Drawing from a remarkable journey that began with a CTO promotion in his late twenties—a rarity in the 1990s—Dominic Jainy has become a leading voice in technology leadership. His path, detailed in his book Digital Trailblazer, wasn’t paved with just coding prowess but with a deep, early understanding of DevOps principles that allowed him to automate, standardize, and ultimately elevate his focus from technology to strategy. Today, we delve into his experience to understand the concrete steps today’s DevOps professionals can take to prepare for the C-suite. Our conversation will explore how to pioneer AI initiatives that actually deliver business value, the crucial mindset shift from a hands-on engineer to a strategic facilitator, and the importance of gaining expertise in often-overlooked areas like data governance. We’ll also discuss practical ways to build cross-disciplinary knowledge and embrace non-technical experiences that forge the business acumen and vision required of a modern Chief Technology Officer.
Studies suggest many organizations struggle to see a return on their AI investments. For a DevOps leader aiming to change this, what’s the first step in identifying an AI initiative with real business impact, and how do you partner with product managers to define its success criteria?
The first step is to resist the temptation to jump on the easiest, most obvious AI experiments. The data is sobering; a recent MIT report found that a staggering 95% of organizations are getting zero return on their AI investments. To break that cycle, a leader needs to actively hunt for opportunities that can create a significant, measurable business impact. Look for initiatives that already have a clearly articulated vision statement, an active and engaged sponsor from the business side, and a team that is genuinely committed to the objectives. Once you’ve identified that opportunity, your role as an agile delivery leader is to form a tight-knit partnership with a product manager. This isn’t just about checking boxes; it’s a deep collaboration where the product manager is empowered to define the targeted user personas, rigorously prioritize the features, and establish the specific, quantifiable success criteria that will prove the AI program’s value to the rest of the organization.
When integrating generative AI into the software development lifecycle, how can a leader establish practical standards for its use beyond just code generation? Could you share a specific example of a “non-negotiable” devops requirement you would implement to ensure AI agents are resilient in production?
Establishing practical standards is about moving past the initial hype and thinking about sustainability and quality across the entire lifecycle. Beyond just generating code, you have to ask how generative AI can improve the quality of our user stories or help us maintain better technical documentation. We need to define where it’s appropriate to “vibe code” versus where rigorous validation is needed. As a leader, your role is to spearhead the creation of these standards, ensuring they are readily adopted because they genuinely make teams more effective. A critical “non-negotiable” I would implement is a robust observability and automated testing framework specifically for AI agents. It’s not enough to test the code; you must ensure that any change to an AI model actually improves its results in a production environment. This means establishing baseline performance metrics and having automated checks that prevent a new deployment from introducing regressions, hallucinations, or other undesirable outcomes, ensuring the agent remains resilient and reliable for end-users.
The transition from a hands-on engineer to a strategic CTO involves a significant mindset shift. How does one move from being the go-to problem-solver to a facilitator who guides teams in making long-term architectural decisions? What does that look like in a daily routine?
This is one of the most difficult, yet most critical, transitions. It feels unnatural at first to step back from being the person with all the answers. The key is to consciously shift your focus from the immediate—getting work done today—to the future, which means prioritizing what work gets done and influencing longer-term decisions. In a daily routine, this means instead of jumping into a code review to fix a bug, you’re leading a meeting where you guide the team to debate the pros and cons of two different architectural patterns. You start asking questions like the ones Martin Davis suggests: “How will this handle future expansion? How will it adapt to changing circumstances?” You spend less time in an IDE and more time in collaborative sessions, facilitating conversations, drawing out opinions, and empowering your team to collectively own the solution. You become less of a doer and more of a conductor, ensuring the entire orchestra is playing in harmony toward a shared architectural vision.
Since data governance can be a blind spot for technical leaders, what specific, hands-on projects can a DevOps engineer volunteer for to build expertise in data quality and dataops? How does this experience directly prepare them for CTO-level conversations about AI strategy and risk?
Data is a massive opportunity for a DevOps engineer to stand out because, frankly, many aspiring CTOs coming from a pure application development background lack deep data skills. A fantastic, hands-on project to volunteer for is an initiative focused on improving data quality to make a specific dataset “AI-ready.” This is a tangible, often underappreciated, but absolutely critical function. You could partner with data specialists to build out pipelines that clean, validate, and govern a key data product. Getting your hands dirty with the challenges of data lineage and quality firsthand is invaluable. As Camden Swita points out, a human can often work around poor data, but an AI agent can’t, leading to hallucinations and bad recommendations. This direct experience arms you for CTO-level conversations because you can speak with authority on the foundational work required for a successful AI strategy. You’ll be able to confidently discuss the real risks of using ungoverned third-party data sources or the intellectual property implications, moving the conversation from a theoretical “we should do AI” to a practical “here’s the data foundation we must build first.”
Rising to the C-suite requires moving beyond a single area of expertise. Beyond formal training, what are some efficient, practical ways for a devops professional to develop a deep understanding of disciplines like enterprise architecture and IT operations while still managing their daily responsibilities?
You’re right, you can’t just take a class in everything; there simply isn’t enough time. The most successful leaders I know are masters of efficient, continuous learning. A powerful and practical way to do this is to formally add learning to your sprint commitments, treating it with the same importance as any other task. Then, the key is to chronicle what you learn, perhaps in a personal journal or, even better, a public blog. The act of writing forces you to synthesize your thoughts and solidifies your understanding in a way that passive learning doesn’t. This also develops a critical CTO skill: sharing knowledge and teaching others. Beyond that, actively seek out peers in enterprise architecture or IT operations for coffee or lunch. Ask them about their biggest challenges and wins. Find a mentor who can give you the high-level view. This combination of self-directed study, active synthesis through writing, and learning directly from peers is the most efficient way to build that broad expertise without derailing your daily responsibilities.
To develop leadership skills, one must embrace experiences outside their comfort zone. Can you describe a non-technical activity, like leading a customer journey mapping session, and explain how that specific experience directly builds the business acumen and soft skills necessary for a CTO role?
Absolutely. Leading a customer journey mapping session is a perfect example of an experience that feels deeply uncomfortable for a technically-minded person but is pure gold for leadership development. In this role, you aren’t solving a technical problem; you’re facilitating a human one. You have to bring together stakeholders from product, sales, and support, and most importantly, listen to actual customers. Your job is to guide the conversation, to uncover pain points in their end-to-end experience with your product, and to document their frustrations and “aha” moments. This directly builds business acumen because you stop seeing your platform as a collection of services and start seeing it through the customer’s eyes—how it helps them achieve a goal or where it causes friction. It hones your soft skills—active listening, negotiation, and strategic thinking—because you’re aligning different departments around a shared understanding of the customer. The path to CTO is about spending more time with people and less time with machines, and an exercise like this is a powerful, direct way to practice that.
Aspiring CTOs must develop a strategic vision that aligns technology with business goals. How can a devops leader start practicing this skill now? Please walk me through the steps you would take to connect a current platform engineering project to a larger company objective.
You can start practicing this immediately, no matter your current role. Let’s say your team is working on a platform engineering project to create a self-service internal developer portal. The tactical goal is to improve developer experience. To think strategically, you first need to zoom out. Go find your company’s latest annual report or investor presentation and identify one of the top three strategic business objectives—let’s say it’s “accelerate time-to-market for new products.” Now, you can connect the dots. The first step is to articulate the link: “Our developer portal project directly supports the company’s goal of accelerating time-to-market.” The next step is to quantify it. You can work with teams to measure how much time the portal will save in provisioning new services, from weeks to hours. Then, you translate that into business impact: “By reducing provisioning time, we can launch new product features 15% faster, directly impacting our competitive advantage.” By consistently framing your technical work in the language of business outcomes, you start demonstrating the strategic thinking that C-suite leaders are looking for.
What is your forecast for the evolution of the CTO role over the next five years?
Over the next five years, the CTO role will complete its shift from being the “chief technologist” to the “chief value creator through technology.” The expectation will no longer be just to deliver stable, scalable systems, but to be a primary driver of business strategy and competitive differentiation. AI will be at the heart of this; the CTO will be responsible not for just implementing AI tools, but for weaving AI into the very fabric of the company to create new revenue streams, optimize operations, and design novel customer experiences. This means the most successful CTOs will be vision painters who can articulate a compelling future and inspire the organization to build it. They will spend as much time on data governance, ethics, and risk management as they do on architecture, and their ability to collaborate with and influence their C-suite peers will be far more critical than their ability to code. The role is becoming less about managing technology and more about leading the business’s digital transformation from the inside out.