Aaron Kesler, Sr. Product Manager, AI/ML at SnapLogic – Interview Series

Aaron Kesler, Sr. Product Manager, AI/ML at SnapLogic, is a licensed product leader with over a decade of experience constructing scalable frameworks that mix design pondering, jobs to be done, and product discovery. He focuses on developing latest AI-driven products and processes while mentoring aspiring PMs through his blog and training on strategy, execution, and customer-centric development.

You’ve had quite the entrepreneurial journey, starting STAK in college and occurring to be acquired by Carvertise. How did those early experiences shape your product mindset?

This was a extremely interesting time in my life. My roommate and I began STAK because we were uninterested in our coursework and wanted real-world experience. We never imagined it will result in us getting acquired by what became Delaware’s poster startup. That have really shaped my product mindset because I naturally gravitated toward talking to businesses, asking them about their problems, and constructing solutions. I didn’t even know what a product manager was back then—I used to be just doing the job.

At Carvertise, I began doing the identical thing: working with their customers to grasp pain points and develop solutions—again, well before I had the PM title. As an engineer, your job is to unravel problems with technology. As a product manager, your job shifts to finding the suitable problems—those which can be price solving because additionally they drive business value. As an entrepreneur, especially without funding, your mindset becomes: how do I solve someone’s problem in a way that helps me put food on the table? That early scrappiness and hustle taught me to at all times leaf through different lenses. Whether you are at a self-funded startup, a VC-backed company, or a healthcare giant, Maslow’s “basic need” mentality will at all times be the inspiration.

You discuss your passion for coaching aspiring product managers. What advice do you want you had if you were breaking into product?

The most effective advice I ever got—and the recommendation I give to aspiring PMs—is: “Should you at all times argue from the client’s perspective, you’ll never lose an argument.” That line is deceptively easy but incredibly powerful. It means you should truly understand your customer—their needs, pain points, behavior, and context—so you are not just showing as much as meetings with opinions, but with insights. Without that, every little thing becomes HIPPO (highest paid person’s opinion), a battle of who has more power or louder opinions. With it, you turn into the person people turn to for clarity.

You’ve previously stated that each worker will soon work alongside a dozen AI agents. What does this AI-augmented future seem like in a day-to-day workflow?

What could also be interesting is that we’re already in a reality where individuals are working with multiple AI agents – we’ve helped our customers like DCU plan, construct, test, safeguard, and put dozens of agents to assist their workforce. What’s fascinating is corporations are constructing out organization charts of AI coworkers for every worker, based on their needs. For instance, employees can have their very own AI agents dedicated to certain use cases—similar to an agent for drafting epics/user stories, one which assists with coding or prototyping or issues pull requests, and one other that analyzes customer feedback – all sanctioned and orchestrated by IT because there’s lots on the backend determining who has access to which data, which agents must adhere to governance guidelines, etc. I don’t consider agents will replace humans, yet. There can be a human within the loop for the foreseeable future but they may remove the repetitive, low-value tasks so people can give attention to higher-level pondering. In five years, I expect most teams will depend on agents the identical way we depend on Slack or Google Docs today.

How do you recommend corporations bridge the AI literacy gap between technical and non-technical teams?

Start small, have a transparent plan of how this matches in together with your data and application integration strategy, keep it hands-on to catch any surprises, and be open to iterating from the unique goals and approach. Find problems by getting interested in the mundane tasks in your corporation. The very best-value problems to unravel are sometimes the boring ones that the unsung heroes are solving daily. We learned a number of these best practices firsthand as we built agents to help our SnapLogic finance department. Crucial approach is to be certain that you’ve secure guardrails on what sorts of data and applications certain employees or departments have access to.

Then corporations should treat it like a university course: explain key terms simply, give people a likelihood to try tools themselves in controlled environments, after which follow up with deeper dives. We also make it known that it’s okay to not know every little thing. AI is evolving fast, and nobody’s an authority in every area. The bottom line is helping teams understand what’s possible and giving them the boldness to ask the suitable questions.

What are some effective strategies you’ve seen for AI upskilling that transcend generic training modules?

The most effective approach I’ve seen is letting people get their hands on it. Training is an amazing start—you should show them how AI actually helps with the work they’re already doing. From there, treat this as a sanctioned approach to shadow IT, or shadow agents, as employees are creative to seek out solutions which will solve super particular problems only they’ve. We gave our field team and non-technical teams access to AgentCreator, SnapLogic’s agentic AI technology that eliminates the complexity of enterprise AI adoption, and empowered them to try constructing something and to report back with questions. This exercise led to real learning experiences since it was tied to their day-to-day work.

Do you see a risk in corporations adopting AI tools without proper upskilling—what are a number of the commonest pitfalls?

The largest risks I’ve seen are substantial governance and/or data security violations, which may result in costly regulatory fines and the potential of putting customers’ data in danger.  Nevertheless, a number of the most frequent risks I see are corporations adopting AI tools without fully understanding what they’re and are usually not able to. AI isn’t magic. In case your data is a large number or your teams don’t know the way to use the tools, you are not going to see value. One other issue is when organizations push adoption from the highest down and don’t take into accounts the people actually executing the work. You’ll be able to’t just roll something out and expect it to stay. You wish champions to coach and guide folks, teams need a robust data strategy, time, and context to place up guardrails, and space to learn.

At SnapLogic, you’re working on latest product development. How does AI factor into your product strategy today?

AI and customer feedback are at the center of our product innovation strategy. It isn’t nearly adding AI features, it’s about rethinking how we are able to continually deliver more efficient and easy-to-use solutions for our customers that simplify how they interact with integrations and automation. We’re constructing products with each power users and non-technical users in mind—and AI helps bridge that gap.

How does SnapLogic’s AgentCreator tool help businesses construct their very own AI agents? Are you able to share a use case where this had a big effect?

AgentCreator is designed to assist teams construct real, enterprise-grade AI agents without writing a single line of code. It eliminates the necessity for knowledgeable Python developers to construct LLM-based applications from scratch and empowers teams across finance, HR, marketing, and IT to create AI-powered agents in only hours using natural language prompts. These agents are tightly integrated with enterprise data, so that they can do greater than just respond. Integrated agents automate complex workflows, reason through decisions, and act in real time, all throughout the business context.

AgentCreator has been a game-changer for our customers like Independent Bank, which used AgentCreator to launch voice and chat assistants to cut back the IT help desk ticket backlog and release IT resources to give attention to latest GenAI initiatives. As well as, advantages administration provider Aptia used AgentCreator to automate one in every of its most manual and resource-intensive processes: advantages elections. What used to take hours of backend data entry now takes minutes, because of AI agents that streamline data translation and validation across systems.

SnapGPT allows integration via natural language. How has this democratized access for non-technical users?

SnapGPT, our integration copilot, is an amazing example of how GenAI is breaking down barriers in enterprise software. With it, users starting from non-technical to technical can describe the consequence they need using easy natural language prompts—like asking to attach two systems or triggering a workflow—and the mixing is built for them. SnapGPT goes beyond constructing integration pipelines—users can describe pipelines, create documentation, generate SQL queries and expressions, and transform data from one format to a different with a straightforward prompt. It seems, what was once a developer-heavy process into something accessible to employees across the business. It’s not nearly saving time—it’s about shifting who gets to construct. When more people across the business can contribute, you unlock faster iteration and more innovation.

What makes SnapLogic’s AI tools—like AutoSuggest and SnapGPT—different from other integration platforms in the marketplace?

SnapLogic is the primary generative integration platform that constantly unlocks the worth of information across the trendy enterprise at unprecedented speed and scale. With the flexibility to construct cutting-edge GenAI applications in only hours — without writing code — together with SnapGPT, the primary and most advanced GenAI-powered integration copilot, organizations can vastly speed up business value. Other competitors’ GenAI capabilities are lacking or nonexistent. Unlike much of the competition, SnapLogic was born within the cloud and is purpose-built to administer the complexities of cloud, on-premises, and hybrid environments.

SnapLogic offers iterative development features, including automated validation and schema-on-read, which empower teams to complete projects faster. These features enable more integrators of various skill levels to stand up and running quickly, unlike competitors that mostly require highly expert developers, which may decelerate implementation significantly. SnapLogic is a highly performant platform that processes over 4 trillion documents monthly and may efficiently move data to data lakes and warehouses, while some competitors lack support for real-time integration and can’t support hybrid environments.

 What excites you most in regards to the way forward for product management in an AI-driven world?

What excites me most in regards to the way forward for product management is the rise of one in every of the newest buzzwords to grace the AI space “vibe coding”—the flexibility to construct working prototypes using natural language. I envision a world where everyone within the product trio—design, product management, and engineering—is hands-on with tools that translate ideas into real, functional solutions in real time. As a substitute of relying solely on engineers and designers to bring ideas to life, everyone will find a way to create and iterate quickly.

Imagine being on a customer call and, within the moment, prototyping a live solution using their actual data. As a substitute of just listening to their proposed solutions, we could co-create with them and uncover higher ways to unravel their problems. This shift will make the product development process dramatically more collaborative, creative, and aligned. And that excites me because my favorite a part of the job is constructing alongside others to unravel meaningful problems.