Highlights from our recent Metis Strategy Summit are below. Check out our Youtube channel and Technovation podcast in the coming weeks for recordings of the conversations.
Organizations across industries are moving beyond initial AI experimentation, focusing on driving implementation, proving and measuring ROI, and developing the next generation of talent as they apply AI to a broader number of business challenges.
As multiple executives emphasized, strong data foundations are essential to any successful AI implementation. Marina Bellini, President of Global Business Services at Mars, noted that the hype around AI has led to more focus on ensuring those foundations are in place. “This is the dream of the CIO: that people will actually start working on data quality.”
This year has also seen increased focus on AI’s ability to deliver value. Augment CEO Scott Dietzen said 2024 “is the year where tech teams are looking for proof and return on investment,” something not always clear or easy to measure for software such as Copilot productivity tools.
Organizations are finding new and innovative ways to apply data and AI to business challenges. Royal Caribbean Group CIO Martha Poulter described how the company transformed traditional food service operations into data-driven processes. Initially, “you would order what you thought, cook what you thought, and serve what you thought. It was gut based,” she explained. By measuring proteins before and after cooking and analyzing everything from ordering to de-thawing to waste, Royal Caribbean was able to generate tens of millions in savings while improving sustainability. “You’d never think food can be an AI problem, but it is,” Poulter said.
Similarly, Avis Budget Group is using an AI-based modeling and prediction system to address asset utilization challenges and ensure cars are on the road for the greatest amount of time. Chief Digital & Innovation Officer Ravi Simhambhatla explained how the company is aiming to break through the 70% utilization ceiling for its vehicle fleet. “If you have physical assets that aren’t being utilized, it’s costing the company money,” he said. “We hit this glass ceiling and asked ourselves why can’t we go to 80% or 90%? It turns out it’s data.”
Technology leaders discussed various approaches to managing and organizing AI initiatives across their organizations. A common thread across nearly all of them was the importance of bringing together technology and business leaders to identify valuable use cases and deliver on them faster. NRG’s Chief Data and Technology Officer, Dak Liyanearachchi, talked about establishing a transformation office that bridges data, business, and technology teams. At Berkadia, an AI Council that includes both business partners and technology leaders drives deeper engagement and keeps discussions focused on value, Chief Information and Innovation Officer Damu Bashyam said.
As mentioned throughout the event, these new organizational structures place particular emphasis on modern technology stacks and data practices. Nicholas Parrotta, Chief Digital and Information Officer at HARMAN International, outlined the company’s evolution from infrastructure-as-a-service to data-as-a-service, and using that data to create more personalized experiences on wheels as the world moves toward autonomous vehicles. “We start with how we do the big stuff with architecture, then product, and now data and being able to drive those as revenue and capabilities,” he said.
Capital One CIO Rob Alexander detailed the company’s platform strategy, explaining how the organization built dedicated infrastructure for machine learning, feature engineering, and now generative AI applications. When it comes to AI, he noted that while it’s “easy to get 70% accuracy out of the box, all the work is getting from 70-75% accuracy, which involves training and fine tuning.” Being in a position to leverage AI today has been a 12-year journey for Capital One, Alexander said, one that has included transforming “everything about who we are” to become a successful technology company and a winner in the banking industry.
Leaders emphasized the need for pragmatic approaches to AI implementation. Mastercard CTO Ed McLaughlin noted three questions a review panel considers when evaluating the feasibility of a new AI initiative: “Does it work, is it worth doing, and does it align to our ethics?” If ChatGPT-style search responses are 10 times more expensive than traditional methods, for example, the question then is whether they can deliver 12 times more value or be that much more useful. McLaughlin underscored the need to assess both the right way to solve a particular problem and whether there are returns on the work being done.
Dietzen added that NPS and engineer satisfaction can also be indicative of value. “If you make engineers delighted, you’ll tend to do well in your organization,” he said.
Chris Davis, Partner and West Coast Office Lead at Metis Strategy, advises technology leaders to ensure that there is product management in every layer of the AI stack, including the application of AI to business processes, the marketplace of horizontal and reusable capabilities across use cases, and underlying foundational models and model development. Business value should be measured relative to components of the stack, especially with generative AI, Davis said.
Effective product management requires teams across the organization to sharpen their product mindset. Cigna’s Chief Digital & Analytics Officer Katya Andresen outlined three elements of that product mindset: identifying real problems for real users, validating through testing and learning, and unlocking value. She cautioned against common pitfalls like “death by a thousand pilots,” in which proofs of concept pile up and eventually become unmanageable. Organizational silos can present a challenge. “We find a lot of opportunities to streamline operations, but there has to be a really deep partnership across tech and ops,” she said. Otherwise, “tech gets upset that ops don’t use their products and ops says well what you gave us didn’t solve our problem.”
Organizations are rethinking their talent development strategies as the landscape evolves. That involves both upskilling internal talent and expanding talent pools across geographies. Land O’Lakes CTO Teddy Bekele described moving from a roughly 50-50 mix of in-house and external talent to a model in which contractors and third parties make up a more significant portion of the talent pool, taking on much of the development work while in-house employees lead the teams. The approach allows for increased flexibility in team sizes depending on shifting enterprise needs. The change was driven by three key factors: accessing expertise, maintaining flexibility to scale teams up or down, and increasing nimbleness.
Upskilling also remains a key focus. At FINRA, Chief Technology Engineering Officer Tigran Khrimian’s team is teaching developers generative AI skills and has seen demonstrable success with using natural language prompting to create “good code” for the company. “Developers with code assistant tools will replace developers who don’t use them,” he said.
Corning’s Chief Digital and Information Officer Soumya Seetharam detailed the company’s three-pronged approach to talent development: creating strategic digital and IT hubs around the world to ensure global talent access; launching a digital literacy program with dedicated “revitalization days” for learning rather than meetings; and expanding the talent pipeline through technology internship and rotational development programs globally. “In the future every person for every function will have some technology in their background,” she predicted.
Technology leadership roles are undergoing significant transformation, reflecting the strategic importance of technology in business operations. According to Katie Graham Shannon, global head of the Digital and Technology Officers Practice at Heidrick & Struggles, the traditional CIO title is becoming less common. Of 23 recent technology leader placements at Fortune 200 companies,18 did not have the CIO title, and 52% were newly created positions with expanded roles. She noted that there is also a shift in reporting structures, with more CIOs reporting to the CEO, and a greater focus on technology leaders’ ability to create and protect value and attract talent, among other responsibilities.
“If we could use the title ‘orchestrator’ it would make more sense,” Shannon added, explaining that today’s technology leaders create value and orchestrate initiatives across the entire C-suite. This expanded scope includes both customer-facing initiatives and internal operational efficiencies with “equal pressure and emphasis” in both areas.
The role is also becoming more business-oriented, particularly in relation to managing technologies like AI. “A properly formatted conversation about AI is not a tech conversation, it’s a business conversation,” observed Henry Man, Co-Founder and Managing Partner at Candela Search. This presents an opportunity for technology leaders to “have a seat at the table” when business colleagues might lean out of technical discussions.
That expanded purview extends to technology leaders on boards or seeking director positions. “There’s no market for a one-issue board member,” said Art Hopkins, who leads the Technology Officers Practice at Russell Reynolds. “You need to show business acumen and a P&L. Go to the CEO and say I’d like to be the executive sponsor of this new incubator. This is a solid step in that direction.”
Companies that educate, explore, experiment, and expand, perpetually, with the right pace and sequencing, are most likely to win with AI
This article was originally published on CIO.com
AI never sleeps. With every new claim that AI will be the biggest technological breakthrough since the internet, CIOs feel the pressure mount. For every new headline, they face a dozen new questions. Some are basic: What is generative AI? Others are more consequential: How do we diffuse AI through every dimension of our business?
Tactically, you can answer these questions in any number of ways. You can build an AI Center of Excellence (COE), launch a strategic task force, or designate a deputy to lead the charge. But whatever you do—if our advisory work and discussions with leading CIOs suggest anything—you’ll have to drive excellence in four related, though not necessarily sequential, streams of work: Educate, Explore, Experiment, Expand. It’s around these four work streams that leading organizations are positioning themselves to mature their data strategies and, in doing so, answer not only today’s AI questions but tomorrow’s.
Educate. You can’t wrangle AI by yourself. Your journey will be fruitful only to the extent that you can instill in those with whom you go to market a digital fluency and a confidence in your ecosystem.
Accordingly, many CIOs have fashioned themselves into the de facto AI professor within their organizations—developing 101 materials and conducting roadshows to build awareness, explain how generative AI differs from other types, and discuss its risks.
To ease collaboration on the topic where it’s likely to surface, Digi-key Electronics, a leading electronic component distributor in North America, has even built networks of influencers. As the company’s CIO, Ramesh Babu, explains, “We identify ambassadors in the organization and position them in the right meetings to drive a common understanding of the many terms floating around.”
Babu also warns against discussing only the benefits of AI. He and his peers make a point of emphasizing the risks. “We’re trying to have balanced conversations,” he says, a practice that underscores the duty CIOs have to develop appropriate policies and usage guidelines in order to mitigate the downsides of AI.
To help educate your own workforce about AI, provide them materials on the topic. Include common definitions, reimagined future states, risks, and policies and guidelines for usage. Have them ready for impromptu meetings, town hall presentations, and other settings. And direct your colleagues to self-service channels so that they may access materials and learn at their own pace.
Explore. To explore is to pose the question: How can I make AI work for my organization?Since the AI landscape is both large and complex, take a two-pronged approach: analyze internally and marry that analysis to marketplace activity.
Internally, start by looking at your value chain or the capabilities that deliver your value proposition. Brainstorm how generative AI could make your processes (and the people supporting those processes) more intelligent and productive. If you’re already using AI for some of the use-cases you brainstorm, no matter – record those too. And pay special attention to use-cases that concern customer service: Of the executives surveyed at the latest Metis Strategy Digital Symposium, 43% said their organizations are prioritizing customer service use-cases for generative AI in 2023.
From all of these sources, compile your use-cases into a backlog and rank them by impact and feasibility. You’ll learn where you can create new ways to win in both the short and long terms while weeding out those cases that are too difficult for their value.
Next, examine the market. At first, you might struggle to wrap your head around the size of it—a $150B addressable market, as estimated by Goldman Sachs—but by doing so you set in motion what should be a continuous evaluation. Search first for vertical-specific and enterprise-wide AI solutions. Categorize them by the capabilities they support. And if your organization permits it, maybe even ask ChatGPT.
Compare and contrast what’s available in the market to your top-ranked use cases and the capabilities you already have. Where an internal capability does not already exist, and the case relies on a large language model (LLM), you will need to determine how you want to proceed: by training and fine-tuning an off-the-shelf model, like Morgan Stanley did with OpenAI; or by building your own, like Bloomberg did.
Experiment. To experiment well is to work through your backlog with urgency and agility and—especially in the case of AI—with a bias for incremental progress. As Baker Tilly CIO Allen Smith explained at a recent panel, “There’s a difference between home runs and singles.” The singles are your friends, says Smith, and a great way to show something tangible, build momentum, and create a vehicle to fuel other interesting ideas.
At the tech juggernaut Lenovo, CIO Art Hu is taking a similar approach. Hu says they are running dozens of proofs of concept. One consequence of being in the early innings of Generative AI, according to Hu, is the rapid pace of development. “Because it’s fast, you can run proofs of concept for not massive investments.” This demonstrates how his team stays in lockstep with the business on investment priorities in a period where economic uncertainty has narrowed the scope of technology investment. “That’s the way you want it. You want small steps for the business without spending or committing a lot of money. They can see the result and decide ‘OK, double down, or shift the investment elsewhere.’”
Many attribute generative AI’s promise to its ascent to the very top of the tech stack, a promise that makes it more approachable than other disruptive technologies that, while undeniably promising, still require technical expertise to be exploited. Acknowledging this nuance, many companies have built experimentation sandboxes in which users from across the organization can try their hand at AI in a controlled environment.
Expand. Research reports have dangled that generative AI could add trillions to the global economy. But generally, these reports assume that AI can be implemented at scale. Here, AI leaps from the Batcave to the streets of Gotham, confronting a new set of challenges.
With regard to creating that scale, Chris Davis, a Partner at the digital advisory Metis Strategy and a leader of his firm’s AI practice, worries less about scaling the technology than he does about people’s role in that scale. “Someone has to develop, train, and supervise the models,” he explains. “…the irony is that people could actually be the limiting factor.”
As a means of overcoming this limitation, he stresses how necessary it is that organizations revisit—and where appropriate, revise—their operating models. “You need to re-envision business strategies with the exponential scale of AI in mind,” he says. “And train product managers on how they might weave AI into anything—core digital products, customer experiences, employee experiences, and so on.” He goes on to explain, that means also ironing out the roles and responsibilities among various players in your organization: “AI laboratories, data scientists, product teams—they all have to know how to work together efficiently every step of the way, from identifying use-cases to building algorithms and models, from following AI operating procedures to monitoring any models that are already in use.”
And there’s plenty of evidence to support Davis’s point. For example, after recently redefining the roles, responsibilities, and delivery methods of its IT product teams to suit its specific AI ambitions, a global financial services provider discovered many gaps in its capacity: some that it could address through upskilling, but also some that would require it to hire new people.
Looking forward. Meanwhile, hyperbolic headlines will continue to outpace adoption; yet, they won’t outpace the exponential rate at which the volume of data is growing, especially as technologies such as 5G and IoT hit their stride. So, if you, too, want to leverage AI to its fullest extent, you must first look in the mirror: Can I manage this growing volume of data? If you can’t convert the data into something meaningful, then, as Lenovo’s tech chief, Art Hu, suggests, you might lose ground: “If you don’t figure out as a company how to (manage a growing volume of data) effectively and efficiently, the competitor that does is potentially going to have a significant advantage.”
As you mature your data strategy, remember that you have many data-driven tools at your disposal, only one of which is AI. It’s wedged between an ocean of use-cases to the North and your core data foundation to the South, and progress in each of these layers is linked to the other two inextricably. There’s no use in thinking of your data strategy as something binary, as if it were a building under construction that will one day be complete. Those that educate, explore, experiment, and expand, perpetually, with the right pace and sequencing, are those most likely to win with AI.
Mike Bertha is a Partner at Metis Strategy
We are thrilled to announce that our Metis Strategy Summit will take place live in New York City. On Oct. 29 from 9 a.m. to 5 p.m., we’ll hear from technology leaders, investors and entrepreneurs about the trends shaping the business and technology landscape today, from the rapid rise of generative artificial intelligence to the macroeconomic and geopolitical shifts impacting global organizations. Other topics include:
Please note, this is an invite-only event for C-level technology leaders. If you are interested in attending, click here to request an invitation. Stay tuned for a venue announcement and agenda updates coming soon. We look forward to seeing you in New York!
Download pdf, 2.11 MB
Click here for highlights from our most recent Digital Symposium, and stay tuned to our YouTube channel for videos of our panel discussions.)
8:30 a.m. – 9:00 a.m.Registrant Check-in
Arrive early to check in and collect your event materials. This time allows you to settle in, familiarize yourself with the venue, and start connecting with other attendees before the day’s sessions kick off.
Additional arrival information will be distributed to ensure a smooth start to your day.
9:00 a.m. – 9:15 a.m.Welcome and Opening Remarks
Peter High, President of Metis Strategy, kicks off the event with a brief introduction of the day’s sessions and the Metis Strategy team.
9:15 a.m. – 9:45 a.m.AI-Driven Customer Experience
This discussion will explore how organizations are using AI to deliver more personalized and dynamic experiences for customers and employees, and how the digital customer experience is evolving in the era of generative AI agents and more powerful models
9:45 a.m. – 10:15 a.m.Project-to-Product’s Next Frontier
The ongoing shift to product-oriented operating models has begun to erode the traditional silos between business and IT and presented technology leaders with new opportunities and challenges. This panel will examine the future of the product model as companies become increasingly experience-centric and AI becomes a bigger part of the equation.
10:15 a.m. – 10:30 a.m.Entrepreneur Spotlight: Augment CEO Scott DietzenThe rise of generative AI sparked a wave of coding assistants promising new paradigms for software development and greater productivity. In this panel, Augment CEO Scott Dietzen will share insights on the current state of the industry and where coding assistants are headed next.
10:30 a.m. – 10:50 a.m.Coffee and Networking Break
Take a moment to grab a coffee and meet fellow attendees. This break offers a great chance to start conversations, share perspectives, and establish connections that will enrich the discussions throughout the day. Use this time to engage with industry leaders and peers before diving into the sessions.
10:50 a.m. – 11:20 a.m.To Innovate at Scale, You Have to Modernize. How Companies Balance Both.
To take advantage of the latest technologies, organizations need a modern tech stack. At the same time, they need to ensure necessary legacy systems don’t become a drag on progress. In this session, panelists will share how they are driving ambitious modernization roadmaps and creating the mindset for change.
11:20 a.m. – 11:50 a.m.Advancing Data Strategy and Measuring AI’s ValueAs AI experiments have flourished, technology leaders are now focused on another acronym: ROI. Panelists will share how they are measuring AI’s business value, identifying initiatives that will drive the greatest impact inside their organizations, and ensuring a strong data strategy to guide it all.
11:50 a.m. – 12:05 p.m.Fireside Chat: The Path from CIO to CEO Mike Clifton joined Alorica as CIO in 2021. This spring, he was named Co-CEO. The former technology and operations leader at Cognizant, Federal Home Loan Bank of Boston, and the Hanover Insurance Group, among others, will share lessons learned throughout his journey and offer tips for CIOs seeking to expand their purviews. He is joined by fellow Co-CEO Max Schwendner.
12:05 p.m. – 1:05 p.m. Lunch and Networking Break
Recharge and refuel while continuing the conversation with colleagues and new connections. Whether deepening discussions from the morning sessions or exploring fresh ideas, this lunch break offers the perfect setting for meaningful exchanges in a more relaxed environment.
1:05 p.m. – 1:35 p.m.Next-Gen Talent Operating ModelsIn today’s talent landscape, a mindset of continuous learning is key to success. This session will explore how companies are upskilling their teams for the future while navigating a world of work in which tech and business teams are more intertwined than ever.
1:35 p.m. – 2:05 p.m. Responsible AI: Value Proposition and Opportunities Operationalizing AI is widely believed to be a compelling and potentially game-changing value proposition but one that comes with a myriad of unique and dynamic risks. Organizations are therefore aiming to practice “responsible AI”, the development, deployment, and use of AI capabilities in a transparent, accountable, legal, and ethical manner. Panelists share their insights and approaches for developing and deploying AI responsibly for the benefit of their respective organizations and their many stakeholders.
2:05 p.m. – 2:20 p.m.Unlocking ROI: Cloud Strategies for the Next AI Wave
This session will explore the symbiotic relationship between cloud and AI, the modernization decisions CIOs can make now to prepare their companies for the next AI wave, and the workload considerations needed to ensure newfound AI efforts deliver ROI.
2:20 p.m. – 2:50 p.m.The Expanding Innovation Ecosystem
Technology leaders today understand that new ways of thinking don’t come only from inside an organization’s four walls. These leaders will share how they are leveraging external partners, peer networks, and new innovation frameworks access to new technologies becomes ever more democratized,
2:50 p.m. – 3:05 p.m.Fireside Chat: Tanium CEO Dan StreetmanAs hackers get more sophisticated and new tools proliferate, today’s cybersecurity landscape is more complex than ever. In this discussion, Tanium CEO Dan Streetman shares how technology leaders can manage through that complexity and protect their organizations from the next wave of threats.
3:05 p.m. – 3:35 p.m.The Changing Role of the Technology Leader: Executive Recruiter Perspectives
The role of today’s digital and technology leaders seems to be changing as quickly as the technology they oversee. In this panel, executive recruiters share perspectives on how the title and purview of the modern technology executive is evolving as advances in AI and other innovations reshape organizations around the world.
3:35 p.m. – 3:50 p.m. Fireside Chat: Remaining Nimble and Resilient in a Constantly Changing World With the US Presidential election just days away and the global economic outlook in flux, companies across the globe are preparing for a variety of scenarios that could impact their strategies going forward. In this fireside chat, the Co-Head of the Goldman Sachs Institute will discuss how technology leaders can put their organizations in a position of readiness and resilience as they prepare for the opportunities and challenges ahead.
3:50 p.m. – 4:00 p.m.Closing Remarks
Peter High, President of Metis Strategy, will reflect on the day’s key takeaways and the insights shared both onstage and off. As the event draws to a close, Peter will set the stage for future discussions on innovation, technology leadership, and transformation.
4:00 p.m. – 5:00 p.m.Reception
Enjoy light refreshments and continue the conversation in a more casual setting. The reception provides a final chance to network, solidify new relationships, and unwind with peers after a full day of learning.
(Click here for highlights from our most recent Digital Symposium, and check out our YouTube channel for videos of the panel discussions.)
This article was written by Leila Shaban, Research Associate at Metis Strategy
Thank you to everyone who attended and participated in the 17th Metis Strategy Digital Symposium. Highlights from the event are below. Check out Metis Strategy’s Youtube channel and Technovation podcast in the coming weeks for recordings of each conversation.
Companies continue to make progress in their AI journeys, deploying the technology to drive efficiency, productivity and innovation. Technology leaders are focused now on driving adoption, generating buy-in for new initiatives, and rolling out new training programs to ensure teams across the enterprise are able to take advantage of what AI has to offer. Below are a few highlights from the event:
Building a foundation for AI at scale
Nearly all CIOs on stage said scalable infrastructure and high-quality, accessible data are key to driving value from AI initiatives. Over the past few years, many organizations have focused on building data platforms, shifting to cloud and rethinking ways of working in order to deliver AI at scale. “Having a really good data infrastructure is foundational to taking advantage of any of these generative AI capabilities,” Priceline CTO Marty Brodbeck said. Many speakers noted their current efforts to get reliable data into the hands of more teams across their organizations.
Nearly half of MSDS attendees said that the rapid evolution of AI, among other macro issues, will have the biggest impact on their organizations in the year ahead
Exploring new use cases
Many organizations continue to train generative AI on internal knowledge bases to streamline processes and enable more self service. CIOs also see potential around developer productivity.
Bristol Myers Squibb receives thousands of calls from physicians and nurse practitioners each day requesting information about specific, often technical, topics, Chief Digital and Technology Officer Greg Meyers said. MDs on the other side of the call often find those answers in internal documents. Now, an AI chatbot trained on the company’s knowledge base can search through the documents to retrieve answers to these questions much faster. With enough fine tuning, Meyers noted the chatbot could constrain search results to trusted documents and help agents provide near-immediate answers to customer queries.
At UPS, Chief Digital and Technology Officer Bala Subramanian recently launched an internal AI tool for email which can process the tens of thousands of customer emails UPS receives on a daily basis, connect relevant information across internal policies and procedures, and generate responses for contact center employees. This ultimately improves worker productivity and reduces response time. UPS also launched an AI chatbot to help employees answer HR questions. Subramanian noted that the company is proceeding slowly due to the sensitive information and personal data in HR systems, and emphasized the critical role of risk management and governance.
At AstraZeneca, AI is significantly reducing the amount of time it takes to conduct research. Cindy Hoots, Chief Digital and Information Officer, described a generative AI-enabled research assistant that quickly searches both internal and external data to answer complex scientific questions. The assistant has helped reduce the time it takes to conduct a literature review from months to minutes, she said. Hoots is now focused on scaling AI adoption. About 15,000 employees use the research assistant, she said, while roughly 5,000 use Copilot solutions and almost 80,000 have access to AstraZeneca’s internal ChatGPT.
At KB Home, employees evaluate a number land deals across 35 markets every week. Aggregating property data from different sources to determine whether to make an acquisition used to take 30-90 days, CIO Greg Moore said. With AI, KB Home can now complete the process in less than two weeks. The faster turnaround now enables the company to make more evaluations and manage more potential deals in the pipeline.
Developer productivity is another area of rapid experimentation. Many of the tools offered by major vendors are in their early days and have room to grow, said Brodbeck of Priceline. The team is exploring solutions that can learn from Priceline’s codebase and provide a richer and more contextual experience. Whether for code generation or another use case, Brodbeck said companies will likely need to deploy retrieval-augmented generation (RAG) to deliver more productivity.
At Augment, CEO Scott Dietzen is thinking about how to retrieve knowledge from internal codebases in a way that protects intellectual property and reduces the risk of leaking sensitive information. The team started with basic engineering tasks that can make developers more productive rather than trying to replace them altogether. Demand for these kinds of tools will last for at least a decade as organizations produce more software, Dietzen said.
The top use cases for digital assistants/copilots that are driving the most value for MSDS attendees are code generation, self-service chatbots, and enterprise search/knowledge management
Bringing the organization along on the AI journey
To drive a common understanding and widespread adoption of AI, CIOs have increased their focus on storytelling and talent development.
At Wilson Sonsini, Chief Information Officer Michael Lucas is focused on cascading AI communications across the firm. His team started with a general awareness campaign. That included employee town halls to communicate the broader strategy as well as AI-centric briefings to partners. Given the sea of media coverage about AI, Lucas encouraged leaders to develop their own elevator pitch to help their organizations clearly understand the company’s AI strategy. Driving a common understanding across the firm is key to driving adoption. “We feel like we need to learn, understand, enrich, and then apply and operationalize,” Lucas said.
At Liberty Mutual, Global Chief Information Officer Monica Caldas is delivering customized employee training and connecting it to the company’s capacity demands across 27 countries. It’s part of a workforce strategy plan called “skills to fuel our future.” First, the company surveyed more than 5,000 employees to determine their skill level around topics like data, data engineering and software engineering. Next, the company mapped over 150 skills, connected them to 18 domains, and assessed how and where to invest in training.
Now, Caldas and her team are helping employees apply that training to a variety of career paths. Instead of a traditional career development ladder, Liberty Mutual is evaluating how to map skills to different jobs and create a “jungle gym” or “lattice of opportunities.” The focus on specific skills, Caldas said, “will help you position your capabilities as a tech organization not just for today, but also plan out where it’s going.”
Education at the executive level is also critical. To bring executives along on the journey, Caldas introduced a program called Executech that helps improve organizational data literacy and elevates the digital IQ of decision makers. Enhancing teams’ tech acumen gives leaders the confidence to start conversations early about important technology topics like API integration.
AI adoption may not be uniform, and there is still lots to learn about how it will impact specific roles. At Eli Lilly, employees who have incorporated AI tools into their workflow are reluctant to give them up, said Diogo Rau, Chief Information and Digital Officer. However, widespread adoption is a continuous and sometimes challenging process, “a lot harder than anyone would guess,” Rau said.
Rau often gets more questions about the risks of AI than how it can be used to improve products and services. Another challenge is that teams excited about creating AI bots aren’t always excited about maintaining or training them. “There are lots of good firefighters, but not every firefighter wants to be a fire inspector,” he said.
62% of technology executives who attended the Metis Strategy Digital Symposium anticipate that the most significant impact that AI will have on talent is increased productivity
Leveraging ecosystem partners
Achieving the transformative potential of generative AI will require collaborating with networks of vendors, startups, peers, and academics. In addition to providing technology solutions, these ecosystem partners can help upskill employees, explore emerging challenges, and prototype new use cases.
Amir Kazmi, Chief Information and Digital Officer at WestRock, draws learnings from both established technology partners and startups. He also brings in academics and peers from other companies to share wins and lessons learned about generative AI.
Regal Rexnord’s Tim Dickson, Chief Digital and Information Officer, uses hackathons and internal events with vendor partners to increase the company’s digital IQ. The company also offers self-paced training from about 10 partners that includes pathways to certification. In the past seven months, more than 100 employees have received training on GenAI fundamentals from Databricks and robotic process automation from UiPath, as well as certifications from Microsoft Copilot. Even if employees don’t use these tools every day, increasing the number of people with technical skills means more individuals “can at least help, or even lead, these initiatives across the organization,” Dickson said.
CommScope CIO Praveen Jonnala, like many other technology executives, is thinking about how to drive a cultural shift around AI. He spends about 80% of his time on organizational change management and culture. He is also leaning into existing partnerships to take advantage of new AI solutions and educate teams. For example, he took business teams to Microsoft for a full day to learn more about the technology and its ability to unlock new business opportunities.
This article was written by Rana Abbaszadeh, a Senior Associate in Metis Strategy’s West Coast Office
As companies look for ways to harness data and AI to deliver on business outcomes, they first need to develop the foundational governance capability that enables them to do so effectively. Data governance requires significant time and resource investment, to be sure, but it ultimately enables organizations to realize the long-term value from their AI and analytics initiatives.
At a high level, data governance refers to the development and management of information about an organization’s data. It includes maintaining a catalog of a company’s data from lineage to definition and utilization. When done well, data governance creates a single source of truth that can be used to unlock trusted insights, inform strategic decision making, and enable personalization at scale.
Companies that implement data governance can:
Metis Strategy takes a strategic approach to data governance and recommends that organizations start with the data that drives significant value. For example, a retail company could focus first on the governance of customer and product data, as this information is core to the company’s growth. Focusing on high-value data helps generate buy-in from key stakeholders and builds momentum for governance initiatives. After that, organizations can turn to other data until governance becomes embedded into the company culture.
This article will outline how to develop a data governance program within your organization, including the different roles and stakeholders involved.
Identifying governance opportunities
Using the Metis Strategy methodology, organizations can quickly realize value while improving overall data maturity. We recommend developing a cross-functional steering committee consisting of senior leaders across business and technology units who will guide the governance process. The steering committee is responsible for setting strategy, direction, and prioritization for the data governance program.
The committee’s primary responsibilities include:
In addition to the responsibilities above, the committee also will evaluate the business case for specific initiatives, approve funding and resource requests, and guide program adoption throughout the enterprise.
Building the Governance Council
In addition to the steering committee, the data governance program should include a governance council that will scope, document, and monitor data assets and lead governance operations. The council should consist of individuals across different business units to provide varied perspectives across domains. Members take on roles such as data owner, steward and custodian to ensure accurate data sets for their respective business units. A high-level overview of this is shown below.
The Data Governance Council consists of several roles with varying responsibilities. Metis Strategy recommends the council have at least the following three roles:
Business unit end users
Business unit end users will have access to trusted data based on their business unit needs and role requirements. They will collaborate with the business data owners to ensure maximum utility of the enterprise data.
Conclusion
Data governance is critical to ensuring the success of strategic data projects across any organization. Having the right structures in place will enable a faster return on investment and allow the governance capability to scale throughout the organization. As more high-value use cases come to life, analytics and AI teams will be empowered to use trusted data to improve business performance, enhance the customer experience and improve operational efficiency.
Companies have had great success in initial governance efforts, unlocking the utilization of customer and product data to help drive product design and improve sales outcomes. For example, after developing a governance program around its consumer and product data, one retailer improved the personalization of a merchandising ad unit by 17% through an enhanced understanding of user engagement and behavioral patterns. Success in this area helped the company make the business case for future analytics and AI use cases. In this case, a strong data governance capability built confidence and momentum for the organization as it continued to scale its analytics efforts.
To learn more about developing a robust data governance program, please contact us at information@metisstrategy.com
Advancements in artificial intelligence have opened the door for innovative ways companies can deliver unique and personalized customer experiences. Join us virtually on May 21 for our next Metis Strategy Digital Symposium where global business and technology executives describe how AI has improved their organizations, how they are continuing to foster a customer-centric mentality, and what the future of technology and digital looks like in the Age of AI.
C-level technology leaders, register here reserve your spot and stay tuned for agenda updates. We look forward to seeing you!
(Click here for highlights from our most recent Digital Symposium, and stay tuned to our YouTube channel for videos of our panel discussions.)
12:00 – 12:15 p.m.
Welcome and Introductions
Welcome and introduction to the Metis Strategy team
Peter High, President, Metis Strategy
12:15 – 12:40 p.m.
Customer Experience in the Age of AI
Marty Brodbeck, Chief Technology Officer, Priceline
Bala Subramanian, Chief Digital & Technology Officer, UPS
Moderated by Steven Norton; Co-Head Executive Networks, Research, and Media; Metis Strategy
12:40 – 1:05 p.m.
Driving Digital Innovation Ahead of Disruption
Greg Moore, Chief Information Officer, KB Home
Michael Lucas, Chief Information Officer, Wilson Sonsini
Moderated by Chris Davis, Partner & West Coast Office Lead, Metis Strategy
1:05 – 1:30 p.m.
Shaping the Story: Future-Oriented Talent and Innovation
Monica Caldas, Global Chief Information Officer, Liberty Mutual
Amir Kazmi, Chief Information & Digital Officer, WestRock
Moderated by Alex Kraus, Partner & East Coast Office Lead, Metis Strategy
1:30 – 1:45 p.m.
Entrepreneur Spotlight: CEO of Augment
Scott Dietzen, CEO, Augment
Moderated by Peter High, President, Metis Strategy
1:45 – 2:15 p.m.
Emerging AI Opportunities in Pharmaceuticals and Healthcare
Diogo Rau, Chief Information & Digital Officer, Eli Lilly
Cindy Hoots, Chief Digital Officer & Chief Information Officer, AstraZeneca
Greg Meyers, Chief Digital & Technology Officer, Bristol Myers Squibb
2:15 – 2:40 p.m.
Blueprint for AI Organizational Readiness
Praveen Jonnala, Chief Information Officer, CommScope
Tim Dickson, Chief Digital & Information Officer, Regal Rexnord
Moderated by Michael Bertha, Partner & Central Office Lead, Metis Strategy
2:40 – 2:55 p.m.
Closing Remarks and Adjourn
Click here for highlights from our February Metis Strategy Digital Symposium, or watch the panels on our YouTube channel. We look forward to seeing you!
Thank you to everyone who attended and participated in the 16th Metis Strategy Digital Symposium. Highlights from the event are below. If you missed the event, check out Metis Strategy’s Youtube channel and Technovation podcast in the coming weeks for recordings of each conversation.
Our next event will take place May 21. More details and an agenda coming soon. CXOs, are you interested in attending? If so, kindly register here. We look forward to seeing you!
The COVID-19 pandemic, global supply chain challenges and the broad resurgence of artificial intelligence created a sense of urgency among many technology leaders to modernize and improve their organizations’ digital capabilities. Today, many companies are beginning to see the results of those investments and talking about the strategic ways technology can continue to enable innovation and resilience. Tech leaders recognize the importance of playing both offense and defense as they continue to navigate an uncertain business landscape, and the continued need to align talent and business strategies as they plan for future growth.
Investing in resilience
Some leaders outside IT may argue that there is never a good time to invest in IT. But given how quickly the competitive landscape is changing, organizations can’t afford to pause. For CIOs, a question is where – and when – to make those strategic investments.
“It’s much more beneficial to invest in a downturn than in an upcycle,” said Gates Corporation CIO Diego Silva. During a downturn, there is more capacity and willingness for people to drive change, put new skills into practice, and move projects forward. The greater acceptance for change gives companies the opportunity to drive productivity and resilience, and ultimately put them in a position of strength when the next upswing comes around.
Indeed, many organizations took advantage of time during the pandemic to invest in digital capabilities. When the world was in “shutdown mode,” Sunbelt Rentals Chief Digital & Technology Officer JP Saini invested in the organization’s omnichannel retail capabilities, talent development, new innovation models, and other initiatives to strengthen the resilience and adaptability of the enterprise. As Metis Strategy partner Chris Davis notes: “Businesses are cyclical, but progress and innovation don’t have to be.”
MSDS attendees shared that the biggest barriers to advancing and maintaining digital capabilities are legacy operating models and legacy infrastructure
Managing both offense and defense
True transformation means not just building innovative products and services but also ensuring that all the processes that support those innovations are running as they should. For CIOs, that means playing both offense and defense well.
At pharmaceutical firm GSK, innovation has long been a core competency. As leaders discussed transforming parts of the organization, there was growing recognition that the company had to balance playing offense and defense, playing to win rather than “playing not to lose.” Offense includes those digital and data capabilities at the core of a company’s strategy, while defense-oriented activities may focus on areas like responsible AI and cybersecurity. It becomes a virtuous cycle, GSK Chief Digital & Technology Officer Shobie Ramakrishnan said. “Defense in service of the offense becomes important.”
Tech modernization is another area where offense and defense must be balanced. As Grainger CTO Jonny LeRoy noted, organizations that have been early adopters have a duty to tend to the IT garden over time, “to keep the weeds out.” Putting that into practice, Grainger is focused on the mechanics of how it grows, using its understanding of processes like customer acquisition and inventory management to guide the continuous development of its systems and solutions. Meanwhile, Grainger keeps an eye on the horizon and experiments with new technology as it comes so it can be ready for what’s next.
Continuously improving
Responding to a fast-changing market requires organizations to deploy new capabilities quickly and pivot when necessary. That requires a mindset of continuous improvement and a constant search for opportunities to align people, process and technology toward a common outcome.
Consider a zero-day cybersecurity vulnerability, one that takes advantage of an unknown or unaddressed issue and needs to be fixed immediately. Jen Felch, Chief Digital Officer and CIO at Dell, said the best way to be prepared is “not only to take care of it early, but figure out how to get fast.” While some may view behavior or process change as antithetical to speed, the efforts to make those changes and continuously improve can be major levers to increase speed and efficiency.
Felch recognizes the desire for continuous improvement among teams as well, not only to build skills but also to see the results of their work. Rapid experimentation cycles have helped, she said: “let’s see what we can do in two weeks and build on that and see how it goes.” Giving appropriate context, bringing in knowledge from across the organization, and encouraging a test-and-learn mindset can also drive empowerment across teams. On the process side, constantly improving data quality, information retrieval methods and learning opportunities have also aided progress.
The top talent efforts that technology executives are focused on to advance AI are widespread education/upskilling and scaling AI-based productivity tools
Adopting new ways of working
Technology leaders are also adapting their talent strategies to better suit their strategic goals. Barry Perkins, COO at Zurich North America, noted that having a majority of technology employees in India limited productivity and agility. Noting “ABCD” – AI, Big Data, Cyber, and Development – as four critical digital capabilities, the company has begun to reassess its talent strategy, including which roles should be closer to headquarters. “We can’t have agility if we’re having conversations thousands of miles away with different time zones,” he said. “It’s much easier side by side.”
Effective talent management also requires leaders to inspire teams about the organization’s future vision and help team members see their place in the plan. As Brinks Inc. CIO Neelu Sethi said, transformation of any sort is less about technology and more about people. She is working to create a true “three-legged stool” of people, process and technology rather than letting a single element be the focus.She also reiterated the need for true collaboration. “You cannot whistle a symphony,” she said. “It takes an orchestra.”
At Travelers, preparing talent for large-scale change has involved a focus on four areas: Customer First; Empower and Act; Test and Learn; and Prioritize. Chief Technology and Operations Officer Mojgan Lefebvre also emphasized the need for effective communication to drive trust and accountability through transparency. “People want to play a role,” she said. “Bringing them along and giving them that capability is important.”
A majority of MSDS participants are either experimenting with Copilots or other generative AI tools to enhance software developer productivity or scaling the adoption of these tools
Advancing generative AI adoption
Naturally, artificial intelligence continues to be a priority in 2024. After a year of initial exploration and education, many organizations are ramping up AI experiments and seeing ways to expand AI across the enterprise. Underpinning all of this exploration is a focus on value delivery and safety.
GSK established an AI policy and set up an AI governance council five years ago when the organization decided to scale AI across the company. Now, Ramakrishnan is thinking about additional risks around adoption and procurement to ensure AI can scale. Similarly, Travelers many years ago set up an AI accelerator team to explore potential use cases and create a framework for responsible AI use. Now, they are prioritizing a handful of use cases and in the process of scaling them across the organization.
“Generative AI is top of mind for every executive to accelerate their workforce and accelerate the products of the business,” said Varun Mohan, CEO & Co-Founder of Codeium. In a poll, participants said the biggest benefit to AI and generative AI adoption is increased productivity (67%), followed by improved products and services (17%). When it comes to advancing AI, 40% of attendees said talent efforts are focused on scaling AI-based productivity tools.
Around two-thirds of respondents see increased productivity as the biggest benefit to AI/generative AI adoption
Many speakers said they are currently using AI for use cases such as developer productivity and internal process automation. A key outcome: speed. “The more we eliminate the drudgery from the process, the more we can start to deliver value,” said Jen Felch of Dell. At Travelers, Levebvre’s team is exploring how generative AI can be an assistant or collaborator, such as quickly searching through and summarizing documents or helping team members access needed information. The company is also exploring how AI can be used to improve job descriptions and recruiting processes. Lefebvre noted that while many of their use cases are internally focused, they want to be able to scale the technology and “make it good before turning it around with customers” as there is also a lot of external value to capture.
At Grainger, LeRoy’s teams are experimenting with generative AI in technology (coding assistants) as well as customer service. Through internal hackathons, the technology team developed tools that are boosting employee productivity and allowing them to do more with a constrained budget. As use of these tools continues to scale, financial management becomes an important factor, LeRoy said. “Some of that is selecting the right model with the right capability level that’s not overly expensive, and managing how much information you put into them.”
Our next event will take place May 21. If you are a CXO and interested in attending, please register here.
The California Gold Rush launched in 1848 when a sawmill operator stumbled upon a literal goldmine while building Sutter’s Mill in Sacramento, California. Nearly two centuries later, a figurative gold rush kicked off as individuals and companies across the globe sought to capitalize on generative artificial intelligence (GenAI).
Looking back at Technovation podcast interviews from 2023, AI and adjacent technologies were easily the most talked about trends. Mentions of ChatGPT and GenAI soared through the rankings, going from a non-existent topic in 2022 to the second most frequently discussed trend on the podcast a year later.
The focus on GenAI brought with it a growing focus on AI more broadly, as well as cybersecurity, chatbots, and robotic process automation. It also spurred conversations about the possibilities of quantum computing and new opportunities to leverage data coming from a range of IoT sensors.
Technologies like blockchain and the metaverse took a backseat to AI this year, but many executives hypothesize that widespread adoption may yet be on the horizon.
When generative AI became widely accessible to companies at the end of 2022, the possibilities seemed endless, spurring conversations about how it could reshape work.
Cisco Sanchez, SVP & CIO of Qualcomm, said he noticed an “anxiousness” within his organization to leverage that technology and show what was possible. Through the company’s Imagine platform, his team identified a number of use cases such as internal documentation search, image creation, and more.
Document summarization piqued the interest of DocuSign CIO Shanthi Iyer, who said GenAI could help clients quickly get answers to questions about their contracts, including which parties were involved, start and end dates, fiscal terms, and even potential risks.
GenAI’s rise also renewed conversations around voice assistants and chatbots. Tracy Kerrins, CIO of Wells Fargo, announced that the company completed 100% consumer rollout of a new virtual agent named “Fargo” that can be accessed through the company’s mobile app. The assistant “helps improve [the customer’s] banking experience and give them the information they may not have even known they needed when they need it,” Kerrins said. Powered by Google’s AI Dialogue Flow solution, “Fargo” is seen internally as the company’s first step toward adopting GenAI and paving the way for its expansion.
Those keeping a close eye on technology trends surely saw GenAI on the horizon, but few could have predicted the speed of its adoption. It’s safe to say that going into 2024, the topic of GenAI will remain strong, with new insights on where it makes sense to deploy the technology, what value it poses to the overall business, and what risk factors need to be considered to drive a successful AI strategy.
To take advantage of the opportunities presented by AI and GenAI, organizations noted the need for a sound data strategy and quality data management practices to act as a foundation. Kristie Grinnell, CIO of DXC Technology, emphasized the need for strong data fundamentals in the age of AI. “Is this data I can count on, take action on, make a decision on?,” asked Grinnell, “Because then, I’m going to run analytics over it to start predicting things for the future.” Without reliable data, she warned, companies could face “disastrous” results.
Filippo Catalano, CIDO of Reckitt, echoed this sentiment as well, describing GenAI as a “lens” on top of the data already collected. “You need to have your data analytics strategy in place,” said Catalano, “Frankly, if you don’t have good data practices… you will not be able to generate competitive advantage.”
Mentions of the Internet of Things and sensor-based technology have steadily declined in mentions over the last few years of podcast interviews. However, this doesn’t appear to be due to declining interest. Rather, sensors are now ubiquitous in many companies, collecting and feeding data back to the IT organization. To many executives, the more pressing topic wasn’t the implementation of sensors themselves, but the data coming from them and the value this data can deliver.
Johnson Controls CIO Vijay Sankaran remains steadfast in the use of IoT sensors in the real estate sector. The data his team collects has a wide range of applications, including mapping facilities to optimize the usage of physical space and improve employee experience. Similarly, at commercial lightning supplier Signify, CDIO Tony Thomas leverages the data about how customers use its smart light bulbs to help the company figure out how to evolve its product and service offerings.
At ConocoPhillips, real-time sensor data is giving the company more visibility into its drill sites than ever before, allowing it to more closely monitor equipment and learn about potential issues before they happen. Using IoT sensors to get real-time data “is allowing us to do deep analytics, machine learning, AI, and monitoring opportunities that we were never able to do before,” said CDIO Pragati Mathur.
Heading into 2024, data remains at the top of the CIO agenda as organizations seek new ways to collect, analyze, and act upon information to drive value.
With data being as valuable as it is, securing it is non-negotiable. The ever-present need to build consumer trust and protect enterprise data ensures that cybersecurity is a trend that will persist and evolve. “Cybersecurity is never a business by itself,” said Gili Raanan, Co-Founder of Cyberstarts. “If technology changes and you’ve got artificial and generative AI,” said Raanan, “you probably need generative AI security.”
The inevitable cyber risks and ethical questions surrounding GenAI’s implementation were not lost on executives. Rajan Kumar, CIO of Intuit, has been on a journey to mature his organization’s data strategy that powers the services offered to clients. While collecting the necessary data is one area of focus, just as important is doing so with “the right guardrails around the security and privacy.”
Alina Parast, CIO of ChampionX, reiterated the need for cybersecurity before leveraging any AI capability. “We need to find a safe and secure home for our data before we apply AI,” she said. Parast applied internal security procedures and protections integrated into the Microsoft platforms she uses to ensure any application of AI doesn’t place data at risk. Parast also explained how cybersecurity practices extended beyond IT to become part of an overall mindset. “We want people to internalize that cybersecurity is something that doesn’t just belong to a small team in the corner,” she said, “but [that] it’s everybody’s responsibility.” To drive home this mindset, she developed an internal cybersecurity training program framed as a murder mystery mini-series, providing a fun alternative to routine corporate training modules.
Juan Perez, CIO of software-as-a-service platform Salesforce, said cybersecurity has been imperative to any initiative he undertook throughout his career. When describing his top five pillars as a CIO, Perez began and ended his list with the need to protect and secure the enterprise. “None of the other [pillars] matter if, at the end of the day, we’re not prioritizing the security of our environments and the security of the information that we have to guard so closely so that we protect the business’s interests.”
As companies run bigger and faster models, the demand for compute has picked up. “You have to get the ROI out of [GenAI], and there’s a lot of compute power that is required,” said Vish Narendra, CIO of Graphic Packaging.
One such avenue is quantum computing. Douglas Lindemann, CIO of ArcBest Technologies, has a team dedicated to researching quantum. This research has informed Lindemann’s perspective on the role that quantum can play as enterprises continue their AI journeys. Through his team’s research, he was able to apply some of the learnings to ‘quantum-inspired optimization’ (QIO) that runs on classical computers to “make more efficient algorithms that can respond and provide responses in a quicker time” and “provide better, quicker model responses with some of our AI.”
Monica Caldas, CIO of Liberty Mutual, says quantum computing could change the way data is recorded and calculations are done. “I just think about the ability of the speed and the processing power that that will bring,” she said. As quantum capabilities advance, “how will that change how we do algorithms? How will that change how we process?”
Vanguard CIO Nitin Tandon agreed, citing the intersection of quantum and AI/ML tools was a trend he was particularly excited about. “We are talking about NVIDIA and GPUs today, but think about if we had qubits, GPUs and CPUs that you can harvest in whatever ratio you want to drive really powerful AI/ML engines.” However, like AI, the advent of quantum poses its share of risks. “It also has huge security implications, which we’re also cognizant of and working on.”
Cloud computing technology has been a trend often mentioned on Technovation, as many companies began their cloud journeys years ago. With strategies in place and transformations in progress, the conversation now is focused on how the cloud has and can continue to enable new processes.
Blockchain seemed poised to be a game-changing technology, but adoption has proved slower than many anticipated. Shubham Mehrish, Global VP of Mars, jokingly contrasted the successful takeoff of AI to the lukewarm response to blockchain. The rise of AI “ is not a blockchain/crypto moment. This is probably more real than that.”
Within the financial services industry, however, some see high potential for value-add use cases. Sumedh Mehta, CIO of Putnam Investments, said during a podcast interview earlier in the year that he sees blockchain as having the “potential for being the backbone of future global financial transactions.”
Lori Beer, CIO of J.P. Morgan, also sees the potential value of blockchain if it is done right. “When you think about all the processes that we have to comply with payments…there’s so many opportunities where you have to connect to other banks to understand information in that [know your customer] process to be able to know that you can go ahead.”
Co-founder and Co-chairman of The Carlyle Group, David Rubenstein, said crypto and the like are here to stay. “Crypto will be perfected at some point,” he said, “and probably it’ll be made more valuable in some ways down the road.”
While the metaverse, augmented reality and virtual reality didn’t make the most-mentioned list this year, the emerging technology may still get its day.
As companies collect more revenue from digital channels, the metaverse could serve as a new frontier for customer interactions. “Whether it is immersive environments like the metaverse, your regular eCommerce shopping, or social media shopping, you can actually put prototypes of new products and designs, and see what the interest of consumers is,” said Katia Walsh, former Chief AI Officer of Levi’s. Metaverse technologies like VR may find some early adoption outside the consumer sector. ConocoPhillips has already started deploying tools to make full use of the capability when it arrives. “Today we use VR headsets for drilling locations and to interact with the machines and people without leaving our desks,” said Pragati Mathur. “How we extend that into the industrial metaverse is something … which is exciting.”
Paul Beswick’s pathway to become Chief Information Officer of Marsh McLennan is non-traditional to say the least. He joined the company via one of its operating companies, the strategy consultancy, Oliver Wyman. He rose through the ranks at that firm to become a partner and global head of Oliver Wyman Labs and global co-head of Oliver Wyman’s Digital Practice. “It all happened because I walked into the wrong meeting one day and got sucked into the project to design Marsh McLennan’s technology strategy with Scott Gilbert, who was my predecessor,” noted Beswick. “Then got sucked into trying to deliver it, which anyone who’s been a consultant should know, you should never do: you should never both write the strategy and take responsibility for delivering it.”
When he weighed the advantages of his pathway, he noted that he has been in executive committee meetings and boardrooms since he was in his 20s. He also acknowledged that having profit and loss responsibilities in various roles along his ascent at Oliver Wyman likely gives him a better appreciation of technology’s power to grow revenue for Marsh McLennan, not just where it can lead to cost savings.
Beswick’s current post has him overseeing technology for a conglomerate that includes Marsh, the world’s largest insurance broker, Mercer, leader in human resources, benefits, and investment consulting, Guy Carpenter which is in the business of reinsurance broking, along with Oliver Wyman. When he took over as chief information officer roughly three years ago, the company was in the throes of moving from a decentralized IT department to one that exerts much more influence from the center. “That’s a fairly new development in terms of how we’ve been organized. When I took on this role, we were starting the process of bringing what had been business unit-specific technology organizations together into one overall organization,” said Beswick. “Prior to that, we’d had different teams by business, but with a shared infrastructure and security organization in the middle. It’s been an interesting journey trying to forge one team out of what were quite independent teams before.”
Beswick sees a primary job of his as increasing the velocity of the business. “We do a lot of work to understand what slows us down, how we get tangled up in our own processes, where there’s bureaucracy that’s unnecessary, where we fail to engineer solutions to problems that we can engineer solutions to that can help things move significantly more quickly,” he underscored. “A huge chunk of where I spend my own time…is focused on trying to change the efficient frontier between speed, agility on the one hand, and security, compliance, robustness, and resilience, on the other.”
A primary pathway to this for Beswick and his team has been in building a platform strategy, building template projects and defining “patterns” that can be deployed readily, streamlining policy, compliance and nonfunctional aspects of every project that his organization undertakes. “One of the things I’ve learned as I’ve come into this job is how important understanding some of the organizational dynamics are and the points of inefficient but stable equilibrium that exist in organization structure that tend to lock you into patterns that are inefficient and thinking very deliberately about how you break through some of those things,” he said.
Beswick is excited about the amount of innovation driven by technology and his team’s ability to convey the art of the possible to the rest of the company. He thinks about technology in the spectrum of hard things to easy ones. “We are not in the game of doing really hard stuff,” he said. “That’s not the organization that we’re built for, but hard things get easier over time, and there’s this constant shift from more complicated and less accessible but powerful technology into things that are increasingly easy to get our hands on. At some point, there’s this tipping point where the hard becomes easy. If we can be there at the point where things become easy and we understand how to put them into action in a real business against our real processes and our real problems, that’s the area where I think we can create the most value. That requires you to be always playing around at the edge of that transition point and make sure you recognize when that transition has happened.”
A case in point is Marsh McLennan’s foray into generative artificial intelligence. It began by partnering with vendor partners, but that proved to be too expensive. However, when Microsoft made the OpenAI back-ends available in a secure fashion, Beswick and his team discovered that with a little bit of extra engineering, they could make that available to the broader company. The goal was to mirror the remarkable uptick in the use of ChatGPT in society. “I didn’t think we needed to spend a lot of time worrying about precisely what the use cases were,” Beswick admitted. “It felt like the use cases would be emergent. Very quickly after we had access to the [Microsoft OpenAI] APIs in a secure fashion, we created the chat interface on top of that, which is what we call LenAI.”
It took only a day and a half to deliver the first version of LenAI to a pilot group within the company. The focus on making IT a driver of velocity improvements were responsible for such a fast path. Soon a few hundred people had access to LenAI and within 28 days, the entire firm had access to it. “I think we’ve identified [roughly] 300 distinct use cases that people have been putting this to,” said Beswick. “Some are very specifically related to some small part of the business. Others are more generic. We’ve kept an eye on that, capturing that information, and we’re using that to then drive our build-out agenda for some of the things that are going to be more scalable implementations of this.”
Beswick believes his team has moved farther faster by turning the typical process on its head. Typically, people gather use cases, find a business co-sponsor, build a business case, assemble a project team, and then get started. Given Beswick’s need for speed, that was too slow. “By going the other way and driving something more generic out and flushing the use cases out, I think we’ve got further faster,” said Beswick with pride. As a result, “we added a couple of extra capabilities into the basics, [such as] internet search document upload. We do a lot of work with documents, so there’s lots of stuff people are doing with document summarization, with data extraction from documents and translation between languages, which these tools are good at. Email drafting, particularly for people for whom English is not the first language when we’re a business that largely operates in English [has been another powerful use case]. A lot of people are using it to tighten up their communications and streamline things.”
Code writing is another layer of value. Beswick noted with excitement that different parts of LenAI were written by LenAI. This will increasingly become the norm. Additional functionality that has been defined has included calculators, stock price lookups, weather lookups, database querying, and the ability to pull from a variety of news sources. “There are clearly some use cases where you can see transformation of various processes that we would run through today and would be fairly manual where we can really divert resources into much more high-value-added work,” said Beswick. “Those are starting to spin out. A lot of it’s around things like document ingestion, processing, and data extraction. Cross-mapping data from one data source to another, one data structure to another turns out to be a pretty tractable problem as well. I think we’re just scratching the surface as to what those sorts of things will be.”
Beswick and his team have made substantial progress in a short amount of time, living up to his goal of being a force multiplier. He believes he and his team are setting a sound foundation, but even higher levels of value will be achieved by building upon that foundation.
It’s been a big year for technology, with the rise of generative AI sparking new conversations about how tech will shape the future of work and society at large. The books below offer a range of perspectives on recent developments in data and AI, as well as resources to help leaders navigate an increasingly complex and fast-moving technology landscape.
The Coming Wave: Technology, Power, and the Twenty-first Century’s Greatest Dilemma, by Mustafa Suleyman and Michael Bhaskar
Suleyman, the co-founder of DeepMind and Inflection AI, has been a pioneer in artificial intelligence. Bhaskar and he believe the coming decade will bring a diverse selection of intensely capable and fast-proliferating new technologies. In The Coming Wave, they explain how these technologies present an existential dilemma as we work to control them: unregulated use on one side, and overbearing surveillance on the other.
Going Infinite: The Rise and Fall of a New Tycoon, by Michael Lewis
Lewis’ latest book tells the psychological story of the dramatic rise and fall of FTX founder Sam Bankman-Fried, the world’s youngest billionaire, who became a leader in crypto almost overnight before losing it all. Lewis tells his story from the vantage point of being in the room to witness the rise and the fall first hand.
Move Fast and Fix Things: The Trusted Leader’s Guide to Solving Hard Problems, by Frances X Frei and Anne Morriss
The informal Facebook motto “move fast and break things” gained a lot of traction across businesses but in a somewhat skewed way. It implied that breaking things, no matter the cost, is simply the price organizations pay for innovation.
Best-selling authors and leadership experts Frances Frei and Anne Morriss believe this way of thinking is deeply flawed and hinders leaders from building a truly resilient company. They argue there shouldn’t have to be a tradeoff between speed and excellence, and that companies can solve difficult problems quickly and fix things at the same time. Drawing on work with leading organizations like Uber and ServiceNow, Frei and Morriss identify five key steps, one per each day of the workweek, that leaders can take to solve their organizations’ most complex problems quickly.
Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity, by Daron Acemoglu and Simon Johnson
Acemoglu and Johnson revisit a thousand years of history and economics to demonstrate how technological progress doesn’t have to lead to a loss of human empathy. Power and Progress explores how technology was once – and could be again – brought under control and used for the benefit of most people.
All-in On AI: How Smart Companies Win Big with Artificial Intelligence, by Tom Davenport and Nitin Mittal
All-In on AI is an insightful look into the magic behind the success of the technology’s leading adopters. While most companies are placing small bets on AI, a select few are embracing the technology to transform their products, processes, strategies, and customer relationships and experiences. Using examples from organizations including Anthem, Ping An, Airbus, and Capital One, Davenport and Mittal explore what AI looks like at the cutting edge and help organizations understand what’s needed to take AI to the next level.
The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI, by Fei-Fei Li
In her memoir, Stanford professor and AI pioneer Fei-Fei Li describes how a Chinese immigrant living in poverty in the United States overcame adversity to become one of the leading contributors to modern artificial intelligence. Whether sharing her own journey or exploring the incredible dangers and opportunities AI poses, she tells a story Reid Hoffman describes as “a testament to the power and possibility of humanity.”
Elevate Your Team: Empower Your Team to Reach Their Full Potential and Build a Business that Builds Leaders, by Robert Glazer
Being a leader is a balancing act. Not only must one find and retain top talent, but he or she must also ensure those teams perform at the highest levels and deliver results while avoiding burnout. A follow up to Glazer’s 2019 book, Elevate, this book provides strategies and tools to help leaders unleash their teams’ full potential and build the leaders of tomorrow.
Data Is Everybody’s Business: The Fundamentals of Data Monetization, by Barbara Wixom, Cynthia Beath, and Leslie Owens
The authors, leaders at MIT Sloan Center for Information Systems Research and UT Austin’s McCombs School of Business, provide a guide to help people across organizations (not just on data teams) think more expansively about how to turn data into money. Covering approaches such as wrapping products with data and selling broader information offerings, show how leaders can drive positive outcomes and generate excitement around new data opportunities.
Think Like a CTO, by Alan Williamson
In this book, Williamson highlights the common themes CTOs should consider as they work to become the trusted leader their company needs. He also adds commentary from industry experts and veteran CTOs to illustrate the book’s focus areas, which include establishing strong relationships with C-suite peers, architecting future-proofed systems, and leading with data rather than passion.
Wiring the Winning Organization: Liberating Our Collective Greatness through Slowification, Simplification, and Amplification, by Gene Kim and Steven J. Spear
Drawing on years of research and insights from organizations such as Amazon, Apple, and NASA, Kim and Spear show how leaders make the “social wiring” that drives results and allows others to thrive. They describe their system for moving problem-solving from risky danger zones to low-risk winning zones and provide a playbook for leaders to rewire their own organizations.
