Measure return on investment

Understanding return on investment (ROI) helps you decide whether to proceed, what to prioritise, and how to track whether AI is delivering value.

When it comes to AI, ROI isn’t only about financial returns. It also includes improvements in:

• quality
• confidence
• capacity
• staff and customer satisfaction
• decision‑making.

Before investing in AI, develop an AI strategy so that AI supports your long-term business goals. Find out how to get ready for AI.

Before investing, it helps to define the:

• problem you are trying to solve
• outcome you expect to achieve
• key signs of progress.

Success measures don’t need to be complex. They may include:

• fewer errors or less rework
• faster turnaround times
• increased revenue
• improved decision‑making
• greater capacity for higher‑value work
• better satisfaction among staff, customers, clients or the community.

Get a complete picture of whether AI is delivering value by considering all foreseeable costs and benefits. Some may take time to become visible and measurable.

Upfront and direct costs

These are the most visible expenses, such as:

• software licences
• subscriptions
• infrastructure
• external support.

Long-term or indirect costs

These include costs that are often overlooked but can be significant over time, such as:

• staff time for training
• testing and change management
• data preparation
• governance activities
• ongoing oversight.

Opportunity costs

Think about the opportunities you may need to give up by investing in AI. This includes the benefits you could miss by delaying or not adopting AI, such as:

• staying competitive with other businesses
• being a workplace that attracts and keeps staff.

For many organisations, the real value of AI appears through a combination of outcomes. Some benefits are easier to measure in the short term, while others become clearer over time.

The following are common ways to measure ROI.

Time and productivity gains

Many AI benefits show up as time saved on routine or repetitive tasks. This is often the easiest place to start.

To estimate value:

1. Measure how long the task takes today.
2. Measure how long it takes with AI support.
3. Multiply the time saved by the cost of staff time to estimate value.

Time saved only delivers value if it’s redirected to useful work, such as serving customers, improving quality or growing the business. 

You may need to track time saved over several weeks or months to get a clearer picture.

Quality and accuracy improvements

AI can reduce errors and improve consistency.

Compare error rates or rework costs before and after adopting AI, then estimate the cost of fixing those issues. 

Learn more about how to strengthen data quality.

Capacity and scale

AI can support more work with existing resources. For example:

• serving more customers with the same team
• processing higher volumes during peak periods
• supporting growth without adding management complexity

Early value often appears as improvements in efficiency, consistency or confidence. Financial returns and other benefits may follow later as processes change, capacity increases, or demand is easier to meet.

Revenue and customer outcomes

AI may support revenue or customer retention. For example:

• better targeting of marketing activities
• faster and more personalised customer service
• improved matching of products or services to customer needs
• new functionality in products or services.

These benefits can be harder to link to AI alone, but even partial improvements can justify investment when tracked over time. 

What matters most is whether outcomes improve after AI adoption and whether those improvements support broader business goals.