How These Companies Balance Experimentation and Stability to Drive Innovation in 2026

Every tech company considers innovation a business imperative. But what does it really mean to innovate?

At healthtech company Optum, innovation is the product of continuous improvement and solving real-world healthcare challenges at scale, motivating technologists to question legacy processes and experiment with emerging technologies. Meanwhile, at safety and security solutions provider Motorola Solutions, it’s rooted in a “laboratory culture,” bridging the gap between social science and engineering in a way that ensures team members build tools that are both mission-critical and technically complex. 

While every company takes its own approach to innovation, the outcome is the same: products that impact both customers and the people who bring these solutions to life. 

Read on to learn how these companies and other organizations like TransUnion and Apex Fintech Solutions cultivate innovation, how this manifests in new solutions and how they balance experimentation and stability. 

ServiceNow’s AI platform enables enterprises to unify AI, data, and workflows through automation, so that enterprises can reinvent how people work across every corner of their businesses.

 

How does innovation show up in your company culture?

At ServiceNow, innovation isn’t a scheduled event — it’s the default operating mode. On my team, we’ve built a culture where curiosity is a job requirement, and the best ideas can come from anywhere in the organization. We treat customer friction as design inspiration and every failed experiment as a signal worth learning from. That mindset has to start at the top: If leaders aren’t modeling intellectual courage and psychological safety, teams won’t take the risks that real innovation demands.

What I’m most proud of is that we innovate with intention. We’re not chasing novelty for its own sake; we’re asking how AI can genuinely reduce the burden on employees and create enterprises that work smarter. That clarity of purpose is what separates interesting experiments from transformational products.

 

“We’re not chasing novelty for its own sake; we’re asking how AI can genuinely reduce the burden on employees and create enterprises that work smarter.”

 

What’s one recent innovation that improved user or employee experience?

One of the most meaningful things we’ve built recently is the AI Control Tower — ServiceNow’s system of record for enterprise AI governance. As organizations deploy AI agents across their operations, they quickly run into a painful reality: No one has visibility into what’s running, what it’s doing or whether it can be trusted. Employees and leaders are left flying blind.

AI Control Tower changes that for our customers. It gives enterprises a single place to discover, monitor, and govern every AI model and agent in their environment, turning an anxiety-inducing black box into something auditable and controllable. The experience shift for the people responsible for enterprise AI is profound: from reactive and overwhelmed to confident and in control. That’s the kind of innovation worth building.

 

How do you balance experimentation with stability?

The honest answer: It’s a constant, productive tension, and I’d be skeptical of anyone who says they’ve fully solved it. My framework is to experiment aggressively at the edges while protecting the core. New AI capabilities can move fast; the foundational workflows that thousands of employees rely on daily cannot afford instability.

In practice, that means tight feedback loops, clear plans and an iterative mindset baked into how we build from day one. The teams that do this well aren’t the ones that slow down; they’re the ones that have earned enough trust to keep accelerating. Speed and stability aren’t opposites — they’re a contract you build with your users over time.

 

 

A UnitedHealth Group company, Optum offers a platform that enables individuals to find nearby doctors, schedule appointments, request medication, manage their health savings account, and more.

 

How does innovation show up in your company culture?

Innovation shows up in our culture through a strong focus on continuous improvement and solving real-world healthcare challenges at scale. Teams are encouraged to question legacy processes, leverage data to drive decisions, and experiment with technology to improve outcomes. Cross-functional collaboration between business, technology and clinical teams enables practical innovation rather than theoretical ideas. There’s also a growing emphasis on automation, AI-driven insights and digital transformation to improve efficiency, accuracy and member experience, making innovation both mission-driven and impact-oriented.

 

“Teams are encouraged to question legacy processes, leverage data to drive decisions, and experiment with technology to improve outcomes.”

 

What’s one recent innovation that improved user or employee experience?

One recent innovation that significantly improved experience is the expansion of AI-enabled tools and automation in workflows. By reducing manual effort in certain reporting, analysis and operational tasks, these tools have improved turnaround times and minimized errors. For employees, this means more time spent on strategic and analytical work instead of repetitive or administrative tasks. For users and stakeholders, it translates into faster insights, improved decision-making as well as service quality.

 

How do you balance experimentation with stability?

Balancing experimentation with stability requires a structured approach. Innovation is typically tested in controlled environments — through pilots, sandbox testing or phased rollouts — before being scaled. Governance, compliance and data security remain foundational, especially in healthcare. By using clear metrics, stakeholder feedback and risk assessment frameworks, teams can innovate responsibly without disrupting core operations. This ensures that experimentation drives progress while maintaining reliability and trust.

 

 

Motorola Solutions builds safety and security solutions that protect people, property and places. 

 

How does innovation show up in your company culture?

At Motorola Solutions, we design technology that prioritizes people and helps save lives. Coming from an academic anthropology background, I bring a “deep hanging out” approach to our work, immersing myself in the world of frontline officers. This allows me to challenge assumptions and identify the unseen forms of labor — social and emotional — that define public safety. I believe true innovation occurs when technology solves a problem customers didn’t even know they had or in a way they never thought possible.

When moving from the field to design, we lean into a “laboratory culture,” uniting UX researchers, designers, project managers and engineers to provide diverse perspectives. By bridging the gap between social science and engineering, we help ensure our tools aren’t just technical achievements but are also mission-critical solutions that enhance situational awareness and improve decision-making.

This holistic approach ensures our innovation is never for its own sake but is a deliberate effort to navigate the complexities of public safety, providing the stability and intelligence responders need to protect the communities they serve.

 

What’s one recent innovation that improved user or employee experience?

We recently launched Assist Suites, role-based AI designed to deliver the right intelligence to the right person at the right time. Public safety agencies face a dual crisis of data influx and critical personnel shortages. Research shows 40 percent of 911 calls suffer from inefficient information exchange, and 50 percent of officers find scene details differ from what they received en route. Our Dispatcher Assist and Responder Assist Suites help solve these challenges by synthesizing 911 audio, body and in-car camera footage, radio transcripts and more into a unified thread of intelligence.

For example, Assist’s real-time 911 call transcription and translation allows call handlers to focus on faster, safer responses, while voice-activated queries of an agency’s data provide officers with eyes-up situational awareness and safety in the field. 

Automating routines addresses the “unseen labor” that causes cognitive whiplash during high-stress incidents; we’re saving time while protecting mental bandwidth. We maintain human-led oversight with audit logs for all AI suggestions to help ensure accuracy and accountability, enabling responders to step away from the screen and return to the community.

 

“Automating routines addresses the 'unseen labor' that causes cognitive whiplash during high-stress incidents; we’re saving time while protecting mental bandwidth.”

 

How do you balance experimentation with stability?

Our customers often operate in high-stakes environments where reliability and accuracy are valued above all else. They need technology that provides unwavering stability when every second counts. When innovating, we must account for the uncompromising realities of their world. 

We balance innovation with reliability by treating our development process as an opportunity for an experimental mindset. Before our solutions hit the real world, we stress-test to help ensure they provide genuine value to our customers. 

The problems we tackle at Motorola Solutions are as varied and dynamic as our customers’ worlds. Just as their conditions, situations, policies and environments are always evolving, our work is never truly “done.” We test, gather feedback, and iterate with deep intellectual curiosity. This allows us to balance fast-paced experimentation with the mission-critical reliability responders need to protect the communities they serve. We don’t just build for the world as it is; we evolve alongside our customers to protect the world as it becomes.

 

 

Fintech company Airwallex offers global businesses fully integrated solutions to manage everything from business accounts and payments to spend management and embedded finance. 

 

How does innovation show up in your company culture?

At Airwallex, innovation isn’t a department; it’s our operating system. We are redefining global banking with AI by discarding the traditional playbook. Every employee here is an AI practitioner. It’s not just engineers using coding agents. Our finance team is “vibe coding” their own applications, our talent team uses AI to streamline hiring, and our operations team is driving massive efficiency gains through automation.

In product and engineering, we’ve embraced a high-velocity “rebuild” mindset. In the AI era, building for the foreseeable future is a myth. We build knowing that as underlying models improve every few months, we may need to rebuild aspects of our product to unlock even more value for customers. This shift even extends to how we grow our team: Our engineering interviews now test for proficiency in working with AI rather than banning it. We aren’t looking for people who can memorize algorithms; we’re looking for “AI pilots” who can leverage these tools to build faster and smarter.

 

“We aren’t looking for people who can memorize algorithms; we’re looking for ‘AI pilots’ who can leverage these tools to build faster and smarter.”

 

What’s one recent innovation that improved user or employee experience?

One of our most impactful innovations is our AI assistant embedded in the Airwallex product, designed to make opportunities obvious and finance nearly invisible. Airwallex offers a broad, powerful product surface that can be complex for new users moving from onboarding to real value.

The assistant acts as an intelligent guide. It understands your context, draws on information you share and relevant public signals, and creates a tailored journey that gets you to outcomes like funding accounts, issuing cards or scheduling international payments much faster. It doesn’t just answer questions. Behind the scenes, specialized agents execute complex tasks on your behalf, from configuring accounts to setting up cards and payments.

It sits on a context layer with access to structured and unstructured data about Airwallex, our products, the user’s business and the user’s Airwallex data. This real-time layer powers experiences across our product and the AI assistant. This is only possible because of advances in agentic architectures and foundation models, an early example of how those breakthroughs create a smoother customer experience and a foundation for the finance agents we’re building now.

 

How do you balance experimentation with stability?

We balance experimentation and stability by being very deliberate about how and where we take risks. Experiments start with clear hypotheses, success metrics and a bias toward running many small, fast tests rather than a few large ones. We learn quickly, kill what doesn’t work, and harden the ideas that do into our core product.

Because we’re a financial platform, stability is non‑negotiable, especially in our core money‑movement and risk systems. Most new ideas — especially AI‑driven ones — start behind feature flags and in constrained surfaces like onboarding or decision support, with offline and online evaluations, strong guardrails and close monitoring. Before promoting an AI experiment into production, we run automated evaluations, red‑team style testing, and monitor for regressions on key metrics so we can catch issues early and roll back safely. This lets teams move fast at the edges of the product while intentionally keeping the underlying financial rails stable.

 

 

TransUnion is a global information and insights company that aims to help organizations understand consumers more effectively and deliver personalized experiences.

 

How does innovation show up in your company culture?

Innovation in our culture is fundamentally about getting better every day. We regularly ask ourselves two simple questions: Are we better today than we were yesterday, and are we actively looking around the corner to anticipate what’s coming next? That mindset keeps innovation grounded in progress and outcomes, not one-off ideas or science experiments.

We focus on innovation that prevents problems before customers feel them, like simplifying platforms, strengthening resilience, improving speed and removing friction. Teams are expected to think ahead, spot signals early, and design solutions that scale. That forward-looking discipline is what allows us to innovate while still operating at an enterprise scale.

 

“Teams are expected to think ahead, spot signals early, and design solutions that scale.”

 

At the core of this is a relentless focus on customer experience. Engineers are expected to deeply understand how customers use our products and design with that intimacy in mind. We encourage candid debate and diverse perspectives but always anchor in delivering a better customer outcome. High-performing teams thrive in this environment because expectations are clear: Move fast, learn quickly, and execute with ownership.

 

What’s one recent innovation that improved user or employee experience?

One recent innovation that significantly improved the employee experience was the expansion of our Innovation Lab and intrapreneurship programs, with a very intentional focus on business impact. The goal wasn’t experimentation for experimentation’s sake; rather it was to help teams explore ideas that could materially improve customer experience, platform capabilities or operational outcomes.

What made this work was clear framing and structure. Engineers worked on real problem statements, had access to hands-on labs, mentoring and external perspectives, and were expected to connect learning back to delivery. It reinforced the idea that innovation is part of the job, not an add-on, and that it should translate into better systems, better decisions and better customer experiences.

The result has been stronger engagement and faster skill development. When teams are trusted to experiment, fail fast, and apply what they learn, they become more confident, more capable and more invested. That directly improves how we design, build, and deliver for our customers.

 

How do you balance experimentation with stability?

I’m very clear that experimentation and stability are not trade-offs; instead, both are required. We encourage teams to move fast and challenge assumptions, but we do so with a strong sense of ownership and accountability. The question is never, “Should we experiment?” Rather, it’s “How do we experiment responsibly and learn quickly?” 

We’re deliberate about where experimentation happens. New ideas are tested in contained environments, proofs of concept, pilots or labs so teams can fail fast, learn, and iterate without risking core platforms or customer trust. Once something proves valuable, we shift quickly into disciplined execution and scale.

This balance ultimately comes down to high-performing teams and leadership behavior. The best teams anticipate issues before they happen, design for resilience, and continuously improve. When experimentation is paired with accountability, speed and customer focus, innovation becomes sustainable and the business gets stronger every day.

 

 

Apex Fintech Solutions’ ecosystem of platforms, APIs and services is designed to help organizations navigate the future of finance, offering fractional share-trading, robo-investing and more.

 

How does innovation show up in your company culture?

Innovation at Apex is embedded in how we approach client enablement. Our team documents features as we’re actively building the product in the development process, advocating for developer experience and identifying integration friction points before they reach production. Our leadership encourages us to continuously evolve our documentation strategy, treating it as a product itself rather than an afterthought. We’re constantly experimenting with new ways to help clients integrate faster, whether that’s interactive APIs, documentation, sample code or software development kits. The culture here is one of continuous improvement: We’re empowered to identify problems, propose solutions, and iterate quickly.

 

“The culture here is one of continuous improvement: We’re empowered to identify problems, propose solutions, and iterate quickly.”

 

What’s one recent innovation that improved user or employee experience?

We recently launched Ask Ascend, an AI-powered assistant that fundamentally changes how users get answers during integration. Previously, users had to navigate between multiple documentation sources — user guides, development guides, API references and SDK documentation — to piece together integration solutions. Now they can ask natural language questions and get instant, contextual answers pulled from our entire knowledge base in one place.

What makes this particularly powerful is how it brings our documentation together. They don’t need to know which document contains the answer or use the exact technical terminology. Ask Ascend connects the dots across our content. The impact has been significant. Users are getting unblocked faster. For our team, this innovation has elevated the importance of our work. We’re not just writing docs that sit passively waiting to be read. We’re creating the intelligence layer that actively helps users succeed and work faster. It’s pushed us to be even more precise, comprehensive, and structured in how we document, knowing that users rely on that quality to deliver accurate responses.

 

How do you balance experimentation with stability?

We use a phased approach. For documentation serving production integrations, stability is paramount. We maintain version control and review processes for core documentation. However, we also carve out space for experimentation through pilot programs with select users with new features or updated documentation formats. Internally, we balance this by dedicating time for exploration, whether that’s testing new documentation tools or content structures, while maintaining our commitment to keeping production documentation reliable and current. The key is treating experimentation as structured learning rather than chaos: We test, measure, learn, and then decide whether to scale or pivot.

 

 

HiBob’s HR platform helps HR teams and managers handle onboarding, compensation, surveys, employee performance and other tasks. 

 

How does innovation show up in your company culture?

At HiBob, innovation is grounded in solving real operational complexity, and nowhere is that more evident than in our U.S. Payroll solution. U.S. Payroll operates in one of the most complex regulatory environments in tech. Between federal, state and local tax requirements, compliance updates, and evolving labor laws, the surface area for error is enormous. Our culture embraces that complexity rather than avoiding it.

We work in focused, cross-functional squads that bring together engineering, compliance expertise, design and data. Innovation is not just about shipping new features. It is about rethinking how payroll should function inside a modern HR platform.

We invest heavily in automation, proactive compliance monitoring and intelligent validation systems. Instead of reacting to regulatory change, we build infrastructure that can adapt quickly and scale confidently. That requires a culture where teams are empowered to challenge legacy payroll assumptions and continuously improve the experience. For us, innovation shows up in how we simplify the most complicated parts of workforce management without ever compromising accuracy or trust.

 

“For us, innovation shows up in how we simplify the most complicated parts of workforce management without ever compromising accuracy or trust.”

 

What’s one recent innovation that improved user or employee experience?

One recent innovation is redesigning critical U.S. payroll flows around real-life employee lifecycle moments, specifically new hires, onboarding and terminations. Historically, payroll and onboarding have lived in separate systems, creating manual handoffs, duplicate data entry and unnecessary compliance risk. We recently introduced new flows that tightly connect onboarding with payroll configuration from day one.

For new hires, payroll-relevant data such as tax elections, state and local jurisdictions, and compensation setup is captured seamlessly during onboarding and validated in real time. The system proactively flags missing or inconsistent information before the employee’s first payroll run, significantly reducing errors.

For terminations, we have enhanced workflows to support state-specific final pay requirements, payout calculations and downstream impacts like benefits and tax treatment. Admins are guided through structured, compliant processes that reduce guesswork.

The improvements are not just operational efficiencies. They directly impact trust: Employees get paid accurately and on time from their first paycheck, and payroll teams spend less time firefighting.

 

How do you balance experimentation with stability?

In U.S. Payroll, stability is non-negotiable. Accuracy, compliance and reliability are foundational, but that does not mean innovation slows down. It means it has to be disciplined.

One way we are evolving the payroll experience is by embedding AI to reduce the most labor-intensive and error-prone aspects of payroll administration. Payroll teams often spend hours reviewing reports, double-checking configurations and investigating anomalies. We are enhancing the system with intelligent anomaly detection, contextual prompts, and automated validation layers that surface potential issues before payroll is finalized. Instead of manually scanning for discrepancies, admins are guided directly to items that require attention.

We balance experimentation with stability through feature flagging, staged rollouts and rigorous internal testing and beta testing. AI-driven enhancements are layered on top of a strong, reliable payroll engine, not replacing it but rather augmenting it.

The goal is not automation for its own sake. It is reducing cognitive load, minimizing human error and giving payroll professionals confidence that the system is working with them, not creating more work.

 

 

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. 

 

How does innovation show up in your company culture?

Innovation at Nexthink is part of our DNA. It shows up in everything we do, from our annual companywide “Innovathon” to the way we rapidly prototype ideas and ship our product to customers.

We see innovation as a process. An idea evolves through conversations, iteration and experimentation until it delivers real value. The rise of LLMs and AI agents has accelerated that process dramatically. What used to take weeks can now happen in days. Teams can explore, validate, and refine ideas faster than ever.

 

“We see innovation as a process. An idea evolves through conversations, iteration and experimentation until it delivers real value.”

 

For us, innovation is not just about adopting new technology. It is about continuously improving how we build better digital experiences for people at work.

 

What’s one recent innovation that improved user or employee experience?

One of our most impactful innovations is Nexthink Spark, the personal IT agent for every employee. Spark was built on a simple belief: Enterprises shouldn’t struggle to unlock value from IT — it should be immediate and accessible. Historically, access to insight and remediation was concentrated in the hands of IT administrators, thanks to Nexthink Infinity. With the maturation of LLMs and agentic AI, we saw an opportunity to put that value directly into employees’ hands.

Powered by AI, Spark adds an intelligent conversational layer on top of our digital employee experience platform. On the surface, it delivers a seamless and frictionless interaction. Underneath, it performs real-time analysis and takes action securely and reliably.

Instead of submitting tickets and waiting, employees can resolve issues instantly through a personal AI-powered IT agent. By making IT value easier to access, Spark transforms IT from reactive support into proactive empowerment, setting a new standard for enterprise self-service.

 

How do you balance experimentation with stability?

Innovation only works if it is built on trust. At Nexthink, our core platform is engineered for stability, reliability and security. That is non-negotiable. At the same time, we deliberately create space for experimentation, especially in fast-moving areas like AI and agentic systems.

We separate exploratory work from mission-critical systems, allowing teams to move fast without compromising customer trust. Once an idea proves its value and robustness, it is integrated into the core through disciplined engineering practices. This dual-track approach allows us to innovate boldly while staying dependable, which is essential when building technology for people at work.

 

 

Clear Street offers a cloud-native brokerage and clearing system that’s designed to add efficiency to the market while transparently minimizing risk and cost for clients. 

 

How does innovation show up in your company culture?

The core of Clear Street has been about driving innovation to revolutionize the infrastructure of global capital markets, basically bringing modern cloud technology to Wall Street and beyond. I am now seven weeks into my role here, so still very early days! But my mandate and challenge is clear: Push the boundaries of what’s possible through AI and design-led product innovation, both for our clients and across the business. That’s a very unique opportunity and rarely exists at a business of this level. 

There is a culture of bravery at Clear Street. We are encouraged to simplify and challenge conventions. Be bold. I love that.

 

“There is a culture of bravery at Clear Street.”

 

What’s one recent innovation that improved user or employee experience?

We’re currently in the process of building a new active trading product. It is an AI-first experience, and we really see it as breaking new territory for what AI can offer to traders through both the technology and the end user experience. We are working to augment a user’s understanding and opportunities, aiming to supercharge them with institutional trading capabilities. This is extremely exciting to design for and is a part of the reason I was brought on board. I see the future of design combined with AI as incredibly exciting; it’s a unique moment in time to explore and influence, and that’s something my team and I plan to fully embrace.

 

How do you balance experimentation with stability?

A great question. Design is about craft and depth of thinking. Whilst AI tooling is incredible for us and represents new opportunities to experiment, the craft and the UX has never been more important — it’s a differentiator. Then when you are designing for financial users, you always have to be mindful of how to introduce new ways of doing things. You have to constantly balance and understand their workflow with the new innovation. Because of people’s reluctance to inherently trust AI, it is also a critical moment for building trust in the way you design for it. That balance on where we put our energy and what value we are adding has never been more important than now.

 

 

SmartBear’s AI-powered solutions are designed to help teams build, test, and ship software at scale. 

 

How does innovation show up in your company culture?

At SmartBear, innovation starts with a clear thesis: As technology transforms how software is built, quality must evolve just as fast. We’ve been innovating across the API lifecycle for years, from foundational design with Swagger to automated testing and observability, and we’re extending that leadership into the AI era. 

Our culture supports innovating alongside our customers. Through SmartBear AI Labs, our product leaders work with engineers and AI specialists to prototype and validate ideas such as autonomous testing and AI-driven governance. We move fast and validate rigorously using real-world customer examples. 

 

“We move fast and validate rigorously using real-world customer examples.” 

 

For example, a beta customer recently rebranded their product, but their legacy automation suite failed immediately due to a shifted cookie banner blocking the login. Our autonomous testing product adapted without interruption and even surfaced a regression. That’s the standard we’re building toward: Systems that self-heal while still catching what matters in production-grade, customer-validated situations.

 

What’s one recent innovation that improved user or employee experience?

The SmartBear Model Context Protocol Server enables AI coding assistants to securely interact with SmartBear tools through a governed interface. Developers can retrieve API definitions, trigger tests, and orchestrate workflows directly from their integrated development environment, bringing quality signals and governance into the same loop where code is written. 

We recently hosted an internal MCP hackathon, and the winning project, API Drift, is on the road to becoming a customer-facing offering. API Drift is an MCP-based tool that automatically detects and resolves discrepancies between live API implementations, Swagger documentation and PactFlow contracts. Instead of discovering misalignment weeks later, teams can identify drift in real time — before it impacts their consumers. 

We also integrated SmartBear Reflect with SAP Cloud Application Lifecycle Management, embedding AI-powered automation into enterprise release pipelines. This integration won SAP’s global ALM-athon and enables teams to test earlier and more frequently, ensuring that their SAP environments remain stable and performant. 

Across these innovations, the goal is the same: Reduce friction for our customers while increasing confidence in their software quality.

 

How do you balance experimentation with stability?

At SmartBear, we balance experimentation with stability by holding ourselves to the same quality standards as our customers do and by using our own products to enforce them. When we introduce new API capabilities, we rely on Swagger Contract Testing to ensure backward compatibility and prevent breaking changes. Our deep test management and automation capabilities validate new functionality continuously across products. And we instrument our platforms with BugSnag to provide real-time insight into application stability, allowing teams to detect and resolve issues early in development.

This approach allows engineers to push forward on agentic AI and autonomous testing while maintaining enterprise-grade reliability. For our teams, that means working on cutting-edge technology with the discipline and tooling required to operate at scale.

 

 

Inspira Financial provides health, wealth, retirement and benefits solutions for over 7 million accountholders. 

 

How does innovation show up in your company culture?

Innovation at Inspira Financial isn’t confined to a single team or initiative. It shows up in how we work every day. Across our organization, design, product, governance and engineering partner closely, reducing the gap between idea and delivery.

Our design team has adopted an AI-powered coding assistant that allows designers to prototype directly on the codebase and ship alongside engineers. We intentionally create space to responsibly experiment with new tools, share what we are learning, and challenge traditional processes. That culture of curiosity helps teams move quickly while staying grounded in strong design and engineering fundamentals. The result is thoughtful, secure experiences for our accountholders and administrators.

 

“We intentionally create space to responsibly experiment with new tools, share what we are learning, and challenge traditional processes.”

 

What’s one recent innovation that improved user or employee experience?

One recent improvement that helped both reimbursement plan accountholders and administrators is our receipt-powered claims experience. Accountholders can upload a receipt and have key details pulled in automatically through optical character recognition, removing manual entry and speeding up the process.

On the back end, that same information helps administrators review claims faster. What used to involve significant manual review now surfaces the right details at the right time. The result is faster reimbursements for accountholders and a simpler workflow for the teams supporting them.

 

How do you balance experimentation with stability?

For employer benefits, stability is not optional. People rely on our platform to securely access their money when they need it. We experiment carefully, focusing innovation at the edges while protecting the core experience. New features are rolled out in stages and tested with smaller groups using feature flags before wider release. Not every idea needs to reach production to be valuable. This approach allows us to explore new ideas while maintaining the reliability users expect from our platform.

 

 

Vertafore offers insurance technology solutions that are designed to connect every point of the insurance distribution channel. 

 

How does innovation show up in your company culture?

Innovation isn’t just about big breakthrough ideas; it’s about continuously improving the way we work and the experience we deliver to our customers. We celebrate our teams with the Vertafore Way, key elements of which are a “Bias to Action,” “Show up Curious” and “Customer Success is our Success,” which each help drive innovation of our own processes (How can we be more efficient for our customers?) and in the software that we deliver to our customers (How can we help them drive their velocity?)

 

“Innovation isn’t just about big breakthrough ideas; it’s about continuously improving the way we work and the experience we deliver to our customers.”

 

What’s one recent innovation that improved user or employee experience?

It’s hard to pick just one. We have so many exciting projects that we are working on for our team members and our clients. One standout has been our implementation of AI capabilities for our development teams. It significantly speeds up their development of new ideas, reduces the turn around time on concepts, and significantly increases our ability to experiment.

 

How do you balance experimentation with stability?

Everything at Vertafore is delivered with a quality-first mindset. Our customers rely on Vertafore solutions to run their businesses so we can’t be showing up with failing software. That being said, we have opened up avenues for experimentation by building an architecture that protects our core while allowing for experiments on top. We work through these experiments in partnership with our customers to ensure we are delivering real value that can be implemented back into our core operations.

 

 

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Enverus’ solutions are designed to help energy companies drive production and investment strategies, enable best practices for energy and commodity trading and risk management, and reduce costs through automated processes. 

 

How does innovation show up in your company culture?

Innovation at Enverus shows up in how often people ask “Why?” and actually get to do something about it. Teams are encouraged to challenge assumptions, test ideas quickly, and learn from real customer feedback, not just slides or theory. We don’t expect perfection, but we do expect ownership. If you have a good idea and a clear point of view, you’ll get support to try it.

What matters most is trust. We give teams real autonomy, and pair it with accountability. That combination creates a culture where people feel comfortable experimenting but are also focused on delivering outcomes that matter to customers.

 

“We give teams real autonomy, and pair it with accountability.” 

 

What’s one recent innovation that improved user or employee experience?

One of the biggest recent improvements has been how we’ve embedded generative AI directly into our products. Tasks that used to take hours — analyzing data, pulling insights and preparing market views — can now be done in minutes. Customers aren’t just faster; they’re more confident in the decisions they’re making.

Internally, giving teams access to AI tools has been a game-changer. It’s helped people automate repetitive work, prototype ideas faster, and spend more time on creative and strategic thinking. The biggest shift hasn’t been productivity — it’s the mindset. When the friction to test an idea drops, curiosity goes up. People are more willing to explore, experiment, and push boundaries, which has been incredibly energizing across the company.

 

How do you balance experimentation with stability?

We’re very clear about where stability is non-negotiable and where experimentation is encouraged. Our core platforms and data foundations are built for reliability, and we don’t experiment recklessly there. On top of that foundation, we create safe spaces for teams to test ideas quickly, and learn fast. The balance comes from discipline — clear goals, guardrails and a willingness to either scale what works or walk away from what doesn’t.

 

 

Sendbird’s AI customer experience platform enables brands to communicate with consumers via voice, video and messaging. 

 

How does innovation show up in your company culture?

Innovation at Sendbird isn’t a top-down mandate. It shows up in how people solve problems day to day. Engineers don’t wait for permission to try something new, and that energy extends beyond product into functions like security and IT. What I’ve noticed is that the best ideas often come from people closest to the friction. Someone on the IT team sees a repetitive process, and instead of just doing it again, they ask, “What if this didn’t exist?” That instinct, to question the default, is what makes the culture feel genuinely innovative rather than just aspirationally so.

 

“Engineers don’t wait for permission to try something new, and that energy extends beyond product into functions like security and IT.”

 

What’s one recent innovation that improved user or employee experience?

One that stands out is our push to make AI a real part of how “Sendbirdians” work, not just a tool that sits in a browser tab. We’ve been focused on practical enablement: helping employees understand where AI actually fits into their workflows and building the internal tooling to support that. The shift we’ve seen is people going from, “I tried ChatGPT once” to genuinely rethinking how they draft, analyze, and communicate. That change in behavior is the innovation. The technology was just the catalyst.

 

How do you balance experimentation with stability?

Be aggressive about where you experiment and ruthless about what you protect. Not everything deserves the same risk tolerance: Core security controls get rigor, a new workflow or integration can move fast. The discipline is knowing which category something falls into before you start, not after something breaks. We operationalize this by building guardrails, pre-approved tools, sandboxed environments and clear AI data handling guidelines so people can experiment without needing to ask security for permission every time. When guardrails are well-designed, they don’t feel like walls. They feel like a running track. You know exactly where you can go fast.

 

 

Sonatus offers AI-powered vehicle software that’s designed to help automakers continuously improve cost, performance, reliability and user experience.

 

How does innovation show up in your company culture?

At Sonatus, innovation is a core company principle, driving our daily operations, decision-making and collaboration to quickly deliver customer value. We focus on solving high-impact, real-world problems for original equipment manufacturers, working within constraints like safety, scalability and legacy systems, and innovating within those realities. This mindset leads to breakthroughs like AI-powered tools that analyze vehicle data to automatically detect failures and find root causes, dramatically reducing testing time and improving scalability.

Our culture emphasizes speed and ownership, where small, cross-functional teams are empowered to experiment, prototype, and iterate rapidly. Cross-functional teams work closely from day one, reducing handoffs and accelerating learning. Our AI initiatives started with small teams and are now integrated into our core platform.

 

“Our culture emphasizes speed and ownership, where small, cross-functional teams are empowered to experiment, prototype, and iterate rapidly.”

 

Design innovation means thoughtful simplification: making our highly complex and technical products intuitive, usable and scalable. Leadership supports this through constructive debate and psychological safety. In short, innovation at Sonatus is defined by focus, collaboration and impact — not just what we ship, but how we build.

 

What’s one recent innovation that improved user or employee experience?

Our recent innovation at Sonatus is a conversational AI agent that revolutionizes troubleshooting for OEM testing engineers. By applying AI directly to vehicle data analysis, we’ve cut issue resolution time from roughly 15 days to as little as two days.

This is a breakthrough in user experience. Instead of digging through data tables, dashboards or documentation, engineers can ask natural-language questions such as “Show me all the diagnostic trouble codes” or “What is the root cause of this sensor issue?” or even “Show me how to fix it step by step.” 

AI’s true power lies in its interpretation of real-time and recorded signals alongside an integrated knowledge base — specs, manuals and diagnostic databases — to deliver comprehensive, actionable insights, significantly reducing diagnostic time and improving fast experimentations. 

The innovation’s impact is defined by its seamless integration into existing OEM workflows, dramatically reducing cognitive load and enabling continuous improvement during testing and even long after vehicles are deployed.

 

How do you balance experimentation with stability?

At Sonatus, balancing experimentation with stability is foundational to how we build products for both safety-critical standard operating procedure and pre-SOP vehicles. We do this by clearly separating where we explore new ideas from where we guarantee production reliability.

Experimentation starts early in controlled environments such as prototypes, simulations and internal tools, often in close collaboration with OEM customers. Small teams are empowered to apply emerging technologies like AI and iterate quickly without compromising vehicle safety or customer trust. Ideas are reviewed by technical/executive leaders, allowing us to move fast while staying grounded.

Once an idea proves valuable, it moves through rigorous quality gates, validation and cross-functional reviews before reaching production. In the automotive industry, stability is non-negotiable. Our software-defined platform supports this balance by enabling new capabilities, such as analytics, optimization or AI-initiatives, to be introduced without disrupting core systems. 

Culturally, teams experiment responsibly. In practice, this balance allows us to innovate fast where it’s safe, while delivering the reliability our customers and drivers depend on.

 

 

FourKites combines real-time digital twins of orders, shipments, inventory and assets with AI-powered digital workers to help companies prevent disruptions, automate routine tasks, and optimize performance across their supply chain.

 

How does innovation show up in your company culture?

At FourKites, everyone is expected to experiment with AI — not just the engineering and product teams. We gave employees across every department access to AI tools early on and encouraged them to find ways to work smarter. As a result, our team has built things like FourSight AI, a conversational analytics assistant that lets anyone query supply chain data in natural language instead of writing SQL or navigating dashboards.

What makes it stick is that we’ve built experimentation into how we evaluate performance. During reviews, employees are explicitly asked how they’ve used AI to become more effective. That makes it clear that experimentation isn’t a side project but is a part of the job.

We also co-innovate with customers through our IdeaExchange platform, which gives them a direct line into product development. That feedback loop has shaped not just features but entirely new AI agents — specialized digital workers designed to solve the specific operational problems our customers surfaced. When you pair a workforce that’s actively experimenting with customers who are telling you exactly what they need, innovation stays grounded in real problems.

 

What’s one recent innovation that improved user or employee experience?

We launched FourSight AI, a conversational analytics assistant that lets employees and customers interact with supply chain data through natural conversation. Instead of building reports or tracking down an analyst, someone can ask a question about shipment status, carrier performance or facility patterns and get an answer immediately. It’s fundamentally changed how people access information.

 

“We launched FourSight AI, a conversational analytics assistant that lets employees and customers interact with supply chain data through natural conversation … It’s fundamentally changed how people access information.”

 

We’ve also deployed a Digital Workforce, or specialized AI agents that handle complex operational tasks autonomously. Tracy, our track and trace agent, proactively monitors shipments, contacts carriers when disruptions arise, and coordinates corrective actions. Sam automates document processing and validation. Alan optimizes appointment scheduling. What makes them different from generic AI tools is that they’re powered by the FourKites Intelligent Network, which contains proprietary insights drawn from over 3 million daily shipments across 1,600-plus companies. That network intelligence lets our agents predict and act on problems that no single company’s data could reveal on its own.

 

How do you balance experimentation with stability?

Every AI agent we deploy follows a structured framework we call Agent Operating Procedures. Before anything goes live, its workflows, decision logic and escalation paths are codified, reviewed, and confirmed. That discipline lets us move quickly while ensuring that what we ship is predictable and auditable.

We also maintain strict governance around data. Our agents are powered by the FourKites Intelligent Network, which draws intelligence from 1,600-plus companies and millions of daily shipments. That requires rigorous multi-tenant data isolation; customer data never crosses boundaries. Those guardrails aren’t there to slow experimentation down. They’re what make it possible to experiment with confidence.

What helps most is broad participation. When people across different functions are working with AI, you get a wider range of perspectives on what works, what doesn’t and what risks might not be obvious to any single team. We pair that with processes we review regularly so our approach evolves as we learn. The goal is to stay disciplined without becoming rigid.

 

 

Cleo’s platform is designed to give organizations visibility into critical end-to-end business flows happening across their ecosystems of partners and customers, marketplaces, and internal cloud and on-premise applications.

 

How does innovation show up in your company culture?

In Cleo, innovation can show up in any corner of the company. There is a common quest for the best ideas, regardless of who or where they come from. That culture starts with our CEO, whose door is always open.

Within engineering, we approach innovation in a systemic way. We recognize that innovative ideas only become great product ideas if they provide significant value to a large portion of our user base, can be built in the market opportunity time window, and provide significant return on investment for Cleo. Thus, in every development squad, we form a triad leadership team to vet product ideas and plan work for the squad that includes a product manager to vet the business value, a software architect to provide feasibility, cost and time assessments, and a user experience expert to ensure features are both valuable and usable for our users. 

Additionally, in the age of AI, innovation is not limited to those working on Cleo’s products within research and development. All teams across Cleo are being asked to reimagine their roles in the age of AI and find innovative ways to both increase the quantity and quality of their work output. Thus, we are all innovating on a daily basis.

 

“All teams across Cleo are being asked to reimagine their roles in the age of AI and find innovative ways to both increase the quantity and quality of their work output.”

 

What’s one recent innovation that improved user or employee experience?

We are in the midst of the most innovative period in Cleo’s history. This is partly due to the unprecedented opportunities that AI brings in terms of hyper-personalization, expert advice on any topic and the ability to take actions on the users’ behalf with generative AI. We are optimizing the user experience dynamically based on who is logged in, with relevant insights based on the user’s job function to orient them to what needs to be done, provide guided recommendations on how to best perform tasks, and offer to automate recurring tasks. The opportunity to accelerate outcomes for our users is unparalleled. 

With the SCO initiative, we are introducing an additional layer of supply chain intelligence on the data moving through our platform that will enable new-business, non-technical personas to orchestrate their supply chain through our platform. Not only will we provide visibility across historically siloed areas of the supply chain, but AI will offer expert guidance and automation for these users to identify and remove inefficiencies, reduce fines, and strengthen business relationships across the supply chain. We have moved from managing bits to managing businesses.

 

How do you balance experimentation with stability?

Given our platform is mission-critical for our customers, if our platform stops, their business stops. So we take stability and security very seriously, and yet we can’t stop innovating. We achieve this in multiple ways.

First off, we do a lot of iterative prototyping of ideas that can be tested and validated with users before they become platform features. This allows for unlimited experimentation without ever affecting the stability of our platform. 

Additionally, given we have a cloud platform, we can push out new features selectively in targeted releases down to a given set of customers and even down to specific users in those customer environments. This allows us to experiment with new features while checking the utility, usability and performance in real environments before doing larger rollouts. 

Lastly, we have continued to optimize our deployment pipelines to decrease the time between deployments. It’s counterintuitive, but changing the product more often with smaller changes leads to more stability and less issues than if code was deployed infrequently in big chunks. If issues are found, they are much easier and faster to fix if less has changed since the last push.

 

 

Sage’s operations management system provides senior living communities with real-time insights into care delivery and operational efficiency.

 

How does innovation show up in your company culture?

Innovation at Sage sits at the core of who we are. We’re reinventing how care is imagined and delivered for older adults. We build for senior care, a uniquely complex industry that demands both technical rigor and deep empathy. Every person at Sage is driven by a shared mission to meaningfully improve the lives of elders and the people caring for them. We don’t just chase shiny tech for its own sake; we stay relentlessly focused on solving real problems for real people.

 

“We don’t just chase shiny tech for its own sake; we stay relentlessly focused on solving real problems for real people.”

 

Our culture is grounded in both excellence and kindness, a combination that truly feels rare. The people at Sage are exceptionally smart and hardworking, with a high bar for what we deliver. We recognize that design, product and engineering each bring critical expertise, and the magic of Sage is how we collaborate with deep respect for one another’s craft. 

We’ve built great products used by tens of thousands of people every day. But as our Chief Product Officer, Ellen Johnson, says, “If we don’t disrupt ourselves, someone else will.” That mindset pushes us to look six months to a year ahead and ensures that we’re not just shipping features. We’re building something that fundamentally expands what’s possible when caring for others.

 

What’s one recent innovation that improved user or employee experience?

Our most recent product, Sage Detect, is an AI-powered fall detection system built for senior living. It’s incredible: The team went from ideation to installation in under six months. Today, Sage Detect is changing how community leaders care for residents by alerting staff when someone is on the floor and enabling authorized team members to review the incident. This has meaningfully reduced hospitalizations and lowered fall rates across communities.

Most falls in senior living communities go unwitnessed. When someone is found on the floor, caregivers often don’t know how or what happened, and many residents can’t explain due to cognitive impairment. Sage Detect gives care teams the insight they need to understand these events and put effective interventions in place.

AI isn’t magic. It’s not a silver bullet, so it’s critical to be thoughtful about its use. Much of the industry is experimenting to see what sticks. But when AI is applied to the hardest, most meaningful problems, like reducing falls with Sage Detect, it completely changes what’s possible. At Sage, we build technology with intention to elevate people with demanding jobs and ensure AI supports, rather than replaces, human care.

 

How do you balance experimentation with stability?

Successful experimentation is how we make big things happen. When Sage has taken big bets in the past, it has meaningfully expanded what our organization can deliver.

Our philosophy is to supercharge teams exploring these bets. That means giving people space to focus without being pulled into day-to-day delivery. It also means getting “one-way door decisions” right. Choices that are difficult or impossible to undo require proper rigor up front before sprinting forward.

Before investing in side quests, we make sure the organization is aligned. Bets are deliberate and shared decisions. When everyone understands why we’re taking the bet, the upside we’re chasing and the go/no-go date, we can build checkpoints to decide whether to continue or off-ramp.

Finally, experimentation without user knowledge is just technical curiosity. At a startup, you can’t wait years to deliver value. We spend real time with users and understand their workflows to build intuition on what will be a non-starter or just slow them down. That intuition ensures our experiments move the needle, driving meaningful outcomes, not just interesting ideas.

 

 

Simply Business’ digital marketplace connects small businesses with insurance coverage solutions. 

 

How does innovation show up in your company culture?

At Simply Business, innovation is reflected in how our teams operate on the day-to-day. We’ve focused on using the benefits of AI to work more effectively and free up headspace for solving tough problems.

What’s one recent innovation that improved user or employee experience?

Small business insurance is notoriously complex. To lower the barrier to understanding coverage, we launched our AI-powered advisor, which provides personalized guidance on coverage details and limits in real time. For the small business owner, this means they can ask the questions that matter to them 24/7. If the chat experience cannot answer a specific nuance, we escalate the conversation to our team of licensed agents. By leveraging the benefits of generative AI, we are empowering business owners to make confident, informed decisions when selecting the right insurance for their business.

 

“To lower the barrier to understanding coverage, we launched our AI-powered advisor, which provides personalized guidance on coverage details and limits in real time.”

 

How do you balance experimentation with stability?

In the highly regulated insurance industry, we can’t afford recklessness with our use of new technologies, but we also can’t afford to stand still. We balance these needs with informed urgency, moving quickly while maintaining rigorous safety guardrails.

Our AI-powered advisor chat experience already leverages AWS Bedrock guardrails to keep the LLM outputs within strict operational boundaries. For nuanced inquiries, we have a human-in-the-loop approach in place today, providing a seamless transition to our licensed agents whenever a human expert is best suited to help.

To further accelerate our deployment speed, we are actively building out LLM evaluations that monitor for accuracy before updates reach production. We are also refining our LLM-as-a-judge architecture to critique our LLM outputs in real time. This ensures that any response failing our internal criteria is intercepted before it ever reaches the customer. 

Ultimately, these methods serve a simple purpose: ensuring that every interaction remains reliable and helpful, providing the dependable experience our customers need and expect to protect the business they’ve built.

 

 

Milestone Systems offers video technology software solutions that are designed to help businesses and people enhance security, efficiency and insight. 

 

How does innovation show up in your company culture?

The engineers on Milestone’s Arcules team are motivated to deliver technology that makes our users’ jobs easier. Our remit is incredibly broad — video, audio, devices, networks, data, AI, UX and cloud-based SaaS. For savvy developers, the opportunities are boundless. As an engineering manager with decades of hands-on experience, I’m serious about our core responsibility: protecting our engineers' ability to innovate and ship. 

 

“The engineers on Milestone’s Arcules team are motivated to deliver technology that makes our users’ jobs easier.”

 

For us, innovation takes many forms. An engineer-led working prototype led to an entirely new UX experience. A new service implementation drove application-wide removal of tech debt. In fact, one of our major product differentiators was created by a developer who simply asked if there wasn’t a simpler way. Engineering voice and leadership are valued here; a functional demo is worth a thousand words. 

We run lean with a few excellent product managers and zero project managers. This requires staff to manage, communicate, and collaborate, and we do it all remotely across North and South America, and occasionally European, time zones. This isn’t the right environment for everyone, but for us on the Arcules team, we wouldn’t have it any other way.

 

What’s one recent innovation that improved user or employee experience?

Just this week, we released an AI-assisted forensic search capability that transforms our users’ ability to find exactly what they are looking for via free-text search of incoming video streams. This shifts our users’ experience from passive video review to active and dynamic search when someone has a safety or security concern. 

The challenges are plentiful: interpreting video accurately and quickly; matching interpretations to each frame at scale; offering query capabilities over wide time domains; building a UX that lets users narrow and widen a search intuitively; and scaling inference in real time while managing cost. 

Our commitment to responsible AI is non-negotiable. Forensic search users always see the source image and video alongside any AI finding to confirm or deny it themselves. We moderate use through automated and manual review of search terms and via terms of service. We build with a focus on ethical use and ensure our product meets European AI Act standards. Our stretch goal is a fully dynamic user interface that learns the behavior of individual users and anticipates their needs. At Milestone, we think of our job as helping the world to see.

 

How do you balance experimentation with stability?

Our informal motto is “Move fast and make things.” Our worldwide customers require a stable experience. As an engineering team that is responsible for our own quality assurance, we know the buck stops with us. For every effort, we know we must ensure stability. We believe in outcomes over output. 

We use feature flags. When appropriate, we apply version-based rate limiting to effect a gradual shift and watch for errors; sometimes we use the front end to communicate contextual information to the back end to ease a transition. Underlying all these techniques is our commitment to a flow-oriented model, which focuses on finishing work in progress before starting anything new. We build large-scale systems and assemble them piece by piece in production, allowing us to experiment, improve, and harden code before users ever see it. 

The most exciting experimentation approach involves partnering with our customers. We spin up what we call our “Flywheel” effort, meeting with customers for a show and tell of experiments and refinements, so that when a concept really resonates, we can create and ship a feature that is both fresh and mature.

 

 

Axle Health offers scheduling and workforce management software for in-home healthcare providers.

 

How does innovation show up in your company culture?

At Axle, innovation shows up in how we choose which problems to solve, not just what technology we use. Healthcare operations look structured and formulaic on paper, but in reality, they are deeply human. It’s why healthcare teams have had to resort to manual solutions for so long. A patient doesn’t say, “I need a 60-minute visit between 10:00 am and noon.” They say, “I get anxious with new clinicians,” “I can only answer the door after dialysis,” or “Mornings are hard because of my medication schedule.” Traditional scheduling systems force those needs into rigid machine-like constraints, which means the system loses the real-life context that matters most.

 

“At Axle, innovation shows up in how we choose which problems to solve, not just what technology we use.”

 

So we applied an LLM to this layer of the product, allowing clinicians and admins to capture the nuance of our patients’ lived realities. Our platform turns organic, free-form patient and clinician preferences into actionable constraints inside the scheduling engine. The innovation wasn’t “add AI” and instead recognized that healthcare is a qualitative domain and required choosing tools that respect that reality. The result is software that adapts to people rather than forcing people to adapt to software.

 

What’s one recent innovation that improved user or employee experience?

We recently launched a customized dashboard with intelligent work queues that schedulers land on when they first start their workday. Our users manage hundreds of visits per day across clinician teams for patients in all different states of care and need. The challenge isn’t just accessing information; it’s knowing what needs attention right now. A late-running visit, a clinician calling out or a patient emergency can quickly turn into missed opportunities for care or expensive compliance-related penalties.

We built intelligent work queues that prioritize visits and patients requiring intervention. The system evaluates clinician availability changes, compliance risks and urgent scheduling needs, and then surfaces the patients who may not get seen without action. Instead of digging through schedules and reports, users are guided through their day. The queue updates continuously as conditions change, so teams can reassign visits, adjust routes, and respond quickly. We changed the product from a system users monitored to one that actively supports daily healthcare operations. Rather than just displaying schedules, Axle helps teams stay on track and focus attention where it matters most

 

How do you balance experimentation with stability?

Healthcare forces you to be thoughtful about how you innovate. Our users are coordinating real patient care, so we can’t treat experimentation casually, but we also don’t believe stability means stagnation. In practice, we introduce changes carefully. We roll features out to small groups first and spend time thinking through how things could fail before they reach customers. When something doesn’t behave the way we expected, we’re set up to respond quickly and adjust.

As we introduce more advanced or experimental technology, we keep humans in the loop. Automated recommendations support decision-making, but human edits always take precedence, and those edits feed back into the system so it can improve over time. The software adapts to how clinicians and scheduling teams actually work, not the other way around.

We’re intentional about where we apply innovation: We focus it where it creates real leverage and competitive advantage, while keeping foundational workflows predictable and reliable. That balance lets us reduce repetitive, manual work without taking control away from the people in the field who ultimately know best.

 

 

Pager Health’s platform is designed to enable healthcare organizations to offer virtual care services. 

 

How does innovation show up in your company culture?

At Pager Health, innovation is a daily practice, not a one-off initiative. Working at the intersection of AI and healthcare means curiosity and responsibility have to coexist. Our culture encourages teams to ask hard questions like, “Should we build this, who does it help, and where do clinicians need to stay in the loop?” before jumping to solutions.

Innovation also shows up in how cross-functional we are. Product, engineering, data science and clinicians collaborate closely, ensuring we’re not just building technically impressive systems, but ones that actually work in real healthcare settings. One of our secret weapons in our products that's been here since day one at Pager is how much clinical thought is baked into them. Our goal is not replacing physicians. We’re giving them AI superpowers so they can enjoy practicing medicine again. And the patients who see them? They receive faster and elevated care. Wins all around. 

At Pager, we value people who think deeply, challenge assumptions, and stay grounded in the lived experiences of patients and clinicians. That balance is what allows us to innovate responsibly.

 

“At Pager, we value people who think deeply, challenge assumptions, and stay grounded in the lived experiences of patients and clinicians.”

 

What’s one recent innovation that improved user or employee experience?

One of our most significant recent innovations was the development of proprietary safety and clinical guardrails that allow us to responsibly expose a LLM directly to members in a healthcare setting. Internally known as Project Helix, this work took over a year and brought together PhDs, medical doctors and an exceptional team of engineers. I’m incredibly proud of the team not just for what they built, but for how thoughtful and rigorous they were throughout.

The goal was to solve one of the hardest problems in healthcare AI: enabling real-time, member-facing AI in high-stakes clinical scenarios without compromising safety or trust. The system actively de-risks situations like crisis emergencies and behavioral health events with real-time mechanisms that detect and intervene when patterns of harm emerge and bring clinicians in when needed.

We also invested in building systems that can identify hallucinations, intervene in the moment, and continuously refine the model. The result is a better experience on both sides: Members get faster, clearer guidance, and clinicians gain confidence that AI is augmenting care responsibly, not introducing risk.

 

How do you balance experimentation with stability?

In healthcare AI, stability starts with accuracy. We’re willing to experiment when the benefits are clear, but only within very high performance standards. An accuracy mistake in healthcare isn’t like getting a recipe wrong; it can directly impact someone’s loved one.

Before any model leaves experimentation, we test extensively across edge cases, real-world scenarios and clinical contexts until we clearly understand its accuracy and behavior. We don’t consider a model stable just because it works most of the time; stability means we can measure its accuracy, trust its outputs, and stand behind it in high-stakes situations.

Once in production, we continuously monitor model performance. If accuracy drift changes, our data scientists are alerted immediately so we can intervene to refine the model. That closed loop is how we innovate while maintaining the standards healthcare demands.

If you’re evaluating a healthcare company that says it’s using AI, ask questions about testing, accuracy and ongoing monitoring. Responsible AI in healthcare requires real rigor and discipline — and teams who lose sleep over getting it right (I have the Slack messages to prove it).

 

 

Caliola Engineering delivers mission-critical communications solutions to U.S. government entities. 

 

How does innovation show up in your company culture?

Innovation is the transformation of ideas into social impact. Caliola Engineering accomplishes this by quickly and collaboratively applying creative ideas to solve complex technical problems and address operational issues. Innovation is a core value of Caliola, and the resulting impact provides trusted solutions for mission critical communications.

 

“Innovation is a core value of Caliola, and the resulting impact provides trusted solutions for mission critical communications.”

 

What’s one recent innovation that improved user or employee experience?

One recent innovation that I have been personally involved in takes an existing communication system, considers its faults, and generates ideas on how best to improve performance. By engineering and implementing these ideas, our team solves real-world issues, helps protect the warfighter, and supports national security.

 

How do you balance experimentation with stability?

Caliola consists of a group of intelligent, driven individuals who have succeeded through collaboration and cooperation. The team’s extensive experience provides a solid base of knowledge on which to build creative solutions. Through collaborative communication and careful resource management, experimentation is allowed to discover the best possible solutions to complex problems while remaining efficient, effective and efficacious.

 

 

Moov Financial’s payments infrastructure platform is designed to make it easy for businesses to accept, store, send and spend money. 

 

How does innovation show up in your company culture?

Innovation doesn’t come from product teams dreaming up big ideas in isolation. The real innovation, the most valuable stuff, comes from the dot connectors, the people who pick up on patterns in the chaos. 

 

“The real innovation, the most valuable stuff, comes from the dot connectors, the people who pick up on patterns in the chaos.”

 

At Moov, those dot connectors are everywhere. Sometimes it’s a customer support engineer noticing the same question about configuring a widget over and over again. That pattern might be the key to what we should add, or more interestingly, what we should remove from the product. Because sometimes the most innovative move isn’t adding something new; it’s removing a feature, simplifying a process or eliminating a roadblock. 

At Moov, we’ve killed features that looked perfect on a whiteboard but wouldn’t move the needle in the real world. It takes courage to admit when something adds complexity instead of value, and it takes humility to roll up our sleeves and fix it again.

 

What’s one recent innovation that improved user or employee experience?

At Moov, our payment link feature lets businesses collect payments or send payouts. Originally, these templated payment forms took up to six seconds to load and become interactive on mobile browsers due to heavy use of JavaScript on the client side. Six seconds is an eternity when someone’s trying to pay you.

Rather than chasing small performance gains or vanity metrics, we rebuilt the experience from the ground up. The team reimagined the entire rendering strategy, delivering critical content first and then progressively hydrating the page as additional assets load. The result was sub-two-second load times on mobile and under one second on desktop. For customers, that time is money. 

Beyond the performance gains, it was also a meaningful win for the team. They had the autonomy and trust to rethink the problem from first principles, collaborate across disciplines, and ship something fundamentally better. Having the opportunity to collaborate on something new and ambitious, and then watching it succeed together, makes the work incredibly rewarding.

 

How do you balance experimentation with stability?

We treat payment infrastructure like home utilities. When you turn on a faucet, water should come out immediately and be clear. That’s the standard. In money movement, “boring” is a feature. 

Boring doesn’t mean we avoid experimentation. It means we’re disciplined about where experimentation happens. If it touches the movement of money, we move carefully. We test aggressively behind the scenes. We introduce change deliberately. Stability and trust come first. If we’re building a new developer experience or internal workflow, we move fast. We prototype. We gather feedback and iterate quickly. Balancing experimentation with stability isn’t about splitting the difference. It’s about knowing what must be rock-solid and where we have room to explore.

 

 

Ellevation Education’s products help U.S. school district administrators and teachers effectively manage English language learning programs.

 

How does innovation show up in your company culture?

We serve 2.5 million multilingual learners across nearly 2,000 K–12 school districts, which is a daily reminder that the technology we build has to work for a first-year teacher in Massachusetts as well as a seasoned educator in rural Texas. That responsibility shapes how we innovate. We have structured our research and development teams around the real journeys educators take in our products every day rather than around product silos, keeping product, engineering and design closely connected to customer needs through intentional feedback loops.

We invest in ideas that accelerate our mission, improving outcomes for English learners, and we are disciplined about saying “no” to everything else. Teams are encouraged to form hypotheses, test ideas quickly, and ship solutions that make a measurable difference for students and families.

 

“Teams are encouraged to form hypotheses, test ideas quickly, and ship solutions that make a measurable difference for students and families.”

 

Seamless cross-functional collaboration is essential to this work. We create shared rhythms like monthly “Heartbeat sessions” that bring transparency to product releases and upcoming marketing campaigns, and weekly “Demo Fridays,” where engineers share works in progress to provide transparency and create excitement across the company.

 

What’s one recent innovation that improved user or employee experience?

One of our most meaningful recent innovations has been expanding our family communication capabilities to deepen family engagement. By thoughtfully combining translation, accessibility and educator-friendly workflows, we have reduced friction for teachers while making it easier for families to stay informed and involved. We are also investing in AI-enabled translation and communication support systems that help districts deliver timely, accurate outreach at scale without sacrificing trust or clarity. This work improves the educator experience by simplifying communication tasks and improves the family experience by ensuring language is never a barrier to partnership. Internally, it has also strengthened collaboration across product, engineering and customer success, aligning teams around real district workflows and shared outcomes.

 

How do you balance experimentation with stability?

We approach experimentation with clear intent and strong safeguards, always upholding the highest standards of student privacy and security. Innovation at Ellevation is grounded in structured pilots and iterative releases, allowing districts to benefit from new capabilities without disrupting core instructional or operational needs. Our teams build on a foundation of secure infrastructure, rigorous testing and thoughtful change management, ensuring reliability as we evolve. We are intentional about where we experiment, focusing on areas with meaningful upside for educators and students while keeping core systems predictable and stable. We also partner closely with customers during product pilots to validate new capabilities in real-world contexts so innovation remains both practical and responsible. Ultimately, we balance speed with care by remembering that our technology supports real people in high-stakes environments every day.

 

 

Aceable’s mobile education platform offers online driving and real estate courses. 

 

How does innovation show up in your company culture?

At Aceable, we constantly ask ourselves, “What’s just now possible?” which helps us move fast and quickly jump on opportunities that were maybe out of reach months if not weeks ago. We don’t fall in love with solutions because we know those might change along the way. Instead, we have clarity on the pain points we need to solve and the outcomes we need to reach. We also know that asking ourselves, “What’s now possible?” only once is not enough. We understand that innovation is not an endpoint; it’s a practice. And leadership does an excellent job of modeling the level and pace of innovation they expect. It really starts with them. They’re willing to roll up their sleeves and adopt new tech and new approaches right along with the rest of the company.

 

"We understand that innovation is not an endpoint; it’s a practice."

 

What’s one recent innovation that improved user or employee experience?

Our regulatory team receives a lot of communications from many different sources — state agencies, internal teams and partners — some related to courses already in the market and some related to courses we’re actively building and in the process of getting approved. Managing the information flow is a challenge and pretty time-consuming with different threads across tools and different teams that need to be notified. We wanted to tackle this problem, and as a first step, we built a “sorting hat” email agent that lets our regulatory lead forward critical “build in progress” information, and the system automatically classifies it into the right category and pushes it to our source of truth documentation that everyone in the company can leverage. It’s a first version, but it’s a step towards reducing the friction of information flow overhead to zero. The broader vision is expanding this approach so that it covers course maintenance as well. 

 

How do you balance experimentation with stability?

It’s an interesting challenge. There’s a lot of experimentation happening, and that raises some tactical — but super critical — questions: What’s the value of training? What does documentation look like in this world? How do we build a roadmap around our innovation efforts? I don’t claim to have all the answers, but we’ve adapted our communication cadence and rituals to account for constant improvement. Instead of one training a month, we do a small training twice a week. Instead of a deck, we demo live and record on Loom so anyone can catch up. We automate updates on Slack. We make things visual and keep a working Figjam. We go out of our way to bring people along because we know we’re moving fast. And communication alone isn’t enough; you need clear objectives and key results to stay accountable for the business value you’re trying to drive. 

Experimentation for the sake of experimentation isn’t what we want. If you’re driving a ton of experimentation but the results aren’t moving the needle on your OKRs, that’s a good opportunity to stop and re-evaluate: Does your team need more stability? Do you need to just let the team work on a current workflow instead of changing a process yet again? Too much experimentation isn’t an issue necessarily, but challenges show up when it’s rolled out at a pace where the team or the user can’t capture the benefits. The OKRs are one signal. Talking to your stakeholders is another. Both tell you whether you’re on the right track, and it comes down to doing the work right alongside them.

 

 

Munchkin Inc. manufactures a broad range of consumer products for children, such as strollers, high chairs and changing stations. 

 

How does innovation show up in your company culture?

WHY Brands Inc., the holding company of Munchkin, Inc., a portfolio of brands that invent, develop, and design creative products across an array of categories, prides itself on its “Moonshot” culture, in which our executive team are tasked specifically to develop moonshot goals that are so ambitious they seem out of reach. Over the last two years, we were able to bring four moonshots to life, which is a testament to the talent and value our entire team places on innovation. 

Innovation is the roadmap for everything we do. It drives our designers, makes parents’ lives easier, and sets us apart as a forward-thinking company that is laser-focused on making the lives of children, caregivers and families easier. Keeping design at the forefront of our company, our CEO and co-founder of 34 years, Steven B. Dunn, implemented a company policy a decade ago that we still practice today. If a product falls beneath a 4.5 rating, our team has six months to correct it. If it does not meet the standard by that time, we pull it from the shelves.

 

“If a product falls beneath a 4.5 rating, our team has six months to correct it. If it does not meet the standard by that time, we pull it from the shelves.”

 

What’s one recent innovation that improved user or employee experience?

Last year, Munchkin launched FLOW Nipple Shield+, the world’s first nipple shield that lets mothers see their milk flow in real time while nursing, marking the launch of a new category. Accompanied by a national campaign to normalize breastfeeding, Munchkin fought for ad space in Times Square and downtown Nashville, becoming the first major brand to openly and beautifully depict breastfeeding and challenge a decades-long taboo. 

In February 2026, Munchkin launched Munchkin Infant Formula. Feeding decisions are among parents’ most important choices. This science-backed formula includes essential prebiotics, proteins and nutrients supporting immune development, brain growth and digestive health, with 10 key ingredients found in breastmilk. This marks a major milestone for Munchkin, completing its 360 Infant Feeding Ecosystem, “Full Circle Feeding,” which includes the FLOW Nipple Shield+, lactation support products and Bond Bottles, providing parents with high-quality, reliable options to feed with confidence and ease during a vital stage of parenthood. Munchkin is the only company in the world offering these 360 supports, no matter how families choose to feed their babies.

 

How do you balance experimentation with stability?

While cutting-edge technology often grabs headlines, WHY Brands Inc. strongly believes that innovation can also be found in simple, thoughtful solutions that address everyday challenges. In the baby lifestyle space, innovation is crucial. As each generation explores their nascent need for change, WHY adapts and grows with them to continually improve the lives of parents, caregivers, little ones and families. Offering more than 350 innovative products — 5,000-plus stock keeping units — Munchkin averages 12 products per household nationwide. By reimagining traditional baby products with intelligent, engaging designs inspired by user insight, we prove that innovation is powerful and accessible. 

By addressing real-world needs and pain points, WHY Brands’ innovations are setting new benchmarks for quality, functionality and style in the consumer goods space. We’re committed to continuous improvement and collaboration, ensuring that our products not only meet but also exceed the needs of modern customers.

 

 

Vacation rental property owners can use AirDNA’s software to access data to make more informed decisions. 

 

How does innovation show up in your company culture?

I deeply believe innovation can come from anywhere. Very rarely do the best ideas come from the key decision-makers. They usually come from places like the customer experience team, who are on the frontlines talking to customers every day, or from the quietest person in the room.

Innovation is also hard. It’s hard because you have to force yourself to think in terms of first principles. You have to zoom out and ask questions that feel obvious in hindsight, but no one slows down to ask. We spend a lot of time in the weeds, but it’s important to spend time in the clouds, too. At AirDNA, we are intentional about giving people the time and permission to question assumptions and rethink how we do things.

 

“At AirDNA, we are intentional about giving people the time and permission to question assumptions and rethink how we do things.”

 

What’s one recent innovation that improved user or employee experience?

We are leaning into AI like most companies right now. A big part of our business is helping people project how much an Airbnb could make before they buy it. We do this by identifying nearby properties that are already operating as short-term rentals. The more precise we are in selecting these “comps,” the more accurate the revenue projection.

Our data team has spent years refining this process and making AirDNA best-in-class. AI gives us new ways to push it further. Right now, it’s internal. Soon, it’s going to fundamentally change how investors predict revenue.

 

How do you balance experimentation with stability?

This is kind of a trick question. Effective experimentation is not “stable” by some definition. If you want meaningful wins, you have to try things that are … experimental. If you optimize for playing it safe, there’s a good chance you never learn anything. It’s easy to say but hard to practice.

When it comes to technical stability, there are excellent tools that let you experiment with guardrails in place. At AirDNA, we use Statsig to run controlled experiments and monitor impact so we can move fast without being reckless. You still have to be willing to take real swings though. That is where the upside lives.

 

 

Analytics8 is a data analytics consultancy that offers services such as data governance, cloud architecture optimization and generative AI implementation. 

 

How does innovation show up in your company culture?

Our consultants work across a wide variety of client environments and challenges. That breadth of experience builds something valuable: the ability to recognize patterns and separate signals from noise when new technologies emerge.

Based on our experience, the best innovations come from practitioners solving real problems. When a consultant discovers a more effective approach on a project, we create space for them to share it with our broader team. That includes a bi-weekly internal series where consultants spotlight unique client and development work from the field, so practical ideas inform how others approach similar ideas. 

We take a deliberate effort to foster an environment where curiosity and calculated experimentation is supported, and team members are empowered to contribute ideas openly. Innovation at Analytics8 isn’t a top-down initiative. It emerges from experienced people doing the work, testing ideas and talking about what they’re learning from their client work. That practitioner-driven approach is a key part of what makes us effective.

 

“We take a deliberate effort to foster an environment where curiosity and calculated experimentation is supported, and team members are empowered to contribute ideas openly.”

 

What’s one recent innovation that improved user or employee experience?

We’ve developed a Model Context Protocol and agentic framework that allows our consultants to securely connect to client data sources and orchestrate complex analytical workflows using AI.

In practice, this means a consultant can configure an intelligent assistant that understands the client’s data landscape. It can very quickly query databases, analyze schemas, and generate documentation, work that previously required hours of manual exploration. The result is that our teams spend less time on discovery and more time on the higher-value activities: architecture decisions, stakeholder collaboration and solution design. 

Our agentic framework also creates project efficiency without sacrificing quality. The question we now ask is: Can we build this once and adapt it across multiple situations?” That mindset of reusable, configurable solutions allows us to invest more in more valuable data insights rather than infrastructure.

 

How do you balance experimentation with stability?

It’s a balance we think about deliberately. The primary filter we apply is scalability: Is this useful beyond a single engagement, or is it a one-off solution? Can other consultants contribute to it and build on it?

Experimentation is valuable, but stability requires real investment, either time to document and maintain something properly, or resources to build it the right way. We take that investment seriously. When something proves to be scalable, practical, and accessible enough that other team members can contribute, that’s when we commit. We formalize the tooling, create shared ownership, and build it into how we work.

Not every experiment needs to become a platform. The ones that don’t scale still teach us something. But when an approach proves its value across multiple client situations, we make the investment to ensure it is durable and well-supported.