There’s no doubt that AI is changing the workplace, and for the following employees, it’s helping them solve real business problems both big and small.
For Krishna Ramaswamy, director of product, data science and analytics at spend efficiency platform Order.co, AI has transformed her data team’s weekly recap meetings, which used to require half a day of combing through Jira, GitHub, Slack and a dozen pipelines in order to provide a clear picture of what shipped, what was blocked, and why.
“Now an agent pipeline does the discovery,” Krishna Ramaswamy said. “It pulls delivery status from Jira, activity from GitHub, signals from Slack and our coding-session metrics via MCP; cross-referencing all and then drafting an evidence-linked recap.”
Half a day of compiling became minutes of editing for Ramaswamy. Now, the whole team starts each Monday with a clear picture of what’s going on.
Ali Hagen, senior product manager at employee experience platform Blink, uses AI for everything from summarizing meeting notes to visualizing product ideas and doing competitive research.
“When we are building quickly it’s been really helpful to use AI prototyping tools for updating visuals or thinking through a new feature,” Hagen said. “It’s great that I can make a real mockup or explore different variations myself before sharing with engineers and design to finalize the details.”
Hagen said that while it’s important to be critical of the AI output and ensure it’s aligned with the company’s goals, she appreciates that the technology gets her team farther faster while suggesting ideas she might not have thought of otherwise.
Built In spoke with tech professionals across industries and departments to get an inside look at how they are harnessing AI to solve business problems, improve efficiency and create better outcomes for customers and colleagues.
Trumid is a fintech company that brings technology and product design to corporate bond trading.
Can you share an example of how you’re using AI in your day-to-day work and what problem it helps you solve?
As an HR team, we use AI to do more than streamline our own workflows. We also help drive Trumid’s broader culture of AI experimentation and advance our AI capabilities across the organization. That means investing in the next generation of AI talent. This summer marked an exciting milestone as we welcomed interns into AI-focused roles for the first time.
One intern joined our AI development team, working across technology and product on product enhancements. Another, our AI automation intern, partnered across HR and technology operations, with one project focused on helping us build an HR bot designed to manage interview scheduling in real time, automatically triggering reminders and coordinating logistics between candidates and interview teams. It’s the kind of repetitive, high-friction process AI is built to solve. For me, that’s one of the most rewarding ways we’re using AI day to day. It’s not just about using AI as a productivity tool; it’s about giving people more time to focus on the conversations, relationships and experiences that matter — creating opportunities for emerging talent while empowering every team to discover new ways of working.
How has your company supported you in experimenting with AI or building new AI-powered solutions?
At Trumid, AI isn’t viewed as the responsibility of a single team. Everyone is encouraged to experiment, collaborate and solve problems, with AI, within the strict guardrails of a regulated industry. For HR, that means thinking beyond traditional recruiting and development paths. Our role is to help create an environment where AI talent can thrive — bringing together the right people and giving them opportunities to use AI in ways that can deliver tangible value.
That mindset extends to how we think about talent. Our AI-focused interns got right to work, contributing from day one. Rather than assigning isolated projects, we embedded them directly within teams, where they could identify opportunities, build practical solutions and improve existing workflows.
And that experimentation wasn’t limited to just our AI interns. Summer interns on our Sales teams were using AI too, building bots to automate repetitive work and proposing new efficiencies for their teams. It was exciting to see that kind of curiosity and creativity extend across roles. Trumid’s cross-functional approach has accelerated learning and helped teams discover new ways of working.
How has working with AI changed the way you think about your own career or future growth?
Working with AI has reinforced that the role of HR is evolving alongside technology. Historically, HR has focused on attracting, developing and retaining talent. Today, we’re also helping build the tools and culture that enable people and AI to work together — an evolution that Trumid has intentionally embraced.
That means thinking differently about the skills we hire for, how we develop employees and how we create opportunities for people to experiment responsibly with new technologies. It’s no longer just about adopting new tools. It is about continuing to build a workforce that is adaptable, curious and prepared for continuous change.
For me, that’s made HR more strategic. Our role is to help people grow alongside AI, while keeping human accountability at the center — equipping employees with the skills, mindset and opportunities to make a greater impact as the nature of work evolves.
NinjaHoldings empowers people overlooked by traditional financial institutions to take control of their finances via a full suite of digital banking and lending products.
Can you share an example of how you’re using AI in your day-to-day work and what problem it helps you solve?
We use AI pretty consistently in our day-to-day work efforts. Sometimes for smaller knowledge aggregating tasks such aggregating information about a particular framework or library. Sometimes for larger tasks such as big, tedious refactors when we know the solution we want, but don’t want to spend significant amounts of time manually updating files.
The biggest wins we’ve had so far are rapid prototyping and developer tooling. With the improvements in AI models, we’re now able to spin up additional tools that make our lives significantly faster and easier. We ended up spinning up an application relatively quickly that we use for team specific alerting and monitoring across several areas of our code base. This is something that would have required significantly more time to care and maintain without having something that could generate the bulk of the project for us.
How has your company supported you in experimenting with AI or building new AI-powered solutions?
Our company is fully onboard with our AI usage, providing accounts to developers it’s pretty hard to hit the cap on. We opened the floodgates relatively slowly during the initial roll-out, but now pretty much every developer is using AI tooling in some capacity. We have it running for immediate feedback on most pull requests and are encouraged to use it for analytics and to get ourselves unstuck when debugging/architecting.
That being said, we do encourage wise usage. Spinning up tons of agents and wasting tokens isn’t where we see most of our use. We typically try to keep it within the realm of raw prototyping to test if things work, or targeted research for solutions. Mostly just automating the annoying parts of software engineering away and speeding up our development cycle. It still has a bit of a way to go in terms of appropriate design patterns and architecture before we would be able to rely on it more for that.
How has working with AI changed the way you think about your own career or future growth?
A little bit. AI tools can do a lot, but they still make quite a few mistakes. I’m still happy with where I’m at in my software engineering career and I don’t think that will change. I’m still finding myself fixing issues or adding new functionality all the time. AI has been a tool that has helped accelerate what I was already doing.
It has shifted some things slightly. Mentorship is now much more important with the increase in development speed. Even though we’re faster, we want to ensure the solution we come up with is the correct one for us, not just what the model is recommending. It’s also changed the way I learn a bit. Things that required reading pages of documentation or several hours of browsing now usually get answered with a simple prompt, which has been a huge time saver.
Realtor.com is an open real estate marketplace.
Can you share an example of how you’re using AI in your day-to-day work and what problem it helps you solve?
AI is my starting point for a lot of tasks: writing code, ramping up on unfamiliar projects, managing Jira, drafting docs. It produces a solid first pass quickly, but still requires real refinement. Because I know the shape of what the solution should look like, I can iterate and guide it there fast. My role has shifted from producing everything from scratch to directing the work and applying judgment on the results.
How has your company supported you in experimenting with AI or building new AI-powered solutions?
They supported it by moving fast to get us access to new models and tools and making adoption an expectation rather than just a perk. Beyond just using AI day-to-day, I was encouraged and given time to actually build AI-powered tools internally. That made it easy to experiment without needing to justify the time.
How has working with AI changed the way you think about your own career or future growth?
It’s made me rethink where I add the most value. As AI handles more of the implementation work, the value shifts to understanding the business and the customer. The more you understand the domain and where the product is heading, the better your technical decisions are. AI compresses execution time enough that you can actually spend more of it there and I think that kind of “product engineer” is where things are heading.
Order.co is a spend efficiency platform that helps businesses automate purchasing, control costs and gain visibility into companywide spend.
Can you share an example of how you’re using AI in your day-to-day work and what problem it helps you solve?
My favorite example is our weekly team recap. The hardest problem on a data team isn’t writing code, it’s shared context — what shipped, what’s blocked and why. Historically, that has been scattered across Jira, GitHub, Slack and a dozen pipelines. Assembling that picture used to eat half a day and was still incomplete. Now an agent pipeline does the discovery. It pulls delivery status from Jira, activity from GitHub, signals from Slack and our coding-session metrics via MCP; cross-referencing all and then drafting an evidence-linked recap. Half a day of compiling became minutes of editing and every Monday the whole team starts from the same picture of reality. The pattern we profess is: agents automate discovery so humans start from synthesis instead of scavenging. We now maintain a shared toolkit of 50-plus agent skills, like triaging an ETL failure directly from a Slack alert, querying Snowflake through Cortex or authoring a PR automatically to our convention standards. New teammates can bootstrap all of this setup in two commands. The ultimate payoff is decreased time to delivery on everything we do: Work starts at “root cause found, fix proposed,” not “let me go look.”
How has your company supported you in experimenting with AI or building new AI-powered solutions?
The support has been comprehensive: from budget to trust and celebration. Everyone who wants them gets paid seats for frontier tools like Claude Code, Cursor or model API access. Usage is encouraged rather than rationed; AI-assisted work is expected to be the default operating model, not a nice to have. The deeper support is trust in production. Our team ships AI as core infrastructure: semantic search over a catalog of a million-plus products, LLM-powered extraction pipelines and AI built dashboarding. When your company lets AI touch production systems, with the guardrails to earn that trust, experimentation stops being confined to a sandbox activity. The experiments compound, too. We point agents at most open questions, not just engineering chores. One engineer went from a raw question to a first class analysis with every claim evidence backed and an executive-ready product brief in two weeks, work historically took months. Results like that drive roadmap decisions and the company gives internal awards for multiplier-style AI work. The result is culture rather than policy — nobody asks permission to try AI on a problem. They ask whether anyone has written a skill for it yet.
How has working with AI changed the way you think about your own career or future growth?
I started my career in finance 25 years ago with no technical background. I learned SQL on the job at TripAdvisor and for years my Python was, charitably, rough. Today, I ship hardened production systems — pipelines, agent harnesses, guardrail automations and its routine. Not because I finally became a great programmer, but because AI collapsed the distance between what I can judge and what I can build. Waiting on execution stopped being my bottleneck. That experience rewired how I think about growth. What we build is becoming a commodity. The same pipeline can come from a teammate, an agent or a fleet of agents running overnight. The durable skill is strategic thinking. Choosing the right problem, framing it precisely enough that any executor can nail it and designing the verification so you can trust work you didn’t do line by line. Those are the muscles I now train deliberately and the ones I hire for. The arc I lived, strategy brain first, technical skills late, used to be a handicap in engineering — AI flipped that. I truly believe that the future belongs to people who know exactly what to build and can now, finally, just build it.
Enverus is an energy SaaS company delivering highly-technical insights and predictive/prescriptive analytics for more than 6,000 customers in 50 countries.
Can you share an example of how you’re using AI in your day-to-day work and what problem it helps you solve?
Two ways — one for me, one for my teams.
My day swings between two very different modes. One hour I’m deep in a technical conversation with engineers about a design, a data model or a tradeoff. The next I’m zoomed out — sequencing work, thinking about where a platform needs to be two quarters from now, making a case to people who won’t touch the code. Both modes need me to arrive with something concrete and the slow part was always producing that first artifact. AI closed that gap. I use it to turn rough thinking into a real architecture doc, to pressure-test a design before I take it to the team, to digest research ahead of a decision. It doesn’t make the call. It gets me to the point where a call can be made.
For my teams, we’re evolving toward an autonomous development lifecycle — planning, implementation, review, testing increasingly agent-driven, with humans in the loop at every step that genuinely requires judgment. Work that used to be a two-week spike now yields a working prototype in a day or two.
How has your company supported you in experimenting with AI or building new AI-powered solutions?
First, tools. Enverus put enterprise AI in our hands early and properly — licensed, governed, secure — rather than leaving people to improvise with personal accounts. That sounds like a procurement detail, but it’s the difference between an engineer trying something on real work on a Tuesday afternoon and having to ask permission first.
Second, exposure. I’ve been supported in attending conferences and building relationships with people working at the frontier of this field. Some of our better architectural decisions started as a hallway conversation with someone solving the same problem elsewhere.
Third — and this is the part I find most energizing — we’re often the ones setting the direction rather than following it. We’re building solutions that don’t have an obvious precedent and then partnering with others so they can build on that foundation in their own products. Work that starts with a handful of engineers here ends up reaching a massive scale. That’s a rare thing to be handed early in a technology shift and Enverus has consistently chosen to lead rather than wait.
How has working with AI changed the way you think about your own career or future growth?
It’s changed what I think is worth getting good at. I’ve spent over 20 years as an engineer and architect and for most of that time my leverage was what I could build. AI made the first draft of nearly everything close to free — so the durable skills turned out to be the ones that are harder to name. Knowing which problem is the right one. Designing clean boundaries. Having the taste to tell a good answer from a merely plausible one. Depth still matters, but range compounds now in a way it didn’t before.
It also moved what I consider realistic. I’d wanted to write a technical book for years and never found the runway. This year I finished one. AI didn’t write it — but it let me organize a decade of scattered research, hold a long argument together across chapters and prepare material at a pace I couldn’t have managed alongside a full-time job. What closed wasn’t the thinking. It was the distance between having an idea and having something real and it closed for writing exactly the way it closed for engineering.
That’s what I tell engineers on my team now. The ceiling moved. Most people haven’t updated their sense of what they could take on.
New York Life Insurance Company is a mutual life insurance company in the United States offering life insurance, estate planning, wealth management and more.
Can you share an example of how you’re using AI in your day-to-day work and what problem it helps you solve?
Data scientists work on cleaning the data and you build AI models for predicting and monitoring. Every time we start building our models, we will spend a significant amount of time just cleaning and validating the data to make sure we have reliable results. Our business partners reach out to us to build AI solutions for the recruiters, because they want to increase agents recruiting retention.
I use ChatGPT for technical research, like how to design the model and then I build a structured model pipeline that I can use to build spot based stands for strategic pipeline in outcome tool. The tool is helping recruiters to get a sense on what kind of action to prioritize during their recruitment process. We want to increase the candidate engagement and increase the full time agent conversion rate.
How has your company supported you in experimenting with AI or building new AI-powered solutions?
We have model governance teams that are continuously monitoring the model’s performance. They make sure the result is valid and accurate. Having really positive feedback from the recruiter, they think that’s really helpful.
So before training, I assumed New York Life is very traditional because of its long history, but I was very surprised by how the company is dedicating and investing in AI. It’s helped me a lot.
I would recommend working at New York Life Insurance Company because as a data scientist, I can grow and learn along with AI.
Fluent is a commerce media solutions provider that connects brands with engaged consumers.
Can you share an example of how you’re using AI in your day-to-day work and what problem it helps you solve?
I’m a business intelligence engineer and a lot of my work involves tracing data discrepancies through a large, interconnected pipeline. I’ve started using AI to help with that tracing: instead of manually re-reading every stage, I walk through the logic with it and it helps me spot where the breakdown is actually happening. It hasn’t replaced the judgment part, knowing what ’correct’ looks like is still on me, but it’s cut down significantly on the methodical work that used to eat up hours.
How has your company supported you in experimenting with AI or building new AI-powered solutions?
Fluent has treated this as a skill to develop, not just a tool to hand out. I’ve had access to a connected AI assistant that can pull context from the systems I actually work in, so it’s not operating blind. My own usage patterns were also evaluated at one point — a useful check, since it’s easy to assume you’re using these tools well when you’ve only scratched the surface. That’s translated into real output: I helped take a new internal product from idea to deployment to reporting in a matter of weeks and separately led a rebuild of one of our core dashboards that’s now fully in production. Both moved faster than they would have otherwise, because I had the access and the confidence to try things without worrying it’d be wasted effort.
How has working with AI changed the way you think about your own career or future growth?
It’s shifted where I spend effort — less time on the mechanical parts of the job (boilerplate queries, repetitive formatting, first-draft documentation) and more time on judgment calls. That’s changed how I think about my own growth: less “learn more tools” and more “get better at the parts of the job that are hard to automate” — knowing which questions to ask, understanding the business logic behind the data, catching things that look fine but aren’t. AI raised the floor on execution speed. It didn’t lower the bar on needing to actually understand the system.
SOPHiA GENETICS is a healthcare technology company that provides software infrastructure to assist data-driven medicine as the standard of care and for life sciences research.
Can you share an example of how you’re using AI in your day-to-day work and what problem it helps you solve?
Like many people, I now use models from the frontier lab companies every day for work and personal purposes. The out-of-the-box capabilities save me significant time in almost everything I do.
But what I’m most excited about lately is the application layer of the AI stack. In other words, using AI models to build custom software and agentic systems for myself, to automate the processes I know well. The AI-native softwares and systems we are building are flexible enough that they also learn over time, getting smarter with each use.
As an example, the strategy team at SOPHiA has used AI to build a sales assistant app that is connected to a rich repository of SOPHiA data, including product specs, sales trainings and even live insights from customer interactions across our channels. Salespeople at SOPHiA can use the app during meetings to get immediate answers to questions. In other words, we’ve agentified the most experienced salesperson at our company and put a digital version of them into the hands of all of our employees.
How has your company supported you in experimenting with AI or building new AI-powered solutions?
At SOPHiA, I believe we are truly cutting-edge in our enterprise AI approach, potentially even in the top percentile in terms of AI efficacy.
I believe our success has come from three places, each of which are unique to SOPHiA.
First, the company has always been extremely diligent about documentation. This is partly due to working in healthcare, but also because we’re a Swiss company that loves precision. In the AI age, proprietary company knowledge is more important than ever. For decades, company knowledge has lived in unopened powerpoints, documents, code repos and manuals. Now, AI converts that unused knowledge into a living tool, accessible by all.
At SOPHiA, we have connected 10,000-plus pages of internal documents and all of our internal systems to Claude. This provides an incredible context window for AI, enabling every employee to access and build upon historic knowledge.
The second reason is our employees. Because SOPHiA itself is an AI company, roughly 120 of our 400 employees hold PhDs, many in AI/ML. Giving a young, AI-centric workforce a strong foundation has led to great results.
Last, our management team has been very supportive of AI and this driver can’t be understated.
How has working with AI changed the way you think about your own career or future growth?
I believe AI, at its core, removes boundaries. At some point, we’ve all felt like we don’t have the right technical skills, are missing a certain expertise, or just don’t have enough time. AI removes a lot of those constraints and that’s exciting.
At the same time, I sympathize with those affected by AI-related job loss. I think it’s a real issue that needs to be solved urgently by regulators.
In my career, I will focus on using AI to help companies that are doing good things for the world. At SOPHiA, our mission is to expand access to best-in-class care for cancer patients and those with rare diseases. We are using AI to do that by analyzing data that is impossible to comprehend without advanced analytics and AI tools. In that way, I feel like we use AI internally and externally to make the world a better place.
Blink’s mobile-first employee experience platform leverages AI to support employers across industries, ranging from healthcare and logistics to retail and construction.
Can you share an example of how you’re using AI in your day-to-day work and what problem it helps you solve?
I’m using AI everyday typically, from getting summarized meeting notes to visualizing product ideas and doing competitive research. When we are building quickly it’s been really helpful to use AI prototyping tools for updating visuals or thinking through a new feature. It’s great that I can make a real mockup or explore different variations myself before sharing with engineers and design to finalize the details. While it’s important to be critical of the AI output and ensure it’s aligned with our goals and thinking, it can get us farther faster and sometimes provide ideas we weren’t otherwise considering.
How has your company supported you in experimenting with AI or building new AI-powered solutions?
At Blink, we are definitely encouraged to use AI tools in any way it can help us create more impact. I’m grateful to not be worried about hitting limits in token usage or being restricted to one tool. Besides the obvious LLMs, it’s neat to try AI features as well in tools like Gong, Notion, Google docs, etc to make it easier to find the information you need. While we're building a new product with a lot of focus on the core functionality at the moment, we are also encouraged to innovate with AI. I love that we are excited to build advanced technology, but stay true to solving the most important problems for our users.
How has working with AI changed the way you think about your own career or future growth?
It’s exciting to me that AI can take away some of the tedious parts of the job, so we have more time to focus on what we should build to delight and positively impact customers. I’m looking forward to focusing on product craft, understanding our users and building in a more hands on way. As a product manager with a technical background, I enjoy how AI has brought me even closer to the development process and increases the value of human connection and thoughtfulness. I’m happy to be in this career where connecting the dots and seeing the bigger picture above the execution level is where the magic happens.
NetBox Labs makes it easier to build, run and govern complex networks and infrastructure for both humans and AI agents.
Can you share an example of how you’re using AI in your day-to-day work and what problem it helps you solve?
I use AI regularly throughout my day and a lot of the time, I use it as a brainstorming partner. Whether I’m preparing for a customer meeting, trying to better understand a technical topic, organizing my thoughts, or figuring out the clearest way to explain something to a customer, it helps me work through ideas more efficiently. It’s especially helpful when I need to take something complicated and make it easier to understand. For me, the biggest benefit is the time it saves. It helps me get through research and prep work faster, which gives me more time to focus on my customers and building relationships.
How has your company supported you in experimenting with AI or building new AI-powered solutions?
My company has been extremely supportive of using AI and encourages us to be creative, curious and willing to experiment with it. I appreciate that it’s not just about being told how to use AI. We’re given the freedom to explore where AI can have a meaningful impact on our work and the way we support our customers. There’s also a strong culture of sharing what we’re learning with each other. As people discover new ways to use AI, those ideas naturally get shared across the team, which encourages everyone to keep exploring.
How has working with AI changed the way you think about your own career or future growth?
It’s made me really excited about where customer success is going. AI can help with some of the more time-consuming parts of the job, giving me more time to focus on what I enjoy most: getting to know our customers, understanding their goals, solving problems and helping them get the most value from our products. It’s also made me realize how important it is to keep learning and evolving. I want to continue finding new and better ways to use AI so I can be more proactive, more strategic and ultimately a better partner to our customers.
Zscaler is an information technology and cybersecurity company that is distributed across more than 160 data centers globally. The Zscaler Zero Trust Exchange platform combined with advanced AI combats billions of cyber threats and policy violations every day and unlocks productivity gains for modern enterprises by reducing costs and complexity.
Can you share an example of how you’re using AI in your day-to-day work and what problem it helps you solve?
In global business systems support, delivering enterprise-grade solutions hinges on cohesively sequenced and complex product roadmaps and the prescriptive product requirement documents behind them. To accelerate the PRD cycle, I built the “PRD Professor” Agent. It is derived from internal data sources such as Confluence, Google Drive and brand-guidelines.
Rather than requiring me to manually draft documentation in a silo, this represents a true “Human + AI” collaboration. The AI agent serves as a co-pilot for co-creation. It handles the heavy lifting of data synthesis and rapid scenario simulation, while I provide the uniquely human elements: strategic intuition, stakeholder empathy and contextual judgment.
By compressing the friction between design and execution, I can bring multiple iterated- solutions to the table in a fraction of the time, reducing business cost, while driving faster value delivery for my customers.
How has your company supported you in experimenting with AI or building new AI-powered solutions?
Zscaler actively fosters an environment of co-elevations by providing business users like me with top-tier models and tools, allowing us to not only experiment with AI but also deploy safe, secure solutions that drive initiatives to real-time business objectives. Learning opportunities are ever present. We have vibrant communities across Slack and other platforms to connect with builders from all domains, cultivating a diverse and collaborative ecosystem. Additionally there have been several opportunities to participate in larger scale workshops, which are broadcast company-wide. This is a true reflection of being committed to each other. We actively build strong, collaborative relationships that ensure everyone is supported and empowered to succeed.
How has working with AI changed the way you think about your own career or future growth?
Working with AI has fundamentally enhanced my ownership and accountability, specifically by reducing the friction of the initial launch phases of new initiatives. Like many professionals, entering a new or highly specialized domain can trigger a sense of imposter syndrome or self-doubt due to a lack of exposure or expertise. While AI doesn’t completely eliminate these challenges, it significantly mitigates the intimidation factor associated with them.
AI acts as an objective sounding board, enabling me to identify errors much earlier and getting me to a high-quality MVP faster. These early experiments save substantial time and effort, ultimately resulting in higher-quality outcomes from the outset. Ultimately, using AI accelerates our “ways of working” by allowing me to execute with urgency, build trust through real impact and maintain a constant focus on the mission.
Confido is building an AI operating system that replaces manual work with AI agents on a single intelligent platform.
Can you share an example of how you’re using AI in your day-to-day work and what problem it helps you solve?
In my day-to-day, I use AI to handle all of the boilerplate or syntax-heavy work that used to require hours of sifting through docs. It goes without saying, but AI also allows me to be much more efficient when coding, since it can work faster than I ever could. From a user-facing perspective, AI allows me to accurately pinpoint and triage common problems reported by users, letting me solve real world problems much more efficiently. The increased development speed that comes with AI is great, but your code can quickly become a big ball of mud if you’re not being careful. I’ve found it increasingly useful to use AI to help me manage my projects, effectively acting as a project manager on my behalf. I use AI not only to help me code, but also to help me manage multiple projects at the same time.
How has your company supported you in experimenting with AI or building new AI-powered solutions?
Confido has been super supportive with experimenting with AI and the vast majority of projects that I work on have been directly related to building new AI-powered solutions. Recently, I’ve been working on building an AI agent called Fido to allow users to interface with the application in natural language. Beyond just the product side, Confido has been very supportive when it comes to using AI to build internal dev tools. I have agents running that scrape error logs, summarize common bugs encountered in the app and deliver daily engineering reports to me via Slack. I really like the fact that Confido lets developers experiment with new models. While we typically use Claude Code in our day-to-day, Confido lets us try out and compare new models to those we currently use. This has allowed me to stay at the cutting edge of the LLM landscape.
How has working with AI changed the way you think about your own career or future growth?
As AI models have become more and more powerful, the actual day-to-day work of a developer has fundamentally shifted; the era of the “code monkey” has come to an end. It’s really hard to predict where things will lead, just as it would have been really hard to predict where the models would be now just a few years ago. Outside the doom and gloom possibility of AI taking over everything and eliminating all humans (the paperclip maximizer), I view the changes that AI has brought to the industry as a jump to a higher layer of abstraction. Programming started with physical punch cards, moved to writing in assembly, then to low level programming languages, then to higher level languages with greater abstraction. I think AI is the next logical progression of this trend. While I don’t know where things will be in a few years, I’m confident that being able to think at the systems level will be increasingly important, along with the ability to have good design taste and make informed decisions around business context that an AI won’t be able to do.
