Jarrett Judkins, director of Strategic Technology Program Delivery at Fidelity Investments, brings over three decades of IT leadership experience, enhanced by his background as an Air Force veteran and holding a doctorate in business administration. He successfully leads and executes strategic technology initiatives across financial, insurance, manufacturing, and retail arenas. Leveraging his strong foundation in AI from doctoral research, Jarrett skillfully integrates traditional and agile methodologies to oversee programs and initiatives. As an innovative problem solver and data-driven decision-maker, he resolves complex issues and delivers solutions that align with organizational goals and desired outcomes.
In an interview with CIOReview, Judkins emphasizes the importance of ethical AI and proactive governance to ensure AI solutions remain effective and responsible. He also highlights the need for evidence-based research and data-driven decision-making in strategic program management.
What are your main roles and responsibilities?
I am a part of a team that manages strategic programs and initiatives. My typical day involves coordinating activities across multiple teams to ensure visibility and foster collaboration. I blend traditional program and project management with agile practices, reflecting our organization's strong focus on agile methodologies. For smaller initiatives, usually under 12 months, I apply agile methods, while for larger projects, I use a more traditional approach to managing dependencies and securing team commitments. This approach helps to highlight inter-team dependencies and ensures that teams meet their targets within a reasonable timeframe. Additionally, I handle reporting, address issues, mitigate risks and engage with relevant teams as needed to ensure timely and high-quality delivery to both external clients and internal teams.
"The goal is to enhance productivity and efficiency—not necessarily by reducing headcount, but by enabling faster and more effective work"
What are the most significant challenges and trends currently impacting the industry?
One of the major trends currently impacting all industries is AI. I was first exposed to AI a few years ago while working on my doctorate, where my dissertation was centered on augmenting human decision-making through the use of AI even before the widespread popularity of ChatGPT, which was just the beginning of AIs popularity. Organizations are now beginning to explore how AI can be leveraged for competitive advantages and to optimize internal processes.
The goal is to enhance productivity and efficiency—not necessarily by reducing headcount, but by enabling faster and more effective work. A key challenge is managing the transition for employees whose roles may be automated by AI. Organizations need to find ways to redeploy these individuals within different parts of the company to retain their expertise. AI remains a highly discussed field that is still very much in the hype phase, with considerable debate on its capabilities, limitations and potential applications.
My hope is that organizations are focusing on implementing AI ethically and responsibly by creating solutions that avoid causing harm. However, we are still in the hype phase, with new AI products and large language models emerging every quarter, each touted as the next big breakthrough. It feels like vendors are leapfrogging one another and no one is an established market leader yet. The continued expansion of competing AI products makes it challenging for large organizations to choose the best solution. With so many options, organizations must carefully evaluate the capabilities and functions of each solution, whether it's a vendor-based AI product or custom-built. They need to ensure these tools perform as intended and avoid unforeseen, potentially harmful behaviors.
Another key area organizations should be focused on is taking responsibility for the performance of their AI solutions. Organizations should begin implementing governance models like those in the security industry, where rigorous testing and threat detection are standard. This approach ensures AI solutions function as intended, are as bias-free as possible, and deliver the expected value.
How do you envision AI evolving in the coming days and what advancements most excite you?
I anticipate that the rise of AI will become more definitive and clearly defined, with major providers emerging as leaders in delivering topnotch AI-based solutions. Although there is currently no dominant player, I expect several key organizations to establish themselves as top contenders across various industries, including finance, retail, transportation and insurance. AI is already significantly utilized in healthcare. In the near future, smaller AI companies are likely to be acquired by larger organizations, reshaping the market and solidifying the positions of the leading players. This trend is likely to increase awareness of how its solutions are implemented in medical settings, even as many individuals may not yet fully grasp these advancements. Furthermore, AI is expected to become increasingly integrated into the criminal justice and legal systems, taking on a more prominent role.
Based on my background, I see substantial potential for AI in the air traffic control industry, which is particularly well-suited for such advancements. This sector is likely to undergo significant changes as it addresses the pressing challenges of an aging workforce, as air traffic controllers are retiring faster. Retail organizations like Walgreens or McDonald's will also increasingly rely on AI to enhance their customer offerings and streamline operations. Any industry that does not start its AI journey in some way risks falling behind and being overtaken by competitors.
What sage advice would you offer to a peer or aspiring professional in the field?
My key piece of advice is to leverage and learn from the experiences of others to avoid repeating mistakes. Organizations should partner with research-based entities or experts in the AI field. While individual companies may hire talented staff, their perspectives can be limited by company constraints or their own backgrounds. Collaborating with third-party organizations can provide unbiased recommendations and guidance, helping accelerate AI adoption and integration with existing tools.
I believe companies should invest in AI governance just as they would with any other technology. Establishing a governance model ensures that the technology does not put the organization at a disadvantage or introduce risks, unethical practices and legal issues. Continuous monitoring is crucial, especially for machine learning models, which learn from data and are influenced by their structure.
We cannot simply implement AI and walk away; proactive oversight is necessary to ensure models function as intended and that no harmful data is introduced. This approach differs from traditional technology solutions implementation, which is often left unmonitored. In traditional technology, issues or performance gaps trigger teams to revisit deployed systems. However, with AI-based solutions, I believe companies shouldn’t take a passive approach. They should be intentional about ongoing monitoring. Some may hesitate due to the added costs of hiring specialized staff, but neglecting this oversight can lead to bigger issues.
If you wait until a problem arises, the damage is already done. It's essential to be proactive in monitoring AI solutions and partner with industry experts to develop ethical and responsible AI roadmaps.
