Latest news with #DavidGucker
Forbes
26-06-2025
- Business
- Forbes
The COO's Roadmap To Building The AI-Era Dream Team
David Gucker, COO, Vultr. The early days of GenAI were fueled by curiosity and experimentation. Now, we've entered a new phase—one where AI innovations like multimodal and agentic AI are delivering real results and reshaping how businesses operate, interact and compete. For today's COO, the question isn't if AI will transform operations—it already has. The challenge is building the kind of team that can use it to create real, lasting value, one that knows how to use AI intentionally, not just enthusiastically. That takes more than tools. It takes people who understand how to apply AI in the real world—and a structure that supports fast learning, smart risk-taking and meaningful progress. Here's a framework my company has used—and refined—to build that kind of team. Prioritize AI-maturity over résumés with buzzwords. Every wave of new tech comes with hype, but transformation doesn't come from tools alone—it comes from the people who know how to use them wisely. It's easy to chase flashy résumés or hire fresh talent from hot programs. But today's most effective teams are led by experienced AI operators who have been down in the trenches and survived. In my experience, many of the strongest leaders thrive in uncertain conditions and know how to adapt under pressure. They don't just 'get' AI—they understand how to apply it to solve business problems. They know that success isn't just about the model but about strategic applications that drive outcomes. They know how to scale what works and avoid what doesn't. They also know when to pump the brakes completely. Individual judgment matters. That said, junior talent still has a role—especially when equipped with the right tools and support. Invest in helping new hires get up to speed quickly by encouraging the use of our AI tools and systems. This helps them build confidence, contribute earlier and develop good instincts for when and how to use AI effectively. With the right foundation, fresh perspectives can thrive. Set shared values and expectations from the start. High-functioning teams thrive on shared expectations. AI changes the decision making process, how teams work together and how success is measured—so it's critical to establish a shared language and operating model from day one. Some key areas to align on include: • How Knowledge Is Shared: AI can accelerate collaboration—but only when paired with a culture that rewards it. Encourage shared responsibility and leverage internal AI tools to centralize documentation and surface insights across teams so the best answers are always accessible, no matter the role or function. • How Decisions Get Made: Define how AI insights inform real-time and strategic choices. What's the process? Who owns what? Who is questioning it and confirming it's right? • How Success Is Measured: Set KPIs that track AI's real value—things like speed, accuracy, scale or efficiency. These will evolve as your AI strategy matures. • How Models Adapt: Agree on a rhythm for reviewing AI performance and adjusting. Don't wait for something to break (or assume it never will)—prioritize iteration over perfection. • Who Owns Risk: Assign clear responsibility for validating AI outputs, managing bias, granting access, ensuring security and compliance and spotting issues early. AI governance isn't a side job—it's essential. • How To Be Accountable: Identify and attribute credit where credit is due. If AI helped out, own it and make it clear from the get-go. Build for the long game—not just the next quarter. Tech innovations are exciting, but hype fades fast. To build something lasting, you've got to ground your efforts in real business needs and build a team that can execute on that. During interviews, go deeper than the résumé and include a foundational approach that was true before the dawn of AI. Ask how they've handled failure. How they adapt. How they help others succeed. And don't underestimate the culture fit, or as I like to say, 'Egos need not apply.' AI promotes collaboration like never before—but only if people are willing to share knowledge and work together. We've seen huge gains by fostering a 'how can I help?' mindset instead of a 'that's not my job' attitude. Magic happens when everyone works toward the same goals and empowers each other to step up when needed. Finally, don't wait to address misalignment. Nearly 70% of employees know within the first month if a role is the wrong fit, and 29% after the first week. Do checks throughout the 30-60-90-day window—and don't be afraid to course-correct quickly. Tech only gets you so far. Hiring in the age of AI is a long-game strategy. With the right framework, hard work (and a little luck), you're investing in an asset that will be with your company for a long time. AI is powerful. But it alone doesn't run your business—people do. So yes, invest in great tools. But invest just as thoughtfully in the people who can make those tools sing. With experienced, low-ego leaders, COOs can build a dream team ready for today's AI moment—as well as future transformation. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
28-03-2025
- Business
- Forbes
How COOs Can Build An AI-First Culture With GPTs
David Gucker, COO, Vultr. getty AI is reshaping how we work, and our role as COO is evolving along with it. At Vultr, I've seen firsthand how AI models like generative pre-trained transformers (GPTs) can drive efficiency across the enterprise. But successful deployment isn't just about adopting the latest technology—it requires strong governance, trust and effective change management. If you're a COO looking to empower your employees with a GPT, here's a straightforward roadmap for success. Many COOs at AI-mature enterprises emphasize the need for an AI Center of Excellence (CoE). But if a CoE sounds overwhelming (and I get it—it does), think of it instead as forming an internal AI committee. This cross-functional team—including your CIO, CTO, VP of Engineering and other leaders—champions AI adoption while ensuring governance, security and compliance. The committee's role is to define policies for safe and responsible AI use, establish data governance that balances speed with trust and deploy an internal GPT client that genuinely empowers your employees. Just as importantly, they set the tone for AI adoption. Three-quarters of business leaders say quick AI adoption will help them maintain a competitive advantage. Employees are 1.5 times more likely to use AI at work—and nearly three times more optimistic about its potential—when their leaders demonstrate confidence and enthusiasm about the tools. Trust is essential when rolling out AI tools. Privacy and security are non-negotiable, but so is creating an environment where employees feel comfortable experimenting with AI without fear of judgment. Treat AI like any other productivity tool—something employees can explore freely to enhance their work. They shouldn't be concerned that their AI interactions might be monitored. To build this trust—or as I call it, the "no judgment zone"—set a clear policy that AI interactions will never be under surveillance or used for performance reviews. At Vultr, the essence of our "Responsible Use of AI Policy" is simple: the principle of least privilege, be responsible with company data, you're accountable for your work product and AI interactions aren't tied to employee assessments. Leadership reinforces this message, ensuring our teams feel safe asking questions and contributing to AI improvements. This removes the "Big Brother" fear and creates a supportive culture of positivity around AI adoption. Securing your GenAI requires the same diligence as any other enterprise system. Role-based access controls (RBAC) protect sensitive data while allowing employees access to relevant insights. A tiered approach—like a wedding cake—balances security and usability: • Outer Layers: Start with the least sensitive data, such as content on platforms like Confluence or intranets. • Middle Layers: Gradually move to slightly more sensitive data, such as customer support systems or IT ticketing platforms. • Inner Layers: Eventually, extend to more privileged data, like financial information, ensuring tighter controls at this stage. • Top Layer: Highly sensitive personal information should remain off-limits to GenAI applications. Identify your organization's biggest pain points and start by applying AI where it delivers immediate value. Focus on processes bogged down by repetitive, time-consuming tasks. Areas like customer support, recruitment, sales and marketing offer big potential. A recent study by 451, in partnership with Vultr, found that 84% of respondents saw improved customer service performance after deploying AI, while 89% reported better results in marketing (pg. 6). At Vultr, one of our biggest pain points was the time our support team spent searching through documentation to assist customers. To address this, we developed a self-hosted GPT client that indexes internal knowledge and customer interaction history, providing real-time, on-demand insights to our support team. The result? Faster issue resolution times, increased efficiency and high customer satisfaction. By tackling high-impact areas first, you can achieve quick wins to build excitement and trust before scaling across other areas. Deciding whether to build your own internal GPT tools or leverage external solutions depends on your organization's technical capabilities and AI maturity. One of the advantages of being a COO for a tech-first company is having extensive resources at my fingertips. Given our expertise in AI infrastructure, it made sense for us to develop a private, self-hosted GPT solution tailored to our needs and integrated into our control plane, ensuring full alignment with our existing governance and security frameworks. For companies with fewer tech resources or those earlier in their AI journey, building and hosting a custom solution can be daunting. Nearly 30% of companies lack the infrastructure and talent to deploy AI effectively, and 21% cite budget concerns (pg. 14). In these situations, partnering with a trusted provider is often the best approach—letting you treat GenAI as an operational expense while relying on an IaaS provider that aligns with your data governance and security standards. This lets you maintain control and keep costs in check without compromising security or performance. AI is evolving rapidly. Staying ahead of emerging trends often means looking beyond your organization for inspiration. Two companies that I find particularly innovative in their use of GenAI are Goldman Sachs and Walmart. Goldman Sachs is empowering its developers with a centralized AI platform that offers access to a range of large language models. This platform enables the rapid creation of secure and compliant AI applications, while their focus on code generation tools has boosted developer efficiency by about 20%. Walmart, on the other hand, is equipping over 50,000 corporate employees with GenAI tools such as "My Assistant" to streamline tasks like drafting and summarizing. Additionally, Walmart's developer-focused tools are revolutionizing the coding and software deployment processes, driving greater efficiency and innovation across the organization. Whether you build in-house or go with an experienced AI infrastructure partner, success depends on more than just technology—it's about creating a culture where AI use is encouraged and strategically integrated. By starting with a strong committee focused on building trust, you can lay the foundation for an impactful AI-first culture within your organization. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
