Bryce Macher is Vice President of AI Engineering at FanDuel, where he leads the application of AI and advanced analytics to accelerate decision-making across the enterprise. His career has been guided by a central question: how can behavioral signals and AI be translated into faster, higher-confidence decisions that drive measurable outcomes?
Bryce began in deeply technical roles, developing natural language processing, computer vision, and cognitive computing applications for some of the world’s most recognized consumer brands. As he moved into leadership, his focus shifted from building models to ensuring they delivered real-world impact. He has consistently observed that the true constraints on AI are not algorithmic, but organizational with decision velocity, clarity of ownership, and trust in how intelligence is operationalized.
At Domino’s, he led initiatives spanning GenAI-powered personalization, digital ad inventory optimization, and cognitive systems designed to understand and evolve brand identity. These production-grade systems are embedded directly into how the business and its customers make decisions, contributing to margin expansion and sustained market leadership in a highly competitive category.
Bryce views AI not as a standalone capability, but as an operating system for scalable decision intelligence. His leadership centers on building teams, platforms, and governance frameworks that make AI measurable, trusted, and repeatable across the enterprise.
As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?
“The most effective data and AI leaders combine technical fluency with a deep understanding of how decisions are actually made by humans and, increasingly, by autonomous agents. Technical skills still matter, but are ultimately table stakes. What differentiates impact is the ability to model decision contexts: who or what is making the decision, under what constraints, and which signals meaningfully influence outcomes.
“In my organization, the most influential skill has been decision literacy: the ability to translate business ambiguity into clear decision frames, and then design AI, ML, and analysis/research systems around them. That includes understanding nuanced linguistic, emotional, and behavioral signals that shape choice: how language primes trust, how visual and sensory cues affect preference, and how timing and context alter intent. As AI systems become more agentic, these same principles apply to machine decision-makers as well: how prompts are framed, how feedback loops are designed, and how incentives are encoded.
“Equally important is the ability to lead across business disciplines. The work that delivers value sits between data science, software engineering, design, and the business. Leaders who can align these groups around shared decision outcomes, rather than models or tools, will build systems that are adopted, trusted, and durable.
Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?
“I have learned that real leverage comes from both people and technology being at the cause of decisions, not at the effect of them.
“Many aspiring data and AI leaders focus on producing better analyses, models, or dashboards. Those are necessary, but they often place you downstream: responding to questions after direction has already been set. C-suite influence comes from shaping the questions themselves: defining which decisions matter, when they need to be made, and what success looks like before data is ever pulled.
“In my career, the moments of greatest impact came when I moved upstream, partnering early with leaders to help frame decisions, clarify trade-offs, and design intelligence directly into workflows. That shift changes how you’re perceived, shifting you from expert advisor to strategic operator.
“This mindset also builds resilience. When you anchor your role to decision causality rather than technical output, your value scales with the business. You become easier to trust, and far more relevant at the executive table.”