Five years ago, generative AI like ChatGPT was still confined to academic research and experimentation. Today, it's practical, pervasive, changing the world — and I have a C-level title in the field.
How did that happen? I assembled a great team of professionals who are experts in the existing technology and deeply curious about the as-of-yet undiscovered ways it can be used.
Creating an effective AI team is about finding the right people with the right skills to embrace complexity, navigate uncertainty, and focus on meaningful outcomes. AI is a transformative force and working with the team that will wield it effectively is one of the most critical decisions an organization can make.
Leadership: Vision, Not Management
Great leadership is the backbone of any successful AI team. I believe leaders don’t just oversee projects, they set the tone, establish the vision, and align the team with the organization’s broader goals. In an AI team, leadership requires a deep understanding of both organizational dynamics and AI’s potential.
This role is less about micromanaging the day-to-day and more about creating clarity. A strong leader answers the big questions: Where are we going? Why does it matter? How will we know when we’ve succeeded? I see leadership as the connective tissue between strategy, execution, and outcomes, ensuring the team stays focused on what truly matters.
Business Analysts: The Bridge Between Vision and Action
In AI, a good business analyst is invaluable. Anna DeGiulio, the lead business analyst on my team, played a critical role in a project that highlights this.
On a help desk ticket triage project, Anna DeGiulio worked with multiple stakeholders across hours of meetings to understand the requirements, find the data to measure the factors of success, and translate all of that into different forms of communication. She created detailed backlog stories for developers and crafted PowerPoint slides for stakeholders, ensuring alignment and clarity throughout the process.
Business analysts dive into the messy details of how departments operate. They uncover use cases, evaluate potential ROI, and critically, envision how AI will reshape workflows and outcomes. They’re the ones asking, “How will this change the way people work? What new possibilities does this unlock?” Their ability to frame these questions in human-centered terms ensures AI initiatives stay relevant and impactful.
Technology Experts: Turning Ideas Into Reality
Technology experts bring the vision to life. This team is often divided into two key groups:
- technology leadership, who map out systems, data strategies, and emerging tech opportunities, and
- the engineers and developers who execute those plans.
Chris Kukla on our AI team exemplifies what makes this role effective. Inspired by conversations with Uber tech leads and research from Bloomberg, he recognized that people don’t necessarily want automation to solve problems for them. They want solutions that help them work faster and smarter.
Within seven days of those initial conversations, Chris and the team implemented a ticket summarization feature in our help desk system. This solution saved minutes per ticket, which, at scale, adds up to saving Impact hours each day. It’s a great example of how agility, clear thinking, and focus can lead to big value, fast.
Product Owners: Sustaining Impact
Once a use case comes to life, the work doesn’t stop. This is where product owners come in, ensuring that the tools and systems developed by the team are not only adopted but sustained over time.
This role focuses on user adoption, iterating based on feedback, and evangelizing the solutions within the organization. Their work ensures that the AI initiatives remain valuable and relevant. Beyond that, they look for opportunities to refine and expand, ensuring that what’s built continues to evolve and grow with the organization’s needs.
Data Specialists: Building on a Solid Foundation
AI runs on data, and without a solid foundation, even the most sophisticated models will fall short. Data specialists ensure that the pipelines are strong, the data is clean, and the metrics are meaningful.
Their work often begins with research: What data do we have? What’s missing? What can we measure that will show progress or success? They maintain the integrity of data systems and support the rest of the team in achieving their goals. These specialists thrive on solving the kinds of murky, complex challenges that arise when building something truly new.
Collaboration: The Glue That Holds It Together
The success of an AI team isn’t just about the roles themselves but how they work together. I’ve seen firsthand how essential collaboration is in our projects. For example, when Chris worked on the ticket summarization tool, he built a great rapport with the developers of the ticketing system. Their cooperation ensured seamless integration and a quick turnaround.
Meanwhile, Anna’s ability to gather user insights and align them with technical needs ensured that the solution was both practical and impactful. Collaboration like this is non-negotiable. It’s what allows an AI team to thrive, delivering solutions that aren’t just technically sound but deeply integrated into the fabric of the organization.
Challenges: Embracing Complexity
Building and managing an AI team is no small feat. Fear and misconceptions about AI often arise from employees worried about job displacement or leaders hesitant to rethink long-held assumptions. Addressing these fears requires a human-centered approach.
Last year, I led the rollout of an AI assistant designed to provide answers to HR-related questions. In that initiative, communication was essential to success. We held regular sessions with teams to address concerns about how the system would impact their roles, emphasizing how it would save time rather than replace jobs.
By involving stakeholders early, we created a shared sense of ownership and trust. Additionally, I made sure technical teams were aligned with leadership’s goals by translating strategic objectives into actionable steps, from prioritizing features to setting realistic timelines.
I believe leaders must emphasize trust, communication, and a shared sense of purpose. AI is not about replacing people but enabling them, helping teams work smarter, faster, and with more insight than ever before. Bridging gaps between processes, technology, and human impact is the real challenge, and the real opportunity.
Measuring Success: Staying Grounded in Outcomes
Success in AI must be defined before the first line of code is written. At the outset of every project, the team establishes metrics that tie directly to the business case. These might include:
- Efficiency improvements, such as hours saved.
- Financial gains, such as increases in net income margin.
- Process enhancements, like throughput or resource utilization.
- Time-to-value, ensuring that projects deliver benefits quickly.
These metrics serve as guideposts, ensuring the work remains grounded in delivering real, tangible value.
The Future of AI Teams
A great AI team is a microcosm of what every organization should strive to be: agile, collaborative, and deeply aligned with its goals. With the right mix of leadership, analytical expertise, technical skill, and data-driven insight, an AI team can unlock incredible possibilities.
The question isn’t just how to build an AI team, it’s how to build one that’s ready to drive transformation in a way that centers people, solves real problems, and creates lasting value. Because at the end of the day, AI isn’t just about technology, it’s about what’s possible when you align vision, tools, and people to create something truly meaningful.
