As digital reliability becomes increasingly critical to business success, organizations must mature their application support operating models to mitigate risks and enable a seamless customer experience. Without a well-defined framework, businesses risk significant financial losses, operational inefficiencies, and diminished customer trust. The 2024 CrowdStrike IT outage, which reportedly caused an estimated $5B in direct losses[1] for Fortune 500 companies, highlights the growing financial risks of digital failures. While this incident stemmed from a faulty software update, it underscores the need for organizations to have resilient disaster recovery plans and adaptable support models to minimize downtime and disruption.
A robust support operating model enhances developer productivity by enabling engineering teams to increase their focus on innovation while maintaining critical business functions, ultimately improving talent retention. New AI tools are aiding this process and improving employee experience as well.
In an era where both internal and external stakeholders demand efficiency and reliability, organizations that invest in resilient support models position themselves for long-term success. To achieve this resilience, organizations must first understand the different approaches available and where they stand on the maturity spectrum.
There is no one-size-fits-all approach to application support. The right model depends on several factors, including business need, size, and complexity. Based on our experience with Fortune 500 clients, we have identified three common approaches, ranging from the least to most mature:
In this traditional model, a dedicated support team is responsible for incident response, troubleshooting and maintenance. While this approach reduces operational burden on development teams, it often results in slower resolution, knowledge gaps, and inefficiencies.
This approach involves collaboration between development and support teams, improving response times. There can be multiple flavors within this category:
At the highest level of autonomy, development teams take complete ownership of applications, including support and maintenance. Enabled by self-service platforms and automation, this model facilitates the fastest incident resolution times and drives continuous improvement by ensuring developer accountability. Growing use of AI-driven observability, self-healing systems, and other automation tools can reduce burnout risk for teams handling both development and on-call support. However, this model may not be ideal for every organization due to the potentially higher costs associated with hiring skilled developers who also have operational expertise.
Each of these models has trade-offs, and organizations must evaluate priorities and capabilities to determine the best approach and target maturity level (the highest may not be the best for every organization). Many organizations may operate in a hybrid model. The key to success lies not just in choosing a model, but in adapting it over time to balance efficiency, productivity and business resilience.
Regardless of which model an organization chooses, success depends on how effectively it is implemented. Here are five best practices to optimize your support model:
When designing an application support model, it is crucial to understand the needs of both internal (developers, business teams) and external (end-users) customers. Organizations should consider:
By adopting a customer-centric approach, organizations can ensure alignment with business needs.
As organizations grow, especially through mergers and acquisitions, support responsibilities often become fragmented, with multiple teams managing the same capabilities across different business units. For one hospitality client, this disjointed structure often led to confusion, inefficiencies, and delayed resolutions. The Metis Strategy team applied a capability-driven approach to ensure teams were organized to deliver business value with the right skills, expertise, and processes. By aligning support teams with core competencies, such as platform reliability, security, or incident management, organizations can reduce redundancies and streamline workflows.
As organizations increasingly modernize and move to the cloud, the application and infrastructure layers become increasingly intertwined. Cloud environments abstract and automate many traditional infrastructure concerns, which can require platform engineering teams to bridge the gap between development, infrastructure and operations by providing automation, self-service tools, and best practices for deployment, observability and cost optimization.
This evolution reshapes understanding of application support. Instead of solely addressing application issues, support teams must take a full-stack, proactive approach—leveraging platform engineering to monitor, automate, and secure both applications and infrastructure. Support teams must collaborate across disciplines to troubleshoot across the entire tech stack to prevent downtime and optimize performance.
Effective application support requires collaboration across multiple functions. Organizations should proactively seek to understand some key perspectives with each group of stakeholders:
By addressing these questions upfront, organizations can avoid roadblocks and create a more sustainable model.
Shifting to a mature support model isn’t just about processes and tools. It requires a cultural shift, making executive storytelling and change management critical. Metis Strategy recently worked with a client shifting to a new model by:
A well-communicated change plan can foster trust, reduce friction, and accelerate adoption.
As organizations redefine their application support model, it is important to recognize that simply implementing the latest AI solution is not enough. Success depends on balancing the right tools with the right people and processes. When properly integrated, AI and human expertise can reduce costs, improve efficiency, and enhance customer satisfaction. Now is the time to build a support model that ensures long-term success.
Future articles will explore how AI is transforming roles, responsibilities, and workflows within different application support models.
[1] https://www.reuters.com/technology/fortune-500-firms-see-54-bln-crowdstrike-losses-says-insurer-parametrix-2024-07-24/
In today’s digital landscape, where user engagement directly correlates with product longevity, the stakes for developing user-centric products have never been higher. Success demands not just initial user understanding, but a continuous cycle of testing, learning, and iterating to refine products based on real-world usage and feedback. When technology product teams rush to build solutions before establishing this foundational cycle, they overlook critical user needs, mistakenly believing they’re accelerating delivery. Organizations that bypass this iterative approach don’t just risk poor adoption rates—they jeopardize their entire product lifecycle and, ultimately, their market position. Yet despite these evident risks, many companies still find themselves in reactive modes, struggling to retain customers and maintain market relevance.
This challenge stems from a complex interplay of organizational dynamics that extends far beyond simple oversight. When technology product teams craft their strategic vision, they often rush to build solutions before fully understanding what users truly need, mistakenly believing this accelerates delivery. This approach, while seemingly efficient, leads to misaligned products that fail to resonate with their intended audience. The reality is that user requirements must be the foundation of product strategy—not an afterthought—and should be shaped by those closest to the user experience.
Our work with organizations across industries has revealed a consistent pattern: companies that do not invest adequate time in upfront user understanding and ongoing iteration inevitably pay the price in form of multiple product iterations and customer dissatisfaction. When combined with aggressive timelines and insufficient user data, this creates a perfect storm that not only undermines product success but also strains organizational resources and team morale.
Through careful analysis of successful product-centric transformations, we have identified five fundamental steps that enable organizations to pursue customer centric product design. These steps aren’t meant to be executed once and forgotten, but rather form a continuous cycle of learning and improvement:
By embracing this framework, organizations can transform their approach to product development. Instead of navigating uncertain returns and user indifference, teams can create products that consistently delight users and drive sustainable business growth. This systematic approach not only enhances product success rates but also establishes a foundation for continuous innovation and market leadership.
To illustrate how these steps transform product development in practice, let’s take a look at a product team tasked with reimagining the customer checkout experience for an eCommerce platform. Composed of Design, Engineering, and Product roles, the team recognizes that successful product development requires seamless collaboration across these critical functions—each bringing unique perspectives that collectively unlock user-centric innovation. Guided by executive leadership’s strategic mandate to reduce customer drop-off rates and improve conversion metrics, the team oversees both web and mobile interfaces and faces a critical challenge: high customer abandonment after cart additions, directly impacting revenue and customer satisfaction. With clear directives from senior management to develop a comprehensive strategy that balances user needs with business objectives, the team must rapidly identify pain points and craft innovative solutions. Under the guidance of an experienced Product Manager, they embark on a systematic journey to uncover user challenges and transform the checkout experience from a point of friction to a competitive advantage.
Image 1: Cross-Functional Team Integration: Ensuring seamless collaboration between Product, Design, UX, and Engineering to aid the product discovery process
To develop customer-centric products, it’s essential to first understand who the product team is designing the experience for. Defining the customer persona is not just a step in design thinking—it is the foundation of a successful product strategy. By clearly outlining user preferences, challenges, and motivations, teams can ensure their solutions are relevant and impactful. Keep in mind that customer persona can represent either a customer or an employee, depending on the product’s focus.
Once a comprehensive persona is created, teams should map out the user’s current experience. Journey maps visually represent the customer’s interactions with the product, enabling teams to identify friction points and tailor enhancements.
For example, the checkout team’s persona might be a 37-year-old working mother of two, often shopping in a rush. Her journey could include:
By understanding these stages, product teams can address user preferences holistically, ensuring that improvements align with both immediate and long-term goals.
After mapping the stages and understanding how customers engage with the app, the next step is to evaluate the pain points they encounter. The ‘jobs-to-be-done’ framework can help product teams understand that users choose products to accomplish specific tasks. By outlining these jobs, product teams can define desired outcomes, segment them and devise strategies to address them. This approach not only fosters empathy for user challenges but also provides actionable insights to define the desired future state.
For the checkout team, this might involve analyzing app usage, metrics, and customer feedback to pinpoint issues like:
Mapping these pain points to the identified journey stages, enables the product team to create a comprehensive overview of specific pain points across the customer journey.
With a clear understanding of user personas and pain points, teams can envision the ideal future-state experience. This critical step requires a nuanced approach that balances user preferences with technical feasibility and business constraints. While user feedback is invaluable, product teams must recognize that not every user suggestion represents a viable or optimal solution—some ideas may introduce unintended complexity or misalign with broader product strategy.
For the checkout team, crafting an ideal experience means carefully prioritizing enhancements that deliver maximum user value while remaining technically and strategically sound:
During a recent engagement with a leading technology company’s mobile application team, this strategic approach enabled the client to boost user engagement and streamline upselling opportunities. By defining an ideal experience that balanced user desires with technical constraints, the team could allocate resources effectively and build toward a cohesive vision.
Once the future state is defined, product teams must identify the capabilities required to bridge the gap. Developing these capabilities proactively—rather than merely reacting to user feedback—empowers teams to anticipate needs and deliver innovative solutions that surprise and delight users.
Leadership support is essential to ensure teams have the resources and authority to pursue these enhancements efficiently. For the checkout team, this might mean prioritizing:
By focusing on proactive development, teams can release iterative changes more rapidly and achieve a continuous cycle of improvement.
Finally, measuring success is as critical as defining the vision. Product teams must identify key performance indicators (KPIs) that align with the desired user experience and overarching business goals. Well-defined KPIs enable teams to validate assumptions, track progress, and refine strategies in real time, making them an integral part of a mature dual-track discovery and delivery process.
For the checkout team, KPIs such as site loading times and drop-off rates provide tangible benchmarks for success. These metrics not only indicate the immediate health of the product but also serve as early signals of deeper issues or opportunities.
Establishing a metrics-driven approach not only elevates the team’s problem-solving capabilities but also creates a culture of accountability and continuous learning. When both product teams and leadership actively engage with KPIs, they foster an environment where decisions are based on evidence rather than assumptions, paving the way for long-term success and innovation.
Through the application of these five steps, the eCommerce checkout team transformed their approach to product development. By deeply empathizing with their users, mapping the customer journey, and methodically addressing pain points, they gained invaluable insights that traditional feature-driven development would have missed. Their systematic approach to defining the future state and identifying necessary capabilities ensured that every enhancement addressed user needs, ultimately resulting in meaningfully lower drop-off rates and higher customer satisfaction.
This framework, though straightforward, opens doors to broader strategic considerations. Teams must handle capability prioritization, cross-functional responsibilities, and resource allocation—challenges that extend beyond any single product team’s domain. Yet these challenges are worth tackling, as they lead to more cohesive, user-centric solutions.
The path to user-centric product development requires more than just following steps—it demands a fundamental shift in how organizations approach product strategy. Success is dependent on leadership’s commitment to empowering teams with both the framework and the organizational support to execute it effectively. By establishing a dual-track discovery and delivery process, teams can maintain their user focus while delivering tangible results. This balance between user needs and business objectives, supported by clear vision and strong change management, creates the foundation for products that truly resonate with users and drive sustainable business growth.
Image 2: Illustrative E2E Product Discovery & Delivery Product for Product Teams
This year on the Technovation podcast, Peter High asked technology and digital leaders about the books they read, TV shows they watched, and podcasts they listened to that fueled their leadership and personal growth. The answers ranged widely across genres and mediums, reflecting the diverse inspirations that leaders draw from. Here are a few highlights of their recommendations and reflections.
Rob Carter, CIO, FedEx
Nothing Like It in the World by Stephen Ambrose
“I like to read about military conflicts. I grew up in a military family, but innovations to me are fascinating to read about… The tycoons of the era weren’t the tech guys like we see them today. They were the railroad barons, and that’s why train stations were so opulent, because it’s where all the money was.”
David Burns, CIO, General Electric, GE Aerospace
The Fog of War (Documentary)
“It’s an incredible documentary about Robert McNamara—his time, his life, and his career. It’s his 11 lessons through his life. I won’t give away the secrets, but from his time as the CEO of Ford to the Secretary of Defense of the U.S. government, there are just a lot of valuable insights that I always take away from that documentary when I watch it.”
Diane Morais, President of Consumer and Commercial Banking, Ally Financial
Essentialism by Greg McKeown
“It’s all about how you spend your time on the things that matter the most and not get distracted by things that, quite frankly, are not going to move the needle or are not important. That mindset at the beginning of COVID really helped us clear the decks of things we might’ve been working on that weren’t really going to be big enough to matter.”
Brad Miller, CIO, Moderna
The Martian by Andy Weir
“You might say, ‘Wow, that’s really not a very insightful type of book or movie.’ However, when you look at what happened with someone being left on Mars, albeit fictional, what that person had to do was figure out how to survive and decompose problems to identify constraints. That resonated with me the most. I love sharing it with others to think about how you attack problems—how you start decomposing them to understand constraints and dependencies and how to unlock them. As a leader, our job is to bring results and operate and, for me, having that skillset has been very powerful in my career.”
Jason Strle, CIO, Discover Financial Services
Financial Intelligence by Karen Berman and Joe Knight
“It’s really important for us to understand how the work we do impacts the company. It provides a lot of very important insight for me to understand how the work we do affects what our shareholders and the investment community see, as well as our employees and our community.”
Sesh Tirumala, CIO, Western Digital
Working Backwards by Colin Bryar and Bill Carr
“One of the things in IT is always about how you tell the story with the end in mind and then work your plan backward. To me, I think that’s a good read, in general, to talk about a finished product in mind and launch. Work your way backward and iron out all the kinks.”
Shri Santhanam, CIO, Experian
The Hidden Brain by Shankar Vedantam (Podcast)
“It’s a podcast about human behavior and how we think and act, particularly in the context of artificial intelligence and understanding the behavior of AI. It’s quite interesting to compare and contrast how we as humans behave, as well as the choices we make.”
Carissa Rollins, Global CIO, Illumina
Wisdom at Work by Chip Conley
“[Conley] talks about those of us who are kind of in our elder stages of our careers. It’s about transitioning from being that person that’s executing and doing the day-to-day to also being this modern elder and helping other professionals through their careers, sharing your wisdom. Often, those of us who have been around for a long time bring a lot of emotional intelligence and perspective and opportunities to mentor others to the workplace.”
Greg Fancher, CIO, PetSmart
Super Communicators by Charles Duhigg
“The concepts laid out are truly about human connection and how driving the right kind of human connection will really allow you to communicate with other people and get through really tough decisions in really tough moments.”
Nand Mulchandani, CIO, Central Intelligence Agency (CIA)
Spies, Lies, and Algorithms by Amy Zegart
“A perpetual favorite is Spies, Lies, and Algorithms by Amy Zegart from Stanford. She’s just an amazing thinker. I recommend that book heavily to folks who want to learn about the CIA, NSA, and other intel organizations. It’s just a great book. I have my son and kids reading it.”
Muneer Mirza, Head of End-User Computing, AWS
My Turn by Johan Cruyff
“The work that Johan Cruyff did was most fundamentally to create advantages for your team on the pitch. As a leader, I think that is one of the most important things you could be asking yourself regularly: ‘What advantages am I creating for my team to be super successful?’ There has to be some way to create that advantage to win in the moments that are super critical for you.”
Leo Bodden, CIO, New York Presbyterian
Outliers by Malcolm Gladwell
“Outliers is a phenomenal book because it really changes your perspective on just about everything that you do and know, and he does it in a data-driven way, which was life-changing for me.”
Dolores Mears, CIO, Hensel Phelps
The Singularity is Nearer by Ray Kurzweil
“You may agree with Kurzweil, you may not, but the knowledge that he has in that book and the data he provides are really good data points that you should consider and think about, especially if you’re thinking about long-term strategy. If you’re thinking ahead 10 or 15 years, pick up his book and pay attention to the data he has.”
Mike Clifton, Co-CEO, Alorica Inc.
CEO Excellence: The Six Mindsets That Distinguish the Best Leaders from the Rest by Carolyn Dewar, Scott Keller, and Vikram Malhotra
“When I was told this is the job we’d like you and (co-CEO Max Schwendner) to do, the first thing I always do is turn to where I can ingest large amounts of data to put a plan together and get my head around the right kind of thinking. It had a lot of great examples of dimensional thinking—how problems were broken down and what you really need to do for your company to set the right path.”
Harry Folloder, CIO, Alorica Inc.
One Second After by William R. Forstchen
“It talks about a connected world where an EMP device goes off in the sub-atmosphere, and within a moment, everything that has a chip in it no longer works. What does society do that’s been so reliant on tech operating everything? If that tech goes away in a moment’s notice, what happens to society? How does it recover?”
Graeme Thompson, CIO, Informatica
Politics, Silos, and Turf Wars by Patrick Lencioni
“It’s a great one to remind us that we need to rally our people around a common objective, not just individual brilliance in their function. That’s one that I’ve read over and over again.”
Ajit Jagtap, CIO, MODE Global
Radical Candor by Kim Scott
“The theme of the book is how can you care personally while also challenging directly. It’s about empathy and action and how it leads to compassion, but it’s not a ruinous empathy where you’re just crying with the person as opposed to trying to help them get over the hump.”
Michael Lucas, CIO, Wilson Sonsini
Hit Refresh by Satya Nadella
“I find [Nadella] to be an extraordinarily insightful human being, not to mention a brave soul, someone who really cares about people. His ability to rally teams of all shapes and sizes and unify them under one vision in this big, huge, behemoth global company is truly outstanding.”
Saul Klein, Founder, Phoenix Court
Start-up Nation and The Genius of Israel by Saul Singer
“I would recommend Start-up Nation, which is probably 12 years old now. It tells the story of tech in Israel and how tech became 40% of GDP. I find Saul’s latest book, The Genius of Israel, even more interesting because it talks about how, at a societal level, Israel has remarkable resilience despite everything that’s going on there.”
Artificial intelligence (AI) has transitioned from a novelty to a cornerstone of modern business, driving productivity, enhancing customer engagement, and enabling operational innovation. Yet, as its influence expands, so do the complexities and risks associated with its adoption.
On the Technovation with Peter High podcast this year, technology and digital leaders across industries once again noted AI and generative AI as the topic occupying much of their time. Conversations in 2024 frequently explored the transformative potential of AI while addressing the hurdles that come with scaling it responsibly. As we look ahead to 2025, technology leaders continue to seek out the opportunities AI can unlock while adopting the appropriate guardrails and governance frameworks needed to to adopt it effectively.
AI has emerged as a game-changer across industries, enabling hyper-personalized customer experiences, enhancing operational efficiency, and powering innovation across a range of business functions. Sanjay Bhakta, Chief Product and Technology Officer at Condé Nast, highlights how AI is transforming user engagement: “We are now starting to also think about how we use our first-party data to provide a better experience to our users, whether it’s personalized newsletters, personalized recommendations, and a very personalized experience as you come to our site.”
Generative AI applications are also reshaping operations and customer service. Shankar Arumugavelu, Executive Vice President of Verizon Global Services, notes: “We’ve rolled out a generative AI-powered research assistant to over 40,000 agents globally, reducing hold times by 20% and enabling faster resolutions for customer queries.” These applications illustrate why AI/GenAI continues to top the trends chart, with 90% of the episodes mentioning it as a significant trend.
Emerging technologies are also gaining traction alongside AI. Digital twins, for instance, were mentioned so frequently this year that they earned a dedicated category in the trends chart. Bala Subramanian, Chief Digital and Technology Officer of UPS, shares how digital twins are optimizing logistics: “We’re making significant investments in creating a digital twin of our network. This allows us to adapt to specific needs like temperature control for vaccines versus other products.” Similarly, AR/VR saw a notable rise in mentions, suggesting it’s further adoption in the enterprise. As CIO of Hensel Phelps Dolores Mears notes: “The ability to take a 3D drawing, get it in front of you on goggles, and see the ‘as-built’ in a facility allows us to walk job sites in real-time and catch errors before they cause rework.”
AI’s integration with advanced data analytics is another key trend. James Ross, CIO of Danaher, emphasizes, “The amount of data that we’re producing is growing, and the need to mine it and use data and analytics and AI to generate insights is going to be more and more critical.”
Despite its promise, scaling AI requires significant investments in infrastructure, data, governance, and workforce training. As Satya Jayadev, CIO of Skyworks, points out: “It’s one thing to put AI systems together; it’s a whole different ballgame to sustain them.” Governance frameworks and clean data are crucial to ensuring AI initiatives deliver their intended value.
Scaling AI effectively also requires businesses to address operational and infrastructure challenges. Prasanna Gopalakrishnan, now the Global Chief Product and AI Officer at ADP, shared during her time as Global CTO at Sky: “We need faster computing power to crunch the enormous datasets being created. Processing data closer to where it’s needed, through edge computing, is critical to solving these challenges.” As the scale and complexity of AI models continue to grow, quantum computing is increasingly seen as a necessary innovation to meet the demand for advanced computational power.
Cybersecurity, which continues to rise as a key focus area on the trends chart, also remains a top concern tied to these advancements. Jason Strle, CIO at Discover Financial, highlights the evolving threat landscape and the implications of quantum computing, noting that it “is going to reset how we think about encryption.” This underscores the urgent need for proactive defenses and a clear strategy to maintain enterprise resilience as these emerging technologies take hold.
Ethical considerations remain a cornerstone of AI adoption. Tilak Mandadi, Chief Data, Digital and Technology Officer of CVS, stresses the critical need for responsible AI: “Applied responsibly, AI has the power to transform industries while minimizing human errors. Without guardrails, the risks outweigh the rewards.” This aligns with a broader industry trend, as companies increasingly adopt ethical AI frameworks and establish internal councils to mitigate risk and guide effective implementation.
Finally, upskilling internal talent is critical to scaling AI effectively. Tim Dickson, CIDO of Regal Rexnord, shares how his organization is equipping its workforce: “We’ve trained over 100 team members in generative AI fundamentals. Upskilling is critical because internal teams understand our business and regulatory challenges, making them better equipped to manage risks.”
This year’s trends highlight AI’s expanding influence across sectors, with technologies like cybersecurity, AR/VR, and digital twins continuing to rise in prominence. These tools are increasingly viewed as essential complements to an AI-led operating model, enabling organizations to unlock the full potential of AI-driven innovation. As leaders on Technovation consistently emphasize, success with AI in 2025 and beyond will depend on how effectively organizations balance innovation with responsibility, ensuring that AI’s transformative power is harnessed sustainably and securely.
As digital strategies have accelerated in the wake of COVID, the relationship between the CFO and technology executives has shifted from a transactional exchange to one requiring deeper collaboration and understanding.
Two key trends—empowered product teams operating autonomously and rising cloud maturity—have altered the landscape of how finance and technology departments work together. This new environment creates both opportunities and challenges as CFOs seek greater financial oversight and CIOs/CTOs push for speed and innovation. Based on our work with Fortune 500 technology executives, we have identified five key areas of friction that characterize this evolving relationship and ways to mitigate the inevitable moments of tension. In future articles, we’ll explore each area in greater detail to show how finance and technology leaders can further enhance their collaboration.
As organizations embrace a product-oriented operating model, technology teams are more empowered than ever. These independent product teams, armed with the resources and freedom to experiment, often operate outside the traditional approval frameworks that once governed tech spending (For more on product funding models, see Metis Strategy Partner Mike Bertha’s article on CIO.com). From a finance perspective, this can feel like handing out a blank check. CFOs worry about losing control over budgets, particularly when spending scales unpredictably. CIOs/CTOs, on the other hand, argue that agile teams need the financial flexibility to drive innovation and respond to market shifts in real time.
Bridging this gap requires balancing innovation with responsible cost management, transforming finance teams alongside product development, and improving how teams measure business value delivered (not just productivity metrics!). In short, product teams build trust by demonstrating continuous tangible business value. One CFO we worked with began by funding one or two empowered product teams. As trust grew and results emerged, the finance team became comfortable expanding the product funding model to the rest of the organization while the product teams spent less time on admin and more time on building products.
As cloud services mature, organizations increasingly rely on serverless infrastructure, microservices, and other cloud-native technologies. While these offer significant flexibility, they often lead to unpredictable operational costs (OpEx) as a percentage of total technology spend. The “pay-as-you-go” model works well for scalability, but it’s easy for teams to inadvertently build “chatty” applications—ones that generate high levels of inter-service communication and drive up usage costs as a result. This surge in OpEx often leaves little room in tech budgets for strategic initiatives. CIOs/CTOs now must focus not only on performance but also on optimizing cloud spend while CFOs demand greater transparency and predictability around operating costs. This challenge is exacerbated as AI demand reshapes the infrastructure landscape. Fortunately, FinOps strategies and capabilities offer a practical path to tackle these growing concerns. FinOps is a collaborative practice that leverages real-time visibility into cloud consumption and cost drivers, employs rightsizing and resource tagging strategies, and encourages aligned financial planning with engineering and operational teams to effectively manage cloud spending.
As organizations achieve greater data maturity, both finance and technology teams have better means to make informed capital allocation decisions. However, the challenge lies in effectively using this data to drive decision-making. CFOs now expect technology leaders to provide real-time insights into technology ROI, cloud spend, and operational efficiency. Conversely, CIOs/CTOs want finance to move beyond historical spending patterns and focus on forward-looking investments and innovation. While the data is there, friction arises in how to interpret and act on it. This can lead to disagreements around which metrics matter most and how capital should be allocated across the technology portfolio.
For organizations transitioning to a product operating model, it’s critical to avoid building entire product teams for products that do not or will not generate substantial ROI. CFOs and CIOs/CTOs can find common ground by clarifying which capabilities—not just technologies—each investment supports and ensuring the annual budget process goes beyond a simple “+X%” increase over last year. Technologists, in turn, should prioritize providing financial data visibility for new products and capabilities, enabling clear communication of ROI, and sharing insights into long-term operating costs from the start.
As organizations rush to adopt new technologies, a key question arises: when should investment levels stabilize? According to the 2024 State of Enterprise Tech report by Insight Partners, only 41% of cybersecurity executives expect increased budgets, down from 51% in 2023. This is a significant shift and suggests that after years of continuous spending to combat evolving threats, companies now face diminishing returns on further investment. While cybersecurity remains critical, over-investment without strategic need or commensurate ROI can lead to inefficiencies. Understanding when a technology has matured—and investment should plateau—requires close collaboration between CFOs and CIOs/CTOs to balance risk management with financial discipline. CFOs and technology leaders can start by jointly assessing current spending’s marginal value and identifying benchmarks for shifting to sustained operations.
As new AI capabilities evolve, measuring their value will remain an ongoing challenge. At our recent Metis Strategy Summit in October, Mastercard’s Ed McLaughlin noted that we do not yet fully know how to quantify AI’s impact and potential benefit, leading to headlines that suggest potential over-investment in the area. This uncertainty underscores the need for a governance model that balances technology teams’ need to innovate and explore, even when ROI is uncertain, with the guardrails that ensure financial accountability.
By establishing a framework for decentralized decision-making within product teams, combined with periodic (quarterly) financial and strategic reviews, CFOs and CIOs/CTOs can support innovation initiatives without compromising fiscal discipline. Matrixing finance partners to the appropriate level of product team structure enables responsible experimentation, helping to prevent reckless spending on high-risk technologies while advancing the organization’s long-term strategic goals.
In 2024, the CFO and CIO/CTO relationship was defined by both shared goals and inherent friction. As empowered product teams drive innovation and cloud costs rise, the need for a balanced, collaborative approach becomes increasingly critical. Updating the governance model, making data-driven decisions, and aligning on spend for emerging technology will be essential to maintaining strong relationships between finance and technology. Successfully navigating the sources of tension above will allow both sides to foster innovation and increase speed to market while ensuring responsible financial stewardship.
CIO turned VC Brian Hoyt draws on his experience prepping companies for IPO and other liquidity events, including his own, to outline a playbook for crossing the start-up to scale-up chasm.
This article was originally published on CIO.com by Michael Bertha, Partner at Metis Strategy and Duke Dyksterhouse, Senior Associate at Metis Strategy
Suppose you lead IT at a VC-backed startup. It just crossed $100M in revenue and is approaching a major liquidity event, such as an IPO. It’s exciting stuff. But as you speak with an expanding cadre of lawyers, accountants, and bankers, you start to appreciate what such an event means for your department.
You start to see the cracks in its foundation. You see the data by which Wall Street will judge your business is scattered across the company, stored and formatted unsystematically. You see the systems that must be repaired (or built) if the firm is to comply with SOX. You even acknowledge that IT has been kept afloat by managed service providers and fractional employees. All of this, you realize, must change. You’ve reached a tipping point at which your IT capabilities must be formalized, but you don’t know where to start.
Fortunately, Brian Hoyt does — and he’s been there, having served as CIO of real-time 3D content creator Unity Technologies, for which he prepared the IT department for IPO in 2020. Today he serves as partner and chief operating officer of Parkway Venture Capital, which invests in software and AI/ML companies across various sectors. He helps the firm’s portfolio companies tackle the intricacies of crossing the start-up to scale-up chasm.
Here, Hoyt shares what he’s learned from scaling IT in rapid-growth companies, both as a CIO and an investor, and what should be considered by digital and technology leaders whose firms are heading for major liquidity events — how they might structure their departments so that the company can weather that liquidity milestone and the S-curves in the years that follow.
There are no exact thresholds for formalizing your IT capabilities, including hiring or promoting a CIO, says Hoyt, but there is one important sign: talk among your colleagues about redoing the company’s ERP system.
“I think people usually go QuickBooks, NetSuite, then onto something else, or they redo NetSuite. And often your accounting team had to implement NetSuite without IT’s support,” he says. “It works for their purposes for the time, but when it needs to be refashioned so that it can scale, that’s the time to bring in someone with some seniority, someone who’s been through it, because it’s really hard.”
Why does an ERP redo or implementation present such a great opportunity to evolve your IT organization? A couple of reasons. First because, as Hoyt observes, ERP implementations are notoriously difficult. Few leaders will volunteer to drive the work and often it will fall to the accounting team. “In many cases that won’t work out,” he says, “because the accounting team must devote themselves to their principal work, which is critical, and because they have lives and implementing ERP software sucks.”
But also, ERP systems rely heavily on IT-supported capabilities, so any work necessary to implement one will likely involve your team anyway. You might as well be the one to command that work; as a tech leader, you’re especially well suited to it, and doing so will give you reason to acquire resources that lend themselves to causes beyond those related to ERP. In other words, remodeling your ERP can afford the chance to remodel your department — to effectively say, “If we’re going to do this, I’ll need the following.”
Of course, the ERP sign is only a rough guide, says Hoyt. The right time depends on your business and industry; the more regulated it is, the sooner you should start. He uses selling into enterprise software as an example. “If you’re going through a lot of security reviews, plan on hiring a CIO earlier. My first question [to entrepreneurs in that space] is: How are you getting through security reviews? And they all groan. The people who have a clear answer are going to do better.”
As it happens, regulation — or compliance — is another area for which you’ll need to hire or promote a leader. Although Hoyt stresses he isn’t one to “insist that you hire from outside,” here you likely need to.
“You need someone who knows the territory, who knows Sarbanes-Oxley and the language of the auditor and preferably someone who’s even worked at your auditor’s firm and can speak their language and prepare everything,” he says. “Otherwise [the auditors] will grind you into a fine powder because they have an endless bench of people to schedule meetings and ask that processes be explained.”
There are plenty of candidates for this role, Hoyt says, but you might need to look outside the box. “They might not be in a job called ‘Head of Compliance.’ It might be ‘IT auditor,’ and they’re tired of flying between Nebraska and New York every week. And I think this has been a huge part of what I’ve done in the past, is finding this person.”
You’ll also need someone to lead infrastructure and ops, which rarely gets the attention it deserves when the firm’s still young and focused on core products and the viability of its business model. Often by the time the firm’s leaders start talking about going public or agreeing to sell, the firm’s foundational technologies may be in need of a closer look. A major piece of this domain is security, which Hoyt says can roll up to the infrastructure and ops lead at first but must eventually become a separate function.
“As your firm grows and starts looking to go public and becomes a bigger topic, you become a bigger target,” he says. “Some attacks from sophisticated bad actors are unavoidable, but you at least want to ensure that you don’t leave yourself vulnerable to avoidable slips.”
Finally, you need someone to lead your financial and business systems. “I love accountants interested in systems,” says Hoyt. “They’re often perfect to lead this area, and a lot of modern CIOs need technically minded leaders that they can trust to know whether a system is out of whack or a piece of work can be executed.”
Why is this leader so vital? Largely, Hoyt says, because of the projects you’ll have to undertake to prepare for your liquidity event.
Hoyt suggests that CIOs can learn much about the projects they’ll have to drive for a liquidity event from the focus of Wall Street earnings calls.
“They’re all about financial predictability and accuracy. Your firm’s viability as a financial asset largely comes down to investors asking, ‘Can we trust this company’s numbers? Will they be delivered on time?’ And if you get your numbers wrong, or they’re late, you can’t fix things by saying, ‘Well, you know, my IT person didn’t come through,’” he says. “And you can’t continue to spend 90 minutes in exec meetings arguing about whether the data you’re all looking at is right or how it’s defined. If those conversations are still going on, then you know there’s work to be done and that you need to start it as soon as possible.”
Getting the numbers right may sound simple, but it’s grueling work, Hoyt says, starting with getting your data right at the source.
“You have to stop fixing problems in the data layer, relying on data scientists to cobble together the numbers you need. And if continuing that approach is advocated by the executives you work with, if it’s considered ‘good enough,’ quit,” he says. “Getting the numbers right at the source requires that you straighten out not only the systems that hold the data, all those pipelines of information, but also the processes whereby that data is captured and managed. No tool will ever entirely erase the friction of getting people to enter their data in a CRM.”
The second piece to getting the numbers right comes at the end: closing the books. While this process is a near ubiquitous struggle for all growing companies, Hoyt offers two points of optimism. “First,” he explains, “many teams struggle to close the books simply because the company hasn’t invested in the proper tools. They’ve kicked the can down the street. And second, you have a clear metric of improvement: the number of days taken to close.” Hoyt suggests investing in the proper tools and then trying to shave the days-to-close each quarter.
Get your numbers right, secure your company, bring it into compliance, and iron out your ops and infrastructure. Do these things and you’re a long way toward being ready for a major liquidity event — or just your company’s next chapter.
Hoyt is so knowledgeable in this area that he answered virtually every one of our questions without hesitating. So it should say a lot that, when asked what one bit of advice he’d leave with CIOs preparing for a liquidity event, he pondered his answer for what seemed like 10 interminable seconds.
“Make sure you have the full support of the top officers — the CEO and CFO, or COO. It’s hard work. And to some extent you’re going to have to make people’s lives harder, so there’s going to be friction,” Hoyt says. “The only way you’re going to overcome it is if you have top-down alignment. As time goes on, more CIOs are reporting to CFOs or COOs or CEOs. But in some firms, the CIO still reports to someone farther down the food chain. That won’t cut it in a firm preparing for an IPO.”
And that’s good news for IT leaders looking to take a step forward with their careers at startups and beyond, he adds.
“CIOs hold a unique position,” says Hoyt. “They can see every part of the company. They see the challenges regarding real estate and ERP and so on. That purview has to be recognized and respected if a firm is serious about playing in the big leagues.”
In transforming your IT workforce, modeling its to-be composition is a necessary and smart first step, but it represents only the tip of the iceberg. Here’s how to pressure-test your model against the realities of execution.
This article was originally published on CIO.com by Michael Bertha, Partner at Metis Strategy and Ishan Prakash, Manager at Metis Strategy
In today’s ever-expanding digital landscape, in which many IT teams operate across geographies, it’s no secret that your ability to leverage technology to its fullest depends crucially on your workforce composition: on how your resources are allocated across various sourcing models, namely, FTEs, onshore, nearshore and offshore. As technology becomes ever more important to strategy, IT leaders are reconsidering their workforce compositions. Where they once optimized predominantly for cost, they’re now weighing that one variable more carefully against many others, including productivity, digital maturity, and the development of longstanding capabilities.
And though modeling the composition of a to-be workforce is a necessary and smart first step, it represents only the tip of the iceberg. In other words, it’s unrealistic to redeploy a role as part of a different sourcing model and expect instant productivity gains. Countless variables must be considered: skillset alignment and maturity, time zone differences, and ways of working (both within a team and across the organization), and so on.
Acknowledging these challenges and complexities, how can one pressure-test their spreadsheet exercise against the realities of execution? Drawing from our collaborations with several Fortune 500 clients on this topic, we suggest an archetype-based approach, one that, when executed well, will allow you to charge ahead with implementing your to-be workforce composition, and with the confidence that it reflects the needs of your organization.
By shifting the composition of your workforce, you are bound to affect certain functions, capabilities, or products. Begin by understanding those entities’ remit and ways of working. Are they developing novel products and services? Configuring a package solution? What key technologies are they using? What skills will enable operations in the new model? Are they working in agile? If so, what flavor do they employ?
As an example, consider the case of a global retail client. In assessing the demand profile of the team that oversees their fulfillment capability, the client examined their inventory management solution, a niche one, and determined that, even though the company might benefit by bringing the solution’s support team nearshore, the right skills would be hard to find in the short run.
Cataloging the answers to these questions provides a fact base that allows you to understand what capabilities, roles, and skills will be needed to support demand. And perhaps equally important, it allows you to understand which of those capabilities are fungible across sourcing models, and which are, at least in the short run, tied to specific locations or vendors.
After constructing a demand profile for each function, look across those profiles. Reconcile and rationalize them. And make sure the various types of demand will be supported by creating a master set of archetypes.
What’s an archetype? In this context, archetypes encompass the roles, responsibilities, and geographies that constitute a specific capability and that are required to support a certain type of demand.
As an example, consider the archetypical teams found at one of our global retail clients. For one, they have an infrastructure delivery team archetype, which consists of a nearshore engineering delivery manager, a scrum team, and both a nearshore and offshore delivery team (i.e., developers), the latter supplementing the former with overnight or follow-the-sun support. They also have single or multi-capability delivery teams that work on, among other things, innovation and product development, and operations and maintenance. As you can imagine, the archetype after which these teams are modeled varies based on the demand profiles they are constructed to satisfy. While every organization is different, we often challenge our clients to limit themselves to six archetypes.
By undertaking this exercise, you not only confirm that your teams are structured such that they can both support demand and satisfy the objectives of the workforce shift; you create a vehicle for scale and standardization in what is likely to be a multi-vendor, multi-geography workforce.
Having established a master set of archetypes, it’s crucial to apply them to each function’s to-be workforce composition. Doing so will reveal discrepancies between those archetypes needed to satisfy demand and those targeted for the to-be workforce compositions, which may have been painted with broad strokes in a top-down exercise that gave little consideration to execution.
By applying the archetypes to your to-be model, you will quickly see where your current workforce easily ports into the new model, and where there are gaps that must be closed through some combination of reskilling, recruiting, and partner sourcing. The time it takes to execute these tactics will inform the speed and sequencing of implementation.
Here you can also take stock of the various ways of working practiced by the different capabilities or functions. If those capabilities and functions work similarly then perhaps it will suffice, as responsibilities change in the new model, to simply recalibrate planning and execution processes. If they work quite differently, however, then a major change will likely complicate both their work and the effort to shift the workforce composition. The discrepancy may indeed merit a broader reset as you move toward the to-be model. It is also at this point that more attention tends to be given to an organization’s horizontal capabilities. One such capability that often surfaces is Knowledge Management: how do we ensure that in changing or relocating a resource, we don’t lose critical institutional knowledge?
By the end of this exercise, you will have effectively pressure-tested your to-be model. You will have uncovered a clear set of gaps that, when accounted for in your roadmap, will help you avoid surprises as you move towards the to-be workforce model. You will also have created a set of archetypes that transcend the seams of your workforce ecosystem and bring harmony and scale to your multi-vendor, multi-geography workforce.
If your organization is anything like those of our clients,’ you will have realized that a shift in global workforce composition, what may initially feel like a lift and shift of resources, is akin to engineering a skyscraper: structural integrity, precision, and attention to detail are paramount to ensuring that the building can withstand any pressures and function according to the needs of its occupiers and stakeholders.
Walgreens’ Stephen Ma shares why fewer architects, with wider domain expertise, may close the gap between EA’s ethos and its perceived value proposition.
Skim recent articles about enterprise architecture (EA) and you’ll notice a contradiction. Of them, plenty suggest that, unless a company develops a strong EA muscle, it will limit itself. Yet just as many seem to question the function’s value, or spotlight material that does. A recent report from Forrester, for example, opens: “[While] enterprise architecture remains a critical capability … many digital and IT professionals view enterprise architecture as a roadblock that adds no real value.”
All this contradiction suggests that EA, as a function, may be suffering an identity crisis, but Stephen Ma — acting chief architect at Walgreens — is not about it.
Ma doesn’t object from a place of defensiveness — he knows the function’s value. But he’s frustrated that the discipline’s value hasn’t been communicated as well as it should be, a grave issue in today’s business climate, in which every role of every function is under scrutiny. He also thinks the solution is straightforward.
“We need a full-stack architect,” he says.
That’s right: According to Ma, the solution to EA’s identity crisis is fewer architects, but ones with the ability to traverse multiple architectural domains.
To understand what makes an architect “full stack,” we first need to define EA. Per Gartner:
“Enterprise architecture (EA) is a discipline for proactively and holistically leading enterprise responses to disruptive forces by identifying and analyzing the execution of change toward desired business vision and outcomes. EA delivers value by presenting business and IT leaders with signature-ready recommendations for adjusting policies and projects to achieve targeted business outcomes that capitalize on relevant business disruptions.”
Not all this work gets done by architects of the same type. By Ma’s count, there are at least four major architect types: Business, Solution, Enterprise, and Technical. Each works across at least two of six major domains of expertise: Business, Product, Design, Engineering, Delivery, and Support. None of them works across all six.
And herein lies the problem, says Ma: When business stakeholders seek to understand EA’s value proposition, or even check the status of a project, they may get different answers depending on whom they ask. It’s the three-blind-men-describing-an-elephant problem: The man who feels the tail describes an animal very different from the one described by the man feeling the abdomen, or by the one feeling the ears and tusks. Though the variety in their descriptions may reflect the function’s comprehensiveness, to the uneducated executive, it sounds like misalignment. That executive feels that, to get a complete picture, he or she must piece it together him or herself.
As Ma sees it, this perception has never been more damning than it is today.
“A lot of people, during COVID, really thought COVID was going to live with us for the foreseeable future, so companies made big digital bets on how people work, how customers interact … and they over-hired,” he explains.
Among these digital bets was greater investment in technological functions believed to critically enable strategy and scale in a remote world — functions like enterprise architecture. Ma recalls how difficult it was to attract talent to CVS, where he was leading architecture at the time.
“I would put a job description out there for a really quality principal architect or director position, and I would get one good resume maybe every three weeks,” he says. “It took me six months to even think about finding somebody good.”
Of course, in the end, many of those bets didn’t pan out. The pandemic ended, many customers returned to their usual patterns of behavior, and about a year ago, companies began revisiting hiring decisions they made under different pretenses, asking, “What exactly does this role do?”
And now, with technologies such as AI emerging, they’re asking twice as emphatically. Ma warns all architects that they need to be able to answer this question clearly, consistently, and persuasively.
“I’ve actually heard fellow architects say, in describing their role, that they’re ‘marriage counselors.’ Relationship-building is very important, but it alone is no longer enough. Architects have to know what they are talking about at a deep technical level,” he says.
To solve this problem, Ma sees the emergence of a full-stack architect who can describe the whole elephant — and enhance EA’s service delivery model by several means.
First, the full-stack architect could ensure the function’s other architects are indeed aligned, not only among themselves, but with stakeholders from both the business and engineering.
That last bit shouldn’t be overlooked, Ma says. While much attention gets paid to the notion that architects should be able to work fluently with the business, they should, in fact, work just as fluently with Engineering, meaning that whoever steps into the role should wield deep technical expertise, an attribute vital to earning the respect of engineers, and one that more traditional enterprise architects lack.
For both types of stakeholders, then, the full-stack architect could serve as a single point of contact. Less “telephone,” as it were. And it could clarify the value proposition of EA as a singular function — and with respect to the business it serves. Finally, the role would probably make a few other architects unnecessary, or at least allow them to concentrate more fully on their respective principal responsibilities. No longer would they have to coordinate their peers.
Ma’s inspiration for the role finds its origin in the full-stack engineer, as Ma sees EA today evolving similarly to how software engineering evolved about 15 years ago. During that time, as social media and e-commerce exploded, so too did the variety of software engineers holding the seams together. Those engineers gradually sorted themselves into realms of expertise — front-end, back-end, data layer, and so on — and ultimately came to be coordinated by the full-stack engineer, now quite common in mature organizations, and expected to become only more so over the next decade.
Ma sees this role as analogous to the full-stack architect: “You can’t really have one person, engineer or otherwise, who’s truly a full-blown expert up and down the stack — I’m not suggesting this is a magic bullet — but the idea here is, let’s have full-stack architects who cover all areas from both a business and technical perspective.”
For some readers, this may beg the question: How would the full-stack architect differ from a product owner? Do they both not link the business and the digital execution? They do, but with different priorities. Product owners, explains Ma, even technical ones, tend to never get sufficiently deep into the weeds of the technology itself, always concerned first with the product. Having worked in organizations that leaned on such roles, Ma has never seen it work.
“Maybe it could if your company is clear about what it does and which roles have exactly which responsibilities, but most are not and don’t. You need a role that can help the in-between, that can be the glue holding together some of the areas that fall under several verticals, and who can be the person that anyone — engineers or businesspeople — can go to when they have a need that concerns both the business and the technology teams,” he says.
Another important justification that Ma offers for such a role is that the problem solved by architects is here to stay, even as advanced technologies enter the equation.
“It’s part of the reason I love this space,” he says. “What’s under it is so many different technology domains — integration, data, APIs, and so on — but also multiple business prongs and industries. There is so much to learn.”
The same can be said for most companies today, as they branch out into new opportunities, often by way of digital transformation, he adds.
“Consider Walgreens, people usually think of our pharmacy, but we also have photo, healthcare clinics, supply chain, and many more functions,” Ma says. “You’re always going to need someone who can bridge the gap between business and technology. You’ll always need someone who underpins it all.”
Underestimating the importance of this role could make or break your operating model transformation: here’s how to think about sourcing the role that will only increase in importance.
This article was originally published on CIO.com by Michael Bertha, Partner at Metis Strategy and Kira Kessel, Associate at Metis Strategy
You’ve seen the virtues of transforming from a project to a product operating model: value-driven work, delivered by dedicated teams rather than through projects led by disparate team members.
But as you embark on this transformation, you’ll have to remember one thing: you can’t do it without a product owner. The strategist, the technical expert, the business savvy leader—those with all three commonly called unicorns, or rock stars—is a person not easy to find.
Product owners are the linchpin of the product operating model; on product teams, a bad engineer is one bad apple, but a bad PO can sour the whole batch. No one else has the end-to-end accountability for a product like the product owner does. No one else so consistently represents customer interests, pushes engineers to adopt DevOps and Agile methodologies, and corrals individuals to execute against a roadmap. This is a leader who thinks of technical features in one conversation and business strategy in the next. The unicorn, if not sourced with proper due diligence, will be viewed only as a creature in fairy tales.
In a traditional project model, a leader is judged based on how well they react to requests and executes on them. In a product model, however, a good product owner anticipates the customer needs and responds to them by prioritizing items on a roadmap based on the capacity of a product team.
Product owners move you from reactive to proactive.
Adopting the product model is no meager mind-shift. Broadly speaking, you have two main options: hiring or upskilling.
You might hire for either of two reasons. The first is that you may not have the luxury of time and mistakes. This is largely a matter of two things: culture and industry.
Let’s start with culture. Ask the following of your company: Is learning tolerated? Is training available? Are there resources you can use to help establish the person in their role? Is there strong leadership and mentorship? Without these variables at play it will be difficult to develop someone internally. You might need someone who has the core PO competencies and fits your culture, someone who perhaps already has the leadership chops you’re lacking—a necessary hire.
Then there’s industry. Also unable to afford time or mistakes are those industries that are heavily regulated, scrutinized by agencies and governments, or uniquely depended upon by their customers. If, for example, you work in medical, military, aviation, and so on, you may not want to risk upskilling when you need someone who can navigate complexities beyond just those inherent to a product owner’s role. These product owners will have to consider certain variables—safety, cybersecurity, geopolitical factors, and many others—that require extra attention when representing the voice of the customer.
Say you’re a technology executive at a biopharma company. Your product owner will not only need to drive innovation—putting the most promising features at the top of the backlog—but will also need to do this while adhering to regulatory constraints. For Shobie Ramakrishnan, Chief Digital and Technology Officer at GlaxoSmithKline, this looks like balancing core values like “accountable for impact” with the pursuit of AI/ML technologies to “supercharge” R&D and clinical trials.
Luxury and time, of course, is only one of the reasons you might hire. The other is that you want a fresh perspective. In particular, someone who offers a point of view you can’t train. Usually that someone comes with a mixed bag of experience: a background in product, engineering, marketing, and finance are most common. What matters is that they offer something new to your company. A leader like this may disrupt the status quo, bring innovation, and offer new ways of thinking about the same problem. They may even serve as a catalyst for change beyond your recent move to the product model.
But could you not solve these issues—the constraints of luxury and time and the desire for a fresh perspective—by outsourcing? Your contractor may not cost as much, but you may face bigger drawbacks. A contractor who doesn’t stick around will take with them the skills and experience you want an employee to share with colleagues so that expertise, new ideas, and growth of the company reinforce one another from within.
In contrast, when you retain someone full time, especially a product owner, you retain institutional knowledge, which is especially valuable when it concerns strategic areas like GenAI, data, cyber and other innovation—areas of central concern to the product owner. A good example of this comes from Zurich North America’s COO Berry Perkins, who has made it part of IT strategy to keep this type of knowledge in-house. Of course, that’s not the only part of the strategy—it also involved the establishment of nearshore competency centers that will depend on Zurich’s employees acquiring new skills and embracing new processes and technologies. Which brings us to upskilling.
You may have a unicorn in your backyard without even knowing it.
What can distract you from that realization? Budget. If it prevents you from hiring, then you may next consider whether you have the funds, and the bandwidth, to pursue training your soon-to-be product owner.
Investing in your training muscle—developing a training capability, establishing career coaching, and encouraging growth from within in other forms and fashions—could do more than just produce the perfect product owner. It will signal to your employees that you want them to stay, that their contributions matter, and that there is space for them to grow internally. Retention could soar, innovation may spike, and revenue would, inevitably, grow.
If training isn’t on the agenda, you may decide to pursue upskilling simply because your employees hold something valuable already: their relationships and their institutional knowledge. The high-performing product owner will have already built relationships with their colleagues, will know the dynamics that exist between teams, will understand the technologies used, and will be better equipped to align IT and business stakeholders. Most importantly, they will have established trust.
Perhaps not even trust is the most important thing. What could be? Institutional knowledge, which, as we’ve seen, an existing employee will already have. They will know the tools, know the processes (which they’ve seen are convoluted at times), and know the customers of the company they are serving (and can speak to the company’s competitive differentiation, not just industry norms). Best of all, they know the product—its thorns, buds, and roses.
Believing in the value of the product operating model is one thing. It’s another to embrace the transformation from project to product with eyes wide open. You should acknowledge the challenges you will encounter, most notably that this one role could make or break the transformation. So before you’re too far into the journey, remind yourself: if you don’t know who your product owner is, at least understand what will dictate whether you hire, contract, or upskill. Better to figure it out now, not later.
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 Michael 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.”
