Common scorecards, clear ownership, unified data: How firms can get a handle on their AI ROI

AI SPENDING is rising fast, but ask most companies what the total cost of AI is and exactly who is spending those funds, and many would struggle to give a clear answer to either question.

That is because AI costs rarely show up in one place. One department may be paying for AI copilots, another experimenting with vibe coding, while engineering teams ramp up cloud-based large language model usage behind the scenes.

The spending is spread across tools, infrastructure (cloud and on-premises), vendors and teams.

AI spending is fragmented across the organisation, with limited visibility into who is using what, where costs are accumulating and whether investments are delivering meaningful business value.

Says Pete Wilson, vice-president for business and general manager for Asia-Pacific for Apptio, an IBM company: “Fragmentation makes it difficult to understand the total spend, which in turn makes it even harder to measure the return on investment (ROI) on one of the most important expenditures on the balance sheet today and justify more spending.

“Not being able to make long-term decisions confidently impacts their ability to scale at a crucial time.”

According to Apptio’s 2026 Technology Investment Management Report, 91 per cent of organisations rank AI or machine learning among their top IT spending priorities, while 67 per cent are funding AI projects through internal capital allocation.

“In other words, AI may be a strategic priority, but it is still competing for finite resources within existing budgets,” says Wilson.

“Business leaders are under growing pressure to show the total cost of AI investments, which of them are delivering value, as well as which deserve more funding and which may need to be reined in.”

This issue is already appearing in how cloud costs are managed. While nearly all financial operations teams manage AI and machine learning workload costs, only 13 per cent are optimising them.

So, while many organisations can see the spend, the lack of active management creates a “black box” that threatens financial accountability as usage grows.

“This is why AI investment management needs to be an important conversation in the boardroom,” says Wilson. “Before organisations can optimise AI investments, their leaders need to get on the same page.”

They can begin with these steps.

1. Define a shared vision for AI ROI

In 2026, 74 per cent of organisations are increasing IT budgets, with a quarter reporting significant jumps.

Increased budgets and growing scrutiny mean leaders are facing mounting pressure to demonstrate transparency around the total cost of AI along with the business value it is meant to be delivering.

Wilson explains that the first step is to align leaders on a shared view of AI ROI. Technology teams may track adoption and system performance, while finance leaders focus on costs, efficiency gains and measurable returns.

Without a common scorecard, teams end up optimising different things.

At a minimum, he says, leaders need alignment on four areas:

• Business outcomes: What strategic priorities is AI expected to effect and how will you measure impact?
• Adoption: Who is using AI, how often, and is it embedded in day-to-day work?
• Cost visibility: What is the total cost of AI across all tools, vendors, infrastructure and teams?
• Utilisation: Where are resources underused, duplicated or wasted – and what can be redeployed?

Visibility into these metrics is essential for aligning leaders and investors on AI transformation, says Wilson.

“It enables everyone to speak the same language and evaluate AI impact with the same data-driven approach.”

2. Establish stronger governance

Fragmented costs also make accountability difficult from the outset. Businesses need a trusted way to track both impact and spend as usage grows, says Wilson.

That means clarifying who is driving the costs and which budgets should carry them.

This is why strong governance matters – establishing clear visibility into both cost drivers and cost owners, ideally with real-time or near real-time data that enables proactive management.

In practice, that requires agreed processes and the right tooling to track all AI-related costs in a consistent and defensible manner across the organisation.

“Without proper oversight, businesses risk reacting to cost increases only after spending has already spiralled,” Wilson says.

3. Build data foundation for faster decisions

With a defined vision and strong governance providing the scaffolding, businesses can use their data to make better investment decisions with greater confidence.

Here, the biggest challenge is having financial, operational and organisational data that is spread across disconnected systems. Overcoming this is critical to gauging AI impact accurately.

Financial intelligence forms this foundation with a unified view of costs across the necessary business data.

That visibility, says Wilson, makes it easier to enforce controls and accountability consistently with timely data that teams can act on.

“Leaders can experiment, iterate and scale AI initiatives with confidence, even as business conditions and technology evolve,” he adds.

Ultimately, organisations that succeed with AI may not necessarily be the ones investing the most aggressively, Wilson points out.

The winners, he stresses, are more likely to be the ones that can measure, govern and optimise those investments effectively over time.

“Having clear visibility into your AI efforts can help you prioritise investments that drive the most impact as you transform your business,” he says.

“By using a purpose-built platform to maximise the returns of your AI investments, you have a better chance of getting ahead of the curve.”

Find out how Apptio can help you get on the right track with AI and deliver impact with your investments.