How AI-powered KPIs can help leaders get better results

The vital question for organizations is: How do they measure success? As companies grow larger and more complex, determining which metrics to use to evaluate performance becomes more difficult.

Traditionally, setting key performance indicators, or KPIs, was the task of senior executives, who relied on their judgment, intuition and experience. But legacy KPIs often capture performance based on suboptimal or even wrong metrics. As companies accumulate larger and more diverse sets of data, legacy metrics that rely primarily on human judgment will be less likely to align performance dynamics with desired outcomes. KPIs need to get smarter.

AI can help by allowing companies to use their own data to better understand what drives performance. In the process, AI can also change how organizations measure, analyze and align performance, replacing outdated static metrics with dynamic and intelligent KPIs that provide more detailed and accurate descriptions of what is actually happening in the business and what is likely to happen. the next.

To understand how executives are using AI to improve measurement and strategic outcomes, and how their organizations have adapted AI-powered KPIs, Boston Consulting Group (BCG) and MIT Sloan Management Review The company collaborated to conduct a global survey of more than 3,000 managers representing more than 25 industries in 100 countries. We also conducted 17 executive interviews to gain greater context and insight into the experience of individual companies using AI to transform their KPIs.

Our research finds that leaders who use AI to prioritize, organize, and share KPIs see better alignment between units or functions, which in turn leads to better overall results. Smarter KPIs can act as an organizational GPS of sorts, streamlining decision-making and momentum across teams. But how are companies using AI to create and manage new intelligent KPIs?

Strategic alignment with AI-powered KPIs

Our executive survey found that using AI-powered KPIs strongly impacts three dimensions of alignment: 1) teams are more likely to agree on which KPIs to prioritize; 2) Interconnected KPIs can be improved across the organization as a group, not separately; and 3) teams are more likely to share information when needed, enhancing accountability and alignment. Let’s explore each dimension.

define the priorities. Our survey found that companies that reported using AI to prioritize their KPIs were 4.3 times more likely to say they had greater cross-functional alignment than those not using AI. Teams are often overwhelmed with a mix of different KPIs that say different things, especially as processes become more complex. Prioritizing KPIs is essential to prevent wasted efforts and resources. AI-based models, as industry-leading companies have shown, help prioritize KPIs by identifying their algorithms that have the greatest impact on desired business outcomes.

Maersk, the Danish transportation, shipping and logistics company, provides a good example of the challenge of prioritizing competing performance indicators. The company sought to determine how best to evaluate performance: Speed ​​(loading and unloading ships or trucks as quickly as possible) vs. reliability (managing the loading process to adhere to a reliable schedule).

The company’s port managers have argued that speed is the best measure of performance on the grounds that this would increase productivity – although additional equipment is needed to handle the increased pace, which increases costs in the short term. However, using AI, the data team at Maersk came to a very different and counterintuitive conclusion: working more slowly improved system-level results. Again using artificial intelligence, they identified an optimal throughput measure for loading and unloading that was slower than the experience of port managers suggested.

To build trust in AI-powered analytics, Maersk developed a model to test the impact of each approach across its value chain. The company discovered that working faster at one port created bottlenecks elsewhere, negating any overall productivity gains. In contrast, adherence to a reliable (albeit slower) schedule resulted in more people arriving on time, which reduced costs. The complexity of these interconnected variables has proven difficult for human judgment alone to decipher, while AI has been able to identify and explain the most useful (“slowest”) performance metric. By prioritizing reliability over speed, AI has facilitated measurably better organizational alignment to deliver measurably better outcomes.

to gather. The metrics that companies, teams, and individuals work on are often interconnected and should not be looked at or optimized in isolation. For example, an IT consulting firm looking to respond to proposals aims to increase the speed at which it can onboard a new project while ensuring fit for purpose. These two goals – speed versus quality – do not require prioritization per se, but rather require joint improvement. Using AI, the company can more accurately predict the “probability of winning” by analyzing the hiring company’s past records and current projects underway. This, in turn, directs the allocation of resources (including staff time) to projects with a high probability of winning.

Clustering is another area where AI’s pattern recognition capabilities typically outpace human judgment and intuition. For example, Pernod Ricard, a $10 billion global spirits brand, uses AI to balance two often competing strategic priorities: increasing profit margin and increasing market share. AI is able to evaluate, weigh and provide insight into how business and marketing investments that improve profits also impact market share goals – and vice versa. In the past, each of these KPIs was siloed: the finance function focused on profitability, while sales and marketing focused on market share. Pernod Ricard is now able to achieve a dynamic balance in its pursuit of profitability and market share, both strategically and operationally, thanks to the compilation of its AI algorithm to assess overall impact.

sharing. Ad hoc teams and functions often end up with ad hoc and siled KPIs, which hurts overall performance. As one executive noted, “We need to do more to share KPIs. … What are the right KPIs to share to make sure one thing doesn’t backfire on the other? Our survey revealed that organizations using AI To create shared KPIs across teams she says they are five times more likely to see improved alignment and three times more likely to be agile and responsive than organizations that don’t use AI to share KPIs.

The basic idea is that AI is able to identify metrics across organizations that require shared accountability. Making KPIs clear and easily accessible also promotes engagement and fosters data-driven conversations between teams. Sanofi has done precisely this with its PLAI app, which uses artificial intelligence to help analyze, process, and deliver numbers that make the most sense to specific audiences within the company. By providing a clear view of enterprise-wide performance, PLAI creates a single source of truth that helps people know where they stand and what needs to be done.

AI-powered KPI management

Adopting AI-powered performance indicators is not as easy as flipping a switch. Companies seeking algorithmic innovation must take three key steps: 1) Organize company data; 2) Build organizational structures to oversee and coordinate the joint development of KPIs/AI; and 3) strengthen their culture of data-driven decision making.

Data. Without exception, the executives we surveyed emphasized that clean, reliable data is critical to transforming KPIs. However, generating clean data has been very difficult for companies. Organizations sourcing their data for AI-powered KPIs should focus on two factors: 1) ensuring systems are in place to generate the necessary data; and 2) prepare the data structure to facilitate the production of KPIs.

John Francis, GM’s chief data and analytics officer, told us that data strategy is paramount because it builds trust in metrics among employees and executives alike. Consideration of telemetry, measurement plans and hardware is vital to ensure data flow and the required KPIs can be provided. A good data strategy makes producing AI-powered KPIs routine. This means that data should be co-located, metrics reporting should be automated using AI, and teams should not need additional technical support to view and manage their KPIs.

Pernod Ricard’s journey to enable AI interventions highlights the centrality of the data challenge. The company decided it needed three years of weekly sales data, but found that nearly 80% of that necessary data was external. Compiling this data took extensive manual effort. This made it particularly important to create a business case for the effort to build internal consensus among employees about its value and gain the required buy-in.

Organizational structures. Organizations that have successfully introduced AI-powered KPIs are often supported by a team or group of teams dedicated to taking a holistic view. The exact approach may vary, but assigning responsibility to one dedicated entity goes a long way in maintaining a successful KPI transformation program.

For example, Schneider Electric established a dedicated Performance Management Office to maintain oversight of key performance indicators intentionally placed in the governance team to ensure a neutral, cross-functional perspective. To help senior leadership focus on the most important information amid multiple KPIs, the management office updates them on what is most relevant to driving business performance. Singaporean DBS Bank, on the other hand, took a more granular approach, integrating cross-functional teams to analyze and improve process drivers and provide a shared, focused view to all team members rather than having thousands of metrics.

culture. Organizations have historically valued experience and intuition regarding data in making decisions. As one interviewee noted, the usual opposition against data-driven decision making is that company leaders were being paid millions for their intuition. That’s why the shift to being data-centric and open to AI-led interventions starts with leaders themselves.

Sanofi is rebooting the way its executives think. “Our 150 senior leaders are trained in bootcamps to become more data-centric, more information-seeking, to ask the right questions, and to really be more digitally savvy in the way they frame their needs,” said Emanuel Freinhard, chief digital officer at Sanofi. . we. “Our goal as part of our culture change is that those 150 people, when they leave, are much more likely to take the data-centric view. We are making sure that our leadership core in the company is trained on how to use the next generation of intelligence-driven KPIs.” Synthetic This top-down approach, combined with data-driven company conversations catalyzed by PLAI, helps drive cultural change at Sanofi.


Achieving strategic alignment within their organizations is an increasingly important priority for senior executives. AI-powered KPIs are powerful tools to achieve this. By getting their data right, using the right organizational structures, and driving a cultural shift toward data-driven decision making, organizations can effectively control the creation and deployment of AI-powered KPIs. Doing so will help them prioritize, aggregate, and share KPIs more effectively—the first steps in achieving the goal of strategic alignment.


Another reading luck Columns by François Candelon.

François Candelon is Managing Director and Senior Partner at Boston Consulting Group, and Global Director of the Henderson Institute at Boston Consulting Group. You can contact him at

Michael Chu is a partner and co-director of data science at BCG

Gaurav Jha is a consultant at BCG, and an ambassador for the BCG Henderson Institute.

Shervin Khodabandeh is a Managing Director and Senior Partner at Boston Consulting Group.

David Kiron is the editorial and research director of the magazine MIT Sloan Management Review The program leads its own big ideas research initiatives.

Michael Schrag is a research fellow at the MIT Sloan School of Management’s Initiative on the Digital Economy.

Some of the companies featured in this column are former or current clients of BCG.

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