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AI is reshaping business fast. But without cost transparency, your boldest investments could quietly drain your budget and stall your strategy. Here’s what leaders need to know.
So you’ve greenlit a few AI projects. Maybe you’re boosting worker productivity, fine-tuning customer service, or improving operations. Everyone’s excited — and rightly so. The potential of AI to transform how we work and compete is real.
But here’s the catch: what’s not clear to a lot of companies is how much this transformation is costing… or whether it’s even delivering the value they hoped for.
That uncertainty? It’s a bigger deal than most people think.
AI’s Financial Blind Spot
The hype around AI has been loud — and fast. But behind the scenes, something quieter is happening. The financial picture of many AI initiatives is fuzzy at best. And in business, lack of clarity can be expensive.
According to research shared by Apptio, 68% of tech leaders expect to increase their AI budgets, and 39% believe AI will drive the largest portion of their department’s future growth. Despite this, many CEOs are still left unsatisfied.
Gartner reports that fewer than 30% of AI leaders say their CEO is happy with the return on their company’s current GenAI investments. That’s despite an average investment of $1.9 million just in 2024.
The hard truth? High spend doesn’t guarantee high impact.
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Why This Blind Spot Matters
Think of AI like the early days of cloud. Back then, things seemed agile and cheap — until surprise infrastructure bills rolled in. With AI, the same thing is happening. And it’s even more complex.
Here’s where the money goes:
- Cloud infrastructure (AI loves compute power)
- Storage and data transfer
- Data engineering and prep work
- Prompt optimization and model tuning
- Token costs from frequent queries
- Security and compliance considerations
In many organizations, different teams spin up their own projects with little centralized accountability. The result? AI sprawl. It becomes harder and harder to tie a dollar spent to a meaningful result. Meanwhile, every dollar invested is a dollar not spent elsewhere.
It’s not just a financial problem — it’s strategic. According to Gartner, over 40% of agentic AI projects will be canceled by 2027. Reasons? Escalating costs, unclear value, and poor risk control.
But without visibility, how do you know which projects to cut… and which ones to double down on?
Traditional Budgeting Won’t Cut It
Old-school budgeting just isn’t built for AI. AI workloads are dynamic. Requirements shift fast. Costs scale quickly and unevenly. You can’t manage this with rigid annual plans or basic spreadsheets.
To keep up, businesses need something smarter. Something that makes it possible to:
- Connect AI spending to specific business outcomes
- Track usage in real-time
- Spot when costs are creeping up and why
- Know when a project is worth scaling — or stopping
The Case for Cost Transparency
When you have financial visibility into AI initiatives, you unlock better decision-making across the board.
Cost transparency helps you answer questions like:
- Are we investing in the right AI projects?
- Are our teams focused on high-value priorities?
- Are we using infrastructure efficiently?
- Could we use a smaller, cheaper model and still achieve our goals?
This also means you don’t burn out your top engineers on interesting, low-impact pilots. By understanding the ROI of their work, you can focus them on what really matters.
And with budgets not infinite (remember, AI might not take your job — but it might take your department’s funding), those decisions make or break future innovation.
A Strategic Framework for Fixing This
So how do you get this kind of financial clarity?
That’s where Technology Business Management (TBM) comes in. It’s a framework made up of three interlocking practices:
- IT financial management (ITFM): Maps IT costs to business priorities
- FinOps: Optimizes cloud costs and ensures accountability
- Strategic portfolio management (SPM): Aligns projects with measurable value
Together, they let finance, technology, and business leaders speak the same language — and make investment decisions based on real data, not gut feelings.
Chances are, your company is already using elements of this. Maybe you’ve dabbled in FinOps or have a solid project management approach. TBM brings it all under one strategic roof.
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AI’s Real Value Isn’t Just in Speed — It’s in ROI
At the end of the day, fast-moving projects aren’t necessarily valuable ones. The winners in the AI race won’t be the companies that spend the most or move the fastest. They’ll be the ones that understand what they’re spending, why they’re spending it, and what they’re truly getting in return.
Companies that adopt a structured, transparent approach to managing AI costs will be the ones who turn AI from a hype-fueled headache into a reliable driver of long-term business performance.
Because AI success won’t come from luck. It’ll come from clarity.
Keywords: AI financial planning, AI success, AI infrastructure costs, Technology Business Management, cost transparency
