The Cultural Benefits of Artificial Intelligence in the Enterprise

Organization-Level Cultural Benefits

The Culture-Use-Effectiveness dynamic is different at the organizational level than it is at the team level. Figure 5 shows the C-U-E dynamic at the organizational level: Organizational culture can improve AI adoption, which in turn improves organizational effectiveness, which in turn improves organizational culture.





Broad Use of AI Requires a Common Language


At PepsiCo, executives view AI as a strategic capability. They also acknowledge that making full use of that capability goes hand in hand with strengthening the company’s culture, says Colin Lenaghan, global senior vice president, net revenue management, for the food and beverage multinational. “PepsiCo is very much an organization and a culture that learns by doing,” he explains. “We view AI as a very strategic capability that helps us solve strategic problems. We are making quite an investment in bringing literacy of advanced analytics across the broader community. We are starting to elevate that literacy among senior management. This is clearly something that has to be driven from the top. It needs cultural change. Over time, we intend to strengthen our AI capability and hopefully the culture at the same time.” Pervasive AI literacy enables communication through a shared language.


A shared language improves communication about (and the identification of) new opportunities. At Levi Strauss & Co., Paul Pallath, the clothing company’s global technology head of data, analytics, and AI, agrees that broad-based adoption of AI demands culture change across the organization. “We need to change the overall culture of the organization, and that depends on getting our people to think in terms of AI,” he says. “If you don’t start thinking in that direction, you’re not going to ask the right questions that can eventually be solved with AI. Thinking in terms of AI — such as asking what solutions might be possible with AI or whether AI could be applied in a particular situation — unveils new opportunities.” Collective thinking in terms of AI depends on a shared language.


“We need to change the overall culture of the organization, and that depends on getting our people to think in terms of AI.”

Paul Pallath
Global technology head of data, analytics, and AI
Levi Strauss & Co.


Changing the culture to make full use of AI across the enterprise is both necessary and difficult, says Chris Couch, senior vice president and CTO at Cooper Standard, which provides components and systems for diverse transportation and industrial markets. “Good companies are going to develop people in all functions, whether it’s finance, purchasing, manufacturing — you name it — that have some sense about where AI tools can be applied. Bad ones won’t,” he explains. “While AI will continue to be something special that only certain experts use, there’s going to be a democratization in the next 10 years. It’s one of those things that is not easy to prepare for, but we have to prepare for it. Otherwise, we’re going to get displaced.” When the organization depends on AI literacy, those who lack literacy add discord.


Innovating With AI Improves Competitiveness


Using AI doesn’t merely help with effectiveness at the team level (such as by improving efficiency and decision quality); managers can also use AI to improve an organization’s competitiveness. For instance, innovating new processes with AI appears to enhance a company’s ability to compete with both existing and new rivals. We compared respondents who said they are using AI primarily to innovate existing processes with those who agreed that their company is using AI primarily to explore new ways of creating value. (See Figure 6.) Respondents who agreed that they are using AI primarily to explore new ways of creating value were 2.5 times more likely to agree that AI is helping their company defend against competitors and 2.7 times more likely to agree that AI is helping their company capture opportunities in adjacent industries. Exploration with AI is correlated — to a greater extent — with improved competitiveness than exploitation with AI.





Organizations can use AI to accelerate these innovation processes for existing processes. Moderna rapidly developed a widely used COVID-19 vaccine with the help of AI. Johnson says Moderna focuses on “having a smaller company that’s very agile and can move fast. And we see AI as a key enabler for that. The hope is that it helps us to compete in ways that other companies can’t. That is certainly the intention here.”


Moderna began automating work that had previously been done by humans, including testing the design sequence of messenger RNA (mRNA) used in vaccines that protect against infectious diseases. “One of the big bottlenecks was having this mRNA for the scientist to run testing,” Johnson says. “So we put in place a ton of robotic automation, and a lot of digital systems and process automation and AI algorithms as well. And we went from maybe about 30 mRNAs manually produced in a given month to a capacity of about a thousand in a monthlong period, without using significantly more resources and with much better consistency in quality.” As a result, employees at Moderna can evaluate many more options for innovation than before; the company’s rapid development of the COVID-19 vaccine was due, in part, to using AI to rapidly test mRNA design sequences. Using AI accelerated innovation, increasing the company’s ability to compete with much larger companies.


But speed is far from the only potential benefit of AI. Amit Shah, president of floral and gift retailer 1-800-Flowers, observes, “If you think about what differentiates modern organizations, it is not just the ability to adopt technologies — that’s become a table stake — but the ability to out-solve competitors in facing deep problems.


“When I think about AI,” Shah continues, “I think about our competitiveness on that frontier. Five years down the road, I think every new employee that starts out in any company of consequence will have an AI toolkit, like we used to get the Excel toolkit, to both solve problems better and communicate that better to clients, to colleagues, or to any stakeholder.” Being a “company of consequence” in the future may require all employees to work with AI to “out-solve” competitors with new ways of creating value.


Using AI to Reassess Key Assumptions, Set New Objectives, and Realign Behaviors


Improving organizational effectiveness is not itself an end goal. After all, organizations can become more effective at the wrong activities: They can achieve misguided objectives, reinforce outdated values, or compete against irrelevant organizations. When CBS’s Subramanyam asked her AI team to assess whether executives had the right assumptions about what factors lead to a successful TV show, she was using AI to reassess what “being effective” means in her organization. Using AI can help a company not only achieve effective outcomes, but also change assumptions about what counts as an effective outcome.


Many executives revealed that their AI implementations were helping them develop or refine strategic assumptions and improve how they measure performance. These changes often lead to shifts in their KPIs. Indeed, our survey found that 64% of the organizations that use AI extensively or in some parts of their processes and offerings adjust their KPIs after using AI. As Pernod Ricard’s Calloc’h says, “We are planning to monitor new KPIs because AI is helping us measure performance more precisely. For example, one algorithm helps us measure the performance of each marketing campaign in isolation, whereas before, campaigns were running on various media at the same time, and it was impossible to isolate the contribution of each media component. Our ability to isolate and better measure a campaign’s performance allows our marketers to be more performance-focused and to make better decisions.”


KLM, for example, used AI to develop a new measure to help make complex financial and operational trade-offs involving crew scheduling and passenger delays. “Rather than optimizing for on-time performance,” Stomph says, “we quantified what it takes not to deliver as promised across different departments. That required us to quantify things that you cannot find in your P&L.” The measure looks at the cost of various situations, such as a two-hour delay to a crew member’s schedule if that person is switched from a flight landing at 2 p.m. to one landing at 4 p.m. “What’s the price of this?” he asks. “If you want to run an optimization across different departments, you need to create a single currency to unify all of these players. And the single currency we created was nonperformance cost.” The single currency enabled everyone to make decisions based on the same criteria instead of relying on individual judgments with uncoordinated decision-making criteria.


KLM’s nonperformance measurement led to changes in a cascade of decisions, including when to swap out crew members. “What I find most intriguing about the solutions we have,” Stomph says, “is even if you will never use the tool, that process of bringing these teams together has been very valuable from a financial and a morale point of view.”


Another way that AI implementations can help organizations revise assumptions about effective outcomes is to enable workers to outperform existing KPIs so consistently and so thoroughly that new KPIs are called for. “People see that they are outpacing the KPIs that they agreed upon because of AI/ML,” Levi Strauss’s Pallath says. “Based on how AI/ML is delivering value to the enterprise, the goalpost keeps shifting.”


New success measures become necessary when AI-based solutions make possible new performance benchmarks, obsolesce legacy KPIs, and/or reveal new drivers of performance. Changes in KPIs often accompany shifts in organizational behavior. Indeed, organizations that revise their KPIs because of how they use AI are more likely to see improvements in collaboration than organizations that don’t make AI-driven adjustments to their KPIs. Sixty-six percent of respondents who agreed that their KPIs have changed because of AI also saw improvements in team-level collaboration.


Significant Change, Advancing Values


Achieving these cultural benefits, particularly at the organizational level, can require considerable change. As Pernod Ricard’s Calloc’h describes it, “Some processes get changed in a significant way because the data and the processing of the data through AI give us more certainty about some of the elements. You can make quicker decisions live, during a meeting. You can iterate more frequently. And you don’t have to wait six months for the return on investment of a campaign to adapt the new wave or to scale it. In fact, you can have more elements. So yes, it’s significantly changing processes of decision-making.” Using AI can accelerate the quality and pace of organizational life extensively, requiring considerable change.


But our research suggests that even when organizations make substantial changes associated with AI, culture does not suffer — quite the opposite, in fact. For example, implementing AI is associated with better morale in general. But when combined with business process change, the effects are even more pronounced: The greater (in both number and extent) the change, the greater the improvements in morale. To wit, 57% of organizations that made few changes in business processes reported an increase in morale, while 66% of organizations that made many changes reported an increase in morale. (See Figure 7.) The more that an organization uses AI, the more opportunities there are for cultural benefit.





A strong culture helps encourage AI adoption, and adopting AI can strengthen organizational culture. This cyclical relationship can build through numerous individual process improvements to enhance the overall organizational culture. Zeighami says that when he introduced AI at H&M, he wanted to avoid the common practice of “making one part of your organization become very good at that, and then the rest are still lagging behind.”


“It’s almost like putting a tire on a car,” he explains. “You don’t screw one bolt really hard and then do the next one. You just do every bolt a little bit and then tighten everything up. And I think that has been a really good approach for us.” Zeighami deployed AI for many company processes, including fashion forecasting, demand forecasting, and price management, along with more personalized customer-facing initiatives. “It’s been a very vast approach,” he observes. “Not going too deep, but a little bit in every area to enhance and elevate and change the mindset for everybody so we can become data-led, AI-led, going forward. And we have seen a lot of interesting results. In some areas we even see that working with the AI product has changed people’s way of working with other stuff, because there’s a proximity impact on the business.” Once an organization introduces AI widely, it can come back and improve not only individual processes but the interfaces between those processes, strengthening the organization as a whole.


Through repeated application and managerial attention, the virtuous cycle between organizational culture and AI use can result in a more cohesive organization, consistently reflecting its desired strategic values. As a result, responsible AI adoption transcends legitimate issues around minimizing bias (in product design, promotion, and customer service) and manipulation (of customers, pricing, and other business practices). Instead, AI becomes a managerial tool to align microbehavior with broader goals, including societal purpose, equity, and inclusivity.


For example, JoAnn Stonier, chief data officer at Mastercard, reports that the financial services corporation launched a data responsibility initiative in 2018 that involved privacy and security issues and included “working hard on our ethical AI process.” Many of her workplace conversations about AI, she adds, “center on minimization of bias as well as how we build an inclusive future.” But the conversations don’t stop there, she says. “The events of this past year have taught us that we need to pay attention to how we are designing products for society and that our data sets are really important. What are we feeding into the machines, and how do we design our algorithmic processes, and what is it going to learn from us?


“We understand that data sets are going to have all sorts of bias in them,” she continues. “I think we can begin to design a better future, but it means being very mindful of what’s inherent in the data set. What’s there and what’s missing?” These discussions help articulate values around which the organization can align, she says. “The whole firm is really getting behind this idea of developing a broad-based playbook so that everybody in the organization understands how to think about inclusive concepts.”



Pervasive change is complex. As founding director of the Notre Dame-IBM Technology Ethics Lab, Elizabeth Renieris is acutely aware of the complexities of these conversations and how they continue to evolve. “The ethics conversation in the past couple of years started out with the lens very much on the technology,” she says. “It’s been turned around and focused on who’s building it and who’s at the table — those are the really important questions.


“The value of ethics,” she adds, “is, rather than looking at the narrow particulars and tweaking around the edges of the specific technology or implementation, to step back and have that conversation about values to ask, ‘What are our values, and how do those values align with what it is that we’re working on from a technology standpoint?’” Stepping back may cause discomfort. But through these conversations, AI can have a profound effect on organizational culture.