Working Smarter, Not Harder: How AI is Empowering These Engineers

Max Zauner never lets AI do things he doesn’t understand. 

“To me, it is important to never give up on the agency over your code,” he said.

Zauner, a senior software engineer I at Dynatrace, has been working on an internal app for the company’s developers called the Dynatrace Console, which he described as a content management system for app releases. He and his peers got to test out different AI coding tools during the early development phase, and while these technologies played a key role in creating the solution, they merely served as support. 

“My approach to AI-assisted coding is to have a clear idea of how to solve coding problems, let it do the cumbersome work, but know exactly what I, as a developer, am doing and what the AI agent is doing,” Zauner said.

AI is redefining the development process for many teams, including marketing platform provider Klaviyo’s K-Service group, where Lead Software Engineer Ivan Bokii and his peers stay busy building a new suite of products that are designed to transform the customer experience for e-commerce brands. 

He said that AI helps him context-switch as he moves between teams, helping him stay on top of changes by summarizing code and system design updates. It also enables his team to surface unknowns and quickly build expertise in certain areas, while allowing them to rapidly prototype new features, test ideas and iterate quickly, putting them in a better position to design an optimal AI UX for the company’s customers. 

“AI hasn’t replaced my role as a software engineer, but it has dramatically expanded what I can accomplish and how efficiently I can do it,” Bokii said.

Below, Zauner and Bokii, along with employees from 16 other companies, share how AI has empowered their teams to work smarter, not harder, giving way to impactful products and high-impact work. 

 

Headquartered in Atlanta, Cox Enterprises operates a multinational portfolio of brands spanning the communications, automotive and agricultural industries. The company’s brands include Autotrader, BrightFarms and Mucci Farms. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

Our entire product and technology group is encouraged and expected to use AI in their respective roles. So, we’re big AI adopters; we’re very committed and on the cutting edge of using AI. 

My team is actually the AI accelerator team, and we’re within the data science org. Our customers are really internal partners at this point who are building products for our customers. We have been tasked with going around the business and identifying opportunities either to build AI into products or to create new workflows to help automate processes with AI. We’ve got a few different agentic AI solutions.

 

“We have been tasked with going around the business and identifying opportunities either to build AI into products or to create new workflows to help automate processes with AI.”

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

One example is what we’re doing for our fleet services team, which repairs heavy duty trucks and trailers that break down on the side of the road. We’re helping them create repair estimates using AI to search through parts availability information. So, if an 18-wheeler broke down, our tools can search and tell them, “For this kind of vehicle, you’re recommended to use these parts, which cost this much and are available at this place.” That allows them to quickly create these estimates and send them off to the shop so they can actually start the repair. It’s automating that whole process of identifying what the issue is and figuring out what actual parts they should use in order to make that repair.

 

What would that project have looked like if you didn't have AI as a tool to use? How has AI changed the way you work, in general?

AI not only makes the processes faster but also helps really improve them as well, because we’re able to use data that they wouldn’t be aware of otherwise. For the fleet services estimates, we’re able to search through and see where their deals are for different shops or how much these parts cost historically. So, they have access to more data now, too, just because we’re connected to all that with AI.

 

 

Capco is a global management and technology consultancy that serves organizations in the financial services and energy sectors. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

At Capco, we build reusable AI assets and bespoke solutions for banks, insurers and fintechs, so clients can move from pilots to safe, production-grade AI. Think genies: assistants that automate business-analyst busywork, streamline data workflows and supercharge claims/service desks with instant summaries and next-best actions. We package these as our Smart Suite, modular accelerators plus services that meet any maturity level and integrate with existing tech stacks. We tackle the real enterprise blockers, such as fragmented data, legacy systems and governance, so AI ships faster, stays compliant and scales across the organization, informing related customer experiences, software development lifecycle processes, product innovations and more.

 

“At Capco, we build reusable AI assets and bespoke solutions for banks, insurers and fintechs, so clients can move from pilots to safe, production-grade AI.”

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

We built a compliance copilot that turns dense regulations and internal policies into an auditable, living workflow. The mission: Remove ambiguity, create a single source of truth for compliance managers, auditors, and product teams, and compress review cycles.

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

It’s like moving from typing a password to using face unlock: faster, fewer steps and more reliable. Without AI, we’d ship a brittle rules-based portal and hand-coded parsers. Hitting similar scope would take about two times longer to reach the minimum viable product, reviews would stay manual and every regulation/update triggers more code and regressions. Outcomes vary by reviewer, and audit evidence is scattered.

Why this excites engineers: You’ll treat AI as power tools; build LLM services with privacy-by-design; stand up vector/retrieval augmented information pipelines; wire eval-as-continuous integration and prompt observability; and co-design with risk/compliance, encountering a modern stack, real production stakes and measurable impact.

 

 

Combining deep observability, AIOps and application security, Dynatrace’s platform helps teams understand the performance of their applications and the user experience. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

Our app team creates an internal app for Dynatrace internal developers who are our “customers.” When other teams want to release an app on the Dynatrace platform, they will soon all need to release it via our Dynatrace Console. Think of it as a content management system for app releases, where people can put their marketing information about the app, add screenshots, add links for more details or add related apps on the platform. Developers can also manage their app releases on the console, being able to unpublish faulty releases or manage the changelog of a specific release. Our Dynatrace Console will soon be integral to the latest Dynatrace Apps platform release process. We’re currently in the middle of migrating existing apps to the Dynatrace Console. We want to make it as easy as possible to allow a smooth release process for our internal app teams at Dynatrace.

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

We were in several pilot phases where we could try out new AI coding tools. One of them was GitHub Copilot. When using these tools, I noticed that there is not a lot of training data included in the models about our internal design system called “Strato Design System,” so I decided to write a small Model Context Provider, which provides documentation data from the Strato docs to the LLM. Now, with the help of this tool, we can significantly improve the quality of our LLM-assisted coding sessions because the AI agents can retrieve the correct usage information about our internal design system components. With the help of this context, the LLMs can produce a vastly better coding output. 

 

“Now, with the help of this tool, we can significantly improve the quality of our LLM-assisted coding sessions because the AI agents can retrieve the correct usage information about our internal design system components.”

 

Just recently, I had to port some of our data tables to a new version of the Strato data table, which would have been a cumbersome task, but with the help of coding agents and the Strato docs MCP, the agent did most of the work. I could then go over the changes, refine them and get the migration through instantly. This would’ve taken me a lot of time otherwise, and the agent could do the heavy lifting for me here, which was a big win for us.

 

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

AI-assisted coding can be very helpful, but I think the key is adopting it with intention. To me, it is important to never give up on the agency over your code. Never let the AI do things that you don’t understand. Never senselessly let the AI generate loads of code. Use this new tool like a new one in your toolbox, and learn how to use it properly. My approach to AI-assisted coding is to have a clear idea of how to solve coding problems, let it do the cumbersome work, but know exactly what I, as a developer, am doing and what the AI agent is doing. Give the agent a clear context, give it a clear purpose and approach it with a solution in mind. 

Having all of these best practices in mind, I have to say that the general use of coding agents has improved the speed and output of code without degrading the code quality or the general reliability of our software. I can definitely say that AI has changed how I work, though not in a way that I let it do my work, but more like I have found a co-worker that never gets tired of my questions and is always up to spar with me and talk about technical problems and their solutions.

 

 

Klaviyo’s platform combines marketing automation, analytics and customer service to enable B2C brands to deliver personalized, real-time customer experiences. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

At Klaviyo, I’m part of the K-Service group, where we’re building a new suite of products to transform the customer experience for e-commerce brands — before, during and after the sale. Think of it like creating an Amazon-style experience for Shopify businesses. Under this umbrella, we’ve developed tools like Customer Agent, an AI chatbot for pre-sales and support, Help Desk for human agents, powered by Klaviyo’s rich data, and Customer Hub, which brings personalization, merchandising and support together on the storefront. Our goal is to help e-commerce brands, whether emerging or scaling, deliver more intelligent, data-driven service that doesn’t just resolve issues but drives revenue and builds stronger customer relationships.

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

I use AI every day both as an engineer and as a cross-functional partner to nearly 50 people across product and engineering. Because I move between teams often, context switching is intense. AI helps me stay on top of changes by summarizing code and system design updates, so I can quickly re-engage wherever I’m needed. Within Customer Agent, our AI-powered solution, we also use AI to accelerate how we learn and explore new domains. Whether it’s prototyping or clarifying a complex feature, AI helps us surface unknowns and quickly build expertise in areas that once required significant time and effort, enabling us to design the best possible AI UX for our customers.

 

“Whether it’s prototyping or clarifying a complex feature, AI helps us surface unknowns and quickly build expertise in areas that once required significant time and effort, enabling us to design the best possible AI UX for our customers.”

 

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

Customer Agent is a complex product, not just a single feature. Without AI, building it would be significantly slower, especially for engineers like me who don’t come from a machine learning background. AI surfaces approaches we wouldn’t know to look for and fills in critical knowledge gaps. It helps me uncover “unknown unknowns,” so I can upskill in real time and immediately apply those learnings to the work. On a practical level, we also use AI to rapidly prototype new features, test ideas and iterate quickly, allowing us to deliver value to customers faster. AI hasn’t replaced my role as a software engineer, but it has dramatically expanded what I can accomplish and how efficiently I can do it.

 

 

Webflow’s platform enables marketers, designers and developers to build, manage and optimize websites without coding. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

We’re building an AI-native web experience platform, one that lets cross-functional teams of marketers, designers and developers visually build, manage and optimize enterprise-grade websites — without hand-coding every detail. Traditionally, a designer creates something in Figma, then an engineer rebuilds that design from scratch in code. That handoff is slow, expensive and full of “this doesn’t look like the mockup” moments. 

 

“We’re building an AI-native web experience platform, one that lets cross-functional teams of marketers, designers and developers visually build, manage and optimize enterprise-grade websites — without hand-coding every detail.”

 

Webflow eliminates that gap. It gives designers and marketers direct control of the web so they can design, build and publish sites themselves, while still maintaining the quality and flexibility engineers expect. When you do need custom logic or integrations, developers can extend the platform with code or APIs, so it scales with your business, not around it. In short, Webflow helps teams ship well-designed, enterprise-grade custom websites fast — without waiting on a dev queue. It’s visual, collaborative and built for flexibility.

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

I lead our developer productivity team, which keeps our platform fast, modern and reliable. That means constantly upgrading our tech stack across many interdependent services without disrupting what 300,000 customers rely on daily. These platform upgrades are notoriously complex; they touch nearly every part of the codebase and have historically taken weeks of manual effort to execute safely. To move faster, we treated AI as an active collaborator, not just a code-generation tool. We use a mix of AI-powered pair-programming tools and integrated development environments, including Cursor, Augment Code, Claude Code and OpenAI Codex, to generate codemods, detect errors and refactor legacy patterns. Engineers pick whichever tool fits their needs; some prefer Augment Code in-context code understanding for large-scale refactors, while others like Claude Code’s reasoning capabilities to interpret errors and recommend safe transformations. 

AI helped map dependencies early, flag risks and cluster related migration tasks so we could parallelize the work across teams. What used to be a slow, error-prone upgrade cycle is now a coordinated semi-automated flow with engineers in the driver’s seat, where AI is doing the heavy lifting. 

 

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

Without AI, this kind of platform upgrade would’ve stretched across multiple quarters — a slow, manual process of dependency mapping, repetitive code updates and endless test cycles. Every small API change in React or Node needed custom scripts and manual reviews. Reliable, yes. Scalable? Not really.

With AI in the mix, we compressed that cycle dramatically. Instead of writing migration scripts from scratch, engineers could prompt AI tools to propose code transformations, validate patterns, spot edge cases and even generate test coverage. The result wasn’t just speed; it was consistency. Teams could move in sync, applying shared migration patterns across the entire codebase and reducing regressions.

AI has reshaped how we approach platform evolution at Webflow. Upgrades aren’t big, disruptive events anymore; they’re continuous, incremental improvements. Engineers have the freedom to experiment with the AI tools that best fit their workflow. And we’re always exploring what’s next — not for novelty, but for how it amplifies creativity, safety and speed. AI has become our quiet partner in keeping Webflow modern, stable and fast.

 

 

Related ReadingHow a Culture of Learning Unlocks Innovation on These Engineering Teams

 

 

Nexthink’s platform is designed to help IT teams resolve issues across any application, device or network by providing real-time visibility, analytics and automation.

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

I’m the tech lead on Nexthink Assist, an AI capability inside Nexthink Infinity, our digital employee experience platform. Infinity gives IT teams deep visibility and remediation across devices, apps and networks so they can diagnose and fix digital workplace issues quickly. Assist is the simple entry point: It turns natural-language questions into the right data retrieval, analysis and actions so users get to answers faster. In short, it drastically lowers the barrier to entry by allowing anyone to get value from Nexthink much quicker, and ultimately reduces the time from “What’s going on?” to “Fixed.”

 

“Assist is the simple entry point: It turns natural-language questions into the right data retrieval, analysis and actions so users get to answers faster.”

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

In my daily work as a developer, AI has become an essential teammate. I use tools like Windsurf and Claude Code at almost every stage of the development workflow, designing, coding, debugging, reviewing and even writing specifications. When I start a new project or dive into an unfamiliar codebase, AI helps me quickly understand the architecture, explain technologies and highlight how different pieces connect. During implementation, I guide the model to generate code that fits our patterns, and my role has shifted from writing code line by line to intensively checking, reviewing and validating what AI produces. It’s a bit like pair-programming: I provide direction and context, and AI does the heavy lifting. 

I also use it to draft clear specifications for my teammates, so they have precise guidance before starting their work. That said, I’m always very deliberate about staying in control of what the AI does; I never just accept its output blindly. Overall, it has made me significantly faster and more effective, allowing me to focus on higher-level design and problem-solving rather than boilerplate tasks.

 

What would that project have looked like if you didn't have AI as a tool to use? How has AI changed the way you work, in general?

Without AI tools, we’d spend more time on boilerplate, digging through docs and manually stitching test scaffolds and refactors, especially when touching unfamiliar areas of the codebase. Work that now takes days would likely take weeks, and we’d ship fewer iterations. AI has fundamentally changed my workflow. It drafts most of my code, allowing me to focus on framing the problem, setting constraints and reviewing the output. It also shortens the onboarding process for unfamiliar projects because it can explain the technology and guide you to the right resources quickly. And when the AI goes in a direction I don’t like, I simply redirect it and correct its trajectory; I’m still fully in control of the implementation. Overall, AI enables me to work faster and smarter while maintaining control over quality and intent.

 

 

Babylist’s platform enables expecting parents to create a baby registry, search for products and garner parenting insights. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

We build commerce at Babylist: search, pricing, checkout and handling out-of-stock items for baby registries across web and mobile. When someone’s carefully chosen stroller disappears, it’s not just lost revenue; it’s someone panicking about whether they’re ready for their baby. We focus on transparent pricing, honest stock status and good alternatives when things go wrong. We handle the technical complexity of e-commerce but for purchases people have put real thought into.

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

Our OutOfStock project tackled what happens when registry items become unavailable across platforms. I did the technical shaping: architecture, service design and rollout strategy for web, iOS and Android. I used AI to draft the technical specification document. I gave it context about our existing systems — how our product data is structured, our API patterns, services we’d integrate with — and worked with it to design the solution. It helped me think through things like how the API for product alternatives should work, what logic to use for recommending similar items and how to structure the work across three platform teams.

AI also helped with risk analysis. It caught edge cases I might have missed, like what happens when there are no good product alternatives and potential performance bottlenecks with stock status checks. It generated code examples, API response formats and implementation phases that I could refine. The whole team works this way now. We treat AI as shared infrastructure for how we coordinate and share context across projects.

 

“We treat AI as shared infrastructure for how we coordinate and share context across projects.”

 

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

Without AI, writing that technical spec would have taken way longer. Probably a week instead of a day or two for all the API designs, code examples and work breakdowns across teams. The constant switching between thinking about architecture and writing detailed documentation is exhausting. AI let me stay focused on the design decisions while it drafted the specifications.

The broader change is that our entire engineering team collaborates differently now: better documentation, more consistent patterns and faster onboarding. AI speeds up the planning and coordination work, which frees up time for the actual engineering problems: keeping stock status data accurate and performant and building a recommendations system that surfaces useful alternatives.

 

 

Lowe’s offers a suite of home improvement products and services for both professionals and consumers. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

Our engineering team builds and supports the enterprise cart and checkout platform at Lowe’s – the technology behind every customer purchase across Lowes.com, the Lowe’s app and more than 1,700 stores nationwide. We focus on creating a smooth, dependable and connected checkout experience that lets customers shop however they prefer, whether that’s online, in-store or a mix of both. From syncing carts in real time to keeping transactions secure and fast, we tackle the complex challenges that come with large-scale, omnichannel retail. Every change we make is about making checkout a little faster, smoother and more reliable for both customers and store associates.

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

One of our most impactful initiatives uses AI to help engineers diagnose and resolve production issues faster. We built a tool that connects to existing monitoring systems, pulling in data from logs and performance metrics. Instead of creating a new standalone product, we integrated intelligent prompts directly into the development environment where developers already work. These prompts help identify errors, surface patterns and suggest root causes and next steps without switching tools, giving teams faster insights with minimal disruption to their workflow.

Next, we’re expanding this into an autonomous AI agent that listens for alerts from communication and collaboration tools, automatically analyzing signals, correlating them with system performance data and summarizing potential causes for on-call teams. At the same time, engineers use a code assistant to handle repetitive coding tasks, generating boilerplate code, improving documentation and writing test cases, as well as custom GPTs that turn user stories into feature scenarios and acceptance criteria, streamlining sprint planning.

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

Before we integrated AI into our workflows, our engineers spent hours digging through logs, analyzing system metrics and manually connecting alerts across services. Root cause analysis could take three to six hours per incident, often requiring multiple team handoffs, and writing test cases or defining acceptance criteria was just as time-consuming and inconsistent.

With AI now built into our tools, debugging and feature planning take a fraction of the time, and we’ve seen measurable gains in developer velocity, consistency and overall confidence. More importantly, AI has changed how we work. By handling the context-heavy, repetitive parts of engineering, it gives our teams more time to focus on innovation, deeper problem-solving and creating better experiences for our customers.

 

 

Jasper’s AI-powered platform is designed to enable marketers to unify their brand voice, connect their workflows and automate the entire content lifecycle. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

At Jasper, our engineering team builds tools that help enterprise marketing teams move from ideas to impact faster — without losing their unique brand voice. Our platform includes Jasper IQ, which learns a company’s style, tone and knowledge base so every piece of content sounds authentically on-brand; Canvas, an AI-assisted editor that helps teams brainstorm, draft and refine ideas in real time; and Agents, customizable AI workflows that take care of repetitive content and marketing tasks.

The problem we’re solving is one nearly every modern marketing team feels: There’s more content to create, across more channels, for more audiences — and scaling high-quality content consistently is tough. Jasper gives teams the best of both worlds: speed and scale from generative AI, paired with the context and control needed to stay creative, strategic and on-brand.

 

“Jasper gives teams the best of both worlds: speed and scale from generative AI, paired with the context and control needed to stay creative, strategic and on-brand.”

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

Jasper is an AI-native company, so AI plays a role in nearly every stage of how we build, from early concepts to deployment. A recent example is Jasper IQ Audiences, a feature that helps customers create LLM-optimized audience profiles. The goal was to make it easier for marketers to generate content that resonates deeply with specific segments.

We used AI throughout the project. During design, AI tools helped our product and design teams explore multiple interface ideas and user flows quickly, almost like having an extra designer in the room. During development, our engineers used AI pair-programming to accelerate coding, improve quality and speed up iteration. And in the refinement phase, we used AI to analyze and optimize how audience data was structured, ensuring profiles were clear, accurate and effective for downstream content generation. The result is a feature that embodies our approach at Jasper — powered by AI, built with AI and designed to help marketers work smarter and faster.

 

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

Without AI, the buildout of Audiences would have taken significantly longer. AI speeds up nearly every part of our process, from exploring design concepts to writing and reviewing code to fine-tuning how audience data is structured. Without those tools, our team would have spent far more time on manual coding, testing and iteration, slowing down the entire release cycle.

More broadly, AI is embedded in almost every facet of how I work. I use it for research, brainstorming, development and even in creating materials that help educate and enable the field. It helps me move faster, test ideas more efficiently and focus more time on strategy and problem-solving rather than repetitive tasks. At this point, it’s hard to imagine working any other way. AI has become a core part of how we build, learn and deliver at Jasper.

 

 

GRAIL develops products that detect cancer early on, such as the Galleri Test, which can detect a signal shared by over 50 types of cancer. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

My team works mostly on internal applications, making it easier for various roles to access and modify production data. I also make developer tools. These days, I mostly work on things I identify as having value, whether it’s a front-end engineer, someone in marketing or research. I am passionate about creating.

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

In the past month, I’ve built new production-grade prototypes for the following: a template management app; an integrated Optical Character Recognition tool; some AI-assistant coding tools; a robust mock-data generation tool; and an automated workflow generation tool. I’ve added command line interfaces for everything I’ve touched. I’ve also been expanding features on existing operations apps. All these things were built with AI, and some of them have AI in their function. I added most of a complex feature to an existing app on Friday alone. I am personally more than 10 times as fast as I was a year ago.

 

“I am personally more than 10 times as fast as I was a year ago.”

 

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

I maybe would have built two of those things, and neither would be nearly as good as it has become already. A number of these projects I would not have even attempted before AI.

 

 

WorkWhile’s AI-powered platform matches organizations with on-demand workers, offering capabilities like flexible scheduling and next-day pay. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

At WorkWhile, the engineering team is building a smarter marketplace that connects hourly workers with flexible, better-paying jobs and helps businesses reliably fill shifts. Behind that simplicity is a set of complex systems designed to predict how likely a shift is to be filled and determine which workers see which opportunities first.

The team uses traditional AI algorithms for shift prediction and tier management, ensuring that preferred workers — those with strong reliability and past performance — are shown jobs first before the pool expands. That helps companies fill shifts faster and workers find consistent, fair work.

 

“The team uses traditional AI algorithms for shift prediction and tier management, ensuring that preferred workers — those with strong reliability and past performance — are shown jobs first before the pool expands.”

 

Beyond the worker-matching engine, the team is also focused on reducing operational overhead. We’ve developed an LLM-powered tool that lets non-technical teams query internal data using natural language, making it possible for anyone to quickly get the insights they need without relying on engineers.

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

One of the team’s recent projects centered on reducing the day-to-day operational burden for support and marketplace operations teams. Historically, these teams often needed engineering help to pull custom data or generate reports, which created bottlenecks. To solve this, our engineers built an internal LLM-powered interface — essentially a natural-language data assistant — that lets anyone ask questions like, “Show me unfilled shifts in San Francisco this week,” and get real-time answers without writing SQL or waiting on engineering time. The goal was to make data access effortless and empower non-technical users to act faster. 

On the engineering side, we also use AI tools like ChatGPT Codex and code-review agents to handle routine tasks. These save valuable hours and keep engineers focused on building scalable systems that power our labor marketplace. AI isn’t replacing our engineers; it’s helping them work smarter and enabling every team to move faster.

 

What would that project have looked like if you didn't have AI as a tool to use? How has AI changed the way you work, in general?

Without AI, the project would have required team members to manually fulfill every data request and maintain custom reporting tools — a process that could take a long time and divert valuable time from product development. By introducing an LLM-powered interface, data access now happens instantly, and non-technical teams can self-serve insights without waiting on engineering bandwidth.

Across engineering, AI has become a powerful enabler. The team is encouraged to experiment with tools that improve efficiency — from code review assistants to auto-completion — but no one is required to use them. Adoption happens naturally when a tool proves valuable. This flexible approach keeps engineers in control while fostering a culture of curiosity and experimentation. AI isn’t a mandate at WorkWhile; it’s a multiplier, helping people work smarter, move faster and focus on building meaningful systems that empower hourly workers.

 

 

Cin7’s cloud-based inventory management software connects businesses to suppliers, warehouses and sales channels in one real-time system, helping them forecast with precision and handle orders with ease.

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

At Cin7, our engineering and product teams are building a comprehensive suite of AI agents, each purpose-built to support a specific stage of a product seller’s lifecycle. Our vision is that everyone involved in managing or selling products will have their own intelligent “AI clone” within our inventory management platform, an assistant that helps streamline their daily workflows, make data-driven decisions and ultimately work smarter.

 

“Our vision is that everyone involved in managing or selling products will have their own intelligent ‘AI clone’ within our inventory management platform, an assistant that helps streamline their daily workflows, make data-driven decisions and ultimately work smarter.”

 

We’re also developing a master agent to coordinate these specialized agents, enabling them to collaborate on complex, multi-step tasks. To power this ecosystem, we’re rethinking our data architecture to ensure our agents have secure, efficient access to the information they need — without compromising privacy.

Unlike generic AI integrations, we’re developing our own proprietary models and algorithms to solve the unique challenges of product sellers. This original approach will transform Cin7 from a system that manages inventory into an intelligent partner that helps our customers run their businesses more effectively.

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

We leverage AI to develop AI, integrating AI tools extensively into our daily workflows. Team members independently select and utilize various AI tools, often switching between them multiple times throughout the day. We also prioritize training our entire organization on effective AI prompting and educating colleagues on the opportunities and risks associated with AI. To encourage AI adoption, we ensure everyone understands the fundamental technologies behind AI. We recognize and address the concerns of those who feel left behind or are hesitant to ask questions, committed to ensuring no one is excluded and that there are no barriers to AI use in any department.

We’ve observed that increased education leads to more creative AI workflow ideas, which are then integrated into our existing tech stack. A dedicated Slack channel facilitates the sharing of these ideas, actively buzzing, as our employees evolve into AI power users.

 

What would that project have looked like if you didn't have AI as a tool to use? How has AI changed the way you work, in general?

At Cin7, AI is integral to nearly every project, making it difficult to imagine our work without it. As an AI-centric organization, we are committed to staying at the forefront of AI technology across all roles and departments. Our dedication to AI begins with recruitment, where a candidate’s familiarity and positive sentiment toward AI can influence their standing. A lack of engagement with AI is seen as a missed opportunity and a disregard for company resources. We believe that when utilized effectively, AI tools can stimulate creativity and lead to innovative solutions.

Rather than dictating our objectives, AI assists us in executing tasks once our goals are defined, allowing us to focus on the quality of the outcomes. While AI is deeply embedded in our workflow, it doesn’t alter our fundamental mission: Enabling product sellers to manage less, sell more and gain complete visibility across their entire inventory ecosystem. This has been our focus long before AI’s widespread adoption and will continue to be so as AGI emerges. It doesn’t change what we do, but it profoundly transforms how we do it.

 

 

AKASA’s AI-powered platform is designed to optimize revenue cycle management for healthcare systems. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

AKASA builds ML systems designed to automate healthcare revenue cycle management, the complex set of processes that connect clinical documentation to billing and reimbursement. The revenue cycle is one of the most complex and error-prone areas in modern healthcare operations, and our goal is to remove friction from administrative processes so providers and staff can dedicate more time to what matters most: delivering excellent patient care.

 

“The revenue cycle is one of the most complex and error-prone areas in modern healthcare operations, and our goal is to remove friction from administrative processes so providers and staff can dedicate more time to what matters most: delivering excellent patient care.”

 

One of our products enables medical coding. This involves creating systems that analyze clinical documentation from inpatient encounters and automatically assign ICD and PCS code, the standardized diagnostic and procedural codes used across the healthcare industry. These codes form the backbone of everything, from insurance billing and reimbursement to quality reporting and public-health tracking.

Traditionally, this has been a highly manual, detail-heavy task, requiring medical coders to review lengthy clinical notes and translate them into precise codes. It’s not only time-consuming but also susceptible to human error and inconsistencies. 

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

Our research and engineering team uses a variety of AI-powered developer tools, including Cursor and Claude Code, to enhance our software development workflow. These tools serve as AI pair programmers, helping engineers brainstorm, design and debug code.

Recently, we have been using AI to accelerate experimental velocity, increasing the rate at which we can stand up prototype machine learning training and evaluation pipelines. Rather than spending hours poring over documentation, writing boilerplate code or manually testing small logic changes, working with coding agents allows us to divert more bandwidth to experimental design and analysis. The focus of hands-on-keyboard coding has shifted to regions of the codebase that are highly technical and/or bespoke, those where the internet would be of no help. For a new engineer ramping up on a codebase as complex as ours, having an AI assistant means being able to learn faster and contribute sooner. This approach embodies one of our team’s core principles: Use AI not just in the products we build but also in how we build them. 

 

What would that project have looked like if you didn't have AI as a tool to use? How has AI changed the way you work, in general?

Without access to AI development tools like Cursor or Claude, each ML experiment would involve significantly more manual effort and incur a longer turnaround time. The absence of AI would also introduce more cognitive overhead and result in higher expenditure of mental energy on mechanical tasks. 

For new team members like me, the onboarding curve would be much steeper, as they’d need to learn both the codebase and the domain context — without the real-time conversational code search and analysis AI now provides. Coding agents also empower those of us on the team who don’t come from traditional computer science backgrounds, broadening the surface area of code we can effectively collaborate on. In essence, AI has become a force multiplier for our teams, in engineering and beyond, helping us move faster, think bigger and deliver more intelligent solutions for our customers.

 

 

Arity is a mobility data and analytics company dedicated to making transportation smarter, safer and more useful. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

Arity’s insurance engineering segment builds data products specifically designed for insurance use cases. Our work helps insurers better understand driving behavior and risk, ultimately enabling smarter decisions and more personalized experiences for their customers. We process managing trillions of telematics data points that include trip events flowing in real time from connected devices and transforming that into insights that power risk models and mobility solutions. We also work on modernization initiatives, such as migrating legacy systems to cloud-native architectures, improving continuous integration/continuous delivery pipelines and enhancing observability and system documentation.

 

“Our work helps insurers better understand driving behavior and risk, ultimately enabling smarter decisions and more personalized experiences for their customers.”

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

Recently, we’ve been using AI tools such as Cursor AI, Copilot, Claude and custom Model Context Protocol integrations within our development workflow to accelerate delivery and improve code quality.

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

Without AI, the development process would have been slower and more manual, especially for debugging, documentation and test generation. Tasks like diagnosing complex build or deployment issues could have taken half a day or more, while writing extensive test cases or documentation would have required multiple team members.

AI tools have significantly: reduced time-to-resolution for production and build issues; increased test coverage and code quality, leading to more stable releases; improved onboarding for new developers through automated documentation; and enhanced productivity, allowing engineers to focus on higher-value design and architectural tasks instead of repetitive debugging or setup work. Overall, AI has changed the way we work by embedding intelligence directly into our development workflow.

 

 

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CAIS’ platform connects independent financial advisors with alternative asset managers, offering greater access to and education about alternative investment funds and products.

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

Our users often need to sift through a long menu of alternative investment products to extract information such as performance data, sector allocation and redemption terms. Relying on our sales or customer service teams to help locate the information often slows this process down.

My team is focused on removing these friction points by building an AI-powered chat interface that integrates across our platform. It enables users to discover, compare and explore alternative investments available through the platform by simply asking natural language questions.

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

For our product chat interface, we recently created the front-end app from scratch, and AI proved useful in several ways. One challenge was learning new technologies, such as Google’s Agent Development Kit. Using GPT-5 to ideate, explore architectural choices and understand how different components fit together was invaluable. Instead of parsing documentation and relying on trial and error, I could work through ideas interactively with the model. That back-and-forth sometimes surfaced approaches I might not have considered on my own, ultimately leading to a stronger implementation and, arguably, a more enjoyable development process, much like pair programming.

Because we were working under tight deadlines, maintaining velocity was crucial. Claude Code, an agentic coding tool, helped us move beyond boilerplate quickly and deploy a functional prototype to our infrastructure for immediate testing. It also made it easier to adapt to large architectural changes, common when working with emerging technologies, allowing us to iterate and test different strategies much faster.

 

“Claude Code, an agentic coding tool, helped us move beyond boilerplate quickly and deploy a functional prototype to our infrastructure for immediate testing.”

 

What would that project have looked like if you didn't have AI as a tool to use? How has AI changed the way you work, in general?

Without AI, setting up the app’s boilerplate, configuring deployments and building a proof-of-concept skeleton would have taken several extra days. Or, it would have required more developer support for areas I’m less familiar with, like our infrastructure repositories. Agentic tools helped me navigate those repositories efficiently and even ran multiple instances across domains, accelerating progress. 

Beyond the setup stage, AI remained valuable. It freed time to refine the product by reducing time spent on business logic; enabled rapid iteration through frequent architectural changes common in greenfield projects; and supported fast, parallel exploration of new features and UX ideas, especially with limited product and design resources. Overall, AI has shifted my focus away from mechanical implementation to creative problem-solving. By compressing build time, it gives me more room to think critically about how to deliver better user experiences.

 

 

AMP is an AI-powered sortation company that aims to modernize the world’s recycling infrastructure and maximize the value in waste, offering solutions that are designed to reduce labor costs, increase resource recovery and deliver more reliable operations. 

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

I work on AMP’s data platform team, and our core product is the data infrastructure around our sortation facilities. We create reliable, well-documented and high-quality data pipelines and datasets. The fundamental problem we solve is data chaos. We ingest raw, traditionally siloed information from dozens of sources and transform it into a cohesive “single source of truth.” This empowers our internal customers to make confident, data-driven decisions without questioning the validity of the underlying information.

 

“We ingest raw, traditionally siloed information from dozens of sources and transform it into a cohesive ‘single source of truth.’”

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

We needed to automate the processing of a critical vendor file used for mass balancing our recycling facilities. This data arrives via email in an esoteric, non-standard format that previously required manual conversion and transformation before it could be used. As an analytics engineer with more experience in data transformation/analysis than Python development, I used AI as a development partner to build an API that would receive the email and handle all the steps required to load it into our data warehouse. After providing a basic overview of what I needed, I received a well-documented and test-supported API that was ready for cloud deployment after some minimal additional development. Our LLM-assisted coding tools made the whole process very straightforward.

 

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

Without AI, the project’s timeline would have easily doubled. Starting a project outside my core expertise like this one would have required much more ramp-up time and additional assistance from a more experienced Python developer. In general, AI has changed my workflow by collapsing the time it takes to get started on basic microservices like this one. Additionally, it handles boilerplate code and model documentation in the blink of an eye, allowing me to focus mental energy on understanding our business logic and developing novel metric tooling.

 

 

Artera’s platform integrates across healthcare organizations’ tech stack, electronic health records and third-party vendors to make patient communications more effective.

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

We are currently building fully autonomous AI agents for healthcare providers. These agents leverage the leading large language, text-to-speech, speech-to-text and generative voice models to automate complex patient communications, engaging patients in a dynamic and realistic manner. Our agents are highly customizable, including voice and speed options, among others, and feature multi-language detection, ensuring they can seamlessly engage diverse patient populations. Our AI agents can handle a wide range of use cases, such as rescheduling, cancelling or confirming appointments. By using AI for routine administrative tasks, providers can reduce staff burnout, optimize efficiency and lower costs.

 

“By using AI for routine administrative tasks, providers can reduce staff burnout, optimize efficiency and lower costs.”

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

Artera engineering has successfully integrated AI into its development processes, starting with Microsoft Copilot in our VSCode IDEs and expanding to include Claude tooling. We utilize Claude agents for various tasks, such as PR reviews, script and Jest test generation, code research, error investigation and “vibe coding,” where agents build fundamental functions and classes based on precise prompts. We’re seeing positive results as we continue to refine our prompts and as LLMs improve. Additionally, we’re using the agents to handle some of the more procedural work, like generating comprehensive pull request descriptions in a matter of seconds, which frees developers from an important but time-consuming task.

With all that said, it is crucial to note that a human review is required for all AI-generated content. AI may not always produce optimal code or understand the best approaches, so our team must review and iterate on its output.

 

What would that project have looked like if you didn’t have AI as a tool to use? How has AI changed the way you work, in general?

At the moment, I’m particularly interested in using AI to create integration and unit tests for our React app. Crafting React component tests has always felt somewhat tedious, especially when it comes to mocking needed and identifying all the branching cases. While generating component tests with AI can be challenging, I’ve found it proficient at recognizing branching logic, though the implementation often results in overly complex test code. To address this, we’re developing clear guideline files — CLAUDE.md files in the code base — that Claude will follow based on our prompts, so tests are built with a consistent set of rules. We’re still learning and growing, but it’s getting better at a pretty fast pace. 

Above all, AI serves as a powerful tool, particularly for managing repetitive tasks and enhancing code quality with innovative solutions I may not have considered. While it’s great for quick checks and speeding up my workflow, I don’t rely on it entirely. Instead, I use it as an enhancement to my process. At the end of the day, it’s just a tool to support my work, not replace it.

 

 

Work & Co is a design and technology company that partners with companies including IKEA, Apple, PGA TOUR, Gatorade, Google and more to launch digital products that transform businesses.

 

What types of products or services does your engineering team work on/create? What problem are you solving for customers?

At Work & Co, our engineering team is known for building digital experiences at scale that people use again and again to improve their daily lives. From websites to mobile apps to chatbots, interactive installations and more for clients across industries, we partner closely with our team members across product, design and strategy to build digital products that make a real impact. 

One thing that I really appreciate in my work is how our clients value the technology team being involved from day one; I believe that really shows in the quality of the work we deliver. Along the way, we also help clients adapt in the age of AI, go to market faster and ship more frequently, which helps increase both conversion and customer satisfaction.

 

Tell us about a recent project where your team used AI as a tool. What was it meant to accomplish? How did you use AI to assist?

While we’re using AI tools internally as part of our process regularly, we’re also highly focused on integrating AI into consumer-facing products for brands. One example is Gatorade. We partnered to bring generative AI-powered product personalization and customization to market with an interactive 3D customizer that lets athletes design their own unique, personalized Gatorade Squeeze Bottles. 

 

 “We partnered to bring generative AI-powered product personalization and customization to market with an interactive 3D customizer that lets athletes design their own unique, personalized Gatorade Squeeze Bottles.”

 

We used AI in a number of ways. Adobe Firefly helped generate nearly endless possibilities for on-brand artwork that gets printed on the bottles. Then, in a less obvious but still crucial way, tools like ChatGPT and Claude helped us enormously to prototype complex animations that required the shader code OpenGL Shading Language. Working with shader language can be challenging, and having “AI as a pair programmer” allowed us to quickly build the gorgeous multi-color gradient loading animation that users see while their AI artwork generates. The ability to have the LLM adjust code via natural language input came in handy as well. We could instruct it to “make the colors more vibrant” or “rotate the direction of the gradient animation by 180 degrees.” Thoughtful comments in the code helped us learn as we built.

 

What would that project have looked like if you didn't have AI as a tool to use? How has AI changed the way you work, in general?

It’s less about what was impossible to do and more about the endless possibilities that were unlocked. We had earlier built a bottle customizer for Gatorade, but by integrating AI and allowing customers to harness it for their own creativity, we super-charged personalization and ensured there was no limit to the bottle designs a customer could create. At the same time, AI enabled my team to move quicker and get faster feedback from designers and our clients. Ramping up work in new frameworks or languages previously came with a significant learning curve, and in my experience, the use of AI tools is now helping tear down that barrier. 

Despite some initial skepticism, there were a few “aha!” moments that changed my perspective on the benefits that AI tools bring to the way I work. ChatGPT is a go-to for all sorts of syntax questions or tasks like initial discovery for technical approaches. Needless to say, AI is not a silver bullet, and we certainly can’t vibe code our way to quality software, but our everyday work benefits from leveraging these powerful and never-tired helpers.