Gal Rimon, Founder and CEO of Centrical – Interview Series

Gal Rimon founded Centrical (previously GamEffective) in 2013 with a clear vision: helping companies empower their employees and make people the center of business success. Before Centrical, he served as CEO of Gilon Business Insight, a leader in business intelligence. When Gilon was acquired by Ness Technologies (NASDAQ: NSTC) in 2010, Gal joined Ness as Senior Vice President and member of its executive leadership team. Earlier in his career, he served as VP of Customer Relations and Operations at Deloitte Consulting and held roles at EDS and Bashan. Gal holds an MBA in Marketing and Information Technologies from Tel Aviv University.

Centrical is an AI-powered employee performance and engagement platform that helps organizations improve the effectiveness of frontline teams through a unified system for performance management, personalized coaching, continuous learning, quality assurance, and gamification. The platform brings together employee performance data, AI-driven coaching, microlearning, recognition programs, and real-time insights to help managers identify opportunities for improvement and guide employees toward better outcomes. Widely used by contact centers, sales organizations, and customer experience teams, Centrical is designed to increase employee engagement, productivity, customer satisfaction, and overall business performance by delivering personalized guidance and motivation at scale.

Before founding Centrical, you spent years in business intelligence leadership, including as CEO of Gilon before its acquisition by Ness Technologies. How did that experience shape your view that enterprises did not just need better dashboards/data, but a system that could turn insights into action for frontline teams?

I spent nearly two decades in business intelligence, working with consulting firms like EDS and Deloitte and later running my own company. We were helping some of the world’s largest organizations make sense of their data, and we were good at it. But I kept running into the same wall. Companies had invested heavily in data infrastructure. The dashboards were sophisticated. The KPIs were well-defined. And still, very little changed.

The intelligence existed. It just didn’t act. The missing link was always the human element. You can put a red flag next to an underperforming employee on a dashboard, but that flag doesn’t tell the manager what to do, and it doesn’t help the employee improve. The bridge between insight and execution runs through people, and no BI tool I ever worked with was built to cross it. That realization became the founding idea behind Centrical. The question was never “how do we give leaders more data?” but “how do we turn data into the right action, for the right person, at the right moment?”

The more point solutions you have, the more data you have, and the more challenging this can be.

Centrical describes itself as building a “Performance Intelligence OS” for the frontline. What does that mean in practical terms for a customer service, hospitality, banking, or telecom team using the platform every day?

Let’s imagine a customer service agent at a large bank: she handles complex calls all day. Her manager oversees a team of 30 people across two sites. Without a performance operating system, the manager spends most of his time pulling reports, reviewing QA scores, and trying to figure out who needs attention. By the time he gets to coaching, it’s reactive, too late, and inconsistent across his team and the next.

With Centrical, the day looks different. The platform starts from the business outcome: a quality improvement target, a new product launch, or a compliance requirement. It takes in signals from KPI data, quality evaluations, learning progress, and employee feedback to identify exactly where the gaps are. When an agent has a specific weakness, say, weak probing questions on retention calls, the platform surfaces that to the manager with a recommended coaching action already prepared, and it triggers a targeted role-play simulation for the agent to practice before the next call.

For the agent in hospitality, it might mean a personalized challenge tied to loyalty enrollment behaviors, with real-time feedback and recognition built into the flow of work. For a telecom team launching a new product, it might mean adaptive learning that adjusts to each rep’s existing knowledge gaps rather than pushing everyone through the same content.

The common thread is that the system connects strategy to execution for every person on the floor, not just the ones whose managers happen to have time that week.

Many enterprises already have BI tools, workforce management systems, learning platforms, and quality assurance software. Where do these systems typically fall short when it comes to improving real-world employee performance?

The problem is not the tools individually, but that they don’t talk to each other in a way that truly benefits the frontline operation.

A QA system flags a quality issue. That flag sits on a dashboard. The manager sees it three days later, if at all. The learning platform has content that could help, but nobody connects the flag to the content. The workforce management system optimizes schedules but knows nothing about skill gaps. And recognition happens separately, in yet another tool.

So insights and decisions never reach the people who need them. Coaching gets disconnected from training, training gets disconnected from outcomes, and the frontline employee experiences a fragmented set of programs that don’t add up to improvement.

And now AI agents are entering the mix, being deployed and optimized in isolation from the human workforce, further compounding the challenge. The answer is not just connecting these platforms. It’s orchestrating them around a shared goal: the right intervention, for the right person, at the right moment, measured against a real business outcome.

The company’s recent customer results include improvements in first-call resolution, sales performance, loyalty enrollment, productivity, and error reduction. What do those outcomes reveal about the kind of frontline work that AI can improve first?

The common thread across all of those results is that they involve work you can measure and improve through behavioral change, through knowledge reinforcement, skill development, and the personalized training, coaching, and motivation that make it stick.

The frontier that AI is opening up now is doing this at the individual level, not just for a segment or a cohort, but for each person, based on their specific gaps, their role, and where they are in their development journey, and what the business needs from them.

TP Samsung’s customer service teams improved first-call resolution by 7.5% while reducing manager administrative work by 70%.

A top five U.S. bank’s fraud back office saw a 66.7% reduction in errors and a 4.8% increase in accounts processed.

IHG Hotels & Resorts wanted their front desk staff to actively recognize and enroll guests into their One Rewards program. We gamified the training into missions, gave staff coins for completing learning and enrolling members, and let properties compete on leaderboards. Hotels using the Centrical platform achieved up to 4x the improvement in loyalty recognition and enrollment efficiency, driving millions in additional revenue and direct bookings.

Centrical is expanding its AI portfolio with AI-assisted coaching, role-play simulations, hyper-personalized performance experiences, and autonomous performance intelligence. Which of these capabilities do you believe will have the biggest near-term impact on enterprise teams?

It depends on where an organization is in its transformation. I’d highlight two capabilities that are creating immediate, measurable impact right now for our customers.

AI-Assisted Coaching is having an outsized effect because organizations are under real pressure to make coaching more efficient, increase manager spans of control, and still improve team performance. Managers are the single biggest influence on frontline performance, and yet they’ve historically spent more time reporting than on coaching. Our AI assistant flips that ratio: it surfaces who to coach, on what, and why, with the right action already prepared. And it’s all prioritized based on the goals of the business. The manager becomes a better coach without needing more hours in the day.

AI Role-Play simulations are equally critical right now, for a different reason. As AI takes on simpler interactions, the conversations that reach human agents are getting more complex: emotionally charged, exception-heavy, high-stakes. At the same time, organizations are mobilizing their workforces into new roles faster than ever. Practice (in the flow of work) is the only way to build confidence and competence in those situations before they happen live. Role-play at scale, driven by actual performance gaps, makes that possible.

Autonomous Performance Intelligence is the next frontier. The vision is a system that identifies opportunities, triggers the right programs, and continuously improves frontline execution without waiting for a manager to initiate.

How does AI-assisted coaching change the role of frontline managers, especially when many managers are already overloaded with administrative work and performance reporting?

Our data shows that managers were spending roughly 60% of their time on data analysis and around 20% on evaluations, leaving less than 20% for actually supporting their teams. That’s the core dysfunction. The people most responsible for frontline performance were spending most of their time doing things a system should do for them.

AI Coaching reclaims that time. The manager gets a prioritized view of exactly who to coach, on what behavior, with a suggested approach already prepared. Sessions can be recorded and auto-documented, so follow-up actions get activated directly from the coaching conversation rather than sitting in a note nobody reads. Personalized goals are created that are both achievable and visible to the employee.

Our data is already showing that managers using our AI capabilities are coaching more, and that their coaching is having a bigger impact on their team’s performance. One of our large hospitality customers saw a 10% improvement in coaching effectiveness thanks to AI, resulting in a measurable improvement in KPIs: all of the KPIs that employees were coached on with Centrical’s AI capabilities improved.

One of the most interesting parts of Centrical’s positioning is the idea of managing both human and digital workers. How should enterprises think about performance management when AI agents become part of frontline operations?

Most enterprises are walking into a problem they don’t see yet. AI agents are showing up from everywhere: one from your contact center platform, one from your CRM, a few from your own teams built, and others bundled into tools you already pay for. We call it the zoo of agents. They’re all doing work, but no one owns their performance. No one can tell you which ones are actually good at the job, which ones are drifting, or which ones should be pulled.

The instinct is to treat that as a technical problem, a model, or an integration. I think that’s the wrong frame. Once an AI agent is doing frontline work, it needs to be governed with the same rigor we bring to human performance: clear goals, measurable outcomes, certification before it handles anything high-stakes, and a feedback loop that catches drift before it does damage.

That’s the layer most enterprises are missing. Not another place to build agents, but a unified layer to manage, certify, and orchestrate them, sitting in the same system that manages your people. Because the work isn’t human or digital anymore. It’s both, on the same team, often on the same task. A person and three agents handling one customer interaction. If you measure the people in one place and the agents in another, you only see fragments of their performance.

Performance management should be seen as one discipline across the entire workforce, human and digital. Same goals, same accountability, same loop of measure, coach, improve.

What safeguards are needed to make sure AI-driven performance systems support employees rather than simply increasing pressure, surveillance, or unrealistic productivity expectations?

This is an important topic because we use AI to help people become a better version of themselves at work.

Pressure can drive short-term results. But over the long term, you need to give people a clear direction: help them build the specific skills their role needs, and create focused practice on the behaviors that matter most. That’s a different deal for the employee. The system isn’t there to watch you. It’s there to make you good at your job. And getting good at your job feels a lot better than being measured on it. It’s a simple thing, but nobody shows up to work wanting to do a bad job.

So the real safeguards aren’t policies, or processes, or some recognition program that sits off to the side. They’re the instructions, skills, and tools built into the daily work to make the job easier, more rewarding, more effective. Skills first, then behaviors, then performance. And underneath all of it, motivation: the employee should see the progress they’re making and feel it. Every step should connect back to something they care about: the customers they help, the team they’re part of, and where they want to go next. That’s what makes it feel like more than a scoreboard.

And because AI handles the admin, the reporting, the prioritization, managers get their time back to actually coach. To be present. That human relationship is what makes the whole thing work.

Centrical recently raised $39 million in Series D funding, bringing total funding to more than $100 million. Over the next 12 to 24 months, how will this new capital accelerate your global expansion and the development of AI tools for managing frontline performance across both human and digital workers?

We’re thinking about growth across multiple areas:

We serve enterprises in 150 countries and 60 languages today, and this round will accelerate our growth globally.

Deepening autonomous Performance Intelligence will be one of our areas of focus. The next stage is a system that identifies opportunities, triggers the right programs, and improves frontline execution continuously without requiring a manager to initiate.

Finally, extending the platform to govern performance across human and AI workforces together. As AI agents take on more frontline tasks, enterprises need the same rigor around performance standards, coaching, and measurement for those digital workers as they have for human ones. We’re positioned to be the operating system for that hybrid reality.

Thank you for hte great interview, readers who wish to learn more should visit Centrical.