A recent survey from Wired found that three in four coders use AI at least once a week, with 17 percent using it most of the time. Microsoft’s CEO Satya Nadella stated in April 2025 that roughly 30 percent of the company’s code is now written by AI.
But for tech leaders who are building their own models, AI is, first and foremost, a tool; and like any tool, it requires sharpening, fine-tuning and craftsmanship to make it worthwhile. Just ask John Bemenderfer, managing consultant at Analytics8.
“We treat AI like any evolving toolset — we stay grounded in core data principles while exploring what’s new,” Bemenderfer said. “Our real value comes from translating that into client impact.”
The approach is similar for Engin Anil, senior manager of AI and ML at Grainger.
“Grainger’s principle of ‘start with the customer’ enables us to understand the root of a problem so we can find the best solution,” Anil said.
Caxy, a software consulting and development agency, introduces customers to AI as a tool to transform their businesses in ways they haven’t even considered.
Founder and CEO Michael LaVista explained the tactical role that the team often takes.
“We had to teach teams how to think about AI not as a department but as a layer — one that touches data, ops, UX and strategy,” LaVista said. “Internally, we built lightweight templates for how to run AI discovery, validate assumptions with fast prototypes and bake in user feedback early.”
Built In spoke with these Chicago leaders and more about how they are using AI as a tool to build even better tools for customers.
Analytics8 is a data and analytics consultancy.
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?”
Grainger is the leading distributor of maintenance, repair and operating products, serving more than 4.5 million customers worldwide.
What is the unique story that you feel your company has with AI?
Grainger is nearly 100 years old and serves a very large customer base. What makes us stand out is our ability to balance established industry leadership with continually innovating technology solutions that aid our core business processes. My team maintains a fast-paced work environment while striving to meet business objectives that help drive the solutions we’re looking for. One of our core principles at Grainger is “start with the customer,” which, for our team, means understanding the root of the problem and finding a quality solution while working fast and completing projects at scale. AI also helps us deliver on Grainger’s purpose to keep the world working by making our processes more accurate and leaving space to continuously make improvements that allow us to solve customer needs.
What was a monumental moment for your team when it comes to your work with AI?
Every day has the potential to be monumental; some interesting moments come out of places we least expect them. One of Grainger’s principles is to “embrace curiosity,” so finding how to best apply new technology to solve business cases is exciting and our values directly encourage exploring new solutions. Simultaneously, we are intentional about remaining customer centered, finding the root of a problem and learning how to solve it. I believe that any company that wants to outlive their competitors needs to be customer-focused. Grainger’s principle of “start with the customer” enables us to understand the root of a problem so we can find the best solution.
I am leading a team of exceptionally talented people who can work laterally across various applications of AI, yet they also have their own specializations. Being able to cross collaborate, make practical connections between customers, the problem and technology, and then adapt the tech quickly keeps me engaged. Our team expects new things to happen and stays agile to shift direction when a new solution comes along. Staying closely connected to our product team also allows us to adopt best-in-class AI technology.
What challenges did your team overcome in AI adoption?
Proving or disproving a case is easy but getting it to work at the scale of Grainger’s business is challenging and requires a lot of data. My team has developed exceptional capabilities in simulating real-world data on which to train our models. We started with no actual data for training models and have since built models that allow us to deliver successful projects. With AI as a continual emerging technology, there’s still a challenge to pave the way in a lot of AI applications which can be a valuable learning experience. We understand that success comes from taking intentional steps and making positive contributions while learning new things.
As for continuous learning, Grainger’s culture encourages team members to be curious and eager to learn. We’re urged to take time to develop or learn new skills, participate in weekly cross-team learning sessions, work on journal collaborations, attend conferences and share knowledge as much as possible. This culture of learning allows us to be both students and teachers, experiment with tools to discover outcomes — and, ultimately, feel like we have unlimited solutions to tap into.
Prosodica uses conversational analytics to humanize the way enterprises improve their business.
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?
Our unique AI journey began more than a dozen years ago, well before AI was prominently featured in commercial products. We turned to AI out of necessity, driven by a desire to solve repetitive, tedious problems without resorting to the kind of brute-force human effort that could drain the joy out of our work. We simply couldn’t accept the idea of hiring talented individuals only to burden them with monotonous tasks, while the truly interesting challenges awaited attention. Instead, we harnessed AI as a willing collaborator to tackle these mundane problems, enabling our team to engage in stimulating, meaningful work. We took inspiration from Tom Sawyer, inviting AI models as eager “friends” to help paint the proverbial fence, keeping our team’s efforts focused on innovation and creativity.
What was a monumental moment for your team when it comes to your work with AI?
For us, as for many others, the monumental turning point was the release of OpenAI’s GPT-3 in 2020. Already deeply involved in machine learning, the sudden availability of this powerful large language model opened up entirely new horizons in our ability to understand and interact with human language. This breakthrough presented an exciting yet challenging moment: we had invested considerable resources into developing specialized, bespoke models for understanding caller intent. GPT-3 offered similar capabilities, with greater flexibility but higher costs. Anticipating that costs would inevitably decrease, we boldly embraced LLMs to position ourselves at the forefront of language-driven AI innovation.
What challenges did your team overcome in AI adoption?
As a company rooted in software engineering and data science, our challenges with AI adoption weren’t typical. Instead of resistance, we faced overwhelming enthusiasm, rapidly integrating AI into our processes. Initially, we believed our in-house, domain-specific models would outperform general-purpose large language models. Yet each quarter, generative AI advances quickly outpaced our custom solutions, prompting us to adopt these models while staying focused on customer needs and preserving the intellectually stimulating aspects of our work.
Today, as AI encroaches on tasks we once saw as uniquely ours, we’re experiencing the same challenges we’ve long helped customers navigate. It’s a demanding but thrilling landscape, rich with opportunities for growth. And as generative AI evolves, we find ourselves once again blending general models with finely tuned, domain-specific solutions — creating uniquely powerful results.
Caxy is a Chicago-based software consulting and custom software development agency.
What is the unique story that you feel your company has with AI?
Caxy’s story with AI starts where most others fall apart — the execution layer. We don’t build AI for AI’s sake. We came up with a plan for AI to free up cash flow. We work with companies that were never built to be tech-first and we make AI real for them.
AI isn’t just possible — it’s profitable. The unique twist is that we rarely lead with the tech. We start by uncovering operational bottlenecks and asking, “What’s the decision here? What’s expensive if you get it wrong?” That lens helps us zero in on places where AI can thrive. We build tools that run quietly in the background, but radically improve speed, accuracy or experience. It’s AI, but without the theater.
What was a monumental moment for your team when it comes to your work with AI?
Our internal motto: AI that ships is better than AI that waits. A turning point came when we helped a company stuck in “pilot mode” — the kind where there’s always another committee review, another data quality fix, another quarter gone. We reframed the problem: instead of trying to solve everything, what’s the $10,000 a day mistake you’re still making because this isn’t live? What’s the ticking time bomb that’s threatening to blow up the business? That conversation changed everything. We deployed a lean, focused solution that solved the real issue in weeks, not quarters. That moment reshaped our entire approach.
What challenges did your team overcome in AI adoption?
The challenge wasn’t getting buy-in on AI — it was getting clarity on what AI should do. PwC found that 94 percent of executives believe AI is critical to future success. But only 14 percent have implemented it at scale. That’s a 5-to-1 gap. Most clients came in thinking they needed a model. What they really needed was a system. We had to teach teams how to think about AI not as a department but as a layer — one that touches data, ops, UX and strategy. Internally, we built lightweight templates for how to run AI discovery, validate assumptions with fast prototypes and bake in user feedback early. That made AI less intimidating and more repeatable.
