If you were to look at a graph showing the rate of change for AI technology, you would see that it has gone from a steady climb upward to a steep, steep incline.
Today, the accelerating rate of change is a point of contention among AI thought leaders.
Some believe that generative AI will soon reach a ceiling when it comes to capability, while others think AGI is just around the corner. But no one feels the pace of transformation more than developers who are building AI today.
Built In spoke with four tech leaders who shared how they are staying aligned with the latest tech, theories and approaches to data management in an unprecedented race to the top.
Workhuman provides SaaS HR solutions that help companies meet today’s biggest human capital challenges.
What is the unique story that you feel your company has with AI?
At Workhuman, we believe the future of AI is Human Intelligence™, which combines rich human data and modern AI to create more meaningful, people-first outcomes. Our journey with AI began over a decade ago as the world’s largest database of social recognition moments beginning with the use of applied techniques like machine learning and natural language processing. These techniques power features like Work Circles, which help personalize recognition feeds and Inclusion Advisor, which coaches users on removing unconscious bias from recognition messages.
With the launch of Human Intelligence™ and innovations like Workhuman iQ and our AI Assistant more recently, we are entering a new generation of insights. AI is no longer just analyzing data. It is amplifying the connection, appreciation and growth that make work human.
What was a monumental moment for your team when it comes to your work with AI?
The most exciting moment is happening right now. We are combining the power of large language models with decades of insights from our client’s social recognition programs and Workhuman’s knowledge base of thought leadership, best practices and platform expertise accumulated over our 25-year history. This gives leaders deeper visibility into their people and culture with clear and actionable next steps.
Imagine seeing which behaviors are most recognized across your team, what skills are emerging and how to lead performance conversations grounded in honest feedback from peers and leaders across the organization. This is not based on gut feeling or recency bias but on repeated, validated signals. That is what we’re delivering with Human Intelligence™ and it is what drives our team forward every day.
What challenges did your team overcome in AI adoption?
One of my team’s biggest challenges was not technical. It was the fragmentation of information. Customer and market feedback was scattered across slide decks, spreadsheets and personal notes. We consolidated everything into a single repository, applied consistent tagging and began using AI to identify common themes and patterns. What once took hours of manual effort now takes a fraction of the time and gives us a clearer picture of what matters most. We still have an opportunity for optimization of the tools we use and our processes, but as AI advances and our utilization matures, we’re better positioned to reduce manual effort and create greater strategic value.
We’re also beginning to use AI to streamline internal documentation and enablement content. Tasks that used to take a full afternoon can now be completed in a matter of minutes. My team works across product, engineering, marketing and our go-to-market teams. We constantly learn from one another, share what’s working, what’s not and challenge each other to better leverage the technology we have at our disposal, especially AI, to reduce effort and increase impact wherever we can.
Smartcat’s client-tailored language AI turns content in any format into any language, from documents to videos to complex websites and software, making global operations simple for any corporate team.
What is the unique story that you feel your company has with AI? If you were writing about it, what would the title of your blog be?
At Smartcat, our AI journey is unique because we operate at the intersection of implementation and innovation. We leverage AI not just internally to optimize our processes but as the core of our multilingual content delivery platform. Our mission is democratizing complex technologies for enterprise users — creating solutions that “just work” despite the sophisticated AI architecture underneath.
We enable translation of documents, files, video, audio and images, with AI enhancing each use case. However, we recognize that reliability trumps novelty in enterprise environments. That’s why we’ve built human review workflows alongside our AI capabilities, ensuring quality when perfection matters.
If I were writing our story, I’d title it: “Making Bleeding Edge AI Work Reliably for Enterprise.” This captures our commitment to harnessing cutting-edge technology while maintaining the dependability that businesses require. We’re not just early adopters; we’re pragmatic innovators transforming how enterprises manage multilingual content at scale.
What was a monumental moment for your team when it comes to your work with AI?
The true breakthrough moment for us has been witnessing the recent explosion of capabilities that we can now integrate into our platform. Take image translation — a process requiring text extraction from visuals, maintaining them as editable layers and then restoring backgrounds seamlessly. This previously impossible workflow is now not only feasible but reliable enough for enterprise use.
What excites us most is how these advancements compound. Technologies like multi-agent collaboration protocol and the just announced Google’s Agent-to-Agent protocol are revolutionizing how systems interact. These developments unlock tremendous potential for creating sophisticated yet manageable workflows that address genuine business challenges.
These technological leaps allow us to build solutions greater than the sum of their parts — a platform where different AI capabilities harmonize to solve complex multilingual content problems that were previously insurmountable. This convergence of technologies represents not just incremental improvement but a fundamental transformation in how enterprises can approach global content delivery.
AI is a constantly evolving field. Very few people coming into these roles have years of experience to pull from. Explain what continuous learning looks like on your team. How do you learn from one another and collaborate?
Our primary challenge has been keeping pace with the accelerating rate of innovation. New AI capabilities emerge weekly and we must constantly evaluate how these technologies can enhance our platform while ensuring they meet enterprise reliability standards. Before this transformation, we operated on traditional development timelines. Now, we’ve had to fundamentally reimagine our velocity — leveraging AI across product development, engineering and quality assurance to move faster than we thought possible.
A fascinating challenge we’re tackling is architectural: how do we restructure our platform to facilitate maintenance and development with a one hundred percent AI-driven approach? We’re exploring how to modularize our UI into independently developed components (“mini-apps”) built without manual coding, yet rigorously tested and reliably deployed.
Nasuni provides hybrid cloud storage solutions, offering scalable and secure data management centered on operational excellence, ransomware protection and support for remote and hybrid work environments.
What is the unique story that you feel your company has with AI? If you were writing about it, what would the title of your blog be?
Nasuni specializes in providing hybrid cloud file storage management, offering solutions that replace traditional file servers and network-attached storage with a cloud-native approach.
Our core value proposition revolves around modernizing file infrastructure. Nasuni can leverage AI to transform unstructured data management and analysis. Cloud-stored files represent vast repositories of potential business intelligence that AI can help unlock.
If I were to write a blog the title would be: “From Storage to Intelligence: How Nasuni is Transforming Enterprise Data with AI.” The blog would explore how Nasuni is evolving beyond just providing hybrid cloud features files to helping enterprises derive actionable insights from their unstructured data using AI.
What are you most excited about in the field of AI right now?
Personally, I’m most excited about agentic architectures in the enterprise right now. I feel that this will be the catalyst for a fundamental shift in how AI operates within business environments and how organizations embrace AI.
AI agents are evolving beyond simple task automation to becoming capable of executing complex, multi-step workflows across organizational systems. These agents can now interface with databases, knowledge repositories, tools and actually, other agents to solve problems that would previously require a lot of human coordination.
Early enterprise AI systems often operated as black boxes, but today’s agentic frameworks provide auditability, controllable autonomy and clear reasoning chains, all of which are needed to build organizational trust in these types of autonomous AI frameworks.
I believe we’re rapidly approaching a tipping point where these types of agentic AI systems will dramatically change and reshape enterprise workflows, moving from isolated point solutions to interconnected, reasoning AI systems that will be able to handle increasingly complex enterprise processes, while still being able to factor in human oversight (human-in-the-loop).
Continuous Learning at Nasuni
- Structured training programs - Internal training as well as promote training inclusive of those from our partners such as Microsoft, Amazon and Google.
- Knowledge sharing sessions - Lunch and Learn sessions where team members present new products or learnings.
- Cross-functional squads - Small, dedicated teams (we call them ‘Tiger Teams’) to spread expertise.
- Internal hackathons - We started experimenting with promoting internal hackathons to increase the knowledge of AI.
- External expert engagement - Workshops with industry leaders and/or academic partners.
- Documentation culture - Comprehensive internal wikis and knowledge bases that preserve and spread our institutional knowledge.
- Dedicated R&D time - Allotting time for experimentation and exploring emerging AI capabilities.
Vestmark provides wealth portfolio management and trading solutions for financial institutions and their advisors.
What is the unique story that you feel your company has with AI? If you were writing about it, what would the title of your blog be?
I think the title would be something like “AI — it’s not about you” or something to that effect. The reason being, there is a tremendous focus right now on how AI will drive time savings for financial advisors (or really, anyone leveraging AI) so they can have more time to do their other, non-AI impacted parts of their jobs (i.e. meet with their clients). But I think the real power of AI comes from the kinds of services and capabilities that AI unlocks as a result of how AI saves us time. Case in point: if we can dramatically reduce the time it takes to generate a bespoke investment proposal — using an AI agent that leverages existing Vestmark software and inputs from the advisor — we also unlock entirely new levels of flexibility for running “what-if” style analyses on those proposals. So yes, we have saved time in the creation of one individual proposal but the same thing that saves us time also unlocks a whole new kind of service — and that is the real power of AI.
What was a monumental moment for your team when it comes to your work with AI?
I don’t think I’ll ever forget the first time we asked our very basic agent to update a client’s email address and saw it reflected in our platform. Just that very simple proof of concept made the possibilities of Agentic AI feel real. Because if we could get an LLM to take a totally unstructured query, find the right set of APIs to execute the task, AND have it reflect in the platform for a user to see and validate, you have all the pieces of the puzzle for a full-blown AI-based assistant. It was that simple proof of concept that we used internally to show people the promise of agentic AI to build additional momentum behind expanding the capabilities out to where it is today.
What challenges did your team overcome in AI adoption?
I think the biggest challenge in AI adoption is having the discipline to know when AI is a good fit to solve a real problem… and when it isn’t (or, isn’t quite yet). It can be very tempting to approach AI as a solution or replacement for whole swaths of human time and effort, but you need to take a step back and appreciate where humans add real tangible value above just their cost or ability to push keys on a keyboard. For example, in wealth management, many of our users, financial advisors, carry a real fiduciary duty to their clients — which means they are going to be held accountable for decision making around which products a client ultimately invests in.
They need to be in control throughout that process because that is the agreement they have with their end client. If as a software platform we were to take the financial advisor out of the loop at critical points in the portfolio management process — even if it “saves time” — it misses the whole point of our platform to our user; to help them fulfill their objectives in serving their clients. And that comes back to a major sticking point I think many firms are going to face in driving AI adoption — building trust.
