AI is becoming part of everyday business operations. Whether it's powering customer experiences, supporting employees or improving decision-making, organisations are moving from isolated AI projects to broader adoption across the enterprise.

But as investments increase, executive teams are asking a new question: What value are we getting in return? 

Here are six practical steps to help connect AI spending to business outcomes.  

1. Inventory your AI services

You can't manage what you can't see. Start by identifying every AI service in use across your organisation—from foundation models and AI platforms to SaaS applications with embedded AI capabilities.

Building a complete inventory provides the visibility needed to understand where AI is being used and where costs are originating.

2. Start with the business problem

Before measuring AI ROI, define the outcome you're trying to achieve.
Examples include:

• Improving employee productivity
• Reducing operational costs
• Increasing customer satisfaction
• Accelerating software development
• Improving decision-making

Without clear objectives, it becomes difficult to determine whether an AI initiative is successful.

Tip: Focus on business outcomes first, not technology metrics.

3. Establish a baseline

You can't measure improvement without understanding where you started.
Document current performance metrics before introducing AI, such as:

• Average ticket resolution times
• Call handling times
• Employee productivity levels
• Development cycle times
• Operational costs

Having a baseline creates a reference point for measuring the impact of AI over time.
 
4. Make AI spending visible

ROI requires visibility into both costs and outcomes. Organizations should seek to understand:

• Infrastructure costs
• GPU consumption
• Token usage
• AI platform spend
• SaaS AI subscriptions
• Operational support costs

A complete picture of AI spending helps organizations evaluate whether business results justify investment levels.
 
5. Measure adoption, not just deployment

Many organizations measure how much AI they've deployed.
Fewer measure how much it's actually being used.
Questions to consider:

• How many employees actively use AI tools?
• Which departments are seeing the highest adoption?
• Which AI services deliver the most engagement?
• Are users achieving measurable productivity gains?

Strong adoption is often the bridge between AI investment and business value.
 
5. Connect outcomes to financial impact

This is where ROI becomes meaningful.
For example:

Organizations that connect AI outcomes to financial metrics can make stronger investment decisions.
 
6. Use ROI to guide future investment

Not every AI initiative will deliver the same value.
Measuring ROI helps organizations:    

• Prioritize successful initiatives
• Expand high-performing use cases
• Reallocate budgets effectively
• Improve executive confidence
• Build a stronger AI strategy

The goal isn't simply to identify costs. It's to understand where AI is creating the greatest business impact.
 

The future of AI success isn't about spending more

As AI adoption matures, organizations are shifting their focus from simply tracking costs to understanding outcomes.

Those that can connect AI investments to measurable business results will be best positioned to scale AI confidently and maximize value.
CloudHealth helps organizations gain greater visibility into cloud and AI spending, making it easier to understand consumption, support governance initiatives and evaluate the impact of AI investments across the business.

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