Tech Talks Daily

What happens when an autonomous AI agent makes a financial decision using inaccurate, outdated, or poorly synchronized data?
In this episode of Tech Talks Daily, I speak with Marcin Kaźmierczak, cofounder of RedStone Oracles and Credora Ratings, about why verifiable data is becoming so important to financial AI agents. RedStone originally developed its oracle infrastructure to supply smart contracts with reliable information from hundreds of sources. The same principles are now being applied as AI agents begin analyzing markets, recommending allocations, processing payments, and executing trades.
Marcin explains what a blockchain oracle does and why smart contracts cannot independently access real world information. RedStone aggregates, cleans, and distributes financial data, including asset prices, liquidity, volatility, and market capitalization. According to Marcin, its network currently secures over $10 billion in total value locked and has operated for six years without a mispricing or downtime event.
The discussion then moves into agent driven finance. AI agents can process information and act at considerable speed, but that speed introduces problems when the underlying information is delayed or the model hallucinates. Marcin describes the synchronization challenge created when an oracle updates every five seconds while an agent makes decisions every second.
One example captures the risk. An AI trading agent reportedly generated $200,000 over three months before losing $250,000 in two transactions. Marcin explains how Credora Ratings can add financial risk context by rating assets and strategies from D to A. Companies can then instruct an agent to operate only within an approved risk range.
We also discuss tokenized assets, the growing interest from major financial institutions, and why blockchain networks offer an attractive operating environment for autonomous finance. Marcin shares practical advice for leaders, including speaking with experienced implementers, testing agents in closed environments, identifying likely failure scenarios, and creating response policies before introducing real money.
What evidence would you require before trusting an AI agent with a financial decision? Listen to the episode and share your answer with me.

What if a restaurant could identify a margin problem while the ingredients were still being unloaded, rather than discovering it several weeks later?
In this episode of Tech Talks Daily, I speak with David Cantu, CEO of Craftable, about AI restaurant back-office technology, margin intelligence, inventory management, purchasing, invoice automation, and the continuing importance of human hospitality.
David has spent decades working in restaurants and technology. He describes an industry dealing with staffing difficulties, rising leases, food inflation, lower traffic, and relentless margin pressure. David cites a National Restaurant Association study indicating that 40% of restaurateurs were not profitable during the previous year.
Craftable connects purchasing, recipe management, inventory, accounting, sales data, and analytics. The company says its platform is used by over 10,000 restaurants, hotels, and venues.
David argues that useful hospitality AI should automate the work that keeps managers, chefs, and operators away from guests. This includes producing sales forecasts, suggesting orders, planning preparation, identifying invoice anomalies, and comparing projected labor with actual requirements.
The conversation becomes particularly practical when David describes a restaurant receiving a ribeye that has increased in price by 20%. If the dish is a popular, high-margin menu item, that vendor increase can quickly reduce its profitability.
Craftable's Invoice AI can detect the change when the invoice is received. The operator can then consider running a higher-priced chef's special, promoting another steak, reviewing the menu price, or adjusting future orders. Waiting until month-end reconciliation would explain the lost margin but leave no opportunity to recover it during service.
We also discuss the difference between AI intelligence and operator knowledge. AI can examine large volumes of information, but an experienced restaurateur understands the atmosphere, the team, the guests, and what is happening at that particular moment.
David believes AI recommendations need to show their work. Managers should be able to inspect how a sales forecast, suggested order, staffing plan, or trend was calculated. Transparency helps people assess the recommendation without forcing them to search through another large analytics report.
Craftable is also testing how to measure whether a recommended action produced a result. If a manager coaches a server with unusually high complimentary items or promotes a menu category with falling attachment rates, the platform can examine whether that action affected sales or margins.
David is skeptical of hospitality AI added as a promotional layer without being built into the daily workflow. At industry conferences, he has seen vendors attach language models to existing products while offering little operational value.
His hope for restaurant AI is highly human. He wants technology reducing administrative pressure while employees welcome guests, serve meals, develop their teams, and create the warm experiences that define hospitality.
Which restaurant decision could protect profitability if the operator received the right information before the next service began? Listen to the episode and share your thoughts with me.

Why do so many enterprise AI initiatives begin with impressive demonstrations but struggle to produce measurable business value?
In this episode of Tech Talks Daily, I speak with Dom Selvon, CTO and value partner at Valiance, about enterprise AI ROI, outcome-based consulting, build versus buy decisions, proprietary data, ontologies, and governance.
Valiance is an AI-native consultancy that charges against client outcomes rather than hours worked. Dom explains why his "value partner" title is deliberate. The company begins by identifying the financial or operational result a client wants and connects its own compensation with achieving that result.
Dom argues that many AI initiatives begin without a clear definition of success. The pressure to adopt AI is real, but companies frequently select technology before agreeing on the business problem, desired outcome, or measurement.
He identifies three recurring mistakes. The first is framing the project around AI rather than the business need. The second is failing to establish a metric and baseline before work begins. The third is using a consulting model that rewards billable time without connecting payment to the client's result.
We also discuss how generative AI is changing traditional build versus buy decisions. Companies historically bought software because custom development was slow, expensive, and difficult to maintain. Coding agents can now reduce the time and cost required to create software for specific internal needs.
Dom does not believe SaaS will simply disappear. However, vendors selling convenience, workflow wrappers, or integration glue face new competition from customers who can create similar capabilities themselves. He argues that stronger SaaS positions will depend on assets a model cannot easily regenerate, including proprietary data, networks, regulatory standing, and deep workflow adoption.
This leads to a wider discussion about competitive advantage. When companies have access to similar models, generated code begins to converge. Dom believes lasting differentiation comes from company data, institutional knowledge, connected systems, employee experience, and the semantic context surrounding that information.
Dom explains why ontologies matter to enterprise AI. Raw data tells an agent what is stored in a particular field. An ontology describes the customers, orders, contracts, payments, relationships, and business rules represented by that data. This context allows people and agents to reason about information in a way that reflects how the company actually works.
Governance also needs to be designed from the beginning. Dom argues that security, permissions, accountability, and compliance allow successful pilots to expand without forcing the business to rebuild everything later.
How can leaders tell when AI is genuinely being adopted? Dom offers a surprisingly simple signal: people stop talking about AI. The technology becomes part of ordinary Monday morning work, and employees focus on completing the task rather than explaining the tool.
Has your company defined the business result, measurement, proprietary context, and governance required to turn AI enthusiasm into operational value? Listen to the episode and share your thoughts with me.

What does a modern contact center need to deliver when the person reaching out may already feel anxious, embarrassed, and unsure where to turn?
In this episode of Tech Talks Daily, I speak with Chris Lovell, service delivery lead for StepChange Debt Charity's contact center and product owner for its Genesys Cloud platform.
StepChange supports hundreds of thousands of people facing financial hardship each year. Chris explains that approximately 40% of its clients receive Universal Credit, over 60% rent their homes, and many are dealing with an additional vulnerability alongside debt.
That context makes the first interaction especially important. People may have delayed asking for support while their financial position became harder to manage. A failed call, long queue, unnecessary transfer, or request to repeat their story can increase stress at the moment they need reassurance and practical help.
Before adopting Genesys Cloud, StepChange relied on fragmented contact center technology that experienced regular technical problems and outages. The charity also lacked detailed insight into why clients were making contact at different stages of their journey.
People could enter the wrong queue, wait to speak with an advisor, and then discover they needed another team. The technology also affected employees. Chris says frontline colleagues eventually stopped proposing improvements because they did not believe the existing platform could support them.
StepChange migrated to Genesys Cloud in four weeks through a three-phase delivery. The team began with lower-risk services, increased the size and complexity during the second phase, and moved the core debt advice operation during the third.
Chris says the migration was completed without downtime. The initial objective was to reproduce the existing service on a stable cloud platform before introducing further capabilities. This sequencing gave the team time to correct early issues and adjust training before the largest group of advisors moved across.
We discuss how improved intent capture and routing helped one team reduce misrouted calls by 60%. Clients reached the right advisor sooner, avoided repeated explanations, and could move toward a suitable debt solution faster. Advisors also began conversations in the right place instead of apologizing for delays or correcting the journey.
Chris argues that contact center success cannot be judged through efficiency alone. StepChange examines whether clients understand their options, complete the advice journey, activate a sustainable plan, and continue toward becoming debt free.
The circumstances remain difficult for many clients. Chris says approximately 28% are still in a negative budget after receiving advice, with an average monthly shortfall of around £600. Some conversations require time, empathy, and experienced human support.
Around 85% of StepChange advice journeys now happen online. Digital access can offer privacy and flexibility, while advisors remain available for the emotional and complicated moments where a person needs reassurance.
We also consider future plans for WhatsApp, web messaging, connected journeys, and AI-powered advisor support. Chris advises leaders to begin with genuine customer behavior rather than selecting a technology and searching for somewhere to use it.
How can your contact center remove unnecessary effort while preserving the conversations where people most need to feel heard? Listen to the episode and share your thoughts with me.

What does it really take to build an AI-ready enterprise when your data is fragmented, teams operate in silos, and years of technology decisions have created complexity that no large language model can magically fix?
In this episode of Tech Talks Daily, I speak with Raymon Ohmori, Senior Principal Software Engineer at Valiantys, and Jiecheng Dong, Senior Software Engineer at Valiantys, about the work that goes into enterprise AI adoption and why successful AI transformation begins long before companies deploy agents, copilots, or autonomous workflows.
Using Valiantys' work with Mercedes as a case study, Raymon and Jiecheng explain how modernizing software delivery and connecting data across teams can create the foundation required for AI systems to deliver meaningful business value. We discuss why fragmented data, organizational silos, poor governance, and unclear business problems continue to prevent many companies from moving beyond AI pilots.
The conversation examines what it means to become AI-ready in practice. Jiecheng explains why enterprises need performant, structured, and queryable data rather than simply feeding huge volumes of information into large language models. Raymon shares why businesses must begin with real problems, stakeholder needs, and clearly defined outcomes to justify the cost of AI and successfully move projects into production.
We also discuss the growing role of agentic AI and autonomous workflows in software engineering. How should engineering teams prepare AI agents to become active participants in software development? What tools, context, permissions, governance, and observability do these systems need to operate effectively? And why might treating an AI agent more like a new colleague than another software tool help teams think differently about deployment?
Raymon and Jiecheng also share their perspectives on AI-assisted software development and developer productivity. As AI becomes increasingly capable of writing code, the role of the software engineer is shifting toward architecture, system design, requirements gathering, trade-off evaluation, and translating business needs into technical specifications. We also discuss the challenge facing junior developers and why companies still need to create pathways for new engineering talent.
Finally, we examine the practical steps CIOs, CTOs, and engineering leaders can take today to build more connected, AI-enabled enterprises. From improving data ownership and governance to identifying costly problems that AI can realistically solve, this conversation offers a practical guide for companies trying to move from AI experimentation to production systems that deliver measurable value.
Where is your company on its AI journey? Are fragmented data, organizational silos, and unclear business problems preventing your AI projects from reaching production, or have you found effective ways to turn experimentation into measurable results? Share your thoughts with me.

 
Useful Links
 
Valiantys Website: https://www.valiantys.com/ 
Valiantys LinkedIn: https://www.linkedin.com/company/valiantys/