5 real-world generative AI use cases in IT by leading CIOs

We are at an inflection point where AI can grow from a technology used for specific use cases to become a defining part of the modern enterprise.

In effect, CIOs and technology leaders find themselves in a position where they must act decisively - where they must capitalize on the current opportunities presented by GenAI and avoid falling behind competitors.

And at the same time, they must also make strategic decisions around areas like data infrastructure, workforce structure, model ownership, and AI governance, that would have long-term implications for the success of the organization.

In this blog post, we feature six CIOs and their thought processes around the potential of GenAI, its applications in their respective industries, and the risks and responsibilities associated with its implementation.

CIOs featured in this edition:

• Alan McIntosh, CIO and CTO of Plexus Worldwide
• Patrick Thompson, Former Chief Information and Digital Transformation Officer of Albemarle
• Andrew Blyton, VP and Chief Information Officer, Incyte
• Yves Caseau, Global CIO of Michelin
• Jim Swanson, Executive Vice President and CIO of Johnson & Johnson
• Cynthia Stoddard, Senior Vice President and Chief Information Officer, Adobe

Dive in.

What CIOs think of GenAI and its potential

Patrick Thompson, Ex-Albemarle

"If you get your governance, security, and your data ingestion right, generative AI can help scale a small company into a big company — and a lean one. My prediction is generative AI will be the most disruptive innovation in business. It will be more disruptive than what Apple did with the iPhone for consumers. And for business users, it will surpass what Microsoft did for workforce productivity.

It’ll help consolidate, optimize, and integrate industries, which will result in new industry performance benchmarks that raise the bar and create greater shareholder value. Companies that don’t embrace generative AI will become obsolete."

Andrew Blyton, Incyte

"People who went through the computer and internet revolution talk about when computers first came online. If you were one of those people who learned how to work with computers, you had a very good career. This is a similar turning point: as long as you embrace the technology, you will benefit from it."

Yves Caseau, Michelin

"After experimenting with both GitHub copilot and ChatGPT for over six months, I’m amazed by the pace at which generative AI is evolving. But in its current state, it’s just a toolbox… Once it’s matured, generative AI will perform many of our mundane tasks — and this will free us to focus on new things.

I do believe that AI will change the way we run our businesses. It is going to intensify digital transformation, by increasing the value that a company can extract from data. The use of AI is going to create new boundaries, new frontiers of what we can do in the future."

Jim Swanson, Johnson & Johnson

"The average hospital generates 50 petabytes of data a year – that’s 50,000 1TB hard drives – and the amount of data generated in healthcare is growing by 47% per year. Yet, only 57% of that data is actually used for making decisions, because too much of it is unstructured, unwieldy, or simply difficult to access.

But that is rapidly starting to change—and we are fast approaching an inflection point when it comes to GenAI in healthcare. With the surge of new technologies and digital innovation, hospitals, health systems, and healthcare companies are pairing GenAI with data and analytics to better serve patients globally."

Cynthia Stoddard, Adobe

"Generative AI evolves the possibility and promise of AI exponentially. The tools are becoming a lot stronger, and you can do more, so you can transform the conversation between the creator and the computer into something that’s easy, more natural to use, intuitive.

Generative AI lets creators at all levels use their own words to generate content. But I don’t think it will ever replace humans. Instead, it’s going to be an assistant to humans."

Consensus

CIOs unanimously view GenAI as a disruptive force with transformative potential. They predict scalability, optimization, and industry evolution, emphasizing its role as a valuable human assistant. Overall, CIOs express optimism about GenAI's exponential evolution and transformative impact across industries.

How the CIOs are implementing AI in IT

Patrick Thompson, Ex-Albemarle

Since the recent pandemic lockdowns, Albemarle has adopted AI as a virtual assistant, ALbot. Initially designed as a self-service chatbot, it has evolved into a versatile tool that assists with various corporate functions.

Over time, it has transformed into a virtual personal assistant that effectively manages federated workflows, streamlining the experience for employees who need to work with multiple systems concurrently without the hassle of logging into each one separately. Through natural language communication, employees can now execute workflows and inquire about information by engaging with the bot, which seamlessly interfaces with their enterprise business systems.

"We were a little ahead of the game, mainly out of necessity. The pandemic forced us to find ways of self-servicing 7,000 employees at home… Interactions become more conversational so you can ask questions and get different insights about the state of equipment. It can be used to curate internal and external industry data that’s then used to train traditional algorithms to deliver agile results."

Jim Swanson, Johnson & Johnson

At Johnson & Johnson, scientists are using generative AI to stay abreast of research.

"No matter how good our scientists are, how to keep pace with all that is really difficult. So you think about being able to use generative AI tools to mine the latest medical information. We’ve been doing some pretty neat things around pulling together that information, and then making that available to our scientists.

Our teams are experimenting with various AI platforms, and we have seen many use cases with promising impact in areas such as augmented employee capabilities, pipeline advancement, regulatory compliance, and quality improvements.

Some examples include understanding and summarizing global regulations and policies as they evolve over time, or identifying areas of unmet needs, particularly in rare diseases. The latter insights can inform our R&D pipelines and investment opportunities and can also help us to bring our healthcare solutions to as many patients as possible, as early in their disease journey as appropriate.

Also, earlier this year, we launched our own internal GenAI Intelligent Chat with a corresponding governance process, being mindful of the risks associated with this technology. Using this tool, our employees can boost their expertise, analyze business information, and generate summaries or pose spontaneous inquiries, all without the risk of exposing this content online or contributing to the training of public large language models."

Andrew Blyton, Incyte (Prev- DuPont Water & Protection)

DuPont leverages AI in various areas such as production scheduling, predictive reliability and maintenance, and sales price optimization.

Moreover, DuPont has made significant investments in multiple technology infrastructures and has found the data lakehouse concept to be highly promising. The data lakehouse combines the benefits of data lakes and data warehouses, providing an open architecture that offers both flexibility and scalability, as well as effective management and high-quality data.

"We have aggregated data across a lot of different technologies over time and I think what we’re finding now is that the lakehouse has the best cost performance straight off, and hence we’ve started investing more heavily in scaling. Making sense of the data of our business has been the main reason to invest in the data lakehouse approach. How do I take multiple enterprise resource planning systems (ERPs) of data, merge them together, and give people almost real-time access to information that previously was being done manually?

We are using the lakehouse and tools like the data lake to build a data foundation and apply ML algorithms to it. We are expanding predictive maintenance using algorithms with third-party sensors to figure out when a machine’s going to break before it breaks, as opposed to after it’s broken. These are good rock-solid business cases, real value being generated from the investment that’s producing a tangible and a measurable return for us."

Alan McIntosh, Plexus Worldwide

Plexus Worldwide uses AI to identify fraudulent account creation and transactions.

"Bias is fundamentally a data problem. We attempt to eliminate bias and incorrect results by leveraging and validating against multiple, complete data sources. We are also in the analysis phase of using AI within the company’s e-commerce platform to gain better insights for predicting and optimizing the customer experience and enhancing personalization. We also see automation opportunities to eliminate many legacy manual and repetitive tasks."

Cynthia Stoddard, Adobe

Adobe employs AI to enhance its back-office processes. The company utilizes automation to analyze customer query tickets and determine if they can be resolved without human intervention. Additionally, Adobe leverages AI to effectively catalog the various software solutions used across different departments, resulting in streamlined spending and a simplified technology stack.

Adobe has also developed a sophisticated "self-healing" platform that automatically identifies and addresses real or emerging technical issues. This platform eliminates the need for a system administrator to be called in the middle of the night to fix such issues, as the AI-powered system takes care of them automatically.

"We internally view AI/ML as being a helper, truly helping our people, and then allowing them to spend more time on other value-added activities. Back when we started this, I would say people were skeptical, but when they saw that they were truly able to spend more time on value added, and in some cases, their jobs were upskilled, people became believers, and they still are believers.

As a company, AI is core to delivering experiences to our customers. Then as the IT organization, we’re following those same principles to inject capabilities into what we do to make it easier for our constituents."

Consensus

From virtual assistants to fraud detection, CIOs have been using AI to streamline workflows, enhance insights, ensure compliance, and deliver better customer experiences. The focus is on scalability, cost-effective solutions like data lakehouses, and measurable returns through ML algorithms, particularly in predictive maintenance. CIOs view AI as a valuable tool for improving efficiency and driving business outcomes.

What CIOs consider the risks and responsibilities of implementing AI

Andrew Blyton, Incyte

"If your entire business model is based on the IP you own, protection is everything. There are many bad actors who want to get their hands on our internal documentation, and the creation of new avenues for the loss of IP is always a concern."

Alan McIntosh, Plexus Worldwide

"Depending on the use cases, the reputation of your company and brand may be at stake, so it’s imperative that you plan for effective governance. It’s critical that CIOs don’t rush to the finish line. Organizations must create a thorough plan and focus on developing a governance framework and AI policy before implementing and exposing the technology. Identifying appropriate stakeholders, such as legal, HR, compliance and privacy, and IT, is where Plexus started its ethical AI process.

We then created a draft policy to outline the roles and responsibilities, scope, context, acceptable use guidelines, risk tolerance and management, and governance. We continue to iterate and evolve our policy, but it is still in development. We intend to implement it in Q1 2024.

I recommend seeking out third-party resources and subject matter expertise. It will greatly assist with expediting the development and execution of your plan and framework. And, based on your current program management practices, provide the same level of rigor — or more — for your AI adoption initiatives."

Jim Swanson, Johnson & Johnson

"What keeps me up at night is all the regulations. You think you can apply it today — and we’re a highly regulated industry, so there's a new law somewhere around the world — and all of a sudden you're out of compliance and now you're at risk.

Without proper governance and supervision, GenAI can pose risks to businesses and society. That’s why all companies should approach the use of AI responsibly, with careful governance and an innovative mindset – and especially those in healthcare, where there’s the potential to significantly impact patients’ lives."

Cynthia Stoddard, Adobe

"It’s important to have the element of diverse oversight through the whole [AI implementation and maintenance] process and to make sure that we have diversity not only of things like ethnicity, gender, and sexual orientation, but also diversity of thought and professional experience mixed into the process and the AI impact assessment."

Consensus

In general, CIOs acknowledge the risks and responsibilities tied to AI implementation. They stress the importance of protecting intellectual property, ensuring effective governance, and addressing ethical considerations. Key concerns include compliance with regulations, potential reputational risks, and the need for diverse oversight throughout the AI process. The collective message is to approach AI use responsibly, with careful planning, involvement of key stakeholders, and consideration for its impact on the business and society.