How Craftable is Using AI to Protect Restaurant Margins and Human Hospitality

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/    

What happens when AI moves beyond writing emails and summarizing meetings and starts influencing who gets hired, promoted, and paid? In this episode of Tech Talks Daily, I speak with David Lloyd, Chief AI Officer at Dayforce, about why HR is becoming one of the highest-stakes environments for artificial intelligence and how companies can introduce AI while protecting employee data, maintaining human accountability, and preparing for growing regulatory scrutiny. HR systems contain some of the most sensitive information companies hold, from salaries and performance records to benefits and personal data. At the same time, AI is increasingly being introduced across recruitment, workforce management, compensation, performance, and employee experience. David explains why this combination creates enormous opportunities but also places greater responsibility on employers to understand how AI systems operate and how decisions are made. A major theme throughout our conversation is the role of AI governance. David challenges the assumption that governance slows innovation, arguing that the right processes can help companies evaluate AI ideas quickly while reducing the risk of introducing systems that lack appropriate data, transparency, or regulatory safeguards. Dayforce recently achieved ISO/IEC 42001 certification for AI management systems and NIST AI Risk Management Framework attestation. David explains what independent validation means in practice and why companies evaluating AI vendors should ask for evidence of how systems are governed, tested, monitored, and audited. We also discuss the principle of "AI by choice." David argues that CIOs and HR leaders should never discover that a new AI capability has suddenly been activated across hundreds of employees without their knowledge. Companies need visibility into where AI is being used, what data employees can provide to models, and whether customer information is being used to train external AI systems. The conversation examines AI literacy and why HR leaders need to become comfortable with the technology themselves before guiding employees through changes to jobs and working practices. Employees are already experimenting with AI, sometimes through personal tools outside approved company systems. Rather than ignoring this behavior, David explains why companies should provide safe environments where people can learn while establishing clear rules around sensitive data. Human accountability remains central as AI takes on more responsibility. David discusses why people using AI should remain accountable for its outputs and why human oversight matters when technology influences decisions involving recruitment, compensation, performance, and careers. For CEOs, CHROs, CIOs, HR technology leaders, and anyone responsible for enterprise AI, this conversation provides practical guidance on responsible AI adoption, employee data, AI bias, model monitoring, regulatory compliance, vendor selection, and building AI governance that can stand up to scrutiny. The lesson is that governance does not have to be a brake on AI adoption. Done well, it can give companies the structure and confidence to move faster, make better decisions about where AI belongs, and continue using the technology when regulators, employees, customers, and boards start asking harder questions. https://www.dayforce.com/ https://www.linkedin.com/company/dayforce/ https://www.linkedin.com/in/dtlloyd/

What separates an impressive agentic AI demonstration from a deployment that produces measurable business value across an entire company? In this episode, I speak with Frank Theisen, Vice President of IBM Technology across Europe, the Middle East and Africa, about how businesses can move AI agents beyond isolated pilots and into the processes where work actually happens. Frank believes the conversation has changed considerably. Most large companies are deploying some form of AI, yet many still struggle to demonstrate a significant commercial return. The difference comes from connecting AI with end-to-end business processes rather than creating another assistant that sits outside the systems employees use every day. IBM has attempted to prove this internally through its "client zero" approach, using its own technology across human resources, IT, procurement, sales and software development before taking those practices to customers. The company reports that AI, automation and hybrid cloud have contributed to $4.5 billion in productivity gains over three years. Frank explains how IBM's AskHR service handles common employee inquiries and helps managers complete administrative tasks without learning how to operate several separate enterprise applications. IBM reports that AI now resolves 94 percent of common HR requests automatically, while similar work is taking place across IT support and procurement. The discussion then turns to orchestration. As companies acquire agents from multiple software providers, the problem becomes far larger than creating individual assistants. Businesses need to understand how agents communicate, which systems they can access, what identities they use and who remains accountable for their actions. Frank expects the number of applications, agents and non-human identities to grow rapidly. Without orchestration and governance, companies risk recreating the same application sprawl they have spent years attempting to reduce, this time with software capable of making decisions and generating additional code. Data presents another barrier. Publicly trained models rarely contain the proprietary information that gives a company its commercial advantage. That information remains distributed across databases, applications, mainframes and cloud services. Frank argues that enterprises need a governed, federated way to bring AI to their data without repeatedly copying everything into another repository. We also discuss digital sovereignty across Europe and the Middle East. Frank describes sovereignty as a matter of control across data, operations and technology. Companies need to decide which workloads require isolation, which regulations apply and where dependence on one provider could limit their future choices. Wimbledon provides a timely example of these principles in practice. IBM Bob helped modernize the tournament's digital platform by mapping and migrating approximately 15,000 articles, videos, photographs and related metadata. IBM says work that would traditionally require four or five specialists over several months was completed by one engineer within four weeks, with the assets themselves extracted in 47 minutes. Frank closes with three practical priorities. Understand where AI could affect the business, determine how successful use cases can be automated across complete processes, then address security, governance and provider dependence before expanding them. If your company already has dozens of AI pilots, should the next investment create another agent or coordinate the ones you already have? Listen to the episode and share your thoughts with me.

How quickly should an AI investment begin producing measurable business results? In this episode, I speak with Monica Kumar, Executive Vice President and Chief Marketing Officer at Extreme Networks, about the growing pressure on technology leaders to prove that AI investments are producing financial and operational value. The conversation draws on Extreme Networks' State of AI for Networking 2026 report, based on a global survey of 200 C-level executives and vice presidents of IT. The findings suggest that enterprise AI has entered a far less forgiving phase. Experimentation continues, but executives increasingly want evidence that deployments are reducing costs, improving productivity or creating better user experiences. The most striking result is the speed now expected. Some 57 percent of respondents said they expect measurable AI impact within weeks or sooner, compared with 16 percent in the previous year. Projects that once might have received six or 12 months to demonstrate value may now face questions within 30 or 60 days. Monica explains why these demands are changing which AI projects receive attention. Leaders are looking for use cases connected with existing operational problems, where results can be measured and communicated clearly. This makes enterprise networking an interesting test case. AI workloads depend on network compute, bandwidth, availability and access to current data. According to the research, 92 percent of respondents said AI is increasing demands on network compute and bandwidth. A fragmented or outdated network may therefore limit the performance of the AI applications running across it. The network can also provide an early opportunity to show what AI produces in practice. Monica discusses performance monitoring, predictive analysis, troubleshooting, compliance checks, capacity planning and security. These are repetitive, data-heavy activities where improvements can be measured in time saved, fewer support tickets and better service availability. A case from Middlesbrough College brings those claims into focus. The college reports that firmware tracking fell from as much as five hours each week to approximately five minutes, while the time spent troubleshooting decreased by around 90 percent. For a small network team, the value comes from giving people additional capacity without asking them to monitor every device or event manually. We also discuss why AI capabilities work better when embedded within normal business systems rather than added as another standalone tool. If an AI service remains outside the daily workflow, employees must move between platforms, transfer information and interpret the result themselves. Integrated AI can monitor the network, identify anomalies, recommend action and automate routine work within the environment where the team already operates. Monica also warns that the quality of AI depends heavily on its data. Before investing in another model or application, businesses need accurate, current and well-managed information. They must also examine whether their network has the capacity to support additional workloads and whether employees understand how to use AI responsibly. If executives expect AI results within weeks, are businesses selecting the right use cases, or simply imposing unrealistic deadlines on complicated technology programs? Listen to the episode and share your thoughts with me.

Could the disaster recovery plan designed to protect your company make a ransomware incident even worse? In this episode, I speak with Darren Thomson, Vice President and Chief Technology Officer for EMEA at Commvault, about Resilience Operations, commonly known as ResOps, and why cyber recovery now requires security, infrastructure, identity and data teams to work from one coordinated plan. Darren argues that many companies are accepting a difficult reality. Even with considerable investment in prevention and detection, a breach may eventually succeed. That does not make cybersecurity controls any less necessary, but it means recovery can no longer be treated as a secondary activity managed by another department. The problem is that security operations and infrastructure teams have traditionally worked toward different objectives. Security specialists concentrate on identifying and stopping threats. Infrastructure teams protect data, maintain backups and restore systems after outages. During a cyberattack, a successful recovery requires both sets of expertise. A backup administrator may be able to restore data quickly, but a forensic specialist must establish whether that data is clean. Without that confirmation, the company risks restoring malware and restarting the incident. Darren explains why a conventional disaster recovery plan may be particularly dangerous during ransomware. These plans were commonly designed for physical failures such as a lost data center. Data would be copied from one location to another so operations could continue. If the source data is infected, however, fast replication can carry the malware into the recovery environment. This is where ResOps enters the discussion. Darren describes it as an operating model rather than a product. It combines established practices from security and infrastructure management into a continuous program for testing, learning and improving recovery. Individual technology projects may come from the program, but resilience itself never reaches a final completion date. AI adds pressure on both sides. Criminals can use it to create faster and more effective attacks, while defenders can use machine learning to inspect large volumes of information, detect patterns and identify the newest clean recovery point. Companies must also protect AI systems as they would any other business application, including the models, data repositories and identities connected with them. Darren offers one practical starting point for CIOs and CISOs: Mean Time to Clean Recovery, or MTCR. This measures how long it takes to restore an application and its data with evidence that both are free from compromise. Before measuring MTCR, leaders must define their minimum viable company. These are the systems and services the business cannot operate without. Once that list exists, teams can test how long a verified clean recovery would take and replace assumptions with evidence. The initial answer may be uncomfortable. Teams may know how to restore an application without knowing whether the backup is clean. Security may know how to inspect the system but lack an established workflow with the recovery team. Darren sees those gaps as the starting point for a useful ResOps program because they provide everyone with a shared problem and a measurable objective. If your most important systems disappeared today, how long would it take to bring the minimum viable company back using verified clean data? Listen to the episode and share your answer with me.

Why can we track a meal traveling across town almost minute by minute, yet an international parcel can seemingly disappear between checkout and delivery? In this episode, I speak with Dieter Van Putte from Crossborder Global, a division of Bnode that provides global commerce and logistics services. Dieter oversees technology and operations across Landmark Global and Apple Express, giving him a close view of what happens when retailers attempt to sell and deliver products across international markets. Dieter explains why cross border delivery remains so difficult to track despite years of investment in supply chain technology. A domestic shipment may involve one main carrier, but an international order can pass between warehouses, road carriers, airports, airlines, customs authorities and final-mile delivery companies. Each participant may record events differently, operate at a different level of technical maturity or provide information at a different speed. We discuss why the customer rarely sees this complexity. They simply expect to know when their order will arrive, what duties and taxes they must pay, plus what will happen if the product needs to be returned. When retailers cannot provide those answers, the problem quickly becomes one of trust rather than logistics alone. Dieter also describes the less visible cost of manual reconciliation. Teams can spend hours checking carrier websites, comparing rates, reviewing spreadsheets, correcting customs information and translating inconsistent tracking events. Mistakes create further work through delayed parcels, billing disputes, customer inquiries and complaints. The direct labor cost matters, but the effect on repeat purchases and customer reviews may prove even more expensive. Our conversation then turns to the role of automation and AI. Dieter explains how technology can support product classification, customs documentation, landed-cost calculations and exception handling. We also examine logistics control towers, which combine data from multiple parties to provide an end-to-end view of an order. With enough reliable information, these systems can detect patterns, predict delays and recommend or initiate corrective action before the customer is affected. There is plenty of promise here, but good software cannot remove every customs rule, carrier handoff or data-quality problem. Retailers still need the right partners, accurate product information and a clear understanding of the promise they are making at checkout. Could better use of data finally make international delivery feel as dependable as domestic shipping, and what would need to change first? Listen to the conversation and share your thoughts with me.

Do you need to understand code before you can build a technology company or lead a team of developers? Five years after our previous conversation, I welcome Sophia Matveeva back to the podcast. Sophia is the founder of Tech for Non-Techies, where she helps founders and business professionals understand how technology products are created, tested, managed and turned into commercial ventures. A great deal has changed since we last spoke. Generative AI tools can now turn a written description into a working prototype within hours. For someone who has spent years believing a lack of coding experience disqualified them from building a technology business, that removes a significant barrier. Sophia believes this is the best time yet to be a non-technical founder, although her reasoning goes beyond AI-assisted coding. Research into billion-dollar technology companies shows that non-technical founders now make up a much larger share of founding teams than they did a decade ago. Many of these businesses sell technology to other companies, where commercial knowledge, customer relationships and an understanding of industry problems matter enormously. We discuss where AI belongs in the founder journey. Sophia recommends using tools such as Lovable or Replit to create a simple test product, show it to potential customers and learn whether people would use or pay for the idea. This allows founders to test their assumptions before committing substantial money to development. The boundary appears when that prototype becomes a real product. Once software stores customer information, processes payments or supports a commercial service, security and technical architecture cannot be treated as optional details. Sophia argues that professional developers are still needed to inspect the code, prepare the product for production and address problems a non-technical founder may not know exist. Her point is simple. AI can help a founder reach the testing stage sooner and at a lower cost. It cannot tell someone with no engineering experience whether the generated code is safe, maintainable or ready to support paying customers. Sophia also shares what she learned from managing her first development team. After raising investment, she attempted to compensate for her technical insecurity by taking a coding course and becoming involved in work she did not fully understand. The result was micromanagement, constant interruptions and frustrated developers. A better approach begins with business priorities. Founders should explain what customers want, ask developers about effort and tradeoffs, agree on what will be delivered during the next work cycle, then give the team the space required to complete it. They should also allow time for technical debt, the less visible maintenance work that prevents hurried development from creating larger problems later. We finish with advice for any business leader who wants greater technology fluency. Sophia recommends joining product meetings, contributing customer knowledge and building relationships with technical colleagues who want to understand the commercial side of the company. Neither side needs to become the other. They need enough shared language to make better decisions together. If AI has removed the cost of testing many technology ideas, what is stopping you from finding out whether yours could work? Listen to the episode, try Sophia's exercise and share your experience with me.