Why we need technology economists

The advent of AI is precisely why organizations need technology economists, not just IT finance professionals.

IT finance is primarily concerned with budgeting, accounting, cost allocation, depreciation, chargebacks and financial reporting. These disciplines remain important, but they assume a relatively stable relationship between technology spending and business outcomes. AI breaks that assumption.

Technology economics asks a fundamentally different question: How do technology investments create, destroy, shift or delay economic value?

AI introduces a set of economic dynamics that traditional IT finance was never designed to evaluate.

AI creates non-linear economics

In traditional IT, spending $10 million typically produced a somewhat predictable capacity increase or operational improvement.

With AI, a $10 million investment might generate $100 million in value. It might generate no value at all. It could increase costs while appearing successful. It could also create strategic advantages that do not show up in financial statements for years.

A technology economist studies the relationship between technology inputs, organizational capability, productivity outcomes and economic value creation.

IT finance largely records the spending.

AI changes the economics of labor

AI is not merely another technology platform. It acts as a form of digital labor.

Organizations now face questions such as:

• Should work be done by humans, AI, automation or a combination?
• What is the marginal cost of an AI-generated transaction versus a human-generated one?
• How does AI affect productivity elasticity?
• When does AI create labor substitution versus labor augmentation?

These are economic questions, not accounting questions.

AI simultaneously creates technology inflation and deflation

A fascinating paradox is emerging: AI can reduce costs in some areas while dramatically increasing costs elsewhere.

For example, fewer coding hours. More GPU costs. Lower service desk costs. Higher cybersecurity costs. Reduced consulting expenses. Increased data management expenses.

Technology economists study entire economic systems and value chains.

IT finance often sees only line items.

AI requires measuring economic outcomes, not technology outputs

Historically, organizations measured projects delivered, systems implemented, budgets achieved and uptime percentages.

The AI era requires measuring:

• Revenue generated
• Margin improvement
• Risk reduction
• Productivity gains
• Decision quality improvement
• Time-to-market acceleration
• Innovation capacity

Technology economists focus on these outcome measures.

This is one reason why AI performance measurement frameworks, including AI-focused balanced scorecard approaches, are becoming increasingly important.

AI introduces massive opportunity costs

One of the largest AI risks is not technological failure.

It is investing in the wrong AI initiatives.

A bank might spend $50 million building an AI solution that saves $5 million annually while ignoring another opportunity that could have generated $500 million in new revenue.

Technology economics focuses on capital allocation efficiency, opportunity cost, marginal returns and portfolio optimization.

These concepts sit outside traditional IT finance.

AI makes technology a strategic production function

Historically, technology supported the business.

Increasingly, technology is the business.

In many industries, AI determines customer experience, operating efficiency, innovation speed and competitive advantage.

Technology is becoming a primary production factor alongside labor, capital and natural resources.

Organizations therefore need experts who understand the economics of technology as a production asset.

AI creates new forms of technical and economic debt

Many organizations are deploying AI rapidly without understanding:

• Long-term infrastructure costs
• Model maintenance costs
• Data quality costs
• Governance costs
• Security costs
• Regulatory costs

A technology economist examines the total lifecycle economics.

The cheapest AI solution today may become the most expensive solution over the next decade.

Why this matters

The central challenge of the AI era is no longer “Can we build it?”

The challenge is, “Should we build it, where should we deploy it, what value will it create, what risks will it introduce and what is the optimal economic allocation of technology capital?”

Those are technology economics questions.

IT finance professionals are essential for controlling and reporting technology spending.

Technology economists are essential for determining whether that spending creates sustainable economic value.

As AI becomes embedded into every business process, the organizations that outperform will not necessarily be those with the biggest AI budgets. They will be those that best understand the economics of technology itself — how AI, data, infrastructure, labor, risk and innovation combine to create measurable business value. That is the domain of technology economics.