Working on a team where you get to use cutting-edge technology can make all the difference — now more than ever.
AI, for example, is already impacting the job landscape, transforming everything from IT jobs and programming roles, according to a recent Anthropic study. Skills like cloud architecture, data security and AI data management are vital developments needed in IT roles. Upskilling is needed across the board though. According to the World Economic Forum, 59 percent of the global workforce will need to upskill by 2030, which includes adopting new technologies.
That’s why it’s never been more critical for tech professionals to find roles that prioritize learning and development, offering employees the time and resources to upskill with new technologies.
Built In spoke with tech professionals across engineering, product and AI roles to see what tools their teams are using to work smarter and faster, and to learn how they are adapting to a changing work environment.
Featured Companies
Gusto provides small businesses with an HR, payroll and benefits platform that assists with team management.
What tools support your day-to-day work?
As a sales leader, I'm constantly looking for ways to make our reps more successful with AI, so the tools I care about most are the ones in their hands. Our reps have copilots that put the right product guidance and customer context in front of them at the exact moment they need it on a call. We've also built a specialized agent that takes on the heavy data, transfer and quality-control work and does in minutes what used to take days, sometimes weeks, so reps spend their time selling and helping customers instead of buried in setup. For me personally, AI is my default tool, not a novelty. I run most of my day through Claude and Cowork, building dashboards, pulling our onboarding data, writing skills that match how my team actually works and turning customer calls into voice-of-customer insight. Less time hunting for answers, more time helping the customer.
How does your team experiment?
We bias toward building our own small version fast and learning in days, not quarters. When someone has an idea, the question isn’t “Is it perfect?” It’s “Can we build a rough version this week, get in front of representatives engaging with customers and start iterating now.” We keep the blast radius small, watch what happens and scale only once the results earn it. A lot of our best improvements started as a scrappy experiment someone ran without waiting for permission. Our live coaching agent is a good example. It's an AI guide that sits with a rep on a call and helps in the moment. We built early versions, handed them to reps and team leads to extend on their own, then took the sharpest ideas from across the whole group and combined them into one master version. The frontline basically co-built the tool.
How does your company adapt to change?
At Gusto, change is the job, not an interruption. The clearest example is how fast we moved on AI. The moment the tools were in our hands, my team didn't wait for a playbook. People just started building and within weeks we'd created dozens of agents and skills across the team. The real test wasn't adopting AI, it was organizing it. We were moving so fast that people were building overlapping versions of the same thing. So instead of slowing anyone down, we gave the work one north star: make our reps dramatically better at getting a customer to their first payroll. Every build now plugs into one connected system, a single onboarding brain, instead of living as a scattered bot. That shift, from a pile of tools to one system, is what's actually moved our numbers. The lesson for me is that the pace is the point. You adapt by building, not by waiting.
PatientPoint delivers digital in-office patient education at no cost to physicians, empowering healthcare professionals to connect more meaningfully with their patients.
What tools support your day-to-day work?
No conversation today about making employees more effective and more satisfied in their roles would be complete without talking about the impact of AI. We've leaned heavily on the Anthropic Claude platform across the company, giving full access to everyone in both technical and non-technical roles. We're in the early stages of using these powerful tools to cut down on the number of apps people need to log into throughout the day and to reduce the constant context switching (and the drain on productivity) that comes with it. Learning how people use these tools helps inform us about gaps in processes, training and features we need to address to make people more productive and satisfied.
How does your team experiment?
Keeping with the AI theme, every day is an experiment because the technology changes so quickly. What was impossible to do a couple of months ago is now a feature everyone takes for granted. One of the beauties of today's models is that you can always just "try it." People ask me all the time if AI can do a specific task or answer a specific question and the typical answer I give is "let's find out." Of course, if you're not careful, AI can give you very confident and very wrong answers, so we need to guard against this when working with new capabilities.
How does your company adapt to change?
Every piece of software our company licenses and the way we use it, has changed more in the last year than in the previous five. It's all been a matter of adapting to the realities of AI through some combination of adding AI-focused features to existing tools, creating new features from scratch, or moving on to newly developed and more capable products. Training is the key here. We moved our enterprise from OpenAI (ChatGPT) to Anthropic (Claude) to leverage more of the features Claude offers for non-technical users, while also aligning with the heavy Claude Code usage among our engineers. While it sounds like a simple "lift and shift," it has been a more involved process with a heavy focus on training people from scratch to ensure a firm foundation. Ongoing training is the key to continuously adapting to changes and our program focuses on these sessions every bit as much as one-off initial training.
Academia.edu is a platform for researchers and scholars to share their work, discover research and connect with fellow academics globally.
What tools support your day-to-day work?
My daily toolkit is focused. Jira for engineering tickets and for coding I've landed on Claude Code and stayed there. I work with a lot of inertia so once I find something that works I'm like, okay, I want to stick to this.
That said, I have to be very skeptical about the output. The scary part is the results that look almost right, but then there's something very subtly wrong about it. My rule is I'll never trust output until I've verified it myself.
If I'm writing a SQL query and having AI assist me, I'll make sure that the results look like what I think they should look like. If someone else has already created a dashboard I'll make sure my query has the same results as that dashboard. It depends how high the stakes are. If you're vibe coding a productivity tool for yourself it's not really going to matter if it's slightly wrong. But if the company is making a decision based on it, it's worth putting in that extra effort to verify it. If I'm ever going to show my work off to someone else, then it's probably worth putting in that extra effort to make sure that what it actually told me is correct.
How does your team experiment?
I'd say it's semi-structured. We've done a lot of experiments where it's like, okay, for this week or two, you're going to try out this new workflow. But I say semi-structured because usually there's not a specific metric that we're tracking. It's more vibes based. Do you feel like you were more productive? Did you enjoy the experience? What things worked well? What things didn't work well? It's a more qualitative evaluation overall.
Especially this year, there's been a lot more people taking initiative and experimenting on their own. Some team members gave a presentation on their AI workflow or how they’re using AI. I even gave a presentation on my own experience. There's been a lot of people just kind of doing stuff on their own, figuring out how it works for them and then sharing it. Academia has a supportive environment to do that sort of thing. Everyone has their own unique workflow, so I ask, "Oh, what are you doing there? That's cool, I haven't seen that before." There's a good mix of styles and tools.
How does your company adapt to change?
It's tough because you have to find a good balance between doing your day-to-day work and taking time to explore and adapt your workflow. Especially today where the tools that are available change really, really quickly. You have to be a little curious.
When we first started experimenting with AI-assisted coding as a team, we were like, "Yeah, we're running this experiment, we want everyone to try this on a temporary basis. We are understanding of the fact that that might cause a slowdown in productivity." That was a good way to approach it. Everyone had a chance to experiment with the new tools and there wasn't immediate pressure to meet deadlines. There was an expectation that you were trying out new things. That protected time to experiment is really nice.
I attended a couple of AI coding workshops and they were going over a lot of basic stuff that I felt like I already knew. “Oh wow, a lot of these people haven't even tried this before.” Whereas at Academia, we had experiments that were kind of baked into every engineer's workflow. My experience has been that my company is staying at the forefront, experimenting with new things and is willing to adapt and change.
Order.co is a spend efficiency platform that helps businesses automate purchasing, control costs and gain visibility into companywide spend.
What tools support your day-to-day work?
Across the company, teams leverage AI tools like ChatGPT and Claude to accelerate research, content creation, problem-solving and workflow design. Recently, Convey has become one of the most impactful tools in our day-to-day work, empowering technical and non-technical employees to create AI agents that can automate processes that previously required significant manual effort. The biggest benefit on top of productivity is giving people more time to focus on strategic, high-value work rather than repetitive tasks.
How does your team experiment?
We take a hands-on approach to experimentation by encouraging every team member to explore the automation and AI tools available to them. Rather than limiting innovation to a single team, we empower individuals to identify inefficiencies, test solutions and share what works. We've already seen significant results, from eliminating time-consuming manual processes to creating entirely new capabilities. One example is our design team building a custom Claude skill that can instantly apply our brand standards to internal content and external assets, saving time while maintaining consistency.
How does your company adapt to change?
One of the reasons we're able to adapt quickly is that change often starts with grassroots adoption. We equip teams with the right tools, encourage experimentation and create opportunities to share successes across the organization. A recent example was our transition to a new HRIS platform that consolidated multiple people systems into a single experience. The platform's built-in AI capabilities allow employees to quickly access information, complete common tasks and find answers independently, improving efficiency while creating a better employee experience. By pairing new technology with employee-led adoption, we've been able to drive meaningful change across functions.
ChowNow is a technology platform connecting diners and independent restaurants for at‑home dining.
What tools support your day-to-day work?
As a product manager at ChowNow, I'm fortunate to work with a fantastic toolkit that genuinely accelerates my work. Claude is my go-to AI tool for documentation generation, prototyping user flows, research and discovery, brainstorming and acting as a thought partner when I'm working through complex problems. Gamma is an AI-powered tool that helps me craft compelling presentations and documents for stakeholders. Google Docs for real-time collaboration and document creation. Confluence is our hub for documentation and process management. Jira is for story tracking and prioritization. Gong is an invaluable resource for listening to user support and sales calls, giving me direct insight into what our customers actually need. Slack is the connective tissue for cross-team communication and collaboration
What I appreciate most about ChowNow is that I've always felt supported with the right tools for the job. Having access to cutting-edge AI tools in particular has meaningfully boosted my velocity and helped me show up as a stronger, more creative contributor.
How does your team experiment?
Experimentation is core to how we operate — especially on our growth team, where instrumentation and a structured approach to testing are essential for learning what moves the needle.
Our process typically starts by clearly defining the problem, aligning on our hypotheses and assumptions and mapping out potential solutions. Then we move fast. What makes this especially exciting is that it's a truly cross-functional effort between: product, engineering, marketing/go-to-market, sales and CX all come together around a shared goal. We've built a genuine culture of experimentation where curiosity is encouraged, learnings are celebrated and iteration is the norm.
How does your company adapt to change?
ChowNow adapts to change by staying close to our customers and acting on what we learn quickly. We're strong advocates for our restaurant partners and the internal teams who support them, like support and restaurant success.
One moment that really stood out to me: we joined a customer roundtable to hear directly from restaurant partners about pain points around their Marketing Onboarding experience (a new product-line we launched in April 2026). We listened deeply, got to the root of the problem and then came back to them — not just with ideas, but with a fully functional prototype. Seeing their reaction to that turnaround was a powerful reminder of what's possible when a team takes genuine ownership and moves with urgency. That's ChowNow in action.
Hireology’s HR technology empowers businesses to build great teams.
What tools support your day-to-day work?
We really lean on a few core tools. Glean brings all our data sources into one place, like sales calls, Jira tickets and customer feedback. That visibility helps when we're deciding what to build next.
Claude is our primary LLM for development and it's deployed across teams. We use Cursor as our integrated development environment, it gives us the flexibility to branch out to other LLMs when we need them for specific things.
And then for design, we're using Builder.io with Claude Design and Claude Code to build the interfaces. It's a pretty smooth flow from design to actual implementation.
So it all connects. The data informs what we're building and we've got the tools to move quickly through the whole process.
How does your team experiment?
The way we approach experimentation has really shifted. We used to spend a lot of time on design upfront, creating mockups, getting customer feedback, and iterating on comps. But as the cost of building has come down, we've basically flipped the script.
Now we build the actual product and show it to customers while we're working on it. We demo something live, get real feedback on the working product and then rebuild based on what we learn. It's so much faster, it used to take three months just in the design phase. Now we can get something built and in front of customers way earlier.
And because rebuilding is cheaper now, we're not as worried about getting everything perfect the first time. We're running more experiments, iterating faster and honestly, we don't really need that long upfront design phase anymore.
How does your company adapt to change?
The AI revolution has been the biggest change we've seen in software design in my 20-plus years of experience. Rather than mandate how we use it, we've focused on giving people the space to grow and discover what's actually possible.
We do a lot of open conversations, on-sites to gather as a team and dedicated work weeks for experimentation. We'll give teams a full week just to try things without pressure, that's when the real learning happens. People find new approaches, new opportunities we wouldn't have thought of otherwise.
We have them study, share findings back with everyone and build new ways of working collaboratively. Getting buy-in from key people early creates momentum.
The philosophy is simple — if you give people the tools and the space to experiment, they'll find growth opportunities that benefit the whole team.
AcuityMD’s intelligence platform is designed to help medical technology companies identify target markets, surface top opportunities and grow their businesses.
What tools support your day-to-day work?
At AcuityMD, our customer success team has quickly become AI-enabled — we leverage Claude and the latest AI technology to run agents and workflows that keep us efficient and focused on client-facing work rather than busy work. Beyond AI, our core stack includes G Suite, Rippling, Planhat, HubSpot, Notion (including Notion AI) and Amplitude.
How does your team experiment?
One of the best parts about working at AcuityMD is the freedom to lean into the cutting edge of AI technology. Our customer success team has built out a roadmap of skills and agents specifically designed to help us experiment with AI in our roles — identifying what delivers the most value and doubling down on it.
A big part of our experimentation is finding the right blend of AI and human touch. We use AI to drive efficiency, but we're intentional about preserving the white-glove experience our clients expect — making sure they feel heard, understood and well-served. That balance also generates better signals for our product team, so client insights flow back into product improvements.
We also run A/B tests on new outreach methods and client engagement approaches, which helps us continuously refine how we support customers and drive adoption. AcuityMD is genuinely one of the best companies I've worked at when it comes to creating space to try new things and move fast.
How does your company adapt to change?
Adapting to the AI era is critical for any SaaS company and AcuityMD takes it seriously. Our mission, helping medical device companies bring innovative technology to market and ultimately improve patient outcomes, only succeeds if we evolve as fast as the world around us.
A great example is how we've rapidly enhanced our product with AI chat and agent functionality. What was already a best-in-class, intuitive interface now includes an AI layer that turns what used to take minutes into seconds giving our clients instant answers on procedure volumes, patient data and market insights so they can launch products faster and more confidently.
The second example is internal: we haven't just adopted AI as a chat tool, we've built a deliberate, agent-based approach to how we work. Our customer success team now uses AI agents to generate client-facing deliverables, score account health and surface insights about where clients need support. This isn't about replacing the human relationship, it's about making every interaction more informed, more efficient and more impactful so we can serve our clients at a higher level.
Agero is a digital driving assistance company that provides accident management, consumer affairs support and connected vehicle services to customers.
What tools support your day-to-day work?
The most impactful tool I've been using lately is AI. I see it being utilized more and more throughout the company and it definitely has had a huge impact on my workflow and productivity. Specifically Claude. Other tools we use are Zoom, Slack and G-suite for collaboration and staying in the loop. Github and CircleCI for reviewing code and ensuring quality stays high. Datadog and Rollbar for monitoring for issues and tracking down problems.
How does your team experiment?
We built an in-house experimentation engine. It allows us to set up different behavior across different populations and compare the results. We feed the results into Sigma which allows us to get impact on our core business metrics. Generally we put any new functionality behind this system as it allows us to turn it on/off easily.
How does your company adapt to change?
Change and adaptation are part of the job. We monitor our experiments and performance and pivot if things aren't going the way we expect. We try to stay current on the latest technology within our company (For example, we are constantly upgrading and migrating to the current best practices). We also look external for inspiration. I was part of the Swoop acquisition, which marked a huge change to how the company's technology operated; and was driven in part to absorb the technology offerings Swoop had. It also meant the entire company needed to adapt our communication and processes to fit one another. The recent Urgently acquisition is another such transition point, we absorbed urgently in part so we could learn from a different seasoned company in the space.
Simply Business is a digital insurance brokerage that allows small business owners to find and buy the insurance they need.
What tools support your day-to-day work?
As a product manager, my day-to-day work requires me to use a multitude of tools so that I can dig deep into customer needs and design the best solutions. Amplitude, Chattermill and Snowflake allow me to track customer interactions and review their feedback, giving us a better understanding of our customers and their behaviors. Figma, Miro and Gemini give me the ability to design and iterate on solutions as I gather information around a customer need and its possible solutions. These tools also allow seamless collaboration with my colleagues, as a crucial part of product management is bridging the gap between the business, customer and engineering. When working with engineering, Jira allows me to communicate requirements and feature expectations to developers to ensure smooth delivery.
How does your team experiment?
In the digital world, improving the product and customer experience is a must. By using data as our compass, we can lead change with confidence, knowing our improvements are truly helping the customer. To do this, we experiment with the customer experience using Amplitude’s A/B testing features. This allows us to test different solutions and track how successful they are with customers. An unsuccessful test isn’t a business failure — it’s knowledge that leads us to better solutions in the future.
How does your company adapt to change?
Simply Business is great at adapting to change. As AI began to boom across the technology industry, Simply Business was fast in adopting AI tools in an effort to drive employee efficiency and excellence. Gemini and Claude are used frequently, but we’re also testing out other AI tools like Google AI Studio and Figma Make to see what works for us as a business. What’s more inspiring is that Simply Business went a step further and offered learning and development courses for employees to learn how to utilize those tools to the fullest, not only for business improvements but really honing in on employees’ personal growth.
DigitalOcean is the “Inference Cloud” — a full-stack, production-ready cloud platform built to run AI applications with predictable performance, sustainable economics, and simpler operations at scale.
What tools support your day-to-day work?
We run the pillar systems you'd expect: finance, HR and CRM, plus Slack and Jira for collaboration. But unlike other companies, we're rethinking the whole stack. DigitalOcean’s own inference cloud, along with AI agents like Claude and Cursor increasingly sit between employees and their underlying systems as a personal interface layer. Any well-built tool becomes interchangeable at the experience layer, as long as it surfaces its data through granular APIs or MCP, because the employee's real interface is their agent.Employees working at DigitalOcean are uniquely positioned here. We already have the infrastructure expertise and engineering discipline to run our own stack, so we’re free to explore moving away from configuration-heavy SaaS toward open-source, API-first, agent-ready tools. For the employees we support, this means they have more control to define how our tools can work best for this. Through natural language prompting, skills to automate repetitive tasks and AI-assisted coding to help them build micro-apps, they are in the driver’s seat for how their work gets done.
How does your team experiment?
We experiment aggressively in a structured way. Before any tool or workflow reaches a wide audience, we pilot it with a smaller group on real work instead of a hypothetical test case. That lets us see how something actually performs in the flow of the business and it keeps the stakes low: if it doesn't earn its place, we've learned that cheaply and moved on. What matters as much as the pilot is what happens after rollout. We don't treat "shipped" as "done." We test and learn, pulling feedback directly from the teams using the tool, because the people doing the work are the ones who know whether it's actually making them faster or just adding a step. That feedback loop is what tells us to double down, adjust, or walk it back. The result is that adopting something new doesn't feel risky or top-down. People trust that we've tested it, that their input shapes it and that nothing gets forced on them just because it's new. That's what keeps the team willing to try the next thing.
How does your company adapt to change?
The clearest example is the work leading up to our Deploy conference earlier this year. It became clear from customer conversations and signals from the market that it was time to reposition DigitalOcean as an AI-Native, Agentic Inference Cloud, which was a bold shift from how we operated previously. Once the decision was made, the most striking part was how quickly everyone aligned. Teams across the company re-prioritized roadmaps and rowed in the same direction, even when that meant setting aside their own plans for the bigger goal.
Adapting to a change that size isn't something one team can do alone. It works because people trust each other enough to move together, fast. The payoff was real: we shipped more products and features than ever before and meaningfully shifted the company's direction, both financially and technically. At DigitalOcean, we know that change is necessary and that this kind of growth mindset, paired with genuine collaboration, is what lets us do the best work of our careers.
NetBox Labs is an AI company that makes it easier to build, run and govern complex networks and infrastructure, for both humans and AI agents.
What tools support your day-to-day work?
My day-to-day workflow starts with a team meeting in Zoom, recorded by Fathom, processed by Claude with meeting notes automatically added to Notion and tasks added to Linear. I proceed to develop with Claude Code in Cmux (though I still use VS Code to read and review code) and manage any asynchronous conversations through Slack. I keep a personal wiki, managed by Claude, with summaries of useful articles, progress on my work projects and other internal notes. We have also just launched an internal agents platform that includes an agent-native data warehouse of our internal business knowledge (Grid) together with a reasoning agent that we interact with in Slack (Flynn). Many of my workflows are now managed by Flynn and Grid gives me quick and searchable access to best practices other engineers are developing in their repositories as well as updates across the company.
How does your team experiment?
Being the AI team, we are constantly experimenting with how best to drive agentic development practices for our team and the company. Everyone is figuring this out and there are new ideas almost every day. We document our learnings and mistakes so they are shared across the company. Failures are first-class findings and we aren't afraid to share them. I run an augmented engineering guild which meets every week to share new ideas and try new tools as they come out. Team members are encouraged to explore and report back to the group. Recent findings range from directory-specific prompts performing ten times better than one monolithic config file to a runaway test-driven development session that burned $250 in tokens. Both are now part of our shared playbook. Our internal agents platform has been one of our biggest experiments and has taught us a lot about designing and operating one, which will lead to future product development work for our customers. On a more structured note, all of our AI products are shipped with an evaluation suite. This allows us to confidently experiment with different model types, prompts and tools to ensure our customers get the best experience.
How does your company adapt to change?
We have the benefit of being nimble and adapting to change quickly. Which is fortunate since the AI landscape shifts every few weeks. Change isn't an event, it's a steady state and we build our processes around it. There are two dimensions here that I face on a regular basis: the products we build and the tools that power them. On the tools side, I learned the 'bitter lesson' of agent engineering first-hand, over-engineering a harness around one model's limitations only to have to back that work out when a better model arrived. At NetBox Labs, we now expect models to improve and keep evaluations in place so we can assess new models and deploy them quickly when they are released. On the product side, we are constantly listening to customers and the community and move quickly to get new products out there into our customers' hands and then iterate on their feedback. We have a publicly defined product lifecycle that allows us to release and adjust as the market shifts.
