AI, Finally Clear for Leaders
AI, Explained Like a Human
If you lead a team or a business, you don’t need more AI buzzwords.
You need clarity—and a path to action.
In this episode, we break down the two most important ways AI is already reshaping work:
- Handling repetitive tasks faster—and smarter—than humans
- Coordinating across systems like a 24/7 digital team
You’ll walk away with:
✅ Real examples from fuel, retail, logistics, and customer service
✅ What’s working right now (and what’s coming fast)
✅ The risks to watch—and why humans still matter
✅ A simple roadmap to get started
🎧 No hype. No jargon. Just what leaders need to know to stay ahead.
This episode is powered by NewTide.ai, Enterprise AI Built for the Fuels and Convenience Industry. Learn more here: https://newtide.ai/
Transcript
Welcome to the Deep Dive.
Speaker A:I'm Alex.
Speaker B:And I'm Spencer.
Speaker A:And this is actually a special edition of Fueling AI.
Speaker A:We're taking a little break from the main episodes you usually hear with Doug Hawk.
Speaker B:Yeah, because there's this topic that's just.
Speaker B:Well, it's everywhere right now, isn't it?
Speaker B:AI.
Speaker A:Exactly.
Speaker A:And specifically we want to dig into AI agents and something called agentic AI.
Speaker A:They sound pretty similar, honestly.
Speaker B:They really do.
Speaker B:Almost interchangeable sometimes, but there's a difference.
Speaker A:And you know, understanding that distinction, we think it's really key, especially if you're trying to figure out how this technology could actually benefit your business.
Speaker B:Whether that's running the forecourt, managing distribution networks or operating convenience stores.
Speaker B:It applies across the board in fuel and retail.
Speaker A:Right.
Speaker A:So we've been looking at some interesting sources.
Speaker A:There's a pretty detailed academic paper and also an article focusing more on how businesses are actually using this stuff.
Speaker B:Yeah, the enterprise angle.
Speaker A:Our goal today really is just to pull out the main ideas, help you understand the difference, maybe spot where the real opportunities are for you, and just think about where all this might beheaded.
Speaker B:Okay.
Speaker B:Yeah, let's do it.
Speaker B:So based on what we've read, maybe the easiest way to think of the first one, AI agents is like hiring a highly skilled digital employee, but one who's focused on a very specific job.
Speaker A:Okay, a specialist.
Speaker B:Exactly, a specialist.
Speaker B:They're built using those, you know, powerful AI models everyone talks about, but they're designed to automate very distinct tasks or workflows.
Speaker A:Like what kind of tasks in our industry?
Speaker B:Well, think about maybe an agent that handles all the incoming customer questions about loyalty points.
Speaker B:Just does that.
Speaker B:Frees up your team.
Speaker B:Or one that automatically pulls the real time inventory data from fuel tanks or even from store shelves and just generates the reports you need.
Speaker A:Ah, okay.
Speaker A:So automating those repetitive things.
Speaker B:Precisely.
Speaker B:They're like the solo performers in an orchestra.
Speaker B:Super efficient, but at one defined thing.
Speaker B:And the sources suggest these are, you know, pretty much ready to go now.
Speaker A:Ready now, yeah.
Speaker B:And more affordable generally than trying to build some massive complex AI system from scratch.
Speaker B:It seems like a really practical place to start.
Speaker B:Some readings even suggested they could bring enough value, like cost savings to basically pay for the underlying AI platform itself.
Speaker A:Huh, interesting.
Speaker A:So it's like onboarding a new digital hire for a very specific role.
Speaker A:You get the value, maybe it's covers the cost.
Speaker B:That's a good way to put it.
Speaker B:Yeah.
Speaker B:Start building that digital capability, that intelligence within your business.
Speaker A:Okay, that makes sense.
Speaker A:Tools for making current jobs Specific jobs, more efficient.
Speaker A:So if those are the solo performers, what about this other term, agentic AI?
Speaker A:What's the next step the sources describe?
Speaker B:Ah, yeah, that's where it starts to get even more interesting.
Speaker B:I think agentic AI is kind of the evolution of that idea.
Speaker A:How so?
Speaker B:So instead of just having one agent doing its one specific task, you have multiple AI agents.
Speaker B:And crucially, they might each have different skills or specializations.
Speaker B:And the key part is they work together.
Speaker B:It's a system, Right, where these digital specialists can actually communicate with each other.
Speaker B:They can collaborate, coordinate, basically tackle much more complex multi step problems.
Speaker A:So less like a solo musician and more like the whole orchestra playing together.
Speaker A:Or you mentioned construction crews earlier when we chatted.
Speaker B:Exactly.
Speaker B:Like a digital orchestra.
Speaker B:Or.
Speaker B:Or yeah, a crew where you have the plumber, the electrician, the carpenter, all different specialists, but they have to work together to build the house.
Speaker B:That's the idea behind agentic AI.
Speaker A:A dream team of AIs.
Speaker B:Maybe you could call it that.
Speaker B:Yeah, because a single agent, you know, might really struggle with a complex challenge.
Speaker B:Something that needs different kinds of data or multiple decision points or actions taken in sequence.
Speaker B:Right, but egentic AI is designed specifically for that kind of collaborative problem solving.
Speaker B:For someone in, say, fuel distribution, this could mean a system that coordinates real time fuel pricing adjustments.
Speaker B:It pulls competitor data, looks at current demand signals from different sites, maybe even weather forecasts, and then recommends or even executes price changes.
Speaker A:Wow.
Speaker A:Okay.
Speaker B:Or maybe it integrates sensors on the forecourt, like vehicle presence, with promotional triggers inside the C store, you know, pump three just finished offering them a coffee discount.
Speaker B:That kind of thing requires coordination.
Speaker A:Yeah, definitely more complex than just answering loyalty point questions, for sure.
Speaker B:Or think about optimizing delivery routes and managing compliance checks autonomously across a whole network of stations that needs different agents talking to each other.
Speaker B:We saw this one example in the material where applying this agentic approach, basically getting different AI parts to work together on an internal business process, cut the time it took from weeks down to just minutes.
Speaker A:Weeks to minutes, that's.
Speaker B:That's huge.
Speaker B:It really highlights the potential jump.
Speaker B:Yeah.
Speaker A:So this whole shift then, it's really about adding that collaboration piece onto the AI's ability to act.
Speaker B:Precisely.
Speaker B:You know, if you look at the evolution early AI, like the first chatbots or image generators they create in content, then AI agents came along and added the ability to act autonomously on specific tasks.
Speaker B:Agentic AI adds that next crucial layer, collaboration and orchestration between those actions, between those agents.
Speaker A:Okay, but like any team, human or digital.
Speaker A:I assume it's not all smooth sailing.
Speaker A:The sources must mention challenges.
Speaker B:Oh, absolutely, yeah, good point.
Speaker B:Just like managing human teams has its pitfalls, managing AI teams does too.
Speaker B:For instance, even single AI agents can sometimes hallucinate, basically make stuff up.
Speaker B:Or they can be quite brittle.
Speaker A:Brittle meaning?
Speaker B:Meaning if the instructions aren't perfectly clear or if something unexpected happens in the data, they might just fail or give a weird result.
Speaker B:They aren't always great at adapting on the fly like a person can.
Speaker B:Now, with agentic AI, where you have multiple agents interacting, will you introduce new potential risks?
Speaker B:You could have agents misinterpreting each other's messages or goals.
Speaker B:Or maybe one agent makes a mistake and because they're connected, that error cascades and causes problems for the other agents down the line.
Speaker A:Ah, like a domino effect.
Speaker B:Kind of, yeah.
Speaker B:So the analysis we looked at really emphasizes the need for strong governance.
Speaker B:You need checks and balances built into these systems.
Speaker B:And critically, you need to keep humans in the loop.
Speaker A:Right?
Speaker B:Oversight, especially oversight.
Speaker B:You can't just set these complex systems loose without any way to monitor them or intervene.
Speaker B:Particularly when you think about integrating them into, you know, real world operations of a fuel station or a delivery network.
Speaker B:You need that human judgment layer.
Speaker A:Okay, so bringing this back down to earth for someone listening right now, maybe they manage a few C stores or they're involved in fuel trading or logistics.
Speaker A:What does this all actually mean for them?
Speaker A:Like today or in the near future?
Speaker B:Yeah, good question.
Speaker B:The practical takeaway seems to be this.
Speaker B:If you have specific, well defined, maybe quite repetitive tasks that are eating up time or resources right now, things like generating standard reports, handling those basic customer service interactions online, maybe doing some initial data sorting or analysis.
Speaker B:Then starting with individual AI agents is probably the most practical first step.
Speaker B:They're focused, the tech is more mature for these specific uses, and they can deliver real measurable efficiency gains pretty quickly.
Speaker B:Immediate value.
Speaker A:Okay, start there for the quick wins.
Speaker B:Exactly.
Speaker B:Get your feet wet.
Speaker B:But then keep an eye on agentic AI.
Speaker B:As those capabilities mature and they are maturing fast, the promise is much bigger.
Speaker B:It's about systems that can handle those really dynamic, cross functional workflows that span different parts of your operation.
Speaker A:So less about just automating single tasks, and more about potentially redesigning how entire complex processes get done.
Speaker B:That's the potential.
Speaker B:Yeah, leveraging that collaborative digital intelligence to tackle bigger challenges.
Speaker A:And the sources seem to suggest this isn't necessarily about, you know, AI taking over jobs, but more about building these powerful digital partners tools that can work with your existing teams.
Speaker B:That was definitely a theme.
Speaker B:Building trustworthy digital partners.
Speaker B:So, yeah, starting small with specific agents makes sense.
Speaker B:It helps you and your team get comfortable with the technology, build some internal experience, learn what works and what doesn't in your specific context.
Speaker B:And all the while, you're watching how agentic AI develops, figuring out where those more complex collaborative systems could fit in down the road.
Speaker A:The roadmap for these, these collaborative AI systems, it sounds like it's becoming clearer, offering some really new ways for businesses, especially in fast moving sectors like ours, to become more agile, more intelligent.
Speaker B:It certainly seems that way.
Speaker A:Okay, so maybe one final thought to leave everyone with.
Speaker A:If agentic AI really works by having these different specialized digital employees, these agents collaborate like a highly effective team, what does that imply for us humans?
Speaker A:How might the design of your own organization, your own teams, need to evolve to work effectively alongside these, these increasingly capable AI collaborators in the future?
Speaker B:That's a fascinating question.
Speaker B:How do human teams best interface with AI teams?
Speaker A:Something to think about.
Speaker A:Okay, that wraps up this deep dive into AI agents and agentic AI.
Speaker B:We really hope this helps clarify the landscape a bit and maybe spark some ideas for your business.
Speaker A:Thanks for joining us.
