What happens when the rush toward AI collides with the messy reality of enterprise data that was never designed for it?

That is exactly where this fast-tracked episode with Kevin Dattolico from Syntax begins. Before we even hit record, we were swapping stories about music, travel, and a certain farewell concert that set the tone for a conversation that was both grounded and unexpectedly human. But once we got going, the discussion quickly shifted to one of the biggest blind spots I keep hearing about at tech conferences around the world. AI ambition is running far ahead of data readiness.

Kevin leads Syntax across the Americas, working with organizations that rely on SAP, Oracle, and complex cloud environments to run their businesses. In our conversation, he shares why many AI initiatives stall or quietly reset the moment they touch real production data. Proofs of concept can look impressive in isolation, but once AI starts interacting with live operational systems, the cracks appear. Inconsistent data, duplicated records, missing context, and governance gaps all surface at once. The result is confusion, unpredictable outputs, and a growing realization that the issue is rarely the model itself.

We dig into why ERP data has traditionally been trusted, while unstructured data across emails, documents, sensors, and logs often tells a very different story. Kevin explains where the real friction shows up when companies try to bring those worlds together, and why assumptions about data quality tend to break long before the technology does. It is a refreshingly honest look at what usually goes wrong first, and why leaders are often blindsided even after years of investment.

One of the strongest themes in this episode is the shift Kevin sees from AI-first thinking toward a data-first mindset. That does not mean abandoning AI spend. It means rebalancing priorities so those investments actually deliver outcomes the business can stand behind. We talk about what consolidation, cleansing, and transformation look like at enterprise scale, especially for organizations carrying decades of technical debt and fragmented systems.

The conversation also takes a thoughtful turn around governance, trust, and leadership. Kevin shares how the role of the chief data officer is changing from gatekeeper to enabler, and why modern governance has to support speed without sacrificing accountability. Along the way, he reflects on the risks of pushing ahead with weak data foundations, particularly in regulated industries where the cost of getting it wrong can be operational, reputational, or worse.

And then there is the moment that caught me completely off guard. When I asked Kevin to look back on his career and reflect on someone who made a difference, his answer led to one of the most moving stories I have heard in thousands of interviews. It is a reminder that behind every transformation story, there are people who quietly shape the path forward.

If you are wrestling with AI expectations, data reality, or simply wondering whether everyone else feels just as overwhelmed by this shift, this episode will resonate. The challenges Kevin describes are far more common than most leaders admit, and the opportunities for those who get the foundations right are real.

So as AI continues to dominate boardroom conversations, are you confident your data is ready to support the decisions you are asking it to make, or is it time to pause and rethink what sits underneath it all?

Useful Links

• Connect with Kevin Dattolico
• Learn more about Syntax

Thanks to our sponsors, Alcor, for supporting the show.

Why do today's most powerful AI systems still struggle to explain their decisions, repeat the same mistakes, and undermine trust at the very moment we are asking them to take on more responsibility?

In this episode of Tech Talks Daily, I'm joined by Artur d'Avila Garcez, Professor of Computer Science at City, St George's University of London, and one of the early pioneers of neurosymbolic AI.

Our conversation cuts through the noise around ever-larger language models and focuses on a deeper question many leaders are now grappling with. If scale alone cannot deliver reliability, accountability, or genuine reasoning, what is missing from today's AI systems?

Artur explains neurosymbolic AI in clear, practical terms as the integration of neural learning with symbolic reasoning. Deep learning excels at pattern recognition across language, images, and sensor data, but it struggles with planning, causality, and guarantees. Symbolic AI, by contrast, offers logic, rules, and explanations, yet falters when faced with messy, unstructured data. Neurosymbolic AI aims to bring these two worlds together, allowing systems to learn from data while reasoning with knowledge, producing AI that can justify decisions and avoid repeating known errors.

We explore why simply adding more parameters and data has failed to solve hallucinations, brittleness, and trust issues. Artur shares how neurosymbolic approaches introduce what he describes as software assurances, ways to reduce the chance of critical errors by design rather than trial and error. From self-driving cars to finance and healthcare, he explains why combining learned behavior with explicit rules mirrors how high-stakes systems already operate in the real world.

A major part of our discussion centers on explainability and accountability. Artur introduces the neurosymbolic cycle, sometimes called the NeSy cycle, which translates knowledge into neural networks and extracts knowledge back out again. This two-way process opens the door to inspection, validation, and responsibility, shifting AI away from opaque black boxes toward systems that can be questioned, audited, and trusted. We also discuss why scaling neurosymbolic AI looks very different from scaling deep learning, with an emphasis on knowledge reuse, efficiency, and model compression rather than ever-growing compute demands.

We also look ahead. From domain-specific deployments already happening today to longer-term questions around energy use, sustainability, and regulation, Artur offers a grounded view on where this field is heading and what signals leaders should watch for as neurosymbolic AI moves from research into real systems.

If you care about building AI that is reliable, explainable, and trustworthy, this conversation offers a refreshing and necessary perspective. As the race toward more capable AI continues, are we finally ready to admit that reasoning, not just scale, may decide what comes next, and what kind of AI do we actually want to live with?

Useful Links

• Neurosymbolic AI (NeSy) Association website
• Artur's personal webpage on the City, St George's University of London page
• Co-authored book titled "Neural-Symbolic Learning Systems"
• The article about neurosymbolic AI and the road to AGI
• The Accountability in AI article
• Reasoning in Neurosymbolic AI
• Neurosymbolic Deep Learning Semantics

Why does healthcare keep investing in new technology while so many clinicians feel buried under paperwork and admin work that has nothing to do with patient care?

In this episode of Tech Talks Daily, I'm joined by Dr. Rihan Javid, psychiatrist, former attorney, and co-founder and president of Edge. Our conversation cuts straight into an issue that rarely gets the attention it deserves, the quiet toll that administrative overload takes on doctors, care teams, and ultimately patients. Nearly half of physicians now link burnout to paperwork rather than clinical work, and Rihan explains why this problem keeps slipping past leadership discussions, even as budgets for digital tools continue to rise.

Drawing on his experience inside hospitals and clinics, Rihan shares how operational design shapes outcomes in ways many healthcare leaders underestimate. We talk about why short-term staffing fixes often create new problems down the line, and how practices that invest in stable, well-trained remote administrative teams see real improvements. That includes faster billing cycles, fewer errors, and more time back for clinicians who want to focus on care rather than forms. What stood out for me was his framing of workforce infrastructure as a performance driver rather than a compliance box to tick.

We also dig into how hybrid operations are becoming the default model. Local clinicians working alongside remote admin teams, supported by AI-assisted workflows, are now common across healthcare. Rihan is clear that while automation and AI can remove friction and cost, human oversight still matters deeply in high-compliance environments. Trust, accuracy, and patient confidence depend on knowing where automation fits and where human judgment must stay firmly in place.

Another part of the discussion that stuck with me was Rihan's idea that stability is emerging as a better success signal than raw cost savings. High turnover may look efficient on paper, but it quietly limits a clinic's ability to grow, retain knowledge, and improve patient outcomes. We unpack why consistent administrative support can influence revenue cycles, satisfaction, and long-term resilience in ways traditional metrics often miss.

If you're a healthcare leader, operator, or technologist trying to understand how AI, remote teams, and smarter operations can work together without losing trust or care quality, this conversation offers plenty to reflect on. As healthcare systems rethink how work gets done behind the scenes, what would it look like if stability and clinician well-being were treated as core performance measures rather than afterthoughts, and how might that change the future of care?

Useful Links

• Connect with Dr. Rihan Javid
• Edge Health
• Rinova AI

Thanks to our sponsors, Alcor, for supporting the show.