AI Economics: What Happens After the Pilot Ends?

June 24, 2025

🕒 7 minutes

From copilots to AI agents, from task automation to full decision-making support—AI is moving fast. But while the headlines are filled with breakthroughs, boardrooms are facing a more practical question: What does all this actually cost? What is the ROI?

In fact, according to Gartner, by 2028, more than 50% of enterprises that have built large AI models from scratch will abandon their efforts due to costs issues in their deployments.

That’s the new frontier of AI economics.

The conversation in board rooms is no longer about whether to adopt AI but how to do it sustainably. And that means understanding not just the upfront investment, but the long-term operational costs, risks, and ripple effects across your business.

So, today we’ll unpack the real economic implications of scaling AI. Because AI isn’t just a line item anymore. It’s an operating model. And understanding its economics is key to staying ahead.

Spending Trends & Budget Models in the New AI Economics

So, AI is increasingly capturing resources at the highest levels of enterprise budgeting. But what’s actually happening behind those rounded-up IT numbers? We can identify three main trends in IT budgets regarding to the next AI economics.

1. Tech Budgets Are Rising—With a Spotlight on AI

According to a December 2024 report by BCG, IT budgets across North America and Europe are set to grow by 4.6% in 2025—up from 3.5% the previous year. But that top-line number hides what’s really happening beneath the surface: artificial intelligence is consuming a disproportionately large share of those increases.

In fact, ISG reports that AI accounts for nearly 30% of the incremental IT budget growth, as companies ramp up investment not only in experimentation, but also in large-scale deployments of AI models, copilots, and autonomous agents.

Gartner’s own forecasts show that total global AI spend will climb to $298 billion by 2027, growing at a compound annual rate of 19%. For context, this puts AI investment growth well ahead of other categories like ERP, infrastructure, or traditional cloud services.

AI economics are skyrocketing tech budgets

2. OpEx is the New Norm

One of the most profound shifts introduced by AI economics is the move away from capital expenditures (CapEx) toward ongoing operational expenditures (OpEx). Unlike traditional software or infrastructure projects, AI systems typically rely on cloud-native, usage-based architectures.

Whether it’s paying per token, per prompt, or per API call, most organizations are now budgeting for AI as a variable cost—not a fixed one.

IDC predicts that cloud spending alone will reach $1.3 trillion by 2025, and much of that growth is being driven by AI workloads, particularly large-scale inference and data processing.

But these costs aren’t always easy to forecast, while AI inference, prompt chaining, and real-time orchestration tools can undermine your budget. That’s why both CIOs and CFOs are now turning to FinOps frameworks—collaborative financial operations models that align technical resource usage with budget governance and business outcomes.

So, in short, AI doesn’t just need deployment plans anymore—it needs cost control levers built into every layer.

3. AI Pricing Is Now Behavior-Driven

Another trend of this new AI economics is quietly reshaping how budgets work: the rise of behavior-based pricing models for AI platforms. Traditional enterprise software followed a predictable path—licenses, subscriptions, maybe some usage tiers.

AI platforms like OpenAI, Anthropic, and Google Vertex now price based on inputs (tokens), model selection (GPT-3.5 vs. GPT-4), and even execution types (fine-tuned vs. zero-shot).

This means that every new AI feature—every customer interaction, sales automation task, or employee-facing agent—becomes a unit of cost. Gartner has noted that 9% of AI-related IT budget increases in 2024 were directly tied to token cost management and API metering tools. That’s not a rounding error—it’s a structural shift.

And as more enterprises move from pilot programs to production, they’re finding that costs scale nonlinearly. But let’s see it on an example to understand this better.

Let’s suppose that a retail company launches a $75K Proof of Concept (PoC) for an AI styling assistant. Once scaled to support 50K customers/month, inference fees hit $300K/year, fine-tuning adds $150K, and integrations with Shopify and Salesforce cost $400K.

Add compliance and monitoring, and the total climbs past $1M—far beyond the initial prototype budget. So, as you can see, the variables in AI economics could make a small detour in your budget planning scale into a huge technical debt problem.

From Licenses to Logic: A New Pricing Playbook

Enterprise software used to follow a clear pattern: license per user, or per seat. SaaS moved that to a subscription model—monthly or annual payments, with predictable tiers based on usage limits or data volume.

But AI economics has broken that mold.

Today, most AI services—especially those involving generative models or AI agents—operate on usage-based or consumption-based pricing. You’re billed by:

The number of tokens processed (input and output),

The type of model (e.g., GPT-3.5 vs. GPT-4 Turbo),

The number of API calls, or

The complexity of the task performed.

So, in our AI economics, cost now scales with behavior.

For example, a chatbot that handles 100 queries per day might be affordable. A customer service agent that integrates with Salesforce, generates emails, and queries three databases per response? That’s a different story entirely.

This is why many enterprises are starting to treat AI pricing models as cloud pricing: real-time, granular, and tied to operational levers like load balancing and routing.

The Rise of Outcome-Based AI Pricing Models

In parallel, some vendors—particularly in the enterprise AI agent and vertical AI space—are offering outcome-based pricing models. Here, customers pay only when the AI system delivers a business result.

For example, a marketing agent might be priced per lead generated, while a legal review tool might bill per contract classified.

In short, these AI pricing models appeals to CFOs and procurement teams because it aligns cost with value. But it also requires clear metrics and tight integrations to prove outcomes, which not all organizations are ready to support.

In fact, according to a McKinsey’s report, over 40% of enterprise AI buyers expect to adopt some form of outcome-based pricing in the next 12–18 months—especially in high-value, high-volume use cases like customer support, finance ops, and marketing automation.

Measuring ROI: Faster Isn’t Always Better

Now, while one of the the big promises of this technology is speed, AI economics don’t always reward velocity unless it’s paired with the right KPIs.

In the early stages of AI adoption, ROI is often measured by efficiency gains—time saved, headcount reduced, response rates improved. These are visible, easy to model, and good for short-term business cases.

But in scaled deployments, leaders are looking beyond simple productivity metrics. They want to know:

Did the AI system reduce churn?

Did it improve cash flow?

Did it generate new revenue, not just cut costs?

The bar is higher. And so is the complexity.

This is why leading enterprises combine operational ROI (cycle time, hours saved) with strategic ROI (customer outcomes, revenue impact) in their evaluations. They’re also building out cost-per-agent and cost-per-API-interaction models to establish more sustainable unit economics for AI.

Where the Money Actually Goes?

In traditional software projects, budgeting is a front-loaded process. You scope the work, estimate resources, secure funding, and run against a clear forecast. AI, especially in its agent-powered form, turns that logic on its head.

But the new AI economics introduces new cost drivers.

With usage-based pricing, unpredictable scaling, and model-specific cost structures, AI spend is often revealed in hindsight—not at kickoff. And that’s a problem for decision-makers tasked with controlling costs and forecasting ROI.

So, let’s break down where the real money goes with our cost drivers table.

Cost Driver	What It Is	Growth Curve	Risk Level
Model Access & Inference	Cost of using models like GPT (per token or call)	Grows fast as usage scales	High – Easy to overspend if not tracked
Infrastructure & Cloud	Compute and storage needed to run AI	Grows with data and traffic	Medium-High – Big jumps with heavy workloads
Integration & Orchestration	Connecting AI to tools (e.g., CRM, databases, APIs)	Steady growth with complexity	Medium – Costs add up in the background
Data Preparation & Quality	Cleaning and organizing data for training/tuning	Upfront + ongoing maintenance	Medium – Poor data = poor results
Governance & Compliance	Tools and staff to ensure safe, legal AI use	Grows with regulations	Medium – Growing fast in regulated industries

Technical Debt: The Time Bomb Behind AI Economics

AI agents promise efficiency, scale, and autonomy—but they also introduce a new kind of risk: invisible complexity. As enterprises rush to automate workflows and augment teams with intelligent agents, they often bypass the foundational work needed to support those systems long-term.

This end up in fragile architectures, costly integrations, untracked behaviors, and “shadow AI” projects popping up across business units. And over time, all of this becomes technical debt—hidden liabilities that slow innovation and increase long-term costs.

Technical debt is one of the biggest roadblocks of AI economics

In fact, AI doesn’t just run on top of existing technical debt—it can compound it, especially when:

AI agents rely on outdated or siloed systems for data access

Multiple models are stitched together with brittle APIs

Business users deploy low-code or no-code AI tools without oversight

No clear versioning, testing, or observability is in place for model behavior

Without strategic controls, every quick win today becomes a rebuild tomorrow.

How to Build AI Without Breaking the Business

To make AI economics sustainable, you don’t need to slow down—you need to build smarter from the start. Here’s a simplified five-step approach adopted by AI-mature enterprises:

1. Architect for Modularity

Design agents as modular services, not monoliths. Keep workflows loosely coupled so they can evolve independently and be reused across teams.

2. Integrate Clean, Unified Data Early

Eliminate silos. AI agents thrive on consistent, real-time data from CRMs, ERPs, and custom systems. Prioritize data integration as step one—not after deployment.

3. Instrument Everything

Monitor token usage, API calls, agent decisions, and latency. Build in observability tools from day one so you can spot inefficiencies and edge cases before they scale.

4. Apply Governance from the Ground Up

Establish role-based access, audit logs, prompt review cycles, and clear escalation paths—especially in regulated industries like finance, health care, and insurance.

5. Tie Costs to Outcomes

Don’t just measure usage—track cost per task, lead, ticket, or workflow. This aligns AI performance with business impact and prevents runaway budgets.

At Inclusion Cloud, we’ve helped enterprise clients bring AI into their operations without falling into the tech debt trap. From AI architecture to data foundations and governance, we work with your teams to ensure your AI investment pays off—not just now, but years from now.

If you’re ready to build AI with confidence, we’d love to support your journey. Schedule a discovery call today—and let’s build something that lasts.