Artificial Intelligence and Business Strategy
In collaboration withBCG
Aligning operations with strategy is a critical leadership task. Turbulent market conditions, agile competitors, and ongoing demands to digitally upgrade operations and processes make strategic alignment more challenging to manage. Digitally savvy leaders are addressing the alignment challenge by improving how they use and develop KPIs.
Drawing on a global survey of more than 3,000 managers and 17 executive interviews, we find leaders across the business spectrum using AI to enhance how KPIs are prioritized, organized, and shared. These enhancements have a direct, measurable effect on strengthening strategic alignment. These leaders also deploy AI to improve the accuracy, detail, and predictive capabilities of KPIs themselves. The improvements individually and collectively generate greater situational awareness and improve how corporate functions work together to achieve strategic outcomes.
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Leaders acknowledge that they need new measurement capabilities and improved metrics to better anticipate and navigate strategic opportunities and threats.1 They recognize that new AI-based measurement abilities can deliver new performance insights and metrics, strengthen alignment, and improve outcomes. Our findings represent a clear and coherent call to action for leaders to create more integrated systems of forward-looking and interconnected KPIs.
These AI-enriched KPIs, or smart KPIs, can effectively act as an enterprise GPS, advising people about where they are, where they need to go, and how best to get there. These smart KPIs offer more detailed and accurate descriptions about what is happening in the business, more incisive predictions about what is likely to happen, and, in some cases, more proactive suggestions for what actions managers should take. Smarter, more forward-looking KPIs improve on legacy KPIs left uncoordinated and unchallenged due to time and executive inertia.
We discuss examples — from several industries — that illustrate how leaders use these new capabilities to achieve their strategic goals and offer specific recommendations for using AI to enrich KPIs and advance strategic alignment.
Strengthening Strategic Alignment With AI
KPIs were always intended to be mechanisms for aligning organizational behaviors with strategic objectives. Most managers, however, do not believe that their KPIs in practice reflect strategic aspirations; they recognize that their KPIs need to be improved.2 Our research shows that leaders who enrich their KPIs with AI are more likely to see alignment benefits, such as improved cross-functional coordination. They are more effective at prioritizing KPIs, identifying and building relationships among KPIs, and sharing KPI-related data across teams. They treat KPIs more as assets to improve and less as targets to hit.
Prioritizing KPIs With AI
Deciding which KPIs to emphasize and prioritize is a well-known challenge of strategic alignment: “We need to do a better job aligning on our KPIs” was a common refrain in our qualitative research. Interviewees widely cited executives’ gut feelings as a key source of KPI disagreement. Survey respondents who reported that their company has used AI to prioritize its KPIs were 4.3 times more likely to see better alignment between functions than those who did not. Prioritizing KPIs with AI improves data-driven decisions and lays the groundwork for stronger strategic alignment.
Maersk, the Danish transportation, shipping, and logistics company, used AI to reassess and define how it measures throughput and the productivity of its network of 65 assets in ports, transportation, and warehouses worldwide. Front-line managers had to decide whether key performance was best defined by loading and unloading ships or trucks as quickly as possible (maximizing throughput) or by managing the loading process so that the transportation could reliably depart as scheduled. Was the right KPI speed or schedule reliability?
Prioritizing KPIs with AI improves data-driven decisions and lays the groundwork for stronger organizational alignment.
This decision would have enormous practical ramifications for the business. Using more equipment in APM terminals for loading and unloading would increase throughput but increase short-term costs (extra equipment meant extra expenses). Using just enough equipment to ensure on-time departures, in contrast, would contain costs but limit throughput. Based on experience, onsite front-line managers believed that speed — unloading and loading ships as quickly as possible — was the right performance measure.
To test this hypothesis, Maersk’s data science team developed digital twins — AI-driven models — to represent each approach and assess their effects across the value chain. They concluded that using less loading equipment would preempt bottlenecks at transshipment points or when making connections with other modes of transport, such as road and rail.
They also discovered that going faster at one port led to slowdowns elsewhere. Keeping to a reliable schedule also reduced costs and resulted in more on-time arrivals. Going more slowly was “a counterintuitive metric,” says Holly Landry, Maersk’s chief data officer. “Using a digital twin for the supply chain both explained and justified using less equipment. It saved in the millions — at just one terminal.”
Now, the company is rolling out digital twins across its value chain. With AI, Maersk prioritized the right KPI, which then led to more efficient, aligned performance across the enterprise and increased customer satisfaction with reliable deliveries.
Ensembling KPIs With AI
Discovering interdependencies among KPIs with AI facilitates the creation of KPI “ensembles” that bundle distinct KPIs for different — but connected — business activities. Examples of KPI ensembles include employee productivity and customer engagement, profit margins and market share, and quality manufacturing output and return on assets. AI plays a critical role in discerning otherwise hidden patterns that link one KPI with another. Since these patterns often span multiple functions and stakeholders, KPI ensembles can break down silos and increase collaboration between different stakeholders, enhancing organizational alignment.
Pernod Ricard, the $10 billion global spirits company, uses AI to describe and deepen the connection between two of its most important KPIs: profit margins and market share. In the past, these KPIs were siloed and separated, 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. Instead of seeking to maximize each individual KPI, the spirits maker now seeks to optimize both KPIs in concert with each other.
“If you can imagine moving a cursor between market share optimization objectives and margin optimization objectives,” says Pierre-Yves Calloc’h, Pernod Ricard’s chief digital officer, “you need to know how the required investments vary to reach these objectives. AI is going to give you that information. With AI, we can better align market share KPIs, margin KPIs, and required investments to reach them.” This capability transformed how Pernod Ricard’s leadership allocates capital and balances its aspirations for profitability and market share.
Making KPIs Shared, Visible, and Trusted
Broadly, sharing KPIs means sharing accountability, sharing information, or both. Sharing accountability for KPIs is typically a leadership decision. Sharing performance information depends on technology and data access. Our research identifies several ways that AI enhances KPI information sharing and promotes collaboration among different parts of an organization.
Using AI to share KPIs offers specific benefits that can strengthen strategic alignment. Companies that use AI to share KPIs are five times more likely to see improved alignment between functions and three times more likely to be agile and responsive than organizations that do not use AI to share KPIs. As one executive remarked, “We need to do more to share KPIs. … What are the right KPIs to share that will allow us to ensure that one thing isn’t counterproductively overriding the other?” Improving visibility into companywide KPI outcomes and performance drivers— with the appropriate data and AI applications — bolsters managers’ abilities to share, discuss, and navigate tensions among KPIs.
At Sanofi, AI brought together the data that drives the pharmaceutical company’s integrated business plan (IBP) — an enormous task for a business that has made 300 acquisitions over the past 50 years. In 2019, incoming CEO Paul Hudson championed data democratization, which required new data standards for quality and governance, as well as new technical infrastructures for processing and distribution. Performance data on key IBP metrics were eventually consolidated and shared with 10,000 top executives worldwide via a smart new digital interface called Plai (for its easy-to-access, easy-to-use, AI-powered functionality).
Companies that use AI to share KPIs are five times more likely to see improved alignment between functions and three times more likely to be agile and responsive than organizations that do not use AI to share KPIs.
This approachable AI tool, also referred to as “snackable AI” by its developers, offers visibility into companywide performance and enables managers to have constructive discussions about performance. These conversations would have been impossible before — not because the data was unavailable but because the algorithm introduced a level of objectivity that made decisions and conversations more organic, trusted, and effective, says Stephanie Androski, Sanofi’s head of global finance operations and transformation.
“We now have one number that’s going behind our sales forecast, and it’s the central point for multiple other KPIs. If we’re predicting a potential out-of-stock situation for a product, not only does it give us the ability to say, ‘Oh, wait a minute — the AI is predicting we might be out of stock of that product in four months. Is that real and can we get ahead of it?’ It also gives us the ability as finance to ask, ‘Are the sales too ambitious for this product? Will we lose market share?’ or ‘What does this do to the overall forecast?’ Because everything is more out in the open, and because you can see it, it’s really helped increase that dialogue and productivity.”
The essential leadership takeaway is that organizing and sharing KPI data with AI can deliver a valuable, trusted platform for collaboration and alignment.
Three Types of Smart KPIs
As KPIs evolve, their contributions to strategic alignment evolve as well. Our research suggests that there are three ways that AI-enriched KPIs improve on metrics that simply track performance. These smart KPIs better describe the world as it is (ongoing and past performance) and better anticipate how it likely will be (future performance). In some cases, smart KPIs also indicate what can or should be done to promote better outcomes. Executive dashboards, for example, typically color-code KPIs: Red indicates that performance is down, and green means performance meets/exceeds expectations. This coding is a simple type of call to action that traditional KPIs and dashboards provide. Smart KPIs go further: They can make more detailed and specific recommendations about next steps and diagnose the implications for other KPIs.
Thus, smart KPIs improve on traditional KPIs in three overlapping senses: They better describe and predict performance and prescribe more detailed and valuable recommendations. These three types of smart KPIs map to a well-known distinction between descriptive, predictive, and prescriptive analytics. This KPI typology is explained in more detail below.
1. Smart Descriptive KPIs. These KPIs synthesize historical and current data to deliver insights on what happened or what is happening. They provide a deeper understanding of performance gaps and their causes, leading to better KPI creation or a better understanding of KPI relationships. An example is Sanofi’s snackable AI tool, which boosts situational awareness by revealing critical interdependencies among different KPIs.
2. Smart Predictive KPIs. These KPIs anticipate future performance by producing reliable leading indicators. They provide visibility into potential outcomes, thus enabling preemptive actions to mitigate risks or leverage opportunities. For instance, General Electric has transformed its KPIs to focus on leading indicators. The company is using AI, for example, to analyze order pipelines by comparing orders against the installed base of products and services. These detailed comparisons help accurately identify opportunities to increase future orders, which, in turn, drives stronger revenue and margins. As Carolina Dybeck Happe, GE’s senior vice president of finance and former CFO, notes, “Utilizing leading indicators creates a much faster and much tighter connection between the strategy and delivering on that strategy.”
3. Smart Prescriptive KPIs. Beyond description and prediction, prescriptive KPIs are enriched with AI recommend actions. They not only indicate performance gaps but also suggest corrective measures. Sanofi’s smart KPIs, for instance, align operations and sales by recommending adjustments to sales KPIs based on supply chain performance.
Enriching KPIs with AI empowers key metrics to drive the right performance and strengthen strategic alignment. By transforming KPIs into smart descriptive, predictive, and prescriptive tools, digitally savvy organizations use them for enhanced situational awareness, more effective decision-making, and improved performance management.
Based on our research, the following actions will help leaders use AI to improve how they use and develop KPIs and how they align operations with strategy.
Treat KPIs as assets. When KPIs are viewed as assets as well as metrics, they invite more thoughtful and intentional investment. Which KPIs should be enriched or improved with AI? Which KPIs are candidates to become more predictive and forward-looking? What investments in AI, data, and people are necessary to strengthen relationships among KPIs and enhance strategic alignment? Much as organizations identify, cultivate, train, and develop their people for greater responsibility and decision-making authority, leaders should identify, cultivate, train, and develop their KPIs to deliver actionable insights and recommendations.
Encourage greater visibility and transparency around performance measures. Making KPIs more visible clarifies accountability and responsibility, encourages discussion, and cultivates a shared sense of purpose. The Sanofi case illustrates that creating a single source of truth about performance that is shareable and visible to top leaders benefits alignment. Increasing cross-functional access to KPIs encourages greater situational awareness and self-awareness. Democratizing access to credible and transparent performance data helps people see where they are and where they need to go. The C-suite should commit to AI-related resources that improve the visibility and transparency of KPIs.
Map KPI relationships and connections. People across the enterprise should be able to see how key performers, key performance, and key performance indicators relate to one another. Visibility and visualization animate how organizational alignment works. Data-driven leaders can use AI to map, model, and manage their performance drivers and KPI priorities, says Hervé Coureil, Schneider Electric’s chief governance officer and secretary general. Describing and representing a company’s “ecosystem of KPIs,” notes Coureil, can be a labor- and resource-intensive first step. These maps and models can both identify and clarify which KPIs should be shared or ensembled. Customer-centric organizations, for example, would likely prioritize shared and ensembled KPIs around customer experience and customer lifetime value metrics. Better mapping and modeling make KPIs better assets.
Our research finds that improving strategic alignment depends not only on properly defining the metrics that matter most but also on continually developing these metrics with AI and better data. But on a more fundamental level, our research highlights that AI is taking on tasks that were once the exclusive domain of executives, such as prioritizing, ensembling, and sharing KPIs. The convergence of AI and KPIs is redefining how KPIs are used and how KPIs contribute to strategic alignment. The ongoing translation of strategy into smarter, better organized, and more valuable metrics is an increasingly critical activity for data-driven leaders.
1. M. Schrage, D. Kiron, F. Candelon, et al., “Improve Key Performance Indicators With AI,” MIT Sloan Management Review and Boston Consulting Group, July 11, 2023, https://sloanreview.mit.edu.
2. M. Schrage and D. Kiron “Leading With Next-Generation Key Performance Indicators,” MIT Sloan Management Review, June 26, 2018, https://sloanreview.mit.edu; and M. Schrage, D. Kiron, F. Candelon, et al., “Improve Key Performance Indicators With AI,” MIT Sloan Management Review and Boston Consulting Group, July 11, 2023, https://sloanreview.mit.edu.