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30+ Digital Transformation Case Studies & Success Stories [2024]

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Digital transformation has been on the executive agenda for the past decade and ~ 90% of companies have already initiated their first digital strategy. However, given the increasing pace of technological innovation, there are numerous areas to focus on. A lack of focus leads to failed initiatives. Digital transformation leaders need to focus their efforts but they are not clear about in which areas to focus their digital transformation initiatives.

We see that digital transformation projects focusing on customer service and operations tend to be more heavily featured in case studies and we recommend enterprises to initially focus on digitally transforming these areas.

Research findings:

• Outsourcing is an important strategy for many companies’ digital transformation initiatives.
• Most successful digital transformation projects focus on customer service and operations

Michelin-EFFIFUEL

Michelin, a global tire manufacturer, launched its EFFIFUEL initiative in 2013 to reduce the fuel consumption of trucks. In this context, vehicles were equipped with telematics systems that collect and process data on the trucks, tires, drivers habits and fuel consumption conditions. By analyzing this data, fleet managers and executives at the trucking companies were able to make adjustments to reduce oil consumption.

• Business challenge : Inability to improve customer retention rates to target levels, due to trucks’ fuel consumption and CO2 emissions .
• Target customers : Fleet managers and operations managers at truck companies in Europe
• Line of business function : Customer success management and sales.
• Solution : By using smart devices, truck and tire performance degradation is detected and maintained from the start. The solution also nudges truck drivers into more cost and environmentally friendly driving.
• Business result : Enhanced customer retention and satisfaction. EFFIFUEL has brought fuel savings of 2.5 liters per 100 kilometers per truck. The company also reduced the environmental costs of transportation activities. According to Michellin, if all European trucking companies had been using the EFFIFUEL initiative, it would have caused a 9 tons of CO2 emission reduction.

Schneider Electric-Box

Schneider Electric is a global company with employees all over the world. Prior to the Box initiative , which is a cloud-based solution, business processes were relatively slow because it is difficult to process the same documents from different locations at the same time. Schneider Electric also needed a way to provide data management and security for its globally dispersed workforce. So Schneider Electric outsourced its own custom cloud environment that integrates with Microsoft Office applications to Box. The platform also ensures tight control of corporate data with granular permissions, content controls and the use of shared links.  Thanks to this initiative, the company has moved from 80% of its content hosted on-premises to 90% in the cloud and has a more flexible workforce.

• Business challenge : Inability to increase operational efficiency of the global workforce without capitulating to data security.
• Solution : Outsourcing company’s cloud-based platform to Box, that ensures data security and integration with Microsoft Office programs to ensure ease of doing business.
• Business result : Schneider Electric connects its 142.000 workers within one platform which hosts 90% of its documents.

Thomas Pink-Fits.me

British shirt maker Thomas Pink, part of the Louis Vuitton Moet Hennessey group, has outsourced the development of its online sales platform to Fits.me Virtual Fitting Room . The aim of the initiative was to gain a competitive advantage over its competitors in e-commerce. Thanks to the online platform developed, customers can determine how well the shirt they are buying fits them by entering their body size.

The platform also helps Fits.me gain better customer insight as previously unknown customer data, including body measurements and fit preferences, becomes available. In this way, the platform can offer customers the clothes that fit them better.

• Business challenge : Lack of visibility into  online sales and customers’ preferences.
• Target customers : Online buyers and users.
• Line of business function : Sales and customer success management.
• Solution : Outsourcing the development of the online platform to Fits.me Virtual Fitting Room .
• Business result : Improved customer satisfaction and engagement. Thomas Pink reports that customers who enter the virtual fitting room are more likely to purchase a product than those who do not. There are many successful digital transformation projects from different industries, but we won’t go into every case study. Therefore, we provide you with a sortable list of 31 successful case studies. We categorized them as:
• System Improvement : changing the way existing businesses work by introducing new technologies.
• Innovation : creating new business practices, based on the latest technology.

If you are ready to start you digital transformation journey,  you can check our data-driven and comprehensive list of digital transformation consultant companies .

To find out more about digital transformation, you can also read our digital transformation best practices , digital transformation roadmap and digital transformation culture articles.

You can also check our sustainability case studies article which include ESG related success stories.

For any further assistance please contact us:

This article was drafted by former AIMultiple industry analyst Görkem Gençer.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

AIMultiple.com Traffic Analytics, Ranking & Audience , Similarweb. Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics , Business Insider. Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are , Washington Post. Data management barriers to AI success , Deloitte. Empowering AI Leadership: AI C-Suite Toolkit , World Economic Forum. Science, Research and Innovation Performance of the EU , European Commission. Public-sector digitization: The trillion-dollar challenge , McKinsey & Company. Hypatos gets $11.8M for a deep learning approach to document processing , TechCrunch. We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million , Business Insider.

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Digital Transformation Examples: 3 Company Case Studies

Learn how three legendary companies—Walmart, Ford, and Anheuser-Busch InBev—improved customer experience by focusing digital transformation around data.

Digital transformation is a process by which a company invests in building out new digital products and services in the effort to rethink the business around digital. An effective digital transformation improves customer experience and enhances the way a company operates behind the scenes.

To digitally transform, your business needs to deploy new products and technologies. With these new products come new ways to connect with your customers and more data to inform roadmaps and strategies.

Once the investment in digital begins, your business can use new products and data to identify growth opportunities. The three case studies below—from Ford, Walmart, and Anheuser-Busch InBev—show how legendary companies went beyond simply creating an app and truly re-thought how digital efforts supported sustainable growth for the business.

• Digital transformation brings about new products and services that improve the customer experience.
• Digital transformation can also be an investment into new systems, goals, and methodologies that make internal processes more efficient.
• Digital transformation gives you more informative behavioral data and more touchpoints with the customer.
• You can leverage the new data gained from digital transformation efforts to further improve the customer experience and drive sustainable growth.
• AB InBev, Walmart, and Ford used investments in digital technology to accelerate internal processes and deploy new digital products that, consequently, provided valuable data on the customer experience and influenced future business investments.

3 examples of digital transformation through data

Here are three examples of legendary companies that embarked on digital transformation with a focus on data. These companies carefully considered how new technology could bring about data that both made internal processes more efficient and produced insights about how to grow customer value.

Brewing company AB InBev underwent a digital transformation by compiling their network of independent breweries into a unified powerhouse . One of their priorities was getting their data in the cloud, and by doing so, employees can now pull data that’s gathered globally and use it to make data-backed decisions.

For example, more accurate demand forecasting means AB InBev teams can match supply with demand—essential for such a large company with a complex supply chain. Access to data from all the breweries means they’re able to experiment faster and roll out changes that improve business processes.

Gathering more data and opening up that data to internal teams was just the first step of the process, though. AB InBev capitalized on their digital investments by launching an ecommerce marketplace called BEES for their SMB customers—the “mom and pop shops”—to order products from. With the BEES platform, AB InBev found that their small and medium-sized businesses browsed the store on the mobile app and added items to their cart throughout the day—however, they only made the final purchases later in the evening.

Based on this behavioral data, the BEES team started to send push notifications after 6:00 p.m., recommending relevant products, which led to increased sales and greater customer satisfaction. By the end of June 2021, BEES had gained over 1.8 million monthly active users and had captured more than $7.5B in Gross Merchandise Volume .

Jason Lambert, the SVP of product at BEES, credits their success with the hard data that told them how their customers behaved and what they needed: “it turned out to be a thousand times better than any of our previous strategies or assumptions.” BEES used behavioral analytics to respond quickly, changing the buying experience to match the needs and habits of their retailers.

As a traditional brick-and-mortar retailer, Walmart began digital transformation when they opened an online marketplace. However, digital transformation is an ongoing process—it doesn’t end at the first website. A digital transformation means companies refocus their operations around digital technology—and this usually happens both internally and in a customer-facing way.

To drive more customer value through digital touchpoints, Walmart set up mobile apps and a website to allow customers to purchase goods online. After analyzing customer behavioral information from their app, they added more services such as same-day pickup, mobile ordering, and “buy now, pay later.”

To be successful with digital transformation, Walmart prioritized data access for everyone on their teams. Breaking down internal silos allowed employees to take ownership; They acted fast and made concrete changes to improve the customer experience.

Walmart’s head of mobile marketing, Sherry Thomas-Zon, notes how critical data—and access to data—are to digital operations. “Our marketing and product teams are always looking at numbers,” Thomas-Zon said. “You can’t work quickly without a self-service data and analytics tool for marketing, especially in an organization as large as Walmart. It keeps our teams agile, despite our size and the increasing amount of data we collect and analyze.”

Ford has embraced several digital transformation initiatives—including using technology to transform and improve the manufacturing process at one of its biggest factories. Not having the correct parts available holds up workers and slows down the production process. Ford introduced a material flow wireless parts system so they could track the quantities of different parts and make sure there were enough available.

In 2016, Ford also introduced a digital product for their customers—the FordPass app . It allows Ford owners to remotely control their vehicles. For example, drivers can check their battery or fuel levels and lock or unlock their car from their phone.

To capitalize on these new digital touchpoints with the customer, Ford leveraged data to improve the experience of the FordPass app. First, the product team grouped customers based on the in-app behaviors they demonstrated. Then, based on each group’s activity, Ford personalized the app experience to provide more value. Jian Wei Hoh, head of business design at Ford, said, “ Designing around cohorts is a game-changer .”

Ford’s success is grounded in the same process as Walmart and AB InBev. They used their digital transformation to gather detailed information about how their consumers interact with their products. Then, they made data-led decisions to provide more value to their customers.

Overcoming common digital transformation challenges

It’s not called a transformation for no reason. You’re changing the way your business operates, which is no easy feat. Here are the common challenges you’ll face and how to overcome them.

Teams undergoing a digital transformation have to:

• Unlearn habits
• Get used to new structures and ways of collaborating
• Deal with changing roles
• Develop new skills

All of this takes time and, as you integrate new systems with the old, there’s a risk that teams will get siloed and chaos will ensue.

A key way of overcoming these challenges is planning. Create a digital transformation strategy roadmap in advance. Outline your integration strategy and detail how this will affect each team. Once you’ve created your plan, share it with the entire company, so everyone can use it as a single reference point. Use a project management tool that allows team members to get a big-picture overview and see granular details like the tasks they’re responsible for.

It takes time for teams to onboard and move away from what was successful under the previous system, for example, shifting from heavyweight to lightweight project planning. Make sure you factor some breathing space into your roadmap—give everyone a chance to get used to the new way of operating.

As part of a digital transformation, you’ll want your team to develop new skills as well. Upskill your team by incorporating digital skills into your employee development plans . Provide people with opportunities to learn and then track their progress.

More challenges arise if you believe there’s an end-state to digital transformation. New technology and new consumer behaviors are always emerging, which means digital transformation is an ongoing process. It’s not something you’ll complete in a week. Rather, it’s a continuous state of experimentation and improvement. At Amplitude, we refer to this process as digital optimization . If digital transformation brings new products, services, and business models to the fold, then digital optimization is about improving these outputs. Both digital transformation and digital optimization are important—digital transformation signals the start of new investments, and digital optimization compounds them.

Tips for building a digital transformation strategy

A digital transformation won’t magically grant you more profit. Examine how each part of the transformation will affect your customers and your employees. Then, you can be intentional and introduce initiatives that positively impact your business.

Diagnose what you want from a digital transformation first

There are different ways of going about a digital transformation. Some companies prefer to implement an all-inclusive digital strategy, and they transform all parts of their organization at the same time. Others opt for a less-risky incremental strategy. Every company is different. To choose the best approach, examine your whole organization and analyze where digital systems could help.

Consider your business goals. Investigate how a digital transformation could impact the customer experience. What new products could you provide? How could you improve your services? For example, you might use artificial intelligence to create a chatbot that reduces customer service wait times—or purchase software that does the same.

You’ll also want to consider your business processes. How could a digital transformation speed you up? Improve your operations? Allow more collaboration between teams? Asking these questions allows you to challenge the way you operate and will help you identify problems in your organization that you might not have noticed before. For example, perhaps your deliveries are often delayed, and you could make delivery smoother by digitizing elements of your supply chain .

Get cross-team involvement

Though different teams may work separately, your customers are affected by each department. Collaboration elevates everyone’s work because it means people can make informed decisions.

Make sure you get input from all of the right stakeholders when you create your digital transformation strategy. Ask:

• What processes hold you up?
• Where are the bottlenecks?
• What data would be useful for you?

Allow everyone to access the data they need without input from anyone else. Help your employees improve their data literacy . Start by providing training so everyone can use the data tools and software in your organization—consider setting up a capability academy for data skills . To help everyone in your organization access and analyze data, adopt easy-to-use self-service tools. Then, lead by example. Provide inspiration by using data storytelling in your presentations to explain the decisions you make.

Encourage collaboration between teams by creating shared resources, so they have spaces to present insights and submit suggestions. This could be as simple as creating a Google Doc for brainstorming that multiple teams can access, or sharing charts directly within your analytics solution like with Amplitude Notebooks . Then, you can start to experiment and make improvements to the digital customer experience like Walmart, Ford, and AB InBev did.

Once your digital transformation is moving, a digital optimization strategy is an opportunity to generate growth. Your digital transformation initiatives will continue in parallel, and the process will become a feedback loop:

• Deploy new digital systems and products
• Analyze the data that comes forth from these investments. Use it to draw insights about your customers or processes.
• Make decisions based on the data and make changes.
• Repeat. (Or, optimize .)

Always focus on your customers

Keep customer needs at the heart of what you do. Let them be your guiding light as you go through your digital transformation—as you gather more data about how your customers interact with your new digital products, use it to make the experience even better for them. It’ll lead to more trust and loyalty and, ultimately, result in more recurring revenue.

To continue your learning about digital transformation and optimization, join an Amplitude workshop or webinar or read our Guide to Digital Optimization .

• MIT Sloan. How to build data literacy in your company
• McKinsey & Company. Digital transformation: Raising supply-chain performance to new levels
• Harvard Business Review. Boost Your Team’s Data Literacy
• Datanami. From Big Beer to Big Data: Inside AB InBev’s Digital Transformation
• Predictable Profits. How Ford Embraced Digital Transformation
• APMG International. Heavyweight v Lightweight Management
• Whatfix. Upskilling Your Workforce in 2022

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Technology Strategy

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Why technology strategy matters.

the revenue growth when leaders double down on investments in technology and innovation

of CIO/CTOs are primarily focusing investment on revenue growth as opposed to cutting costs

of CIOs are focusing on an overall business transformation, versus a single function, in 2024

of enterprise transformation projects fail to meet expectations

Start with technology, then reinvent

Use everything technology offers to build a better business.

What you can do

Get out of tech debt and into tech value.

Curb your tech debt and focus your tech spending on the activities that will power your business growth. Give your management team a shared understanding of how tech can deliver more value.

Design an enterprise architecture that allows your business to soar

Make the most of digital core technologies and techniques to improve business continuity and reduce your risk. Reinvent every aspect of your IT using generative AI.

Build an operating model that’s as innovative as your team

Respond to changing market demands by being nimbler. Design and implement an intelligent operating model built for business agility, resiliency and growth.

Have a clear vision for your tech transformation

Prioritize what you want to achieve, set the success criteria, and establish a transformation office to deliver it.

What you’ll achieve

A tech strategy everyone supports

Create a coalition for change with a plan that also fires up your business, tech and finance teams.

Total transparency on where your tech spend is going

With a clear view, you can decide where to reduce, redistribute and expand your tech investments.

A vision for your future architecture

Get the outcomes your business needs while continuously transforming your organization at scale.

An operating model that is your competitive edge

Get an intelligent operating model that moves at the same pace as your customers.

A transformation that meets expectations

Track and communicate the value of your enterprise transformation and get insights to help you make better decisions.

What’s trending in technology strategy

This is a singular moment for CIOs: here is how they can take advantage to unlock true business value across their enterprise.

How do you simplify a complex enterprise transformation; Accenture’s Jason Sain recommends starting with a clear vision and value creation story.

By focusing on new opportunities provided by cloud, data and AI, CSPs can accelerate their legacy technology transformation to resolve tech debt and position themselves for new product and service growth. 

CIOs can bring the greatest value to transformation. Accenture’s Greg Douglass explains how CIOs address new challenges in today’s business.

If Agile is challenging, meet multi-speed; a model combining the best of both worlds to gain agility.

Accenture’s Kit Friend explains what’s at risk when CIOs mistake a hybrid solution for Agile, and why multi-speed isn’t always the answer.

Five imperatives the C-suite must address to reinvent in the age of generative AI.

Greg Douglass from Accenture advises how to boost a tech-savvy board as embracing digital transformation requires tech expertise across enterprises.

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Top 40 Most Popular Case Studies of 2021

Two cases about Hertz claimed top spots in 2021's Top 40 Most Popular Case Studies

Two cases on the uses of debt and equity at Hertz claimed top spots in the CRDT’s (Case Research and Development Team) 2021 top 40 review of cases.

Hertz (A) took the top spot. The case details the financial structure of the rental car company through the end of 2019. Hertz (B), which ranked third in CRDT’s list, describes the company’s struggles during the early part of the COVID pandemic and its eventual need to enter Chapter 11 bankruptcy. 

The success of the Hertz cases was unprecedented for the top 40 list. Usually, cases take a number of years to gain popularity, but the Hertz cases claimed top spots in their first year of release. Hertz (A) also became the first ‘cooked’ case to top the annual review, as all of the other winners had been web-based ‘raw’ cases.

Besides introducing students to the complicated financing required to maintain an enormous fleet of cars, the Hertz cases also expanded the diversity of case protagonists. Kathyrn Marinello was the CEO of Hertz during this period and the CFO, Jamere Jackson is black.

Sandwiched between the two Hertz cases, Coffee 2016, a perennial best seller, finished second. “Glory, Glory, Man United!” a case about an English football team’s IPO made a surprise move to number four.  Cases on search fund boards, the future of malls,  Norway’s Sovereign Wealth fund, Prodigy Finance, the Mayo Clinic, and Cadbury rounded out the top ten.

Other year-end data for 2021 showed:

• Online “raw” case usage remained steady as compared to 2020 with over 35K users from 170 countries and all 50 U.S. states interacting with 196 cases.
• Fifty four percent of raw case users came from outside the U.S..
• The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines.
• Twenty-six of the cases in the list are raw cases.
• A third of the cases feature a woman protagonist.
• Orders for Yale SOM case studies increased by almost 50% compared to 2020.
• The top 40 cases were supervised by 19 different Yale SOM faculty members, several supervising multiple cases.

CRDT compiled the Top 40 list by combining data from its case store, Google Analytics, and other measures of interest and adoption.

All of this year’s Top 40 cases are available for purchase from the Yale Management Media store .

And the Top 40 cases studies of 2021 are:

1.   Hertz Global Holdings (A): Uses of Debt and Equity

2.   Coffee 2016

3.   Hertz Global Holdings (B): Uses of Debt and Equity 2020

4.   Glory, Glory Man United!

5.   Search Fund Company Boards: How CEOs Can Build Boards to Help Them Thrive

6.   The Future of Malls: Was Decline Inevitable?

7.   Strategy for Norway's Pension Fund Global

8.   Prodigy Finance

9.   Design at Mayo

10. Cadbury

11. City Hospital Emergency Room

13. Volkswagen

14. Marina Bay Sands

15. Shake Shack IPO

16. Mastercard

17. Netflix

18. Ant Financial

19. AXA: Creating the New CR Metrics

20. IBM Corporate Service Corps

21. Business Leadership in South Africa's 1994 Reforms

22. Alternative Meat Industry

23. Children's Premier

24. Khalil Tawil and Umi (A)

25. Palm Oil 2016

26. Teach For All: Designing a Global Network

27. What's Next? Search Fund Entrepreneurs Reflect on Life After Exit

28. Searching for a Search Fund Structure: A Student Takes a Tour of Various Options

30. Project Sammaan

31. Commonfund ESG

32. Polaroid

33. Connecticut Green Bank 2018: After the Raid

34. FieldFresh Foods

35. The Alibaba Group

36. 360 State Street: Real Options

37. Herman Miller

38. AgBiome

39. Nathan Cummings Foundation

40. Toyota 2010

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Artificial intelligence in strategy

Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform .

Joanna Pachner: What does artificial intelligence mean in the context of strategy?

Yuval Atsmon: When people talk about artificial intelligence, they include everything to do with analytics, automation, and data analysis. Marvin Minsky, the pioneer of artificial intelligence research in the 1960s, talked about AI as a “suitcase word”—a term into which you can stuff whatever you want—and that still seems to be the case. We are comfortable with that because we think companies should use all the capabilities of more traditional analysis while increasing automation in strategy that can free up management or analyst time and, gradually, introducing tools that can augment human thinking.

Joanna Pachner: AI has been embraced by many business functions, but strategy seems to be largely immune to its charms. Why do you think that is?

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Yuval Atsmon: You’re right about the limited adoption. Only 7 percent of respondents to our survey about the use of AI say they use it in strategy or even financial planning, whereas in areas like marketing, supply chain, and service operations, it’s 25 or 30 percent. One reason adoption is lagging is that strategy is one of the most integrative conceptual practices. When executives think about strategy automation, many are looking too far ahead—at AI capabilities that would decide, in place of the business leader, what the right strategy is. They are missing opportunities to use AI in the building blocks of strategy that could significantly improve outcomes.

I like to use the analogy to virtual assistants. Many of us use Alexa or Siri but very few people use these tools to do more than dictate a text message or shut off the lights. We don’t feel comfortable with the technology’s ability to understand the context in more sophisticated applications. AI in strategy is similar: it’s hard for AI to know everything an executive knows, but it can help executives with certain tasks.

When executives think about strategy automation, many are looking too far ahead—at AI deciding the right strategy. They are missing opportunities to use AI in the building blocks of strategy.

Joanna Pachner: What kind of tasks can AI help strategists execute today?

Yuval Atsmon: We talk about six stages of AI development. The earliest is simple analytics, which we refer to as descriptive intelligence. Companies use dashboards for competitive analysis or to study performance in different parts of the business that are automatically updated. Some have interactive capabilities for refinement and testing.

The second level is diagnostic intelligence, which is the ability to look backward at the business and understand root causes and drivers of performance. The level after that is predictive intelligence: being able to anticipate certain scenarios or options and the value of things in the future based on momentum from the past as well as signals picked in the market. Both diagnostics and prediction are areas that AI can greatly improve today. The tools can augment executives’ analysis and become areas where you develop capabilities. For example, on diagnostic intelligence, you can organize your portfolio into segments to understand granularly where performance is coming from and do it in a much more continuous way than analysts could. You can try 20 different ways in an hour versus deploying one hundred analysts to tackle the problem.

Predictive AI is both more difficult and more risky. Executives shouldn’t fully rely on predictive AI, but it provides another systematic viewpoint in the room. Because strategic decisions have significant consequences, a key consideration is to use AI transparently in the sense of understanding why it is making a certain prediction and what extrapolations it is making from which information. You can then assess if you trust the prediction or not. You can even use AI to track the evolution of the assumptions for that prediction.

Those are the levels available today. The next three levels will take time to develop. There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis. From there, you go to delegating certain decision authority to AI, with constraints and supervision. Eventually, there is the point where fully autonomous AI analyzes and decides with no human interaction.

Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.

Joanna Pachner: What kind of businesses or industries could gain the greatest benefits from embracing AI at its current level of sophistication?

Yuval Atsmon: Every business probably has some opportunity to use AI more than it does today. The first thing to look at is the availability of data. Do you have performance data that can be organized in a systematic way? Companies that have deep data on their portfolios down to business line, SKU, inventory, and raw ingredients have the biggest opportunities to use machines to gain granular insights that humans could not.

Companies whose strategies rely on a few big decisions with limited data would get less from AI. Likewise, those facing a lot of volatility and vulnerability to external events would benefit less than companies with controlled and systematic portfolios, although they could deploy AI to better predict those external events and identify what they can and cannot control.

Third, the velocity of decisions matters. Most companies develop strategies every three to five years, which then become annual budgets. If you think about strategy in that way, the role of AI is relatively limited other than potentially accelerating analyses that are inputs into the strategy. However, some companies regularly revisit big decisions they made based on assumptions about the world that may have since changed, affecting the projected ROI of initiatives. Such shifts would affect how you deploy talent and executive time, how you spend money and focus sales efforts, and AI can be valuable in guiding that. The value of AI is even bigger when you can make decisions close to the time of deploying resources, because AI can signal that your previous assumptions have changed from when you made your plan.

Joanna Pachner: Can you provide any examples of companies employing AI to address specific strategic challenges?

Yuval Atsmon: Some of the most innovative users of AI, not coincidentally, are AI- and digital-native companies. Some of these companies have seen massive benefits from AI and have increased its usage in other areas of the business. One mobility player adjusts its financial planning based on pricing patterns it observes in the market. Its business has relatively high flexibility to demand but less so to supply, so the company uses AI to continuously signal back when pricing dynamics are trending in a way that would affect profitability or where demand is rising. This allows the company to quickly react to create more capacity because its profitability is highly sensitive to keeping demand and supply in equilibrium.

Joanna Pachner: Given how quickly things change today, doesn’t AI seem to be more a tactical than a strategic tool, providing time-sensitive input on isolated elements of strategy?

Yuval Atsmon: It’s interesting that you make the distinction between strategic and tactical. Of course, every decision can be broken down into smaller ones, and where AI can be affordably used in strategy today is for building blocks of the strategy. It might feel tactical, but it can make a massive difference. One of the world’s leading investment firms, for example, has started to use AI to scan for certain patterns rather than scanning individual companies directly. AI looks for consumer mobile usage that suggests a company’s technology is catching on quickly, giving the firm an opportunity to invest in that company before others do. That created a significant strategic edge for them, even though the tool itself may be relatively tactical.

Joanna Pachner: McKinsey has written a lot about cognitive biases  and social dynamics that can skew decision making. Can AI help with these challenges?

Yuval Atsmon: When we talk to executives about using AI in strategy development, the first reaction we get is, “Those are really big decisions; what if AI gets them wrong?” The first answer is that humans also get them wrong—a lot. [Amos] Tversky, [Daniel] Kahneman, and others have proven that some of those errors are systemic, observable, and predictable. The first thing AI can do is spot situations likely to give rise to biases. For example, imagine that AI is listening in on a strategy session where the CEO proposes something and everyone says “Aye” without debate and discussion. AI could inform the room, “We might have a sunflower bias here,” which could trigger more conversation and remind the CEO that it’s in their own interest to encourage some devil’s advocacy.

We also often see confirmation bias, where people focus their analysis on proving the wisdom of what they already want to do, as opposed to looking for a fact-based reality. Just having AI perform a default analysis that doesn’t aim to satisfy the boss is useful, and the team can then try to understand why that is different than the management hypothesis, triggering a much richer debate.

In terms of social dynamics, agency problems can create conflicts of interest. Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business. AI provides a neutral way based on systematic data to manage those debates. It’s also useful for executives with decision authority, since we all know that short-term pressures and the need to make the quarterly and annual numbers lead people to make different decisions on the 31st of December than they do on January 1st or October 1st. Like the story of Ulysses and the sirens, you can use AI to remind you that you wanted something different three months earlier. The CEO still decides; AI can just provide that extra nudge.

Joanna Pachner: It’s like you have Spock next to you, who is dispassionate and purely analytical.

Yuval Atsmon: That is not a bad analogy—for Star Trek fans anyway.

Joanna Pachner: Do you have a favorite application of AI in strategy?

Yuval Atsmon: I have worked a lot on resource allocation, and one of the challenges, which we call the hockey stick phenomenon, is that executives are always overly optimistic about what will happen. They know that resource allocation will inevitably be defined by what you believe about the future, not necessarily by past performance. AI can provide an objective prediction of performance starting from a default momentum case: based on everything that happened in the past and some indicators about the future, what is the forecast of performance if we do nothing? This is before we say, “But I will hire these people and develop this new product and improve my marketing”— things that every executive thinks will help them overdeliver relative to the past. The neutral momentum case, which AI can calculate in a cold, Spock-like manner, can change the dynamics of the resource allocation discussion. It’s a form of predictive intelligence accessible today and while it’s not meant to be definitive, it provides a basis for better decisions.

Joanna Pachner: Do you see access to technology talent as one of the obstacles to the adoption of AI in strategy, especially at large companies?

Yuval Atsmon: I would make a distinction. If you mean machine-learning and data science talent or software engineers who build the digital tools, they are definitely not easy to get. However, companies can increasingly use platforms that provide access to AI tools and require less from individual companies. Also, this domain of strategy is exciting—it’s cutting-edge, so it’s probably easier to get technology talent for that than it might be for manufacturing work.

The bigger challenge, ironically, is finding strategists or people with business expertise to contribute to the effort. You will not solve strategy problems with AI without the involvement of people who understand the customer experience and what you are trying to achieve. Those who know best, like senior executives, don’t have time to be product managers for the AI team. An even bigger constraint is that, in some cases, you are asking people to get involved in an initiative that may make their jobs less important. There could be plenty of opportunities for incorpo­rating AI into existing jobs, but it’s something companies need to reflect on. The best approach may be to create a digital factory where a different team tests and builds AI applications, with oversight from senior stakeholders.

The big challenge is finding strategists to contribute to the AI effort. You are asking people to get involved in an initiative that may make their jobs less important.

Joanna Pachner: Do you think this worry about job security and the potential that AI will automate strategy is realistic?

Yuval Atsmon: The question of whether AI will replace human judgment and put humanity out of its job is a big one that I would leave for other experts.

The pertinent question is shorter-term automation. Because of its complexity, strategy would be one of the later domains to be affected by automation, but we are seeing it in many other domains. However, the trend for more than two hundred years has been that automation creates new jobs, although ones requiring different skills. That doesn’t take away the fear some people have of a machine exposing their mistakes or doing their job better than they do it.

Joanna Pachner: We recently published an article about strategic courage in an age of volatility  that talked about three types of edge business leaders need to develop. One of them is an edge in insights. Do you think AI has a role to play in furnishing a proprietary insight edge?

Yuval Atsmon: One of the challenges most strategists face is the overwhelming complexity of the world we operate in—the number of unknowns, the information overload. At one level, it may seem that AI will provide another layer of complexity. In reality, it can be a sharp knife that cuts through some of the clutter. The question to ask is, Can AI simplify my life by giving me sharper, more timely insights more easily?

Joanna Pachner: You have been working in strategy for a long time. What sparked your interest in exploring this intersection of strategy and new technology?

Yuval Atsmon: I have always been intrigued by things at the boundaries of what seems possible. Science fiction writer Arthur C. Clarke’s second law is that to discover the limits of the possible, you have to venture a little past them into the impossible, and I find that particularly alluring in this arena.

AI in strategy is in very nascent stages but could be very consequential for companies and for the profession. For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today. It’s conceivable that competitive advantage will increasingly rest in having executives who know how to apply AI well. In some domains, like investment, that is already happening, and the difference in returns can be staggering. I find helping companies be part of that evolution very exciting.

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New advances in technology are upending education, from the recent debut of new artificial intelligence (AI) chatbots like ChatGPT to the growing accessibility of virtual-reality tools that expand the boundaries of the classroom. For educators, at the heart of it all is the hope that every learner gets an equal chance to develop the skills they need to succeed. But that promise is not without its pitfalls.

“Technology is a game-changer for education – it offers the prospect of universal access to high-quality learning experiences, and it creates fundamentally new ways of teaching,” said Dan Schwartz, dean of Stanford Graduate School of Education (GSE), who is also a professor of educational technology at the GSE and faculty director of the Stanford Accelerator for Learning . “But there are a lot of ways we teach that aren’t great, and a big fear with AI in particular is that we just get more efficient at teaching badly. This is a moment to pay attention, to do things differently.”

For K-12 schools, this year also marks the end of the Elementary and Secondary School Emergency Relief (ESSER) funding program, which has provided pandemic recovery funds that many districts used to invest in educational software and systems. With these funds running out in September 2024, schools are trying to determine their best use of technology as they face the prospect of diminishing resources.

Here, Schwartz and other Stanford education scholars weigh in on some of the technology trends taking center stage in the classroom this year.

AI in the classroom

In 2023, the big story in technology and education was generative AI, following the introduction of ChatGPT and other chatbots that produce text seemingly written by a human in response to a question or prompt. Educators immediately worried that students would use the chatbot to cheat by trying to pass its writing off as their own. As schools move to adopt policies around students’ use of the tool, many are also beginning to explore potential opportunities – for example, to generate reading assignments or coach students during the writing process.

AI can also help automate tasks like grading and lesson planning, freeing teachers to do the human work that drew them into the profession in the first place, said Victor Lee, an associate professor at the GSE and faculty lead for the AI + Education initiative at the Stanford Accelerator for Learning. “I’m heartened to see some movement toward creating AI tools that make teachers’ lives better – not to replace them, but to give them the time to do the work that only teachers are able to do,” he said. “I hope to see more on that front.”

He also emphasized the need to teach students now to begin questioning and critiquing the development and use of AI. “AI is not going away,” said Lee, who is also director of CRAFT (Classroom-Ready Resources about AI for Teaching), which provides free resources to help teach AI literacy to high school students across subject areas. “We need to teach students how to understand and think critically about this technology.”

Immersive environments

The use of immersive technologies like augmented reality, virtual reality, and mixed reality is also expected to surge in the classroom, especially as new high-profile devices integrating these realities hit the marketplace in 2024.

The educational possibilities now go beyond putting on a headset and experiencing life in a distant location. With new technologies, students can create their own local interactive 360-degree scenarios, using just a cell phone or inexpensive camera and simple online tools.

“This is an area that’s really going to explode over the next couple of years,” said Kristen Pilner Blair, director of research for the Digital Learning initiative at the Stanford Accelerator for Learning, which runs a program exploring the use of virtual field trips to promote learning. “Students can learn about the effects of climate change, say, by virtually experiencing the impact on a particular environment. But they can also become creators, documenting and sharing immersive media that shows the effects where they live.”

Integrating AI into virtual simulations could also soon take the experience to another level, Schwartz said. “If your VR experience brings me to a redwood tree, you could have a window pop up that allows me to ask questions about the tree, and AI can deliver the answers.”

Gamification

Another trend expected to intensify this year is the gamification of learning activities, often featuring dynamic videos with interactive elements to engage and hold students’ attention.

“Gamification is a good motivator, because one key aspect is reward, which is very powerful,” said Schwartz. The downside? Rewards are specific to the activity at hand, which may not extend to learning more generally. “If I get rewarded for doing math in a space-age video game, it doesn’t mean I’m going to be motivated to do math anywhere else.”

Gamification sometimes tries to make “chocolate-covered broccoli,” Schwartz said, by adding art and rewards to make speeded response tasks involving single-answer, factual questions more fun. He hopes to see more creative play patterns that give students points for rethinking an approach or adapting their strategy, rather than only rewarding them for quickly producing a correct response.

Data-gathering and analysis

The growing use of technology in schools is producing massive amounts of data on students’ activities in the classroom and online. “We’re now able to capture moment-to-moment data, every keystroke a kid makes,” said Schwartz – data that can reveal areas of struggle and different learning opportunities, from solving a math problem to approaching a writing assignment.

But outside of research settings, he said, that type of granular data – now owned by tech companies – is more likely used to refine the design of the software than to provide teachers with actionable information.

The promise of personalized learning is being able to generate content aligned with students’ interests and skill levels, and making lessons more accessible for multilingual learners and students with disabilities. Realizing that promise requires that educators can make sense of the data that’s being collected, said Schwartz – and while advances in AI are making it easier to identify patterns and findings, the data also needs to be in a system and form educators can access and analyze for decision-making. Developing a usable infrastructure for that data, Schwartz said, is an important next step.

With the accumulation of student data comes privacy concerns: How is the data being collected? Are there regulations or guidelines around its use in decision-making? What steps are being taken to prevent unauthorized access? In 2023 K-12 schools experienced a rise in cyberattacks, underscoring the need to implement strong systems to safeguard student data.

Technology is “requiring people to check their assumptions about education,” said Schwartz, noting that AI in particular is very efficient at replicating biases and automating the way things have been done in the past, including poor models of instruction. “But it’s also opening up new possibilities for students producing material, and for being able to identify children who are not average so we can customize toward them. It’s an opportunity to think of entirely new ways of teaching – this is the path I hope to see.”

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Helping to solve economic disparity in China through online entrepreneurship

Dr Wei Miao (UCL Faculty of Engineering Sciences) used UCL-Zhejiang University Strategic Partner Funds to understand the impact of regional promotional strategies on large e-commerce platforms.

10 May 2024

The Covid-19 pandemic revealed a range of socio-economic inequalities across the world, when government-imposed restrictions impacted the ability of some individuals to earn money. This was heightened in less economically developed countries. However, there are opportunities for entrepreneurs and small and medium-sized businesses to use e-commerce platforms such as Amazon and eBay to sell goods and earn a living. This is a particularly beneficial option during a mass crisis such as a pandemic because business can continue more or less as usual. However, the visibility of sellers on these platforms can vary, and in some instances, sellers and their products don’t get enough attention to make a living.

In response to this, Dr Wei Miao wanted to explore whether promoting whole regions – and not just individual sellers – on e-commerce platforms could improve the economic prospects for entrepreneurs. Following a successful application to the UCL-Zhejiang University Strategic Partner Funds, he partnered with Professor Liqiang Huang from Zhejiang University in China, to explore the topic in depth.

Wei and Liqiang in front of Qiushi Lecture Hall (the iconic building of ZJU)

Wei and Liqiang in front of Zhejiang University's main gate

Wei and Liqiang in Zhejiang University School of Management

The power of collective promotions online

“Taobao is the largest e-commerce platform in China,” Wei explained. “Our partners at Zhejiang University have contacts there, so through this project, we were able to collect very detailed sales data on a large scale. This covered many provinces and regions in China. Although this project is China-specific, we believe our findings could also be generalised to other developing economies.”

When buyers browse e-commerce platforms such as Taobao, Amazon or eBay, they commonly see endorsements. However, these endorsements are pitched at the level of products from individual sellers – using terminology such as ‘Amazon’s choice’ or ‘eBay bestseller’. Wei and his collaborators wanted to explore the benefits of endorsing whole geographical regions instead – known as a collective promotional strategy. Fortuitously, Taobao implemented a strategy like this in 2014 and is the only known large e-commerce platform that has done so. The platform would calculate the total sales of all sellers in a particular rural town. If the total sales exceeded certain thresholds, an endorsement called ‘Taobao Town’ would be given. As a result, this collective would be promoted to buyers across the whole Taobao platform.

“It means that we, as consumers, will know that a rural town might be under-developed in terms of its economy. But it produces some unique items that customers like,” Wei said. “We thought this type of promotion could be quite effective. We conducted causal inference econometric analyses to compare uncertified towns with certified ‘Taobao Towns’. The certified towns experienced an almost 32% increase in their sales because of this endorsement. That was our first major finding – collective promotion strategies can indeed help rural towns boost their sales.”

The team also found that the collective promotion strategy can help mitigate regional disparities between richer areas and underdeveloped areas. It is the least developed areas that have the most to gain from the ‘Taobao Town’ endorsement. And when a town gets the endorsement, people are more motivated to pursue an e-commerce career. This was particularly the case for female entrepreneurs and younger entrepreneurs, who tend to be underrepresented in entrepreneurship.

In addition, the research dispelled concerns that a ‘Taobao Town’ certification in one town might negatively impact the economy in neighbouring towns, or exacerbate inequalities in a region as a whole. “Our analysis shows that towns neighbouring certified towns also benefit from the endorsement of the focal town,” Wei said. “We believe this is due to the ‘encouraging effect’. People in neighbouring towns see the successes, learn how to do e-commerce well, and then thrive in their own businesses. It's a win-win situation for both the certified towns and the neighbouring towns.”

Improving regional economies in a changing world

Wei and his collaborators want to share the findings from this study to policymakers across the world, particularly in developing economies. “Regional economic disparity issues are common in other countries,” Wei explained. “Rolling out similar collective promotion strategies on other platforms can boost the visibility of remote locations and rural towns. This can help local entrepreneurs to develop their businesses and succeed in this post-Covid world.”

Together with his collaborators at Zhejiang University and Professor Christopher Tang from the University of California Los Angeles (UCLA), Wei has also recently completed a paper about these findings, which will be submitted to leading academic management journals for publication. The team is attending a range of academic conferences to share their findings with other academics. Crucially, Taobao has reacted very positively to the results, and the company is in the process of allocating more resources to work on its collective promotion strategy. Taobao also plans to run more training programmes to the sellers in certified ‘Taobao Towns’ so they can maximise the benefits of this endorsement.

Next, the team plans to analyse the data further to understand pre-Covid and post-Covid experiences. As there have been concerns that some demographic and ethnic groups were impacted differently by Covid-19, this information could help governments plan better for future crisis situations. Protecting the income and earning potential of everyone could be crucial in the future.

“Our successes so far mean we are trying to deepen this collaboration between UCL and Zhejiang University,” Wei said. “We want to involve more academics from each side, plus launch some joint teaching programmes and PhD programmes. I would really encourage other academics at UCL to apply for funding through UCL Global Engagement. Getting this funding is not the end of the story for us, it’s really the starting point.”

• The UCL-Zhejiang University Strategic Partner Funds is one of UCL's Partner Funds . It opens for applications on September 6, 2024.
• A Rising Tide that Lifts All Boats: The Effects of Collective Recognition Programs on E-Commerce Sellers and Regional Economic Development  (working paper)
• Dr Wei Miao's academic profile  
• UCL Faculty of Engineering Sciences
• UCL in East Asia
• UCL and China
• Aerial View of Rural Chinese Town and Farmland
• Credit: iStock

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May 14, 2024 • 2 min read

Strategic alliance with Nano One to deploy its One-Pot process technology

We’ve partnered with Nano One, a technology company with patented processes for the sustainable production of lithium-ion battery cathode active materials (CAM), to accelerate commercial scale deployment of its One-Pot process.

The process is anticipated to produce high quality cathode materials with a lower environmental impact . It eliminates wastewater and a sodium sulphate waste stream, a major challenge in current cathode material production processes.

The strategic alliance will accelerate the development and deployment of a new generation of battery cathode material plants sold and licensed to operators globally.

We’ll work with Nano One to integrate the One-Pot process into a design package, including specialized equipment. This will enable customers to create competitive CAM production facilities to meet growing market needs. The initial focus will be on lithium ferro phosphate (LFP) cathode chemistries and will extend to other chemistries in time.

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Our  Worley Chemetics  teams will design and fabricate the process reactors, incorporating metal alloys that are specifically designed for corrosive environments.

We’ll also work with Nano One on process and plant designs to create a design-one-build-many growth strategy.

“We expect this partnership to add value and significantly de-risk the One-Pot process, with Worley Chemetics’ patented metal alloys, reactor design and fabrication capability, and the technical and delivery capability of the broader Worley organization. We have found in Worley a collaborative, insightful and visionary team that is just as passionate about changing the way the world makes battery materials as we are,” said Dan Blondal, Nano One CEO.

“Nano One's innovative One-Pot process has the potential to transform the production and performance of battery materials. Combining Nano One’s technology with our expertise and scale in battery materials will advance our growth strategy while accelerating the deployment of this important technology,” added Laura Leonard, Group President, Technology Solutions.

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The forward-looking information herein includes expectations about the success of the alliance, the development and benefits of the CAM package, and the expansion and scalability of technology at the Candiac facilities. Additional operational and company risks for Nano Ones are detailed in the MD&A and Annual Information Form as of March 27, 2024, and other securities filings available at www.sedarplus.ca .

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How Apple Is Organized for Innovation

• Joel M. Podolny
• Morten T. Hansen

When Steve Jobs returned to Apple, in 1997, it had a conventional structure for a company of its size and scope. It was divided into business units, each with its own P&L responsibilities. Believing that conventional management had stifled innovation, Jobs laid off the general managers of all the business units (in a single day), put the entire company under one P&L, and combined the disparate functional departments of the business units into one functional organization. Although such a structure is common for small entrepreneurial firms, Apple—remarkably—retains it today, even though the company is nearly 40 times as large in terms of revenue and far more complex than it was in 1997. In this article the authors discuss the innovation benefits and leadership challenges of Apple’s distinctive and ever-evolving organizational model in the belief that it may be useful for other companies competing in rapidly changing environments.

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Idea in Brief

The challenge.

Major companies competing in many industries struggle to stay abreast of rapidly changing technologies.

One Major Cause

They are typically organized into business units, each with its own set of functions. Thus the key decision makers—the unit leaders—lack a deep understanding of all the domains that answer to them.

The Apple Model

The company is organized around functions, and expertise aligns with decision rights. Leaders are cross-functionally collaborative and deeply knowledgeable about details.

Apple is well-known for its innovations in hardware, software, and services. Thanks to them, it grew from some 8,000 employees and $7 billion in revenue in 1997, the year Steve Jobs returned, to 137,000 employees and $260 billion in revenue in 2019. Much less well-known are the organizational design and the associated leadership model that have played a crucial role in the company’s innovation success.

• Joel M. Podolny is the dean and vice president of Apple University in Cupertino, California. The former dean of the Yale School of Management, Podolny was a professor at Harvard Business School and the Stanford Graduate School of Business.
• MH Morten T. Hansen is a professor at the University of California, Berkeley, and a faculty member at Apple University, Apple. He is the author of Great at Work and Collaboration and coauthor of Great by Choice . He was named one of the top management thinkers in the world by the Thinkers50 in 2019. MortentHansen

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