Analytics8 Innovation, Technology & Agility

What is the unique story that you feel your company has with AI? If you were writing about it, what would the title of your blog be?

The blog title would be “Clean Data In, Real Results Out: Our Ground-Level Advantage in AI.” Our story with AI starts where we’ve always been strong — data quality. Everyone talks about model performance, but we focus on the part that’s often overlooked, clean, consistent and well-defined data. That’s what makes AI actually work. 

We’ve helped clients across industries wrangle messy, siloed data into usable formats — whether it’s structured, semi-structured or unstructured — and those same skills are critical for AI success. The principles we’ve used for decades still apply; if the data isn’t clear and governed, the insights won’t be either. We’re not chasing shiny tools — we’re applying real data expertise to real AI use cases.

What are you most excited about in the field of AI right now?

I’m excited by how quickly AI is becoming usable. Until recently, if you wanted to build something with generative AI, you needed full-scale development — custom UIs, infrastructure and engineering support. Now, that’s changing. The frameworks are maturing and the vendors we already partner with are embedding AI into their tools in a way that lets us build fast, useful solutions for clients. Whether it’s applying RAG techniques to unstructured data or using semantic models to add context to structured data, we can now turn proprietary information into actionable insight — without reinventing the wheel. That shift is a game-changer.

AI is a constantly evolving field. Very few people coming into these roles have years of experience to pull from. Explain what continuous learning looks like on your team. How do you learn from one another and collaborate?

We treat AI like any evolving toolset — we stay grounded in core data principles while exploring what’s new. Everyone on the team keeps an eye on the latest model releases and vendor updates, but our real value comes from translating that into client impact. We test tools in private preview, share lessons through internal whitepapers and video walkthroughs and collaborate to figure out where new frameworks can plug into existing analytics work. It’s not about chasing trends — it’s about asking, “what’s worth trying and how do we apply it responsibly to real data problems?”