Thank you to everyone who participated in the 15th Metis Strategy Digital Symposium. Check out some event highlights below, and stay tuned to the Metis Strategy YouTube channel and Technovation podcast in the coming weeks for full sessions.
For businesses across the globe, 2023 was the year of generative AI. Since ChatGPT’s launch and meteoric rise last November, digital leaders have been experimenting with a range of new GenAI products and services as they searched for the most effective, and least risky, way to bring the technology to their organizations. As GenAI (and the hype around it) took off, it prompted important and complex conversations about the future of work and how to accelerate innovation while managing new and significant risks.
A little over a year in, leaders continue to experiment with new tools as a means to drive new value and improve the experience for customers and employees. They are also turning their focus back to the fundamentals, building strong data governance and data hygiene practices to ensure their organizations have the strategic and operational foundation needed to take advantage of their data.
GenAI is still the shiniest thing out there, but as technology leaders look to 2024, they are focused on integrating it, and AI more generally, into their organization’s operating model and championing use cases that can produce tangible value at scale.
If year one of generative AI was about deciphering its risks and enabling organizations to experiment safely, year two will be about finding ways to drive value at scale. With new use cases emerging regularly, technology leaders are figuring out how to prioritize the initiatives that show the most promise to the business.
At BNSF Railway, CIO Muru Murugappan and his team use a value feasibility matrix to assess technical feasibility, timing, and complexity of new AI initiatives versus the expected payback. Some companies are also using business interest and executive sponsorship as criteria for deciding which initiatives to pursue. One overarching lesson: the more ambitious the project, the more challenges it is likely to face.
In addition to delivering on generative AI’s opportunities, CIOs now are contending with a new set of costs as well. Speakers also highlighted the fact that simply “turning on” a new AI tool does not guarantee value.
For many, the path to unlocking AI’s value means getting back to basics. “This is reinforcing the need for analytics fundamentals,” said Filippo Catalano, Reckitt’s Chief Information & Digitisation Officer. “If you don’t have good data practices, at best you’re going to use whatever others are using, but you will not be able to generate competitive advantage. Great data practices … become even more important.”
While new AI tools have helped organizations explore the art of the possible, they also have created a number of new risks, from more advanced cyberattacks to the negative impact of training algorithms on biased data. Just over one quarter of attendees cited data privacy as the largest AI-related risk to their organizations. The delicate balance for CIOs: managing the new risk landscape while empowering teams across the organization to experiment and innovate.
Martin Stanley, who leads the Cybersecurity and Infrastructure Security Agency R&D portfolio, is currently assigned to the Trustworthy and Responsible AI program at NIST. Among his team’s goals is promoting adoption of the AI Risk Management Assessment Framework, which provides a construct for deploying AI responsibly and managing risk among a diverse set of stakeholders. The framework means to address a few key concepts: building a language around AI risk beyond simply monitoring potential vulnerabilities, creating a shared understanding of how to manage that risk across the enterprise, and driving a trust-driven, “risk- aware culture” that influences how people interact with the technology.
CIOs are working to build trust into every layer of the process. As Vishal Gupta of Lexmark noted, technology is only as good as people’s ability to adopt it and trust what it’s saying. “Otherwise, you really can’t do much with it.” At Lexmark, Gupta is taking a layered approach, creating trust in the underlying data via stronger governance and management practices; driving trust in AI and machine learning models by setting up an AI ethics board and rigorously vetting use cases; and continuously testing to validate AI’s ability to truly drive business outcomes.
New AI tools such as developer copilots have the potential to drive significant productivity gains and reshape how many of us do our jobs. As humans and technology continue to interact in new ways, CIOs are focused on optimizing the digital employee and customer experience while helping teams navigate a changing world of work. Indeed, 50% of MSDS attendees plan to apply AI and generative AI to impact employee experience and productivity, with 30% planning to use it to improve the customer experience.
At TransUnion, CIO Munir Hafez and his team are taking a human-centered approach to the digital experience with a focus on ensuring tech equity and establishing policies that allow teams to safely experiment with new tools, among other initiatives. When investing in employee experience, “our goal was to create a consumer-grade experience that enables employees to be engaged and productive in an environment that is integrated, modern, frictionless, and connected anywhere,” Hafez said.
AI’s ability to deliver frictionless employee experiences and deliver real productivity gains far beyond IT is likely to be a big focus for 2024. Many panelists noted how access to accurate, AI-enabled real-time data can help field managers make decisions more quickly, and how technologies like digital twins can streamline design processes and speed time to market.
Navigating an uncertain economic environment and rapid technological advances are top of mind for CIOs in the year ahead. The convergence of these two factors continues to underscore the importance of bringing a strategic, value-based lens to AI development and adoption.
The hype around generative AI may come back down to earth in 2024 as companies begin to understand its complexities in the enterprise. “I think there is going to be…a little bit of a trough that we’ll hit with GenAI,” said Graphic Packaging International CIO Vish Narendra. “The commercialization of that is going to take a little longer in the enterprise than people think it’s going to.”
As technology becomes embedded across a broader range of products and services, the spotlight will be on CIOs to show the art of the possible, create future-ready workforces, and manage risk. Given their broad purview that spans horizontally across organizations, CIOs are well positioned to influence and shape enterprise strategy in the year ahead, setting their companies up for continued resilience and growth.
This article was written by Marjorie Freeman.
A data strategy is a plan of action to manage an organization’s data assets across its technology, processes, and people. In practice, that entails understanding how data is generated, where and why it is consumed, and how its use helps organizations achieve strategic objectives.
On Metis Strategy’s Technovation podcast, Peter High has interviewed many global, digital-forward CIOs about their data strategies. Below are insights from those leaders about how companies can use enterprise data assets to their fullest potential.
Artificial intelligence has been top of mind for many organizations, even more so in 2023 with the rise of ChatGPT and increased discussion around generative AI. This has prompted a multitude of conversations around AI’s core facet: data, and how it can drive the business forward.
During the February 2023 Metis Strategy Digital Symposium, Krzystof Soltan, the Chief Information Officer of Vulcan Materials Company, and Anupam Khare, the Chief Information Officer of Oshkosh Corporation, shared their experiences building data strategy into complex, scaled organizations.
At Oshkosh, Khare leads with the question: “How do we extract financial value from data by bringing people and data together?” The company is working to become what Khare calls a predictable enterprise, using four fundamental principles to guide the journey:
For Vulcan Materials, data strategy is linked to the organization’s technology strategy. “It always comes back to business value, the time to value, how fast we are able to provide the insights” Soltan said. Vulcan Materials’ looks to the following principles to guide its data and analytics work:
Both Khare and Soltan’s stories underscore the need to tie data strategy to business value, work toward a common tech stack, and engage people at every level of the organization in the data journey.
OneDigital, which provides customizable and cost-effective HR solutions to organizations and their workforces, acquires around 30 organizations per year. This is no easy feat, but CIO Marcia Calleja-Matsko strives to create a seamless experience for every organization that is onboarded.
When acquiring a new organization, especially one in a different vertical or industry, it is important to ensure there is a consistent record across multiple platforms, Calleja-Matsko says. Cue the single source of truth, or what she calls the “golden record.” Once that record has been created, it must be maintained.
Over the years, Calleja-Matsko has been working to build OneDigital’s data strategy in three key ways:
If data is the new oil and speed is the currency of business, then data governance is the link that fuses the two. For more, see Michael Bertha’s commentary: Data Strategy at the Speed of Business.
CIOs play an instrumental role in creating a common language around data and making sure teams across the enterprise have the tools and concepts they need to harness data effectively. To develop this data literacy, many organizations have built enterprise-wide curriculums and training resources.
Monica Caldas, the CIO of Liberty Mutual, which has its own professional development training programs, including one specifically geared toward executives, said it well:
“Technology is everybody’s responsibility these days in terms of understanding what it can do. … Everyone that sits around the table needs to be beyond, ‘How do I click this?’ and [be] somewhat well versed [in topics like] what can an API do, and why does that matter.”
Many organizations have launched digital academies to train employees on digital skills, including technology and data literacy. In 2019, for example, Toyota launched an academy to knock down the invisible wall often found between IT and the business and give end users greater knowledge of the software they use every day. “The idea was to not just train IT, but everyone across the organization.” said then Chief Innovation, Strategy and Digital Officer, Vipin Gupta. The approach has empowered associates across the business to truly understand how to capitalize on the tools, data, and processes at their disposal.
Data literacy is also key to enabling citizen development, an approach that encourages those outside IT to contribute to software development, often via low-code/no-code tools. Paired with increased data literacy, this can make it easier for teams across organizations to apply data and analytics to their work and accelerate time to insight.
Chief Information and Digitization Officer of Reckitt Benckiser Group, Filippo Catalano encourages executives to create opportunities for properly governed self-service data access:
You want to also make sure that, as much as possible, everybody in the company becomes a data scientist. … Get out of the way so you can unleash creativity, empower people everywhere in the organization to do what they need to do on data and analytics, but also to do it on the right platforms so that things are done in a fair way, but also in a safe way.
CIOs, CDIOs, and CDOs are in incredible positions to influence the change they’d like to see within their organizations. Directly engaging individuals in the company’s data journey through hands-on learning opportunities can not only build knowledge and morale, but also can catalyze new competitive advantages.
Any successful data strategy needs a compelling, ambitious vision and a clear path to success that resonates across an organization. CIOs, then, need skillful storytelling to get buy-in from multiple stakeholders and create forward momentum.
Telling the story effectively means, once again, putting business outcomes front and center. “I can talk all day about ‘hey, you should have data governance and you should think about a data lake or a single view of the customer,’” said Dak Liyanearachchi, Head of Data and Technology at NRG Energy. “All of those are really interesting, but what does it really mean to the organization?”
One useful move includes thinking about data as an enterprise asset that requires strong partnership across every part of the business. While companies can notch small wins leveraging data within silos, the real benefit comes when that great work logically connects across the organization.
“If you think about connecting the dots across the value chain, that’s where you start to see some significant business opportunities,” Liyanearachchi said. When that happens, “the value you bring multiplies at a faster rate.”