Decision Intelligence in Practice: Quantifying AI’s Impact on Business Outcomes 

The last few years have been the era of AI experimentation. Now, it’s time to move away from that experimental mindset. This is the year where operationalizing AI and quantifying its outcomes will be critical.

Yet, many businesses are still measuring the wrong things. We’ve moved beyond AI adoption rates and how many employees use AI tools. What matters now is measuring AI business value.

This is where decision intelligence ROI matters. It uses the right AI performance metrics to surface the business impact of AI, not just how much AI you have in play.

Because if you can’t measure how your AI is performing, you have no insight into its real value.

Why Measuring AI Business Value is Not the Same as Measuring AI Activity

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You may think you have AI performance metrics, but they probably look something like this:

• Number of AI tools deployed
• Number of use cases launched
• How many employees use AI
• What percentage of processes are automated

These are adoption metrics. They say almost nothing about value.

Now, consider just two of the headline findings from a recent European EY report:

• 56% of respondents saw increased profit or reduced cost from AI
• On average, the use of AI has brought profits or savings of €6.24 million

These metrics don’t come from tracking adoption numbers. They come from decision intelligence. That’s how you measure not just the quality, but the consistency and the speed of the decisions AI supports.

Business outcomes are driven by the many decisions made every day in the business. To understand the business impact of AI, that’s where your focus must be. With the right decision intelligence framework, you can focus on the metrics that matter, not the ones that just look good. 

Understanding Decision Intelligence ROI

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At its simplest, decision intelligence ROI is the measure of how AI improves decision making. Put differently, it’s the process of quantifying AI benefits. You aren’t measuring AI systems themselves. You’re measuring the outcomes, or what that AI use changed.

This creates a clear link between AI investments and the operational performance improvements they generate. It then guides where further investment will show the most results. After all, your company didn’t invest in AI to create models. You invested to improve business performances. 

Your decision intelligence framework is the bridge between these two.

Creating a Decision Intelligence Framework

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McKinsey suggests five critical layers to a framework that supports decision intelligence ROI determination:

• Financial impact: Where AI delivers revenue and enterprise value
• Strategic outcomes: Where AI shifted business performance
• Operational KPIs: The AI performance metrics that show how work changed
• User adoption and engagement: How AI is used (and trusted) in workflows
• Technical performance: The reliability of the AI system

To create a decision intelligence framework that supports quantifying AI benefits this way, you should focus on 4 areas.

Identifying High-Value Decisions

Not every decision benefits from AI support. The areas that do are typically:

• High frequency
• High impact
• Repetitive
• Data-intense

This could be production scheduling and inventory matters, demand forecasting and maintenance planning, customer prioritization and service, and areas where there is repetitive manual labor. These are where the business impact of AI becomes visible the fastest.

Measuring Decision Performance Before AI

Unfortunately, many companies have rushed to adopt AI without establishing their original baseline. If you do not know what the time taken to make decisions or the error rates in a specific process were, you cannot measure how they improved. That is critical to measuring AI business value.

Measuring How AI Changed Processes

Once AI has been implemented, you can start quantifying AI benefits. Look for improvements from your baseline in:

• Decision speed
• Accuracy
• Confidence
• Consistency

These offer meaningful AI performance metrics, rather than simple usage stats. 

Translating Decisions into Business Outcomes

Many businesses have these first three steps in place. They simply stop too early. Measurable outcomes are critical to measuring AI business value, and so also decision intelligence ROI.

Here’s just some of the AI performance metrics you could be tracking, and the decision that led to them, using our earlier examples.

• Faster production scheduling, leading to increased throughput
• More accurate inventory handling, leading to lower inventory costs
• Better demand forecasting, leading to fewer stockouts
• Faster customer prioritization, leading to improved retention

Now, you have truly begun quantifying AI benefits, and your decision intelligence ROI, in practical terms. 

Decision Intelligence ROI as a Strategic Advantage

The enterprises that succeed with AI aren’t the ones throwing the most tools in the mix. Instead, they are the ones that can use that AI to consistently make better decisions faster than their competitors. When you know your decision intelligence ROI, you can get the real measure of your AI success.

Enterprises that adopt decision intelligence this way are thinking differently about AI. They’ve moved it from the “tech initiative” bucket into a decision-making capability. That decision intelligence ROI lets them make smarter future AI investments, and get better results from the AI they are already using.

That quickly becomes an advantage over competitors stuck in the pilot loop. 

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