Have you ever wondered how an industry known for delays and uncertainty suddenly starts operating with the pace of a tech company? That thought stayed with me as I spoke with Eppie Vojt, the Chief Digital and AI Officer at West Shore Home. His team is bringing applied AI into home remodeling in a way that feels practical, grounded, and surprisingly human.

Eppie explains how a strong data foundation allowed them to introduce agentic systems without the usual chaos. Those systems now handle scheduling, permitting, forecasting, and communication in the background. The result is a level of certainty that customers rarely experience in remodeling. When someone signs a project, they already know the installation date. Hours of operational work happen silently, and that alone changes the entire experience.

We also talk about the culture that made this possible. Instead of forcing new tools onto teams, leadership encouraged small experiments and curiosity. That simple move flipped the mood internally. Departments began approaching Eppie with ideas rather than waiting to be pushed. The rollout was gradual, giving people time to shift into more valuable work without fear or disruption.

Looking ahead, Eppie sees huge potential in letting customers start their journey in different ways. Tools like photogrammetry and digital twins could help people get early pricing guidance without a full in-home visit. It reflects a bigger change across physical industries as AI becomes something that quietly supports accuracy, safety, and convenience.

If you care about real AI adoption rather than hype, this one offers a clear view into what works. I'd love to hear what stood out to you after listening.

Useful Links

• Connect with Eppie Vojt on LinkedIn
• Learn more about West Shore in this video

What does it really mean to run a company that aims to be "good" before it ever thinks about becoming "great"? That was the question sitting with me as I sat down with Appfire's CEO, Matt Dircks. The conversation took us straight into the heart of modern leadership, purpose, and the realities of running a global SaaS business during a period of change.

Matt has led organisations through rapid growth, mergers, cultural resets, and shifting market expectations. What stood out in our discussion was how open he is about the parts of leadership that are messy. He talked about transparency, dealing with hard decisions, and the challenge of building a culture where people feel safe enough to be honest without losing accountability. His philosophy is grounded in something simple. You cannot scale trust unless you behave in ways that earn it every day.

We explored how Appfire is evolving beyond its acquisition roots, expanding from Atlassian aligned tools into cross platform solutions that support enterprises across Microsoft, Salesforce, GitHub and more. Matt explained why the company is investing heavily in new AI native products and why being close to customers is becoming a priority as their needs become more complex. He also shared how openness, active communication, and a willingness to be challenged guide the way he leads through uncertainty.

The more we talked, the clearer it became that Appfire's next chapter is a blend of product innovation, cultural maturity, and a renewed focus on service. Matt's story offers a useful lens for anyone wrestling with questions about values, growth, and the human side of technology. What does a "good company" look like in practice, and how does that shape the road to long term success?

I'd love to hear what resonated with you, so let me know your thoughts.

• Connect With Matt Dircks on LinkedIn
• Learn more about Appfire
• The No Asshole Rule: Building a Civilized Workplace and Surviving One That Isn't by Robert I. Sutton
• Range: Why Generalists Triumph in a Specialized World by David Epstein

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What happens when AI adoption surges inside companies faster than anyone can track, and the data that fuels those systems quietly slips out of sight? That question sat at the front of my mind as I spoke with Cyberhaven CEO Nishant Doshi, fresh from publishing one of the most detailed looks at real-world AI usage I have seen. This wasn't a report built on opinions or surveys. It was built on billions of actual data flows across live enterprise environments, which made our conversation feel urgent from the very first moment.

Nishant explained how AI has moved out of the experimental phase and into everyday workflows at a speed few anticipated. Employees across every department are turning to AI tools not as a novelty but as a core part of how they work. That shift has delivered huge productivity gains, yet it has also created a new breed of hidden risk. Sensitive material isn't just being uploaded through deliberate actions. It is being blended, remixed, and moved in ways that older security models cannot understand. Hearing him describe how this happens in fragments rather than files made me rethink how data exposure works in 2025.

We also dug into one of the most surprising findings in Cyberhaven's research. The biggest AI power users inside companies are not executives or early career talent. It is mid-level employees. They know where the friction is, and they are under pressure to deliver quickly, so they experiment freely. That experimentation is driving progress, but it is also widening the gap between how AI is used and how data is meant to be protected. Nishant shared how that trend is now pushing sensitive code, R&D material, health information, and customer data into tools that often lack proper controls.

Another moment that stood out was his explanation of how developers are reshaping their work with AI coding assistants. The growth in platforms like Cursor is extraordinary, yet the risks are just as large. Code that forms the heart of an organisation's competitive strength is frequently pasted into external systems without full awareness of where it might end up. It creates a situation where innovation and exposure rise together, and older security frameworks simply cannot keep pace.

Throughout the conversation, Nishant returned to the importance of visibility. Companies cannot set fair rules or safe boundaries if they cannot see what is happening at the point where data leaves the user's screen. Traditional controls were built for a world of predictable patterns. AI has broken those patterns apart. In his view, modern safeguards need to sit closer to employees, understand how fragments are created, and guide people toward safer workflows without slowing them down.

By the time we reached the end of the interview, it was clear that AI governance is no longer a strategic nice-to-have. It is becoming a daily operational requirement. Nishant believes employers must create a clear path forward that balances freedom with control, and give teams the tools to do their best work without unknowingly putting their organisations at risk. His message wasn't alarmist. It was practical, grounded, and shaped by years working at the intersection of data and security.

So here is the question I would love you to reflect on. If AI is quickly becoming the engine of productivity across every department, what would your organisation need to change today to keep its data safe tomorrow? And how much visibility do you honestly have over where your most sensitive information is going right now? I would love to hear your thoughts.

Useful Links

• Connect with Cyberhaven CEO Nishant Doshi on LinkedIn
• Learn more about Cyberhaven

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Have you ever wondered what it looks like when an enterprise finally breaks free from spreadsheet-driven decision paralysis and lets AI take the wheel? That was the question at the back of my mind as I sat down with Bianca Anghelina, the founder of Aily Labs.

In our conversation, Bianca explains how her career inside large global enterprises shaped her view of the world. She saw first hand how companies could gather astonishing amounts of data but struggled to translate it into choices that actually mattered.

That friction pushed her to imagine something bolder, a decision intelligence platform that could remove the hand-stitched chaos of manual analysis and replace it with real-time clarity. She shares how she took the leap during an uncertain moment in 2020, trusting the idea that disruption often grows during difficult periods. Hearing her describe that early stage reminded me how many founders take quiet risks long before the public sees any success.

What stood out most was the simplicity of her philosophy. Every company will eventually use AI, but only the ones that rewire their culture and everyday routines will turn it into measurable value. Bianca talks about the shift from pilots to production, the widening gap between firms that run AI at scale and those still treating it as a side project, and how leaders need to rethink their role if they want to see material financial impact.

She also shares how Aily Labs uncovered a hundred million dollars in opportunities instantly for one enterprise, and how their AI agents connect previously isolated functions to solve resource allocation, supply chain shocks, and board-level scenarios in minutes instead of months.

We also look ahead. Bianca outlines her vision for fully autonomous decision-making agents and the long path toward an operating model where strategy, execution, and action flow through a single intelligent layer. Her optimism about where this can lead Fortune 500 organisations over the next five years left me thinking about how quickly boardrooms will need to adapt.

At the same time, she grounds that vision in her own story, acknowledging the mentors and supporters who helped her grow from corporate leader to founder.

If you are wrestling with the business case for AI, or trying to understand why so many firms still struggle to get past experimentation, this episode offers a clear window into what happens when AI is built into the centre of how an enterprise thinks. It is a rare mix of founder story, practical insight, and a glimpse of the future.

What part of Bianca's thinking resonates most with your own experience, and how do you see decision intelligence reshaping leadership teams in the years ahead? Let me know your thoughts.

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Is AI quietly pushing us to work alone when creativity has always thrived on collaboration? I'm joined by Joseph "Coop" Cooper, co-founder of Nullshot, to unpack a different vision for how AI should support creators, builders, and teams. And before any Interstellar fans get too excited, this is not the cinematic space explorer navigating wormholes; this is a serial entrepreneur building something very real on Earth, where conversations turn into working applications in real time.

Coop shares how his journey from modding video games as a child to launching multiple ventures across crypto and developer tools shaped his belief that current AI tools lean too heavily towards individual productivity. He explains the thinking behind Nullshot's jam rooms, where multiple people can co-create with AI in a shared space, instead of building in isolation. We explore how this model encourages quieter voices to contribute, removes the pressure of pitching ideas, and replaces it with live prototypes that speak for themselves.

Alongside the enthusiasm for this collaborative future, Coop also addresses the tougher questions around ownership, fair credit, and how contributions should be recognised without being gamed. There is an honest discussion about whether AI-powered creation lowers barriers for everyone or risks shifting too much power towards the platforms that control it. The balance between opportunity and risk feels central to what Nullshot is attempting to achieve.

As teams, founders, and creators look for better ways to bring ideas to life together, this conversation offers a grounded look at what shared AI creation could mean in practice. Are we ready to move from solo prompts to collective building, and what might that shift say about how we define creativity in the next phase of digital work?

What do you think, could collaborative AI change the way your team builds, and how would you feel about sharing ownership in a live creative space?

Useful Links

Learn More About Nullshot

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Is the UK quietly slipping into the role of a cautious observer while other nations shape the future of AI with greater confidence and intent?

In this episode of Tech Talks Daily, I sit down with Rav Hayer, Managing Director at ThoughtWorks and Head of BFSI, to explore why our approach to AI regulation may be slowing progress at a time when momentum matters.

We move beyond the headlines of multi-billion pound investment announcements and look at what is really happening on the ground for business leaders trying to innovate in an environment shaped by uncertainty, shifting guidance, and risk aversion.

Throughout our conversation, Rav shares his perspective on how this climate is affecting founders, scaleups, and established enterprises alike. We examine why so much British innovation still finds its way overseas, and what that says about ownership, long-term competitiveness, and the confidence gap holding many organisations back.

I also ask Rav to compare the UK's position with regions such as Singapore, Brazil, and Saudi Arabia, where proactive regulation is being used to encourage innovation rather than create friction. Together, we unpack the hidden costs of ambiguity, from time lost in legal interpretation to talent being drawn away from building meaningful progress at home.

We close the episode on a more human note as Rav reflects on his personal journey, the role his parents played in shaping his work ethic, and the values that continue to guide his leadership today.

As the UK weighs protection against progress, should we continue to step carefully, or is it time to show greater conviction and direction in our AI strategy? I would love to hear your thoughts on where that balance should sit. What do you think, and how should the UK move forward from here?

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What happens when a seasoned finance leader steps into the world of enterprise software and decides to rebuild the financial close with AI at its core?

In my conversation with Darren Heffernan, CEO of Trintech, we look at the shifts taking place inside the office of the CFO and how automation is reshaping a discipline that has relied on spreadsheets and manual routines for generations. Darren's story spans public practice, GE Capital, and twenty years inside Trintech, which gives him a rare view of both the pressure inside finance teams and the opportunities created when workflow, data, and intelligence finally come together.

Across our discussion, Darren explains how Trintech has spent decades refining the financial close by embedding intelligence directly inside the workflow rather than bolting it on. He talks through real examples of AI identifying exceptions, writing rules, scanning volumes of transactions, and reaching back to a human for review so the outcome remains transparent and traceable. His point is that trust and clarity matter as much as speed, especially in a profession where regulators, auditors, and boards expect every action to be explainable. It is a reality check for anyone comparing providers claiming to deliver AI without the decades of grounding needed to understand how finance actually works.

We also talk about the human side of transformation. Darren believes the people who learn to work with AI will thrive, and he pushes back against the idea that automation threatens finance roles. Instead, he sees a future where agents and humans collaborate while accountants focus on judgment, interpretation, and value.

His reflections on leadership, mentorship, humility, and the maturity that comes from doing almost every role inside a company add a personal texture to the story. It is clear that his philosophy of making time count is not a slogan, it is a way of working that shapes how Trintech designs its products and how teams support customers.

As we look toward 2026, Darren shares his view on the next frontier in finance. He describes a future where AI powered workflows not only detect issues but take action, improve continuously, and still respect the need for control. His message is simple.

Finance runs on trust, and AI must strengthen that trust, never weaken it. So how should leaders approach this moment, and what might the financial close look like once AI becomes a reliable partner rather than a confusing buzzword? I would love to hear what you think.