$10M AI Revenue Surge: What’s Actually Bought

Originally published June 10, 2023 — excerpt from Medium (AI in 2024: The Future is …?)

In mid-2023, I documented an unprecedented market phenomenon that predicted the enterprise AI crisis we see today. A network of independent developers I track generated over $10M in AI contracts within just a few months — with an unexpected twist about who was buying.

The Unprecedented Revenue Data

The numbers were remarkable:

• Over $10M in total AI-related contracts;

• 30% of participants successfully selling AI solutions;

• Individual teams achieving seven-figure revenues;

• Project completion times: typically 2-3 months;

• Team sizes: mostly 1-2 person operations.

But the most surprising insight wasn’t the revenue scale — it was who was buying.

The Adoption Curve Inversion

Traditional technology adoption follows a predictable pattern: early adopters embrace new technology first, followed by early majority, late majority, and finally laggards.

AI adoption in 2023 inverted this pattern completely.

The primary buyers weren’t tech-forward companies. They were the most conservative industries:

• Insurance companies;

• Banking institutions;

• Healthcare organizations.

These are typically technology laggards — the last industries to adopt new solutions. But in 2023, they became the first buyers of AI services.

What They Were Actually Buying

The conservative industries weren’t purchasing AI tools or platforms. They were buying "bespoke AI integration" — custom solutions that their existing vendors couldn’t deliver.

Typical projects included:

• Document processing automation for insurance claims;

• Customer service augmentation for banking operations;

• Workflow optimization for healthcare administration;

• Data analysis capabilities for regulatory compliance.

Critical insight: Nothing sold was an asset or platform. Everything was custom integration work that existing enterprise vendors couldn’t provide.

Why Existing Vendors Failed

The conservative industries had urgent AI needs but faced a critical vendor capability gap:

Enterprise software vendors weren’t ready:

• Too slow to adapt AI capabilities into existing products;

• Bureaucratic approval processes prevented rapid development;

• Legacy architecture couldn’t support AI integration;

• Sales cycles too long for urgent AI demands.

Systems integrators couldn’t deliver:

• Lacked AI-native development expertise;

• Focused on traditional integration approaches;

• Required lengthy discovery and planning phases;

• Couldn’t provide rapid prototyping and iteration;

The Temporary Market Window

This created an unprecedented opportunity window where small, agile teams could outcompete established enterprise vendors.

Why small teams won:

• Speed: Could deliver solutions in months, not years;

• Expertise: AI-native development capabilities;

• Flexibility: Could adapt quickly to specific requirements;

• Cost: Lower overhead than enterprise consulting firms.

The irony: Conservative industries abandoned their traditional preference for established vendor relationships because they desperately needed AI capabilities immediately.

What This Predicted

This revenue surge was a leading indicator of the enterprise AI crisis we see today:

1. Massive demand for AI capabilities across all industries;

2. Critical vendor gaps in enterprise AI delivery;

3. Architectural problems preventing AI integration;

4. Relationship disruption as competence beats connections.

The conservative industries that bought "bespoke AI integration" in 2023 were essentially paying to solve the same architectural problems that later caused enterprise AI failures.

The Sustainability Question

I recognized this as a "temporary fluke" that wouldn’t last indefinitely. The window existed because:

• Urgent demand surge created immediate opportunities;

• Vendor preparation lag left capability gaps open;

• Technical barriers were low for initial AI implementations;

• Market education was minimal about AI integration complexity.

Eventually, enterprise vendors would adapt, demand would stabilize, and the opportunity window would close.

Lessons for Enterprise Strategy

For established companies:

• Urgent AI demand can override traditional vendor relationships;

• Capability gaps create competitive vulnerabilities;

• Small, agile competitors can temporarily outperform established partners;

• Architectural readiness determines implementation speed.

For vendors:

• Conservative industries will abandon partnerships for immediate capabilities;

• AI integration expertise becomes a competitive requirement;

• Speed of delivery matters more than relationship history;

• Technical competence beats marketing relationships.

The Consulting Opportunity

This revenue surge revealed exactly the market dynamic that created the current enterprise AI consulting opportunity:

Companies need AI capabilities urgently, but their existing architectures and vendor relationships can’t deliver them. This creates demand for consultants who can both diagnose why AI integrations fail and provide architectural solutions that make AI implementation possible.

The $10M surge proved that even the most conservative industries will pay premium rates for AI solutions that actually work — when their traditional vendors can’t provide them.

Key Takeaway

The 2023 AI revenue surge wasn’t just about money — it was a market signal about enterprise AI readiness. Conservative industries became early AI buyers because they faced urgent capability needs that their existing vendor relationships couldn’t satisfy.

This pattern predicted the wave of enterprise AI failures we see today, where companies spend millions on AI initiatives that deliver chatbots instead of business value.

The market opportunity exists for consultants who can bridge the gap between AI promise and enterprise reality — exactly what the independent developers were doing at seven-figure rates.

This revenue analysis directly informed my understanding of why enterprise AI integrations fail and what architectural solutions companies actually need.