Start managing what you measure—with AI

Performance metrics in business are a double-edged sword: On one hand, companies are infinitely more quantifiable in the digital age, and the explosion of big data and key performance indicators (KPIs) has pumped new life into the 20th-century maxim that you can’t manage what you don’t measure. Companies today don’t just have thousands of new metrics to consider; executives have KPI dashboards to squeeze them all into view.

On the other hand, many businesses frequently—and sometimes disastrously—conflate metrics with business strategy. (Wells Fargo’s ill-fated strategy with cross-selling in the mid-2010s is a case in point.) Others can wind up leaning so heavily on the same old KPIs (such as Net Promoter Scores) that they miss out on opportunities to better align resources to strategy.

A new era of performance management

But this is 2024, with 180 million-plus people already using ChatGPT, so you could almost predict this next sentence: Enter AI.

Just as AI applications are triggering operational changes and overhauls in every major function of business, they are making inroads in the world of KPIs and performance management. For companies keen to understand how to measure the ROI of all these new AI initiatives, an impressive new study suggests that AI will itself play a big role in solving the puzzle. 

In a global survey of more than 3,000 managers in more than 25 industries and 100 countries conducted by BCG and MIT Sloan Management Review, results show that leading companies “are not just using AI to enhance performance but to redefine it by challenging long-held assumptions about the drivers of business success.” 

How AI-enabled KPIs are showing impact

The study revealed three top-level takeaways about the immediate impact of using AI to enhance performance measurement:

• Significant early adoption. 34% of organizations are using AI to create new KPIs.

• Performance improvement. 90% of those using AI to create new KPIs are seeing their KPIs improve.

• Bottom-line impact. Organizations using AI for KPIs are three times more likely to see financial payoffs than organizations that don’t.  

New types of ‘smart’ KPIs

1. Descriptive KPIs combine historical and current data to deliver insights on what happened or what is happening; they can provide a deeper understanding of performance gaps and their causes. 

2. Predictive KPIs anticipate future performance and project reliable indicators for that. They can also identify patterns that other techniques or humans can’t, allowing them to draw on a richer range of potentially counterintuitive patterns. The huge benefit: Predictive KPIs can enable preemptive actions to mitigate risks or take advantage of hidden opportunities. (Imagine the potential impact of predictive KPIs for huge logistics companies at the onset of the COVID-19 pandemic in 2020.)

3. Prescriptive KPIs use AI to recommend actions to optimize performance. They can identify performance gaps and take corrective measures.

The BCG study is worth studying for early examples of companies putting AI-enabled KPIs into practice, with impressive results. 

Read also: Andrew McAfee: 4 steps to fast-tracking generative AI

Maersk redefines productivity 

One case in point is Maersk, the global shipping giant, which the report notes is using AI to recalculate how it measures the productivity of its global network of assets in ports, transportation, and warehouses. Managers sought to understand whether key performance was best defined by loading and unloading ships and trucks as quickly as possible or by optimizing the loading process for better on-time transportation and departure. To find out, they used AI-driven models to represent each approach and assess its effects. 

“These models concluded that reliable departures that used less loading equipment would preempt bottlenecks both at trans-shipment points and during connections between different modes of transport, such as road and rail,” the authors report. “With AI, Maersk prioritized the right KPI, overcoming a human bias to maximize speed. This approach led to more efficient, aligned performance across the enterprise and, in turn, increased customer satisfaction with reliable deliveries.”

Pernod Ricard identifies new connections between profit and market share

The BCG study shows how other major brands are experimenting with AI for similar KPI initiatives: Pernod Ricard, the $10 billion global spirits company, is leveraging AI to more accurately define and deepen the connection between two major KPIs: profit margins and market share. In the past, these KPIs were siloed, each with its own set of measures. The finance function focused on profitability, while sales and marketing focused on market share. 

The company “now deploys AI to deliver insights into how commercial and marketing investments that improve profits—such as media or in-store activation—also influence market-share objectives and vice versa,” according to the study. “Instead of seeking to maximize each individual KPI, the spirits maker now seeks to optimize both KPIs in concert with each other.”

What is the takeaway for companies that have not yet sought AI assistance in KPIs? Lead author François Candelon, a BCG managing director and senior partner, summed it up neatly in Fortune: “It’s a well-worn maxim that companies can’t manage what they don’t measure. But using AI transforms both what companies can measure and what they should manage.”