What does it actually take to turn AI from an experiment into measurable growth? In this episode, I caught up with Jason Riback, President of MediaMint, to unpack what “agentic growth services” really mean in practice. MediaMint works with leading organizations across media, entertainment, retail, and technology to scale front-office operations across marketing, sales, media, and data. But this conversation was less about buzzwords and more about execution. Jason shared his journey from engineering at the University of Michigan to McKinsey, and then into the heart of the San Francisco startup ecosystem. That blend of operational rigor and startup agility clearly shapes how he thinks about growth today. For him, AI is only valuable when it produces definable improvements in real workflows. That means fewer manual handoffs, fewer errors, faster cycle times, and better output quality. Otherwise, it is just another system sitting on the shelf. We spent time breaking down the gap between data-driven marketing in theory and decision-making in reality. Reporting is always retrospective, Jason reminded me. The real challenge is using insights in near real time to influence spend allocation, targeting, and optimization before a campaign ends. That requires governance, clean data, and clear accountability. Without those foundations, organizations risk operational exposure and opaque decision logic that no one can confidently explain. One of the most thoughtful parts of our discussion centered on human oversight. Jason was clear that while AI can technically retrain models and adjust guardrails on its own, handing over full autonomy creates a black box problem. Enterprises need the right governance layer, where recommended changes are reviewed and approved against clear outcomes. Automation should feel invisible within the workflow, not like another dashboard demanding attention. We also explored MediaMint’s Intelligent Assistant platform, MIA. What stood out to me was the pragmatic approach. Rather than offering a one-size-fits-all tool, MediaMint customizes AI agents around each client’s tech stack, data connectors, and workflow steps. That flexibility is essential because no two marketing organizations operate the same way. The goal is applied agentic execution embedded into daily workloads, not theoretical AI capability. Finally, we turned to the people side of transformation. As automation becomes embedded in front-office operations, roles will inevitably shift. Jason believes teams will move away from repetitive execution and toward managing, interpreting, and optimizing AI-driven processes. That shift demands AI literacy, cross-functional alignment between marketing, tech, and finance, and shared agreement on what good looks like. Accountability does not disappear simply because an agent executes a step. If you are wrestling with how to apply AI inside marketing and revenue operations without creating new risks or unnecessary complexity, this episode offers a grounded perspective. It is a conversation about discipline, governance, and measurable outcomes, not hype. What would change in your organization if automation genuinely reduced cycle times by 50 percent while improving quality and transparency at the same time?
