Podcast Pieces: Why Agentic AI Promises an Exciting Future for Teams

In the lives of busy marketers, the goal is clear: Spend less time on repetitive but necessary tasks, and more time on high-impact strategy. Traditional automation promised to make things a little easier — but agentic AI has the potential to take it much further.

In this episode of the Local Marketing Beat podcast, host Christian Hustle talks with Uberall CTO Ana Martinez, who leads the company’s R&D team across product, design, and engineering.

Ana shares why she sees agentic AI as one of the most transformative shifts since the early days of the internet, and how Uberall is evolving from static automation to AI-native, goal-driven orchestration. With innovations like the Location Performance Score (LPS) and UB-I, the platform is becoming smarter, more proactive, and ready to work alongside marketers like a trusted teammate.

Where Agentic AI Differs from Traditional Automation

Ana Martinez: “With orchestration and agentic AI, you can get very close to proactive intelligence and operate in a much more dynamic way. I always say it’s not that AI will replace humans. AI will do the things we honestly don’t like to do; it will do the heavy lifting and help the humans stay strategic. Teams can scale without chaos. I think that that’s the main change of what’s possible here.”

Traditional automation is great for simple, repetitive tasks. But as Ana Martinez points out, today’s customer journey is anything but simple. A customer might start with an AI recommendation, add a product to their cart, wait for a discount, and finally make the purchase through a link shared by an influencer. The path to purchase is fragmented, unpredictable, and unfolding in real time.

That complexity creates a data overload. Rules and workflows can only go so far: they cover some scenarios, but not all. And by the time a business identifies a new pattern and writes a rule for it, the opportunity may already be gone. At the same time, consumers now expect highly personalized experiences — something rigid automation simply can’t deliver at scale.

This is where agentic AI makes the difference. Rather than relying on pre-set rules, it reasons, adapts, and responds dynamically to customer behavior as it happens. For Ana, it’s about adopting a new mindset, one where AI works proactively to help businesses meet rising customer expectations in the moment.

UB-I: Agentic AI in Practice

Ana Martinez: “What we've done is embed agentic intelligence across our core workflows. What UB-I (Uberall’s AI agent) can do is operate across thousands of locations. What we ensure is the human is always in the loop and in control; everything that UB-I does and all the agents behind it is 100% auditable and 100% traceable.”

For Ana, Uberall’s AI agent UB-I is the storefront and conductor of Uberall’s agentic platform. Behind the scenes, a swarm of specialized agents work in harmony, while UB-I serves as the performance manager for every location, 24/7. It tracks each site’s Location Performance Score (LPS), understands Location Performance Optimization (LPO) practices, and knows how to act on them efficiently. In Ana’s words, it’s transformational.

Developing UB-I, she says, has been very different from building traditional software. Instead of deterministic requirements where engineers know exactly how the system will behave, UB-I is built on top of a large language model (LLM). That means interactions can be unpredictable — and often pleasantly surprising. Early adopters have asked UB-I questions the team didn’t anticipate, only to find that UB-I could handle them thanks to its deep integration with the Uberall platform through MCP and contextual data.

To make this work in the real world, the team invested early in guardrails and feedback loops. UB-I is designed to stay on-topic, on-brand, and aligned with customer trust. That requires continuous testing, monitoring, and even stress-testing to “break” the system and learn from its responses. As Ana puts it, it’s a shift in how software is built — moving from static development cycles to ongoing evolution in production. The result is a smarter, safer, and ever-improving agentic AI that customers can rely on.

For multi-location brands, AI agents like UB-I mean consistency at scale. They can expect less manual work, faster execution, and greater online visibility. That frees teams to focus on outcomes instead of day-to-day tasks. And when combined with LPO practices, Ana notes, businesses should see not only stronger revenue growth but also improved efficiency and healthier margins.

How Companies Win the AI Game

Ana Martinez: “Even if you have the best intern in the world coming to work in your company, you don’t expect that person to be effective from day one. You’re still going to be onboarding that person. You still need to give them context. You need to explain how you do things at your company. With agentic AI, it’s kind of the same. It can understand context. It can make decisions autonomously and act proactively if you let it, but you need to work with it and give it that context. You’re going to give it feedback, and that in turn will get better and better.”

Consumers are adopting AI at breakneck speed. As Ana points out that in the near future, people will expect to interact with agents directly for everything from product discovery to purchase. And as agents become more widespread, software platforms will follow suit, connecting human and machine collaboration in powerful new ways.

For marketing teams, this shift creates two urgent priorities: adapting to AI-driven consumer behavior and rethinking internal operations. Content strategies must evolve for AI search, while businesses need to identify high-impact areas where AI can replace manual effort or handle edge cases more effectively than workflows and rules. Ana advises starting small — with a 30-60-90 day plan — to test use cases, measure outcomes, and scale what works. The focus, she stresses, should be on goals and outcomes, not just replicating existing processes.

Above all, she urges businesses not to fear AI, but to treat it as a superpower. The best results come from collaboration between humans and machines, guided by proven frameworks like LPO. Much like onboarding a new intern, success requires context, feedback, and patience. AI will make mistakes, but with guidance, it improves rapidly — and those who invest early gain a “booster” that makes it hard for competitors to catch up. As Ana puts it: The transformation is happening either way. The question is whether your business will lead it or follow it.