Aaron Kesler, Director of AI Product Management at SnapLogic – Interview Series

Aaron Kesler, Director of AI Product Management at SnapLogic, is an authorized 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 recent 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 tired of 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 correct problems—those which are value solving because in addition 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 all the time 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 all the time be the inspiration.

You speak about your passion for coaching aspiring product managers. What advice do you would like you had while you were breaking into product?

The very best advice I ever got—and the recommendation I give to aspiring PMs—is: “In case you all the time argue from the client’s perspective, you’ll never lose an argument.” That line is deceptively easy but incredibly powerful. It means it is advisable 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, all the things becomes HIPPO (highest paid person’s opinion), a battle of who has more power or louder opinions. With it, you develop 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 firms 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—akin 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 quite a bit on the backend determining who has access to which data, which agents must adhere to governance guidelines, etc. I don’t imagine agents will replace humans, yet. There shall 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 firms bridge the AI literacy gap between technical and non-technical teams?

Start small, have a transparent plan of how this suits in along 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 what you are promoting. The best-value problems to unravel are sometimes the boring ones that the unsung heroes are solving day by day. We learned loads of these best practices firsthand as we built agents to help our SnapLogic finance department. Crucial approach is to be sure you may have secure guardrails on what kinds of data and applications certain employees or departments have access to.

Then firms should treat it like a university course: explain key terms simply, give people a probability 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 all the things. 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 arrogance to ask the correct questions.

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

The very best approach I’ve seen is letting people get their hands on it. Training is an important start—it is advisable 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 that 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 firms adopting AI tools without proper upskilling—what are a few of the commonest pitfalls?

The largest risks I’ve seen are substantial governance and/or data security violations, which might result in costly regulatory fines and the potential of putting customers’ data in danger.  Nevertheless, a few of the most frequent risks I see are firms 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 multitude or your teams don’t know methods 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 consider the people actually executing the work. You may’t just roll something out and expect it to stay. You would like champions to teach and guide folks, teams need a powerful data strategy, time, and context to place up guardrails, and space to learn.

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

AI and customer feedback are at the guts of our product innovation strategy. It isn’t nearly adding AI features, it’s about rethinking how we will 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, in order that they can do greater than just respond. Integrated agents automate complex workflows, reason through decisions, and act in real time, all inside 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 recent GenAI initiatives. As well as, advantages administration provider Aptia used AgentCreator to automate considered one of its most manual and resource-intensive processes: advantages elections. What used to take hours of backend data entry now takes minutes, due to 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 important example of how GenAI is breaking down barriers in enterprise software. With it, users starting from non-technical to technical can describe the final result 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 an easy 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 repeatedly unlocks the worth of knowledge across the trendy enterprise at unprecedented speed and scale. With the power 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 rise up and running quickly, unlike competitors that mostly require highly expert developers, which might decelerate implementation significantly. SnapLogic is a highly performant platform that processes over 4 trillion documents monthly and might 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 concerning the way forward for product management in an AI-driven world?

What excites me most concerning the way forward for product management is the rise of considered one of the most recent buzzwords to grace the AI space “vibe coding”—the power 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 have the opportunity 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.