Tech Talks Daily

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.