How leaders can drive the coveted project-to-product transformation
This article was originally published on CIO.com by Chris Davis, Partner at Metis Strategy, and Kelley Dougherty, Associate at Metis Strategy.
In this time of fluid markets, fierce competition, and constant disruption, the modern enterprise must stay innovative and agile. It must be ready to evolve at any moment, and deliver quickly, consistently, and reliably through its large-scale software operations.
But it can hardly do so through traditional, monolithic ways of working, particularly those organized around projects. Many companies are therefore reorienting their operating models around end-to-end products. Done well, these transformations make a company nimble. Done poorly, they exhaust the organization and produce little value.
Leaders must transform their organizations methodically along a path that minimizes redundancies, builds momentum, and creates immediate and tangible business value. In this article, we outline the steps to start a product operating model journey, coloring the steps with stories told on the Metis Strategy Podcast by executives from companies like Ascension, Condé Nast, and Hyatt.
First, leaders must identify the products around which their operating model will be designed. We define a “product” in this context as:
“a capability or portion of a capability, brought to life through technology, business process, and customer experience, with a continuous value stream, and an ability to measure success independently.”
Therefore, leaders should draw the capability map of their business, showing how value streams and assets are positioned, how they relate to each other, and which of them are immature or missing. These capabilities can then be translated into end-to-end products calibrating for the organization’s size, offerings, and business model.
If an organization has uniform customer offerings and go-to-market motions, then its products should be aligned to the company’s value chain. Such is the case at Ascension, as explained by its Chief Marketing and Digital Experience Officer, Raj Mohan: “We’ve organized our teams particularly broken up by the consumer journey into product teams down that path, and then staff those teams along those journeys itself.”
In practice, products aligned to a customer-facing value-chain might include: Development → Marketing → Sales/Order Management → Fulfillment → Customer Success
Aligned to internal value streams, they might include Financial management, HR management, Legal Management, IT Management, Facilities Management, and Data and Analytics.
In contrast, if an organization has multiple business units, offerings, or go-to-market processes, its products must be defined so they account for each BU’s customers, geographies, and so on. This way, products can still be aligned to value chains but also arranged into broader groups, lines, and teams, each constituting a “deeper” aspect of the value chain.
This is how products have been defined at Condé Nast.
Sanjay Bhakta, Chief Product and Technology Officer at Condé Nast explains that his organization’s product offerings result in them having “some capability within the brands, especially the big brands, that focus on things that may be bespoke or have specific requirements.”
Next, leaders must define the capabilities around which they’ll organize resources and configure the product teams such that they can deliver value autonomously. Mohan suggests that a product team can stand on its own “if, over at least a three-year horizon, you can see clearly that a durable team can bring value that you can sign up for.”
How many product teams should you have? As a rule of thumb: about one tenth as many employees as there are in the organization. Ideally, each product team should comprise seven to nine people, and they should include a product manager, scrum lead, technical lead, and engineers. These might be supplemented by user experience leads for consumer products, other engineers, shared services, or specialists.
A project-to-product transformation requires that an enterprise think first in terms of products, and this shift hangs on the structures and processes by which the company manages its portfolio. A company should organize its portfolio around the outcomes it seeks, and those should in turn dictate the capabilities initially staffed to mature at a higher rate. When resources are limited, start by productizing 2-5 key areas, do it well, and scale from there.
Hyatt, for example, has organized its portfolio around customer-focused capabilities, and so has caused the enterprise at large to think in terms of customer outcomes. As Hyatt’s Global CIO, Eben Hewitt, has explained: “Moving to a product mindset, to me, means, number one, it’s for a customer… You’re thinking about the outcomes that people want.”
Further, an organization will do well to manage its portfolio according to Agile principles and to align its product teams to business outcomes. Not only will product teams then naturally align to each other and their shared objectives; the organization itself will think in terms of products and outcomes.
To manage portfolio by capabilities, use annual planning sessions to craft roadmaps aligned to outcomes and segmented by capability. Such roadmaps can then inform the teams who support those capabilities, and ensure their own roadmaps align to enterprise objectives. These planning sessions also give leaders a chance to decide how to allocate funds. As a rule, the product teams should receive roughly 80% of the organization’s budget, and that allocation should cover their needs end to end to build and manage the lifecycle of the product. The remaining 20% should go toward broader initiatives.
Adopting an Agile mindset and common ways of working early in the journey will help reorient a company reliant on waterfall, project-based operating models towards continuously delivering value. However, frameworks such as Scrum and Kanban are a means to an end. Some organizations conflate a “product” transformation with an “Agile transformation,” and lose themselves in the minutia of adhering to specific ‘rules’ and ceremonies. The key is to create a baseline for teams to form, storm, and norm by reducing confusion of how to transition from a rigid waterfall process to a mindset in which an entire agile product team establishes a shared identity founded in the problem the product solves; not their title or role on a waterfall assembly line.
Bhakta emphasizes that Agile should extend to the relationship between product and engineering. He explains: “[It] helps us do faster decision-making, helps us to get products out into the market faster.”
If organizations are already practicing Agile when they start transforming, then they should focus on infusing into their processes the product mindset. If an organization isn’t so mature, however, then it should train teams on core Agile practices to which they can align their processes.
Ultimately, this transformation largely depends on whether people can successfully serve the role as a Product Manager, and balance the business value, viability, usability, and feasibility to focus teams on shipping products and experiences that users love, adopt, and help improve with feedback.
Therefore, each team needs a Product Manager, who can:
Identifying, training, and upskilling Product Managers, especially for internal products, is often the hardest part of the journey. But to be successful, Product Managers must also have clear scopes of responsibility, the power to execute on them, and feedback loops by which they can measure performance and course-correct.
Each of the steps we’ve covered critically enable teams to scale, and once they’ve been carried out the first time, they tend to act as a flywheel, sustaining themselves with their own momentum and creating excitement within the organization to productize more capabilities.
To gauge success of your product operating model journey, start by:
The journey of maturing a product team is never really complete. Once the teams are launched with the steps outlined in this article, leaders should then do the following at scale, working team by team:
It is our firm belief that adopting a product operating model is the only way to successfully support a scaling organization. But don’t take it lightly; this is a commitment that requires leaders to dedicate at least a year of their time to successfully transform an organization’s mindset.
Personalized customer experiences, automated business operations, and data science-driven insights all depend on the quality and volume of your data. That’s why your data privacy strategy must be more than a policy on ethics.
This article was originally published on CIO.com by Chris Davis, Partner at Metis Strategy and Elizabeth Tse, Associate at Metis Strategy.
Companies continue to face implementation challenges as they rush to comply with data privacy regulations such as Europe’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). This is due largely to a mismatch between their management of data and the stringent requirements set by the regulations.
Organizations can address the complexities of privacy regulations via a well-defined data governance framework, which leverages people, processes and technologies to establish standards for data access, management and use. Such a framework also enables companies to address elements of privacy, including identity and access management, consent management and policy definition.
As leaders implement data governance models with privacy in mind, they may face challenges, including lukewarm executive buy-in, lack of a cohesive data strategy, or diverging opinions about how data should be used and handled. To address these obstacles, leaders should consider the following actions:
While a Chief Data Officer or CIO may lead the implementation of a data governance framework or model, data governance should be a shared responsibility across a company. At a minimum, the IT department, privacy office, security organization, and various business divisions should be involved, as each has an important stake in data management. Bringing in a variety of stakeholders early allows firms to establish key data objectives and a broader data governance vision. This collaboration can take the form of a dedicated task force or may involve regular reporting on data governance and privacy objectives to the executive board.
Data privacy, similarly, is also a shared responsibility. All employees have a part to play in maintaining data privacy by following accepted standards for data collection, use and sharing. Indeed, implementing a successful data governance model with privacy in mind requires educating employees on governance concepts, roles and responsibilities, as well as data privacy concepts and regulations (e.g. the definition of “personal information” vs. “consumer information”).
After establishing a governance vision and driving employee awareness, organizations can define their desired data governance roles – such as data owners, data stewards, data architects and data consumers – and tailor the roles to their needs. Some companies may distinguish between data stewards and data owners, for example, with the former responsible for executing daily data operations and the latter responsible for data policy definition. For one client with a large and complex IT department, Metis Strategy established a governance hierarchy with an executive-level board, combined data steward/owner roles, and other positions (e.g. data quality custodians). This structure facilitated ease of communication and enabled the client to scale its data management practices.
In the long term, firms should incorporate data governance and management skills into their talent strategy and workforce planning. Given the expertise required and the shortage of qualified people for some data-intensive roles, organizations can consider enlisting the help of talent-sourcing firms while focusing internal efforts on talent retention and upskilling. As companies’ strategic goals and regulatory requirements change, they should remain flexible in adjusting their data governance roles and ownership.
To respond adequately to consumer privacy-related requests for data, organizations should establish standardized procedures and policies across the data lifecycle. This will allow companies to understand what data they collect, use and share, and how those practices relate to consumers.
For example, the CCPA provides consumers with the right to opt out of having their personal information sold to third parties. If a retailer needed to comply with such a request, it would need to be able to answer questions in the following categories:
Establishing policies and standards for the above can help organizations quickly determine the actions needed to respond to customer requests under privacy regulations. Companies should communicate policies widely and ensure that they are being followed, as failing to do so can propagate the use of inconsistent templates and practices. At one Metis Strategy client, for example, few stakeholders had sufficient awareness of data management and access standards, despite the fact that the client’s IT department had established extensive policies around them.
To successfully implement data governance frameworks and ensure privacy compliance, firms may also need to address challenges posed by legacy infrastructure and technical debt. For example, data often is stored in silos throughout an organization, making it difficult to appropriately identify the source of any data privacy issues and promptly respond to consumers or regulatory authorities.
Firms also need to evaluate the security and privacy risks posed by outsourced cloud services, such as cloud-based data lakes. Those using multiple cloud providers may want to streamline their data sharing agreements to create consistency across vendors.
Some technologies can help companies leverage customer data while mitigating privacy risks. In a Metis Strategy interview, Greg Sullivan, CIO of Carnival Corporation, noted that data virtualization enhanced his organization’s analytics capabilities, drove down operational and computing costs and reduced the company’s exposure to potential security and privacy gaps.
Companies can also consider new privacy compliance technologies, which can enhance data governance through increased visibility and transparency. Data discovery tools use advanced analytics to identify data elements that could be deemed sensitive, for instance, while data flow mapping tools help companies understand how and where data moves both internally and externally. These tools can be used to help organizations determine the level of protection required for their most critical data elements and facilitate responses to consumer requests under GDPR and CCPA.
Although legacy technology overhauls can be time-consuming and costly, firms that are decisive about doing so can reduce their privacy and security risks while avoiding other challenges related to technical debt.
As the global data privacy landscape evolves, organizations should continuously adapt their data governance models. Companies should proactively address their obligations by designing data governance roles, processes, policies, and technology with privacy in mind, rather than reacting to current and forthcoming privacy legislation. Companies doing so can not only improve risk and reputational management, but also encourage greater transparency and data-driven decision-making across their organizations.
Company-first CIO Krzysztof Soltan and his team helped transform the construction-aggregates giant with a focus on digitizing operations, modernizing infrastructure, and overhauling how IT goes about its business.
This article was originally published on CIO.com by Mike Bertha, Partner at Metis Strategy and Chris Boyd, Manager at Metis Strategy.
In a recent “all-hands” meeting, Krzysztof Soltan, CIO of Vulcan Materials, announced his IT organization would continue its “laser focus on digital transformation.”
Digital technology, he explained, would remain a central focus of the construction-aggregates industry and would underpin customer-grade experiences increasingly expected from industry leaders. Vulcan, based in Birmingham, Ala., is the nation’s largest construction aggregates company, producing materials such as crushed stone, sand, and gravel, with strategic downstream assets like asphalt and ready-mixed in select markets. Soltan, previously a tech leader at Johnson Controls, ABB, and GE, became the company’s first CIO just two years ago and is at the forefront of the company’s digital transformation efforts.
Soltan and his fellow leaders attribute Vulcan’s success to many things, but chief among them is the company’s attitude toward key activities like operating and selling — “The Vulcan Way,” as it is widely referred to within the company. This orienting force has become so strong that, to Soltan and his team, it seemed only right that they should rethink IT in terms of how it might amplify the approach. As Soltan explains: “If we were going to keep up with the pace of change in the industry, IT would have to be recalibrated.”
Here, Soltan and his IT leadership team share the story behind those efforts. They highlight the mindset and approach necessary to leverage new technologies to best compete in the digital age.
As Soltan’s IT leadership team explains, Vulcan’s digital transformation turned a corner with the advent of the Vulcan Way of Selling, an enterprise-wide initiative that, through technology, aimed to turn the company’s highly manual relationship-based sales model on its head. And so it did.
Since the initiative’s launch in 2017, Vulcan has deployed myriad proprietary technology solutions that serve up real-time market insights, thereby improving experiences for sales reps, customers, and the truckers responsible for transporting goods to job sites. For sales reps, these improvements show up as more time spent talking about solutions with customers, and less time on administrative work like quoting. For customers, real-time location-tracking of materials shipment translates to better labor planning. For truckers, a seamless, paperless experience when picking up materials at a Vulcan quarry means faster delivery.
As Vulcan SVP Jerry Perkins put it at the company’s 2022 investor day, “Time is money in the construction and trucking industry, and these tools make our truckers and customers much more efficient and productive.”
The success of the Vulcan Way of Selling brought the company to an inflection point. Enterprise-wide, tech-enabled transformation programs would no longer be one-off events; instead, they were destined to become fixtures in Vulcan’s pursuit for continuous improvement.
Enter Soltan. After learning the business and getting acclimated with the effort to integrate US Concrete, which the company had recently acquired, Soltan got to work charting IT’s path forward. “Between the US Concrete acquisition and other major initiatives, we hadn’t taken a step back in awhile to reflect on how we were managing our own shop,” Soltan says, noting this isn’t unusual for companies during periods of growth.
The path to cementing Vulcan IT’s value proposition, says Soltan, would be two-fold: Invest continuously in enabling business-driven initiatives, and modernize how they manage the business of IT.
As just one example, the company has commenced VulcanX, an initiative that extends the Vulcan Way of Selling by providing best-in-class tools to the company’s Sales teams to help them win more business and deliver better experiences to customers, in the form of seamless and secure interactions. These efficiencies, the company hopes, will drive more quotes and, subsequently, higher quote-to-order conversions, all while allowing the team to spend less time on administrative tasks.
Just as important is the technical foundation on which Vulcan operates its plants. And so the company has launched another initiative in partnership with its business units to modernize the organization’s technical infrastructure, including improving the speed, connectivity, and mobility of its networks in service of Vulcan’s 10,000+ employees — qualities that will become only more vital as the company multiplies its digital capabilities.
“One reality of our business is that we have to enable modern day technology in the rugged, remote locations that are home to our plants and quarries,” says Soltan. “VulcanX enables scale and mobility in the plant with cloud-based solutions, and our modernized networks will improve our ability to capture data and to quickly drive insights for the folks running our operations.”
Vulcan’s employees can leverage digital capabilities in the field only to the extent that the company’s IT and OT systems are integrated. This reality — understood by Vulcan’s business unit leaders as well as anyone — has ultimately stood to justify, incentivize, and propel the company’s transformation.
A great deal of Vulcan’s success in managing the business of IT can be traced back to the department’s operating model. “The capabilities you deliver within IT the roles and responsibilities, and the ways of working — getting these things right — creates a solid foundation for execution,” Soltan says. To Vulcan’s leaders, it made sense, then, that the operating model should be among the first things they strove to modernize.
First, there was talent strategy — how the company would recruit and train. Of particular concern was the department’s IT career paths, which stood to be refreshed. As Soltan recalls, “We needed our paths to be more indicative of the work we’re doing. This not only helps us attract new talent but allows our team to feel confident they are adding modern skills to their toolkits.”
To this end, Vulcan leaders did two things. First, they developed a new set of career paths, including specific tracks for product management, DevOps, Data Engineering, and other sets of skills that, as Vulcan advances, will become indispensable. Second, the leaders expanded its talent pool by opening a second hub in Dallas, home to Vulcan’s US Concrete acquisition, and the fourth largest metropolitan area in the United States.
The second facet concerned projects, which experienced high demand. As Soltan explains, when digitally transforming at the pace Vulcan has, “priorities change daily, and without rigorous governance processes, it’s nearly impossible to have visibility into your IT investment portfolio.”
To rein in demand, and ensure resources were allocated impactfully, Vulcan formalized its IT Project Management Office (PMO). “The goal is to manage IT like a business,” says Soltan. “That means being clear about investment criteria for IT projects and establishing expectations for project execution that allow us to monitor value capture.”
For Vulcan, each new project introduces new applications and integration patterns into the technical estate. To ensure these can be properly absorbed, Vulcan also invested in maturing its enterprise architecture muscle. “Standards around technologies, integration patterns, and security are becoming more important,” says Soltan.
“Architecture ensures that new solutions do not render old ones redundant and that we construct things in a manner conducive to easily capturing and integrating data,” he explains, noting this will only become more important as IT/OT convergence accelerates to enable capabilities such as predictive maintenance in the plants.
For CIOs in similar sectors just starting out on digital journeys, the prospect can be unsettling, especially in light of recent technological changes — the AI craze, the pace at which IT and OT are converging — not to mention the list of demands from the business. And still, as Soltan says, one thing is certain: Technology will increasingly enable you to compete and differentiate yourself.
So if your company is like Vulcan Materials, if it has climbed to great heights despite preceding the dawn of digital, Soltan suggests you get started: “Your business leaders are smart. They know the importance of technology and of modernizing IT to compete. They have your back. So look honestly at where you are, rip off the band-aid, and start moving, piece by piece, towards your future state.”
First-ever McWane CIO Lynn Lovelady fast-tracked the global manufacturer’s corporate IT makeover by emphasizing A-teams, smart centralization, and establishing trust.
This article was originally published on CIO.com by Mike Bertha, Partner at Metis Strategy.
In 2018, the day after his employer publicly announced it was being acquired, Lynn Lovelady, then VP of IT at Energen, received a pivotal phone call that would reshape his career.
It was from Charlie Nowlin, then CFO at McWane, who for more than a year had been searching for the company’s first chief information officer.
After a long courtship that included lunches with members of McWane’s C-suite to ensure a cultural fit, Lovelady signed on to helm IT as CIO at the global manufacturer of ductile iron products, valves, hydrants, fittings, plumbing products, fire extinguishers and suppression systems, and steel pressure vessels.
The Birmingham, Ala.-based McWane’s growing corporate IT department had existed only since 2008, and for Lovelady there was a lot of work to do. =
“We were transitioning from a decentralized IT model to one that increasingly relied on corporate IT, which necessitated enhancing the planning process, governance, and implementing consistent policies on cybersecurity,” says Lovelady, reflecting on a department that was responsible for supporting the more than 20 operationally diverse businesses under the McWane umbrella.
To address the growing pains, Lovelady reinforced the importance of strategic planning for IT. In addition to rationalizing applications and other tactics you would expect, Lovelady knew establishing influence across McWane would be essential for the IT makeover to succeed, and that in turn would require over-communicating, driving accountability, measuring success, and rewarding high performance. With these principles in mind, Lovelady and his team launched their strategy, dubbing it “fifteen in five,” representing their bold ambition to drive fifteen years’ worth of transformation in the next five, and to shore up IT capabilities in doing so.
But executing wouldn’t come without challenges: multiple ERP implementations, a reluctance to adapt to new ways of working at a storied company, and perhaps most daunting, the reality that IT in each of the 20-plus businesses had grown accustomed to operating independently.
According to Lovelady, his team’s ability to overcome these headwinds hinged on three pillars that go beyond technical implementation.
Lovelady admits that in the early innings there were some who questioned whether hiring a CIO was necessary. The company, after all, had been successful historically.
To buck the trend, Lovelady prioritized meeting with all senior executives upon his arrival: to sell his strategic plan, share how he planned to make improvements, and most importantly, state his intention to earn their trust by establishing a relationship based on frequent and transparent communication. “Whether they’re personal or business, relationships take work, but that work is how you establish trust,” says Lovelady, “and picking up the phone or walking down the hall fills the trust bank over time.”
Lovelady’s focus on communication earned him respect and support from the executive team, which included the general managers of the 20-plus portfolio businesses. It showed when he presented to them. Mr. McWane himself and other EVPs started endorsing Lovelady’s initiatives, and “this backing, coupled with some early efficiency wins, helped the GMs get behind the vision and get comfortable with the new chargebacks,” Lovelady says.
To ensure his team embraced and embodied his philosophy, Lovelady purchased everyone a copy of Excellence Wins, by Horst Schulze. “While we can’t do everything the Ritz Carlton does, I think the spirit of having a customer-first mindset is critical. Following up, not assuming a problem is solved, paying attention: It’s critical we all share these values.”
Before Lovelady arrived, many major IT initiatives, especially those — like ERP projects — meant to drive efficiency across the businesses, were viewed as non-strategic. This kept top talent on the sidelines. Lovelady turned this approach upside down. “Do you really want the software you are going to run for the next 15 to 20 years being designed by just anyone?” posits Lovelady. “Or do you want it designed by your A players?”
Combined with an outstanding internal ERP implementation team, Lovelady worked with business units to put their best talent on the ERP initiatives, which in 2023 alone, led to four on-time deployments. It also led to the consolidation of seven separate CRM environments. Those two efforts combined have enabled for the first time ever end-to-end visibility of McWane’s value chain for select businesses, from the manufacturing of products, through the sales process, all the way to recognizing revenue.
An ERP veteran, Lovelady knows that technology alone isn’t what makes ERP implementation successful. “It’s about having the right people, following the right processes, and avoiding common pitfalls like customization.”
And for Lovelady and McWane, the right people are often those with substantial IT experience. “Around here, young talent are people in their thirties,” explains Lovelady, “and a lot of our team members came with backgrounds as directors, vice presidents, or even CIOs at well-respected companies.”
It is to this talent philosophy Lovelady attributes McWane’s ability to service their approximately 6,000 employees across its global footprint with less than 30 full-time corporate IT employees, and only a handful of longstanding strategic partners.
Key to reigning in and forging partnerships with the portfolio of operationally diverse companies was the deployment of what McWane refers to as “smart centralization.” Through this strategy, Lovelady and his team have struck the often difficult to balance attributes of business unit flexibility with enterprise scale.
“At corporate, we focus on things that can be done globally,” says Lovelady. These include network management, help desk, establishing and enforcing policies related to information security and risk management, and several other IT functions. “These are strategic capabilities for IT, and we have more purchasing power when we address them horizontally across our portfolio,” says Lovelady. “Besides, our businesses shouldn’t have to worry that outdated network equipment is putting their operation at risk.”
Still, the businesses operate with a high degree of local decision-making authority, Lovelady says. “We’ve simply implemented guardrails and policies to make sure we are influencing the domains where we have expertise, and we are making decisions that serve the greater good of McWane, not just an individual business.”
About five years have passed since Charlie Nowlin phoned Lovelady in 2018, and McWane’s corporate IT is firing on all cylinders. IT’s seat at the table has been cemented for many reasons. Chief among them are a rationalized, simplified, cost-effective ERP footprint; a maturing IT security and risk management capability that includes regular audits; a help desk that receives positive ratings from more than 90% of users; and a successful data center migration, which included moving more than 400 servers in real-time, so seamlessly, Lovelady says, that nobody even noticed.
Communication from corporate IT is proactive, includes regular site visits, frequent updates to demonstrate progress against the strategic plan, and plentiful impromptu calls and drop-ins. Business and IT are rowing in the same direction, with the shared goal of making the right decisions for the greater good of McWane.
Lovelady, who announced his retirement in the fourth quarter of 2023, will leave a legacy of transformation at McWane — one that will be synonymous with service excellence, integrity, and collaboration. The results he achieved are enviable, so we asked him what advice he’d share with CIOs pursuing similar journeys. He vehemently referred to the annual strategic plan that started it all, highlighting the importance of trust.
“It takes years of hard work to build trust, and it can be lost in an instant,” says Lovelady. “Don’t breach that trust, and you’ll go far.”
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
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
CHEVY CHASE, MD., May 15, 2024 – Metis Strategy, a strategy and management consulting firm purpose-built for digital and technology leaders, is proud to receive the 2024 Great Place To Work Certification™ for the second consecutive year. Great Place To Work is the global authority on workplace culture, employee experience, and leadership behaviors proven to deliver market-leading revenue, employee retention, and increased innovation.
The prestigious award is based entirely on what current employees say about their experience working at Metis Strategy. This year, an outstanding 90% of our employees have affirmed that Metis Strategy is a great place to work, significantly surpassing the national average. Furthermore, our employees unanimously rated our services as “excellent” and expressed that they felt welcomed upon joining our company.
“We are honored to have earned the Great Place To Work Certification™ for the second consecutive year,” said Metis Strategy President Peter High. “I am profoundly grateful to everyone at Metis Strategy for fostering a welcoming culture and consistently embodying our core values. I extend my heartfelt thanks to our team for their trust and endorsement, and I am proud of them for this well-deserved recognition, which reflects the collaborative workplace they foster at our firm.”
From premier C-level counsel to strategy-setting and execution, clients partner with Metis Strategy at critical points in their business journeys. With a focus on enriching business leadership through in-depth content and active relationships, Metis has earned a reputation as the trusted advisor to senior executives at the nexus of business, technology, and innovation. Metis Strategy has also been recognized as one of the Top 50 Boutique Consulting Firms to Work for in North America by Vault and one of the Fastest-Growing Companies in the Americas by the Financial Times for two consecutive years.
About Metis Strategy: With more than two decades of experience, Metis Strategy is a boutique strategy and management consulting firm focused on the intersection of business, technology, and innovation. Serving mainly Fortune 500 and Forbes Global 2000 companies, areas of specialty include business strategy, digital transformation, technology strategy and operations, growth and scale strategy, and organizational change. We help define new products or services for clients, design improved customer and employee experiences through digital capabilities, and advise organizations on how they can achieve favorable business outcomes more efficiently and effectively.
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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.”
