits your ability to win more often, charge more, or scale profitably?

2. Map the hidden intelligence
   What do your best people know that doesn’t scale? What patterns exist in your data that no one has extracted?

3. Start with augmentation
   Use AI to make your best performers meaningfully more effective—not replaceable.

4. Measure business outcomes
   Track revenue per employee, win rates, client quality, and lifetime value—not just productivity.

5. Reinvest the gains
   When AI improves outcomes, reinvest in deeper capability and accelerate growth.

Here’s My Take

Efficiency plays are being commoditized. Everyone has access to the same automation tools.

Business model reinforcement is still wide open. It requires understanding how value is created, proprietary data, and operational discipline. Your competitors can buy the same tools. They can’t copy how you see what actually drives value.

Don’t ask:
“How can AI make us more efficient?”

Ask:
“How can AI make our business model more profitable, scalable, and defensible?”

The first leads to incremental improvement. The second changes your odds of success. The differentiator won’t be who adopts AI first. It will be who uses AI to fundamentally improve the math of their business.

Next week: The Factory Mindset—how manufacturing discipline is being applied to knowledge work using AI.