Your AI Strategy Needs a CFO

AI was supposed to lower the cost of getting work done.

That was one of the simplest and most powerful assumptions behind the entire AI boom. If a model could write code, answer customer questions, analyze documents, or automate repetitive tasks, companies would need fewer hours to produce the same output. Productivity would rise. Costs would fall.

For the past two years, most discussions about AI focused on whether the technology could perform the work. Much less attention was paid to a different question:

What happens when the bill arrives?

That assumption is starting to face its first real test.

Over the past several months, a growing number of technology companies have begun discussing a challenge that received far less attention during the early AI boom. As AI systems move from experimentation into daily operations, costs are rising faster than many organizations expected.

The issue is not simply model pricing. It is the accumulation of everything surrounding the model: agent loops, retrieval systems, monitoring, evaluations, memory layers, infrastructure, governance, and thousands or millions of API calls running continuously across the business. Individually, those costs can seem manageable. At scale, they become something else entirely.

For some organizations, the total cost of operating AI systems is beginning to rival the labor costs those systems were originally expected to reduce. That does not mean AI is failing. It means the industry is entering a different phase.

The first phase of AI adoption rewarded speed. Companies rushed to deploy copilots, automate workflows, and integrate models into products. The goal was learning. Teams wanted to understand what the technology could do before competitors did.

That approach made sense. When a technology is changing rapidly, experimentation creates valuable information. Organizations that waited for perfect clarity risked falling behind.

But experimentation and operations are different disciplines.

An AI prototype that saves a few hours a week looks very different from an AI system supporting thousands of users, running around the clock, and touching critical business processes. As AI becomes part of normal operations, the questions start to change.

Can we afford to run this at scale?

How much value is it creating?

Which workflows justify the cost?

Those are not technical questions. They are economic ones.

The most important shift is not in model capability. It is in how AI is being used.

Early deployments were often simple. A chatbot here. A coding assistant there. A few experiments inside product teams.

Today's AI systems look very different. Companies are building agent workflows that operate continuously. Models call other models. Agents trigger additional actions. Systems retrieve information, verify outputs, evaluate performance, and maintain memory across interactions.

The result is that AI increasingly resembles infrastructure rather than software.

And infrastructure has economics.

Cloud computing went through a similar evolution. Early adopters focused on flexibility and speed. Eventually, organizations discovered that cloud spending required governance, optimization, and accountability.

The same pattern may now be emerging around AI.

For most of the current AI cycle, competitive advantage was largely tied to adoption. The companies deploying AI fastest often appeared to be winning.

That may not remain true.

As costs increase, efficiency starts to matter as much as capability. A company that generates the same business outcome using half the tokens, half the inference cost, or half the infrastructure may ultimately have a stronger advantage than a competitor running larger and more expensive systems.

That shifts the conversation considerably. Instead of asking which model is smartest, leaders may increasingly ask which workflow delivers the best return on investment. Instead of measuring activity, they will need to measure outcomes.

The challenge becomes generating more business value per dollar of AI spend.

That is a very different optimization problem.

The companies moving fastest are already beginning to treat AI spending the same way they treat cloud spending. They are monitoring usage, evaluating costs, measuring outcomes, and building processes to understand where AI creates real value.

Some are experimenting with model routing strategies that send simpler tasks to cheaper models while reserving frontier models for higher-value work. Others are exploring open-source alternatives or hybrid architectures that reduce dependency on expensive API calls.

The goal is no longer simply deploying more AI. It is deploying AI efficiently.

That requires a different mindset. Product teams, engineering leaders, finance teams, and operations leaders increasingly need to work together because AI decisions now affect both technology strategy and operating margins.

This issue of Redeployed is brought to you by Tecla: As AI becomes part of everyday operations, the challenge is no longer simply adopting the technology. Organizations need people who understand the tradeoffs between cost, performance, infrastructure, and business outcomes, and they increasingly need help building and running those systems, not just staffing them. The teams moving fastest are pairing AI expertise with operational discipline, bringing in talent that can optimize systems, manage complexity, and build sustainable AI-driven workflows. Tecla helps AI-driven companies hire elite AI-ready talent, build AI, and manage it in production, all through one vetted network across the Americas.

The biggest risk may not be that AI fails. It may be that AI succeeds in ways organizations are not prepared to manage.

Many companies still lack clear visibility into the total cost of their AI systems. Teams deploy agents independently. New workflows appear across departments. Usage grows gradually until the cumulative spend becomes difficult to explain or control.

This creates a form of operational debt. The systems generate value, but the economics become increasingly difficult to understand.

There is also a risk that organizations focus on AI activity rather than business outcomes. More prompts, more agents, and more automation can create the appearance of progress even when the underlying economics remain weak.

The important metric is return on investment, not token consumption.

This shift is changing what organizations need from technical teams.

The demand is no longer only for people who understand models. Companies increasingly need engineers and leaders who understand the economics surrounding those models.

Cost optimization, model selection, orchestration design, infrastructure efficiency, and workflow architecture are becoming critical skills. Organizations that build those capabilities early may gain an advantage that is difficult for competitors to replicate.

Once AI becomes infrastructure, managing it efficiently becomes just as important as deploying it. The companies that learn how to generate more output per dollar of AI spend will have more room to experiment, scale, and invest in future innovation.

The companies that benefit most from the next phase of AI will not necessarily be the ones running the largest models or deploying the most agents.

They will be the ones that understand the economics best.

They will build visibility into AI costs. They will measure outcomes alongside usage. They will invest in governance, optimization, and operational discipline. And they will make AI spending accountable in the same way they manage every other strategic resource.

Because the next competitive advantage in AI may not come from intelligence alone.

It may come from learning how to use that intelligence profitably.

Connect With Other Technology Leaders

If you want to connect with other technology leaders having real conversations about AI and how it is changing business, check out GILD Curated Circuit.

More to come…

✔️ I asked 4 executives how they measure AI ROI. None started with AI tokens. — Business Insider

✔️ Gartner Forecasts Worldwide AI Spending to Grow 47% in 2026 — Gartner

✔️ Nvidia VP Says AI Costs ‘Far’ More Than Human Employees — Entrepreneur