Tech Talks Daily

What happens when an industry that has barely changed for generations suddenly finds itself at the center of one of the biggest shifts in modern work?
In this episode of Tech Talks Daily, I'm joined by Kate Hayward, UK Managing Director at Xero, for a conversation about how accounting is being reshaped by technology, education, regulation, and changing expectations from clients and talent alike.
Kate describes this moment as the largest reorganization of human capital in the history of the profession, and as we talk, it becomes clear why that claim is gaining traction.
We explore how AI is shifting accountants away from pure number processing and toward higher-value advisory work, without stripping away the deep financial understanding the role still demands.
Kate shares why so many practices are reporting higher revenues and profits, and how technology is acting as a catalyst for rethinking long-standing workflows rather than simply speeding up broken ones.
We also dig into research showing that pairing AI with financial education strengthens analytical thinking while leaving core calculation skills intact, a useful counterpoint to the more dramatic headlines about machines replacing people.
Our conversation moves into the practical reality of how firms are using tools like ChatGPT today, from scenario planning to preparing for difficult client conversations, while also discussing where caution still matters, particularly around data security and core financial workflows.
Kate also explains how government initiatives such as Making Tax Digital and the digitization of HMRC are changing client expectations and deepening the relationship between accountants and the businesses they support.
We also spend time on the future of the profession, including how hiring strategies are evolving, why problem-solving and communication skills are becoming just as valuable as technical knowledge, and why private equity interest in accounting is accelerating digital adoption across the sector.
Kate rounds things out by sharing how Xero is thinking about product design in 2026, what users can expect next, and why keeping the human side of the profession front and center still matters.
So as accounting moves further into an AI-assisted, digitally native future, how do firms balance efficiency, trust, identity, and long-term relevance, and what lessons can other industries take from this moment of change?
Useful Links
Follow Kate Hayward on LinkedIn
Accounting and Bookkeeping Industry Report
Xero Website
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What does sales leadership actually look like once the AI experimentation phase is over and real results are the only thing that matters?
In this episode of Tech Talks Daily, I sit down with Jason Ambrose, CEO of the Iconiq backed AI data platform People.ai, to unpack why the era of pilots, proofs of concept, and AI theater is fading fast. Jason brings a grounded view from the front lines of enterprise sales, where leaders are no longer impressed by clever demos. They want measurable outcomes, better forecasts, and fewer hours lost to CRM busywork. This conversation goes straight to the tension many organizations are feeling right now, the gap between AI potential and AI performance.
We talk openly about why sales teams are drowning in activity data yet still starved of answers. Emails, meetings, call transcripts, dashboards, and dashboards about dashboards have created fatigue rather than clarity.
Jason explains how turning raw activity into crisp, trusted answers changes how sellers operate day to day, pulling them back into customer conversations instead of internal reporting loops. The discussion challenges the long held assumption that better selling comes from more fields, more workflows, and more dashboards, arguing instead that AI should absorb the complexity so humans can focus on judgment, timing, and relationships.
The conversation also explores how tools like ChatGPT and Claude are quietly dismantling the walls enterprise software spent years building. Sales leaders increasingly want answers delivered in natural language rather than another system to log into, and Jason shares why this shift is creating tension for legacy platforms built around walled gardens and locked down APIs.
 We look at what this means for architecture decisions, why openness is becoming a strategic advantage, and how customers are rethinking who they trust to sit at the center of their agentic strategies.
Drawing on work with companies such as AMD, Verizon, NVIDIA, and Okta, Jason shares what top performing revenue organizations have in common.
Rather than chasing sameness, scripts, and averages, they lean into curiosity, variation, and context. They look for where growth behaves differently by market, segment, or product, and they use AI to surface those differences instead of flattening them away. It is a subtle shift, but one with big implications for how sales teams compete.
We also look ahead to 2026 and beyond, including how pricing models may evolve as token consumption becomes a unit of value rather than seats or licenses.
Jason explains why this shift could catch enterprises off guard, what governance will matter, and why AI costs may soon feel as visible as cloud spend did a decade ago. The episode closes with a thoughtful challenge to one of the biggest myths in the industry, the belief that selling itself can be fully automated, and why the last mile of persuasion, trust, and judgment remains deeply human.
If you are responsible for revenue, sales operations, or AI strategy, this episode offers a clear-eyed look at what changes when AI stops being an experiment and starts being held accountable, so what assumptions about sales and AI are you still holding onto, and are they helping or quietly holding you back?
Useful Links
Follow Jason Ambrose on LinkedIn
Learn more about people.ai
Follow on LinkedIn
Thanks to our sponsors, Alcor, for supporting the show.

In this episode of Tech Talks Daily, I sat down with Keith Zubchevich, CEO of Conviva, to unpack one of the most honest analogies I have heard about today's AI rollout.
Keith compares modern AI agents to toddlers being sent out to get a job, full of promise, curious, and energetic, yet still lacking the judgment and context required to operate safely in the real world. It is a simple metaphor, but it captures a tension many leaders are feeling as generative AI matures in theory while so many deployments stumble in practice.
As ChatGPT approaches its third birthday, the narrative suggests that GenAI has grown up. Yet Keith argues that this sense of maturity is misleading, especially inside enterprises chasing measurable returns. He explains why so many pilots stall or quietly disappoint, not because the models lack intelligence, but because organizations often release agents without clear outcomes, real-time oversight, or an understanding of how customers actually experience those interactions.
The result is AI that appears to function well internally while quietly frustrating users or failing to complete the job it was meant to do.
We also dig into the now infamous Chevrolet chatbot incident that sold a $76,000 vehicle for one dollar, using it as a lens to examine what happens when agents are left without boundaries or supervision.
Keith makes a strong case that the next chapter of enterprise AI will not be defined by ever-larger models, but by visibility. He shares why observing behavior, patterns, sentiment, and efficiency in real time matters more than chasing raw accuracy, especially once AI moves from internal workflows into customer-facing roles.
This conversation will resonate with anyone under pressure to scale AI quickly while worrying about brand risk, accountability, and trust. Keith offers a grounded view of what effective AI "parenting" looks like inside modern organizations, and why measuring the customer experience remains the most reliable signal of whether an AI system is actually growing up or simply creating new problems at speed.
As leaders rush to put agents into production, are we truly ready to guide them, or are we sending toddlers into the workforce and hoping for the best?
Useful Links
Connect with Keith Zubchevich
Learn more about Conviva
Chevrolet Dealer Chatbot Agrees to Sell Tahoe for $1
Thanks to our sponsors, Alcor, for supporting the show.

In this episode of Tech Talks Daily, I sit down with Imran Nino Eškić and Boštjan Kirm from HyperBUNKER to unpack a problem many organisations only discover in their darkest hour. Backups are supposed to be the safety net, yet in real ransomware incidents, they are often the first thing attackers dismantle. Speaking with two people who cut their teeth in data recovery labs across 50,000 real cases gave me a very different perspective on what resilience actually looks like.
They explain why so many so-called "air-gapped" or "immutable" backups still depend on identities, APIs, and network pathways that can be abused. We talk through how modern attackers patiently map environments for weeks before neutralising recovery systems, and why that shift makes true physical isolation more relevant than ever. What struck me most was how calmly they described failure scenarios that would keep most leaders awake at night.
The heart of the conversation centres on HyperBUNKER's offline vault and its spaceship-style double airlock design. Data enters through a one-way hardware channel, the network door closes, and only then is information moved into a completely cold vault with no address, no credentials, and no remote access. I also reflect on seeing the black box in person at the IT Press Tour in Athens and why it feels less like a gadget and more like a last-resort lifeline.
We finish by talking about how businesses should decide what truly belongs in that protected 10 percent of data, and why this is as much a leadership decision as an IT one. If everything vanished tomorrow, what would your company need to breathe again, and would it actually survive?
 
Useful LInks
Connect with Imran Nino Eškić
Connect With Boštjan Kirm
Learn More about HyperBUNKER
Lear more about the IT Press Tour
Thanks to our sponsors, Alcor, for supporting the show.

What happens when the AI race stops being about size and starts being about sense?
In this episode of Tech Talks Daily, I sit down with Wade Myers from MythWorx, a company operating quietly while questioning some of the loudest assumptions in artificial intelligence right now. We recorded this conversation during the noise of CES week, when headlines were full of bigger models, more parameters, and ever-growing GPU demand. But instead of chasing scale, this discussion goes in the opposite direction and asks whether brute force intelligence is already running out of road.
Wade brings a perspective shaped by years as both a founder and investor, and he explains why today's large language models are starting to collide with real-world limits around power, cost, latency, and sustainability. We talk openly about the hidden tax of GPUs, how adding more compute often feels like piling complexity onto already fragile systems, and why that approach looks increasingly shaky for enterprises dealing with technical debt, energy constraints, and long deployment cycles.
What makes this conversation especially interesting is MythWorx's belief that the next phase of AI will look less like prediction engines and more like reasoning systems. Wade walks through how their architecture is modeled closer to human learning, where intelligence is learned once and applied many times, rather than dragging around the full weight of the internet to answer every question. We explore why deterministic answers, audit trails, and explainability matter far more in areas like finance, law, medicine, and defense than clever-sounding responses.
There is also a grounded enterprise angle here. We talk about why so many organizations feel uneasy about sending proprietary data into public AI clouds, how private AI deployments are becoming a board-level concern, and why most companies cannot justify building GPU-heavy data centers just to experiment. Wade draws parallels to the early internet and smartphone app eras, reminding us that the playful phase often comes before the practical one, and that disappointment is often a signal of maturation, not failure.
We finish by looking ahead. Edge AI, small-footprint models, and architectures that reward efficiency over excess are all on the horizon, and Wade shares what MythWorx is building next, from faster model training to offline AI that can run on devices without constant connectivity. It is a conversation about restraint, reasoning, and realism at a time when hype often crowds out reflection.
So if bigger models are no longer the finish line, what should business and technology leaders actually be paying attention to next, and are we ready to rethink what intelligence really means?
Useful Links
Connect with Wade Myers
Learn More About MythWorx
Thanks to our sponsors, Alcor, for supporting the show.