Eight examples of artificial intelligence in action

Until recently, many companies have existed in a kind of limbo regarding artificial intelligence (AI) development, carrying out endless pilots and proofs of concept, but bringing very few AI-enabled projects through to enterprise production.

That’s changing quickly. According to the latest McKinsey Global Survey on AI, conducted in July 2024, 78% of respondents say their organisations use AI in at least one business function, up from 72% in early 2024 and 55% a year earlier. The use of generative AI (GenAI) has also increased, with 71% of respondents telling McKinsey that their organisations regularly use GenAI in at least one business function, up from 65% in early 2024. And AI investments continue to grow. More than three-quarters (78%) of business leaders said their organisation plans to increase its overall AI spending in the next financial year, according to the Deloitte AI Institute’s 2024 State of Generative AI in the Enterprise report.

How much business value AI has delivered so far, however, may be a matter of debate. For example, 21% of C-suite survey respondents reported that they feel GenAI is already transforming their organisation, compared to only 8% of non-C-suite respondents, according to the Deloitte report. And that’s just one type of AI, a catch-all phrase for cognition-like capabilities, including everything from computer vision and natural language processing to deep learning and agentic AI.

At this stage, there may be as many ways to get AI wrong as to get it right. However, exploring enterprise AI in action can demonstrate its potential, covering a wide range of tasks, including:

• Accelerating drug development
• Reinventing telecommunications business processes
• Designing toy cars
• Pollinating crops
• Increasing efficiency in large-scale manufacturing

Looking at what different companies are doing with AI—and how they are doing it—can provide inspiration for others as they envisage AI applications of their own.

Example 1: Goldman Sachs uses AI to write code

GenAI tools not only produce written language and images but also generate computer code. Goldman Sachs is conducting a “proof of concept” for assisted coding tools powered by GenAI.

Even while industry peers J.P. Morgan, Citigroup, Deutsche Bank, and Bank of America were reportedly preventing their employees from using GenAI, Goldman Sachs was placing the technology in the hands of its programmers to assess its initial value. CIO Marco Argenti explains that while it would not be wise to immediately entrust all important processes to AI, it is imperative that businesses attempt to envisage its potential. The firm has several proofs of concept underway in areas that include software development and document classification, which have shown some promising initial results.

Taking things a step further, Goldman began testing an autonomous software engineer from AI start-up Cognition in 2025 that is expected to soon work alongside the firm’s 12,000 human developers.

Takeaways

Goldman Sachs envisions AI as a companion to software developers rather than a replacement for them. It is also testing out large language models to upgrade the document classification management processes currently performed by traditional AI. The company is keeping the names of the tools it’s using under wraps, along with the specific departments that are testing the technology, but it has shared some early results.

• Developers have been able to use GenAI to write as much as 40% of their code automatically, which Argenti says could improve the productivity of human developers by somewhere in the low double figures.
• The bank is using the software not only to generate code but also to test it.
• The large language models are at least as good as humans at managing the company’s millions of documents.
• Goldman Sachs’s CIO regards GenAI as one of the greatest disruptions of his lifetime, on a par with the Internet, apps, or the cloud.
• The greatest challenges, he says, are adapting internal controls to mitigate any risks to the company or its customers and finding talent with expertise in large language models.
• The company’s use of GenAI has expanded, with its GS AI Assistant now available to all Goldman Sachs employees after successful trials with approximately 10,000 staff members.
• Argenti says he regards the autonomous software engineer, named Devin, as a “new employee” who can begin carrying out tasks on behalf of developers.

Learn more

• CIO Marco Argenti shared some early GenAI results in this Wall Street Journal¹  Q&A.
• Argenti tells CNBC how he envisions having hundreds—and, ultimately, thousands—of autonomous software engineers² working on behalf of the company, depending on the use case.
• In a recent podcast episode³, two analysts from Goldman Sachs Research discussed the power of automating more day-to-day enterprise processes, such as software development.
• Reuters reports on Goldman Sachs’ company-wide launch⁴ of its AI assistant in 2025.
• The Wall Street giant is “aggressively scaling AI⁵ for increased productivity and efficiency,” according to PYMNTS.

Example 2: Australian telecommunications company Telstra evolves from AI assistants to agentic reinvention

In mid-2023, Telstra developed a pair of GenAI tools—Ask Telstra and One Sentence Summary—to enhance customer support after service activation, as part of a strategic five-year agreement with Microsoft Azure OpenAI Service. These tools help agents quickly access accounts, product details, and summaries of past interactions, enabling faster troubleshooting and more personalised care. The GenAI systems reduced follow-up contacts by 20%, with 90% of its customer service employees reporting time savings and improved post-sales support quality, according to Telstra.

“We’re unlocking value and growth as technologies such as generative AI proliferate rapidly,” says Kim Krogh Andersen, Group Executive, Product & Technology at Telstra, in an article on the Microsoft website, adding that the company was “on track to improve all key business processes with AI by 2025.” Indeed, the company made an even greater leap forward with the announcement of its joint venture with Accenture to use AI to reinvent business processes and develop agentic workflows.

Takeaways

As Forrester Vice President and Senior Research Director Frederic Giron explains, Telstra’s newer approach to AI indicates a fundamental shift from a more piecemeal approach to automating existing tasks and business processes, to automating isolated tasks along existing business processes, to “reimagining how work gets done.” It addresses what Forrester has called the “process chasm”—the gap between a focus on productivity optimisation and true business reinvention.

• Gaining a competitive advantage with AI requires more than simply deploying GenAI assistants—it necessitates a complete rethinking of how tasks are carried out in the first place.
• Telstra is combining its crown jewels—its extensive telecom expertise—with Accenture’s AI assets and talent to create AI solutions for everything from process optimisation to customer experience.
• The approach is to develop AI solutions for internal use, perfect the system, and then turn it into a product for sale.
• While other companies are juggling multiple partnerships, Telstra has reduced its AI collaborators to two, with an emphasis on co-innovation.

Learn more

• Forrester’s Giron explains how Telstra’s “build, then sell⁶” approach amortises large substantial up-front AI development costs across a broader revenue base and also creates new revenue streams.
• Telstra executives explain the company’s development of specialised co-pilots⁷ for customer service agents.
• The Guardian reports that Telstra’s Chief Executive Officer, Vicki Brady, told investors that AI will play a major role⁸ in Telstra reinventing itself over the next five years and that its workforce would probably decrease by 2030.
• A press release on the Accenture website explains how the seven-year joint venture Telstra will use Accenture’s £3 billion AI investment⁹ to reinvent business processes end-to-end through new capabilities such as agentic AI¹⁰.

Example 3: Moderna’s data and AI application accelerate mRNA development

Biotech company Moderna’s AI investments have paid off for drug development at a time when speed was vital for marketplace success. Founded more than a decade before the COVID-19 crisis, the company spent years building integrated data science and AI software to support the repeatable development of thousands of different mRNA-based medicines and vaccines. The web-based application includes reusable code for workflow automation, data capture, and model building. This assists scientists in designing novel mRNA constructs, improving their efficacy, and ordering samples through a high-throughput, preclinical production line.

Digital and analytics-based from the outset, Moderna was built to overcome the odds of traditional high-risk, high-return pharmaceutical development. Pharmaceutical companies typically spend a billion pounds developing a medicine in the hope of receiving many billions in return, but see a success rate of less than 15%. AI can improve those chances to 50/50 and reduce the time to market, as well. The AI gamble paid off for the Cambridge, Massachusetts, company, which was able to develop a leading COVID-19 vaccine in record time, demonstrating around 95% efficacy in preventing illness from the virus, according to the U.S. Centers for Disease Control and Prevention. COVID-19 vaccine developed in record time, demonstrating approximately 95% effectiveness in preventing illness from the virus, according to the U.S. Centers for Disease Control and Prevention.

Takeaways

While the company’s COVID-19 vaccine attracted the most attention, it is only one of Moderna’s products developed using its AI and data science tools for mRNA development. The company approached it as a software capability. “For over a decade, Moderna has built a vast library of data, which is our greatest asset as a digital-first biotech company,” Brice Challame, the company’s VP, Data & AI Transformation, wrote in a 2023 blog post, “This library has enabled us to create our own integrated data ecosystem to drive and refine our algorithms.”

• Many companies become preoccupied with the initial infrastructure investment for an AI project. However, Moderna recognised the value that an application could deliver across multiple products in the long term.
• Early investments in cloud infrastructure, IoT, analytics, and automated processes laid the foundation for AI work. “We relied on digitalisation early on, not for the sake of digitalisation but for generating data,” explained Moderna’s former chief digital and operational excellence officer Marcello Damiani in an article by the Digital, Data, and Design Institute at Harvard. “This enables us to develop better algorithms, which helps to create the next generation of medicines,” Damiani says.
• An mRNA vaccine is itself an information-based product, a synthetic molecule that sends instructions to the body’s cells for training its immune system. So, Moderna developed a web-based drug design application to streamline the process of changing the information encoded in a synthetic molecule. Behind the scenes, numerous AI algorithms help inform decision-making, particularly in preclinical products, for which they provide data-centric predictions on the best sequences to code for a certain protein much more quickly than humans can. The software collects data throughout the process to develop new and improved algorithms over time.
• The company also uses AI to improve clinical trial planning, quality control, and even its call centre operations.
• Starting in 2023, Moderna partnered with OpenAI to develop its own instance of ChatGPT. Today, GPTs are embedded across Moderna's business functions—from legal, to research, to manufacturing, to commercial—as purpose-built assistants.

Learn more

• The Digital, Data, and Design Institute at Harvard offers background¹¹ on Moderna’s early adoption of AI.
• Brice Challame of Moderna shares some lessons learned on the company’s “journey to becoming a real-time AI organisation.”¹²
• MIT Sloan Management Review’s Me, Myself, and AI podcast interviewed¹³ Moderna’s former Chief Data and AI Officer Dave Johnson about the role of AI in vaccine development.
• ZDNet describes¹⁴ Moderna’s drug development technology as an “AI factory.”

Example 4: Mattel adopts AI image generator DALL-E early for product development

GenAI burst into the public consciousness with widespread rollouts of AI language models capable of generating human-like text. Months earlier, toy manufacturer Mattel began working with a generative image-creation tool, OpenAI’s DALL-E system, to create realistic images and artwork based on natural language inputs.

Mattel has no plans to replace its product designers with artificial counterparts any time soon. Instead, the toy manufacturer is using AI to assist designers in generating ideas for new Hot Wheels cars. More recently, Mattel signed a 2025 agreement with OpenAI to develop AI-powered products and experiences based on Mattel’s brands.

Takeaways

Mattel’s GenAI exploration illustrates the potential benefits of human-machine collaboration in a creative field.

• Mattel began using the image generator in October 2022.
• Designers can enter a natural language request for a “scale model of a hot rod convertible,” and the system may provide variations on a bespoke image based on a 1938 Ford in yellow with whitewall tyres.
• The user can work with the image generator to explore different options—a colour change, a hardtop, a tweak in body type—to deliver a final rendering of a new toy car.
• GenAI enables designers to think more broadly, providing models or options they might not have come up with on their own. “Ultimately, quality is the most important thing,” says Carrie Buse, the director of product design at the Mattel Future Lab. “But sometimes quantity can help you find the quality.”
• The toy manufacturer, which encountered some difficulties a decade ago with its “Hello, Barbie” doll that recorded and uploaded children’s conversations to the cloud, said in a press release it will “emphasise safety, privacy, and security in the products and experiences that come to market” as a result of its latest OpenAI collaboration.

Learn more

• The Associated Press highlights¹⁵ Mattel’s early entry into GenAI.
• Microsoft Source details¹⁶ how human designers work with DALL-E to generate and refine new Hot Wheels designs.
• In the Harvard Business Review¹⁷, Thomas H. Davenport, who is Professor of IT and Management at Babson College, and Nitin Mittal, who is a principal at Deloitte Consulting, explore how GenAI will change creative work.
• PYMENTS¹⁸ delves into the 2025 Mattel-OpenAI collaboration on AI-enabled toys.

Example 5: Costa Group deploys computer vision-powered pollinators instead of bumblebees

If there is any buzzing about in the tomato greenhouses of Australia’s Costa Group in Guyra, New South Wales, it is not coming from bumblebees. Using natural pollinators (such as bees) for indoor farming is illegal there—native honeybees struggle in enclosed environments. And for biosecurity reasons, Australia has long prohibited the import of non-native European bumblebees, which are often used for greenhouse pollination in the Northern Hemisphere. Instead, the produce grower is using robotic pollinators—powered by computer vision—on one million tomato plants.

In summer 2021, Costa Group began using Polly robots from Israeli firm Arugga AI Farming. Early results showed the machines produced a 15% higher yield compared to manual pollination and up to a 7% higher yield compared to bumblebees.

Takeaways

For the Costa Group, the robotic pollinator offers advantages beyond efficiency. While they are not permitted to use bees (which is how most greenhouse pollination is carried out), the agribot option is not only more efficient than manual pollination, but it also reduces the spread of viruses as it requires no human contact with the tomato plants.

• Tomato plants need to be shaken to self-pollinate, a task typically carried out by commercial bumblebees, or by workers shaking trellises or using vibrating wands.
• The Polly robot moves between the rows of plants, using computer vision and deep learning to identify which flowers are ready for pollination. On the back end, the Arugga system uses a streaming analytics toolkit to process raw video from greenhouses. The imagery is then annotated, and the model is trained on the resulting datasets, which are updated monthly. The model improves over time and is exported to the robots, which can run the algorithm individually and are essentially edge computing devices.
• The robot then emits compressed air pulses to vibrate the flowers that are due, similar to the way bumblebees perform buzz pollination.
• Arugga’s AI is becoming more intelligent. In 2024, it stated it could pollinate one hectare (about 2.5 acres) with 3.5 robots, with expectations to reduce this to 2.5 robots thanks to an improved algorithm.

Learn more

• Costa Group’s AI-powered pollinators are just one example of the agricultural computer vision applications¹⁹ in an Imaging & Machine Vision Europe article.
• Produce Plus²⁰ explains how the robots determine which flowers are ready to be pollinated.
• Arugga’s co-founder and Vice President of Business Development explains how the company’s AI pollination, a replacement for managed bees, can help growers increase their yields²¹.

Example 6: Procter & Gamble deploys AI, machine learning, and edge computing to streamline production

P&G is seeking to digitalise and analyse data from its more than 100 manufacturing sites. It is developing AI capabilities, such as machine learning and computer vision, to maximise equipment health and availability, assess product quality in real time on the production line, and improve energy and water usage. The consumer packaged goods manufacturer has concentrated its initial efforts on its paper products and baby care segments with pilot schemes in the United States, India, Japan, and Egypt. An early project used AI to predict the finished lengths of paper towel sheets, thereby delivering the correct amount of product to customers—just one of many efficiencies the company hopes to achieve.

“We want everything done in real time, on the line, with technology, so that we know instantly if something is wrong,” Chair, President, and CEO Jon Moeller explained in a discussion with Goldman Sachs Asset Management. “And with AI, we can start moving towards knowing instantly—knowing before it happens, not just when it happens.”

The company is also using AI in supply chain management. Previously, it required bringing together data from disparate software used for manufacturing, supply chain, marketing, quality assurance, and laboratory information systems, a tedious, manual task requiring hundreds of labour hours each time. It is now partnered with analytics provider phData to develop an AI-powered system using KNIME’s open-source analytics to automate data integration. It can then analyse supply, demand, and inventory data in real time to forecast future inventory requirements and highlight potential supply chain disruptions.

Takeaways

P&G’s goal is smart manufacturing at scale, said former CIO Vittorio Critella, with a focus on predictive maintenance, predictive quality, and sustainability optimisation. AI could help enable fully contactless operations in the future.

• A digital enablement office is prioritising product manufacturing, packaging use cases, and packaging processes that could be implemented organisation-wide.
• The company can transmit data from production line sensors to the cloud to build and train machine learning algorithms that are then deployed back to the edge on the factory floor.
• P&G has trialled the use of AI to minimise overpacking of paper towel rolls (by more accurately predicting finished sheet lengths) and is using computer vision for real-time quality control of nappies and sanitary pads (where the precise, high-speed assembly of multiple layers of materials is essential).
• Large volumes of complete datasets from its paper machines can be used to train machine learning algorithms to predict which machines require maintenance and identify opportunities to reduce energy usage.
• Machine learning and AI must be embedded into operations and culture to deliver value, P&G’s CIO says.

Learn more

• Consumer Goods Technology provides an overview²² of P&G’s digital system, which utilises IoT sensors and AI.
• P&G’s former CIOs spoke to Deloitte’s CIO Journal about the opportunities and challenges of deploying AI at scale²³ in manufacturing.
• CIO.com describes how P&G has been able to progress beyond AI experimentation²⁴ to scale increasingly sophisticated use cases.
• Microsoft Source posted news about its work with P&G²⁵ on digital manufacturing.
• CEO Moeller told Goldman Sachs²⁶ that he sees AI potential in “moving innovation from the laboratory bench to very sophisticated computers” to speed up molecular discovery, explore more areas for innovation, and come up with more successful product ideas.

Example 7: UK high-speed railway team develops a better construction plan with AI simulator

The High Speed 2 (HS2) railway running from London to Manchester is the largest transport infrastructure project in the UK. The joint venture company Align formed a working group involving three international infrastructure companies and is responsible for the design and construction of one of the most challenging sections of the project: the so-called C1 section. This 21.6 km (13.42 mi) stretch north-west of London includes the 3.4 km (2.11 mi) iconic Colne Valley Viaduct and a ‘twin-bored’ tunnel of approximately 16 km (9.94 mi). At the height of the five-and-a-half-year project, more than 1,500 people will be working on site. Align invested in ALICE—an intelligent construction sequencing product—to model contingencies and devise optimised and realistic construction schedules for HS2.

Pre-construction—the first phase of a project during which companies plan and schedule the entire scope of a job, estimate costs, and analyse requirements—is a crucial stage. But much has changed over the past couple of decades in the critical path method that architectural, engineering, and construction companies use to plan their projects. It has typically been a Herculean challenge to come up with one or two plans, given the effort required to create a timetable. By using AI, companies can produce hundreds or thousands of options in a few hours, with a complete analysis of their impact on cost and schedule.

Takeaways

Align already had a construction schedule in place that helped it win the C1 project but used ALICE to double-check its assumptions and look for opportunities to improve the plan for its viaduct substructure works. Scheduling a major infrastructure project, with its complex interdependencies and constraints, is difficult to do well. The construction simulator incorporates these interdependencies in an algorithmic equation that can analyse thousands of scenarios and review them based on the company’s goals.

• The construction simulator helps companies to virtually create construction plans with a variety of different inputs before actual building work begins.
• Align used the construction simulator to create dozens of scheduling options in just 10 minutes.
• It then ran “what if” analyses on the software to decide how to construct the viaduct more efficiently.
• AI can rapidly manipulate vast numbers of parameters that can affect construction—such as labour, equipment, and material availability, construction methods, and planning issues—in ways that human planners alone could never achieve.
• Align replicated three years of planning work in just six weeks.
• More recently, HS2 signed a 2024 agreement with Futuria to be its AI delivery partner in a newly formed consortium led by Atos, to implement AI-driven solutions for improving rail operations, streamlining construction workflows, and enhancing asset management.

Learn more

• Management consultancy Roland Berger describes how AI can increase efficiency throughout the construction value chain²⁷.
• Construction Dive covers a range of pre-construction AI applications²⁸.
• In 2022, the Align-ALICE partnership won the award for industry innovation²⁹ from the British Construction Industry Awards.
• Railway Technology explains how a consortium led by Atos will create digital twins³⁰ of HS2 infrastructure, with railway-specific expertise from Arup and AI from Futuria.

Example 8: Thomson Reuters streamlines the development of AI tools for journalists, lawyers, and compliance professionals

Dating back to its founding in 1851 as a news agency, Thomson Reuters has always been about data, in one form or another. Today, it provides data-driven business services in the fields of legal, tax, and accountancy, as well as news. What is now its legal division introduced natural language search 30 years ago, and its R&D group has been testing and delivering AI-enabled innovations ever since.

In 2022, Thomson Reuters developed an AI application to accelerate its machine learning innovation by implementing common data and model governance, and standardising its model release process.

Takeaways

Thomson Reuters’s AI application provides not only a shared workspace for AI oversight, but also a system for managing AI-specific risk with the aim of balancing speed and governance. There are a host of challenges to effective AI model performance, such as the potential for algorithmic bias and changes in the distribution of data. These challenges only become more complex as companies expand their deployment of AI-enabled systems, eventually surpassing the ability of data scientists to manually monitor them over time.

• Thomson Reuters collaborated with Amazon Web Services to develop a tool that provided a standard “clickable” user interface which data scientists and model owners across the company could use without writing code.
• The application offers AI workspaces, data service, model registry, AI annotation, and monitoring for issues such as data or model drift and bias.
• The application streamlines the traditional performance modelling carried out by human data scientists, who monitor model deterioration and work with the appropriate teams to make the necessary adjustments.
• User adoption was the most important success factor and the greatest challenge. Agile development with weekly demonstrations for users to gather feedback was key.
• The company’s portfolio of AI-enabled offerings helps human journalists spot trends, provides predictive mapping capability for tax compliance, and offers natural language queries and AI-powered search algorithms for legal, tax, and accounting professionals.
• Thomson Reuters emphasises human-in-the-loop AI to reduce errors and improve model performance.

Learn more

• Maria Apazoglou, Vice President for AI, Machine Learning, and Business Intelligence, shared details on the AI platform³¹ with Deloitte’s CIO Journal.
• The AWS Machine Learning Blog offers an in-depth look³² at the development of AI technology.
• In a 2025 article, Thomson Reuters explains how three levels of human-in-the-loop testing³³ by 2,500 subject matter experts helped create its GenAI assistant, CoCounsel, which can conduct in-depth research, analyse extensive and complex data, and generate diverse types of content quickly.

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