Every tech CEO is making the same mistake. They're cutting services to fund AI. They're racing toward a cliff.

At TSIA World ENVISION, J.B. Wood, President and CEO of TSIA, stood before 500 technology executives and told them something they didn't want to hear: "You're all wrong." And Wood wasn't referring to just one thing, but to many: AI eliminating services, startups having the advantage, and what it takes to make money in the AI era.

"AI startups can afford to be unprofitable," Wood said. "You can't." That single statement exposes the trap swallowing the technology industry whole. While established providers slash service teams to fund AI initiatives, they're destroying the only capability that makes AI profitable. The irony is brutal: AI doesn't reduce the need for services—it multiplies it.

Welcome to AI Economics, where everything you thought about profitability just became obsolete.

Key Takeaways

• AI is changing the economics of technology, not just the technology itself. Companies that fail to adapt their pricing and service models will lose margins and market relevance.
• Services are not disappearing—they’re becoming your most significant advantage. Success depends on reinvesting automation savings into new service capabilities that make AI consumable and profitable.
• Incumbents can win the AI race. With domain expertise, global scale, and customer trust, traditional tech companies are still best positioned to lead—if they evolve now.

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The Three Lies Killing Your Business

Lie #1: AI Eliminates Services

What everyone believes: AI automates human work. Therefore, we can cut staff and bank the savings.

What's actually happening: Every successful AI deployment requires more human expertise, not less. Data scientists to prepare training sets. Integration specialists to connect AI to existing workflows. Adoption engineers to ensure users actually change behavior—and model operations teams to monitor drift, bias, and compliance.

Early AI pilots are failing at alarming rates—not because the technology doesn’t work, but because companies don’t have the services infrastructure needed to deploy it effectively. Poor data quality, flawed integrations, untrained users, and undefined outcomes aren’t new challenges—they’re classic service problems, now magnified by the complexity of AI.

The reality check: AI isn't the end of services. It's the era of services. The companies that understand this will own the next decade. 

Lie #2: Startups Will Win the AI Race

What everyone believes: AI-native startups are lean, fast, and unencumbered by legacy systems. They'll run circles around incumbents.

What's actually happening: Venture capital is betting billions on a fantasy. Yes, startups have simple codebases and agile teams. But they lack everything that matters: customer trust, domain expertise, global infrastructure, and the ability to operate at scale profitably.

Here's the asymmetry nobody talks about: Startups can burn cash for years chasing growth. You can't. But that's not a weakness—it's your strategic advantage.

"The advantage is the incumbents’ to lose," Wood declared, and he's right. You already have:

• Domain specialists who understand how customers actually create value.
• Existing relationships with buyers who trust you to deliver outcomes.
• Scale infrastructure that startups will spend a decade trying to build.
• Profitable operations that can fund R&D without begging for another funding round.

The race isn't startups versus incumbents. It's incumbents versus themselves—specifically, whether they'll sabotage their own advantages by cutting the experts who know how to turn AI into customer outcomes.

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Lie #3: AI Sells Itself

What everyone believes: AI's capabilities are so obvious, so transformative, that customers will adopt it immediately. Just ship the technology.

What's actually happening: Without services, AI fails 95% of the time. Every technology wave promises to "sell itself." Hardware didn't. Software didn't. Cloud didn't. AI won't either.

Services fill the gap between AI's potential and realized value—the unglamorous work of making technology actually function inside a customer's messy reality. That's why Wood emphasized a fundamental shift: when your AI makes decisions inside your customers' businesses, you share responsibility for the outcome.

This changes everything about how you organize, sell, and deliver technology. Product teams can't work in isolation. Services can't be an afterthought. Customer success can't be reactive; it's a call center.

The companies winning in AI Economics have already reorganized around Outcome Product Managers who package solutions, define KPIs, and ensure measurable results. They've embedded services into the sales conversation from day one. They've instrumented value tracking across the entire customer journey.

They've stopped selling technology and started delivering outcomes. Because in AI Economics, that's the only thing that renews.

Related: Pricing-Led Transformation Under AI Economics

The New Service Portfolio

If you accept that services are mandatory for AI profitability, the next question is obvious: which services?

Wood outlined what an AI-era portfolio needs to succeed:

• AI Readiness & Data Services (ARGS): These services establish the foundation—managing data quality, building governance frameworks, and creating preparation workflows that ensure reliable inputs for AI systems.
• Value Optimization Services (VOS): AI models drift, develop bias, and degrade in performance over time. Monitoring model health, detecting anomalies, retraining systems, and maintaining compliance are essential. This isn’t traditional IT operations—it’s a new discipline that requires data scientists and domain experts working side by side.
• Outcome-Oriented AI Services (OOAS): Perhaps the most critical—and most often overlooked. These services define what success looks like, align workflows to capture value, and validate that AI delivers on its promises. They track KPIs, prove ROI, and ultimately determine whether customers renew or walk away.

Here's the trap most companies fall into: they attempt to automate their existing services (support, implementation, monitoring) and declare victory. That's not AI Economics—that's just cutting costs.

AI Economics demands you automate the commoditized work and then reinvest every dollar saved into building these new capabilities. Automation isn't the endgame. It's how you fund the services that make AI profitable.

Wood was explicit: "Keep the experts who understand customers and how to achieve value from your technology." The companies cutting their domain specialists to hire more data scientists are making a catastrophic mistake. You need both.

Related: Why Advanced Services Are Defining the Next Era of AI

The Real Profitability Formula

AI Economics rewrites the profit equation. In the old model, profitability came from cutting costs—automate everything, reduce labor, and ship more software to boost margins.

In the new model, automation targets commodity work. The savings are then reinvested into outcome-driven services that deliver measurable value to customers. That’s where the real margin expansion happens.

The difference is subtle but fatal if you miss it. Most companies are stopping at step one. They're automating, cutting costs, and watching their margins temporarily improve—while destroying their ability to deliver successful AI to customers.

Meanwhile, their pricing models are collapsing. Per-user and per-device models die when AI helps customers achieve more with fewer people and fewer machines. Per-transaction models crater when AI automates transactions.

If your pricing still measures seats, licenses, or units, AI will erode your revenue faster than you expect.

The solution is simple: price for outcomes—not effort. Not hours. Not users. Not API calls. Price for results. For tangible business impact. For the real value your customer captures.

This is why services become your competitive moat—because you can't price for outcomes unless you can measure, prove, and guarantee them. That requires a services infrastructure that most companies haven't built yet.

Related: The AI Services Era: Why Services Are Now Your Greatest Advantage

What Happens Next

In 18 months, the AI Economics winners will be decided.

They won’t be defined by who built the most advanced models, raised the most funding, or shipped the most features. They’ll be defined by who built the service capabilities that make AI reliably profitable for customers.

The companies that win will have made three key moves:

1. They automated, then reinvested: They used AI to cut costs in commoditized areas—support, monitoring, basic implementation—but reinvested those savings into building ARGS, MOS, and OOAS capabilities.
2. They repriced for outcomes: They moved away from labor- or user-based pricing and adopted models tied directly to customer value. Because they instrumented ROI from day one, they can prove the impact.
3. They productized what works: Instead of selling one-off AI projects, they turned successful deployments into repeatable, scalable solutions—proven outcomes packaged as productized services.

The companies that fail to make these moves? They’re the ones VCs and startups will pick apart—not because the newcomers have better technology, but because the incumbents will have dismantled their own advantages.

Margins will vanish. Pricing power will erode. Customers will defect to competitors who can deliver outcomes, not just ship software. The advantage is still yours to lose. You already have what they don’t: domain expertise, customer relationships, profitable scale, and trust. But only if you stop cutting the very capabilities that make AI valuable—and start rebuilding for the era of services.

J.B. Wood didn’t take the stage to inform. He took it to warn. The race has already begun. You’re either building services-led AI profitability—or you’re already dead. You just haven’t stopped moving yet.

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FAQs

What exactly is “AI Economics”?

AI Economics refers to the new financial and operational models emerging as AI changes how technology is built, delivered, and monetized. It examines how AI shifts value creation from products to services, and from features to measurable outcomes.

Why are services becoming more important in the AI era?

Because AI increases complexity, it requires expertise in data quality, adoption monitoring, model optimization, governance, and ethics. Services bridge the gap between potential and value—ensuring customers actually achieve the outcomes AI promises.

What’s the first step for established tech companies?

Start by mapping your current services portfolio against your AI roadmap. Identify where automation can create savings, and redirect those resources into building AI-specific service capabilities that accelerate adoption and prove value.