Have you ever wondered what happens when the browser stops being a simple window to the web and starts becoming the control point for how AI touches every part of enterprise life? That was the starting point for my conversation with Michael Shieh, founder and CEO of Mammoth Cyber.

What followed was a detailed look at why the browser is turning into the foundation of enterprise AI and why the shift is arriving faster than many expect.

Michael shared why employees already spend most of their working lives inside a browser and how this makes it the natural place for AI to support decisions, speed up routine work, and act as the interface between people, applications, and data. But we also spoke about the uncomfortable reality behind that convenience.

When consumer AI browsers rush ahead with features that harvest data or request wide-reaching permissions, the trade off between speed and governance becomes harder to ignore. Michael explained how this gap leaves security teams unable to see where sensitive data is being sent or how shadow AI creeps into daily workflows without oversight.

During our conversation he broke down what makes an enterprise AI browser different. We talked about policy controlled access, device trust, identity federation, and the safeguards that protect AI from hazards like indirect prompt injection. Michael also described how the Mammoth team built a multi layer security model that monitors what the AI can view, what it cannot view, and how data moves across applications in real time. His examples of DLP at the point of use, low friction controls for workers, and granular visibility for security teams showed how the browser is becoming the new enforcement boundary for zero trust.

We also covered the growing tension between traditional access models like VPNs or VDI and the faster, lightweight deployment Mammoth is offering to large enterprises. Hearing Michael explain how some customers replaced heavy remote access stacks in weeks made it clear that this is more than a new product category. It hints at an early move toward AI shaped workflows running directly at the endpoint rather than through centralised infrastructure. As he looked ahead to the next few years, Michael shared why he expects the browser to operate as a kind of operating system for enterprise AI, blending native AI agents, web apps, and policy controls into a single environment.

This episode raises an important question. If the browser becomes the place where AI reads, writes, and interprets information, how should enterprises think about identity, trust, and control when the pace of AI adoption accelerates again next year? I would love to hear your thoughts.

What happens when a former NHL player who once faced Wayne Gretzky ends up running a global data company that sits at the center of the AI boom? That question kept coming back to me as I reconnected with Mike McKee, the CEO of Ataccama, seven years after our last conversation. So much has shifted in the world since then, yet the theme that shaped this discussion felt surprisingly grounded. None of the big promises of AI can take hold unless leaders can rely on the data sitting underneath every system they run.

Mike brings a rare mix of stories and experience to this theme. His journey from the ice to the C suite feels like its own lesson in discipline, teamwork, and patience, and he openly reflects on the way those early years influence how he leads today. But the heart of this conversation sits in the reality he sees inside global enterprises. Everyone is racing to build AI powered services, yet the biggest blockers are messy records, inconsistent metadata, long forgotten databases, and years of quality issues that were never addressed. It is a blunt problem, and Mike explains why the companies winning with AI right now are the ones treating data trust as a foundation rather than an afterthought.

Across the discussion, he shares stories from organisations like T Mobile and Prudential, where millions of records, thousands of systems, and vast volumes of structured and unstructured data must be monitored, understood, and governed in real time. Mike walks through how teams build confidence in their data again, why quality scores matter, and how automation now shapes everything from compliance to customer retention. What stood out most is how quickly the expectations have shifted. Boards and CEOs now treat data as a strategic asset rather than an operational chore, and entire roles have emerged above the chief data officer to steer these programmes.

This episode is also a reminder that AI progress is never only about models or GPUs. Mike pulls back the curtain on why organisations struggle to measure AI readiness, how they can avoid bottlenecks, and what it takes to prioritise the work that actually moves the needle. His point is simple. Without trustworthy data, AI remains a promise rather than a practical tool. With it, businesses can act with confidence, respond faster, and make decisions that genuinely improve outcomes for customers and employees.

So as AI reaches deeper into systems everywhere, how should leaders rethink their approach to data trust, governance, and quality? And if you have been on your own journey with data challenges, where have you seen progress and where are you still stuck? I would love to hear your thoughts.

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What happens when a former Microsoft leader walks away from tech, immerses himself in personal wellbeing, and accidentally discovers one of the biggest blind spots in the global spa, salon, and wellness industry? That question sat with me as I spoke with Sudheer Koneru, founder and CEO of Zenoti, who has shaped one of the most influential platforms powering beauty, wellness, and fitness operations in more than fifty countries.

This conversation takes an interesting path. Sudheer began his career inside Microsoft during its high-growth era, then built and exited a successful enterprise software company, only to step away from the industry entirely. Those two quiet years focused on health and family revealed something surprising.

The spa and salon sector he was engaging with as a customer lacked modern tools, consistent experiences, and operational systems that could help both staff and guests thrive. That realisation moved him from passive observation into building Zenoti, a platform designed for large brands with multi location operations. Today, Zenoti supports more than twelve thousand businesses and processes millions of bookings each year.

Across our discussion, Sudheer explained why staff turnover shapes guest trust far more than most of us realise. He shared the emotional aspect of returning customers wanting familiar faces, the operational pressure this creates, and the measurable business impact when those connections are lost.

We also talked about the role of AI. Unlike many narratives that focus on automation replacing creativity, Sudheer was clear that AI is strengthening the personal side of the industry. He described how tools like Zeni and Hyperconnect reduce missed calls, increase upsells, support new staff with real context, and free human teams to offer better on site care.

Hearing how Zenoti has grown as a profitable unicorn while staying selective about its customer base added another layer. Sudheer credits this discipline as one of the company's strongest decisions, along with a willingness to focus on brands that truly benefit from the platform's depth.

As the wellness and beauty sectors move further into AI supported operations, the question becomes whether businesses can adopt new capabilities without losing the warmth and familiarity that keep guests returning. After listening, how do you feel about AI supporting personal service industries, and where do you see the right balance landing? I would love to hear your thoughts.

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