Tech Talks Daily

How can an independent safety evaluation remain meaningful when the AI inside a product may change after its next update?
In this episode of Tech Talks Daily, I speak with Dr. Robert Slone, Senior Vice President, Chief Scientist, and Innovation Officer at UL Solutions. Robert has spent almost 30 years leading science, research, product development, and innovation teams. He now helps guide UL Solutions' scientific work across safety, security, and sustainability.
Many listeners will recognize the UL Mark without knowing what happens behind it. Robert explains how UL Solutions tests products to their limits, which can involve setting them on fire, finding their breaking points, inspecting manufacturing facilities, and determining whether they meet defined safety requirements.
That work began over 130 years ago when electricity was introducing unfamiliar risks. Today, the same broad question applies to artificial intelligence: how can society benefit from a new technology while understanding and managing the harm it could cause?
The need is becoming increasingly visible as AI moves into healthcare, transportation, manufacturing, financial services, infrastructure, and consumer products. Robert recalls being approached about evaluating an AI-enabled teddy bear capable of talking with children. It is a memorable example of how decisions made inside an AI model can reach directly into everyday life.
Robert organizes AI product safety around three pillars. The technical pillar considers robustness, risk management, functional safety, and whether the system performs its intended purpose. The ethical pillar includes fairness, bias, privacy, transparency, and explainability. Governance covers data management, product updates, accountability, and the complete operating life of the system.
We also discuss one of the hardest problems in AI certification. Traditional products and software can be evaluated against a defined version, but AI systems may be updated, retrained, or affected by changing data. Robert explains why meaningful safety assurance requires version-specific testing, annual reviews, disclosure of significant changes, and eventually telemetry capable of identifying problems much closer to real time.
For business leaders buying AI, the conversation provides a practical vendor checklist. Where did the training data come from? How was performance measured? What are the system's known limitations? How were privacy and bias assessed? Who takes responsibility if its behavior changes?
Independent testing cannot promise that an evolving product will remain safe forever. It can provide evidence about the version evaluated, expose gaps, establish accountability, and create a process for monitoring future changes.
What proof would you demand before allowing an AI product to influence an employee, patient, customer, or child? Listen to the episode and share your thoughts with me.

Could allowing patients to choose their own appointment times help reduce missed visits and shorten NHS waiting lists?
In this episode of Tech Talks Daily, I speak with Alison MacDonald, European Lead and Senior Vice President at Nordic Global. Alison brings an unusual combination of clinical and technology experience as a registered nurse who moved into digital health over 15 years ago.
Her career began in community nursing, where she was asked to lead an electronic health record project because colleagues thought she was good with computers. What initially appeared to be a simple exercise in converting paper forms into digital records encouraged her to question whether healthcare could redesign the process rather than copy it onto a screen.
We discuss Nordic's work with Cambridge University Hospitals NHS Foundation Trust on patient self-scheduling and automated earlier appointment offers. Before the program, missed outpatient appointments were removing valuable clinical capacity while administrative teams spent time calling patients and rearranging bookings.
Cambridge introduced self-scheduling through Epic MyChart, allowing patients to select appointment times through the patient portal. Alison says the DNA rate fell from 5% to 2.2% during the program.
According to the supplied results, over 20,000 patients successfully scheduled their own appointments and over 3,000 accepted offers to attend earlier when cancellations created availability. Patients moved appointments forward by an average of 16 days.
Forty percent of accepted earlier appointments occurred within seven days of the offer, while 7% took place on the same or following day. Administrative teams also saved an estimated ten minutes for every self-booked appointment.
Alison explains why patient control can improve attendance. People can choose times that work around employment, caring responsibilities, travel, and family life instead of receiving a fixed appointment through a letter or telephone call. Patients can also cancel or reschedule without waiting for somebody to answer the phone.
The operational lesson goes beyond appointment booking. Healthcare systems may be able to recover existing capacity by examining missed appointments, theater scheduling, waiting list processes, pre-visit questionnaires, and patient communications before concluding that every problem requires additional staff or facilities.
We also discuss where AI is producing practical results in healthcare. Alison points to medical imaging, emergency department triage, waiting list management, clinical documentation, and workforce deployment. She warns against discussing AI as one generic solution because each application requires a defined use case, suitable data, workable processes, governance, and staff adoption.
Ambient clinical documentation offers one example. An AI scribe can record a consultation, prepare a structured note, and pass it to the clinician for review and correction. Alison cites an NHS evaluation reporting a 23.5% increase in direct patient interaction time and an 8.2% reduction in appointment length.
Interoperability remains another major challenge. Healthcare journeys cross hospitals, primary care, community services, and specialist providers that may use different systems or a mixture of electronic and paper records. Even basic differences, such as one organization measuring pain on a five-point scale and another using ten points, can prevent reliable comparison.
Alison recommends agreeing on common data standards, defining the minimum patient information required during care transitions, including interoperability requirements in procurement, and avoiding bespoke integrations that make future information sharing harder.
Digital access also requires balance. Online services can improve convenience, but healthcare providers must retain appropriate alternatives for patients who lack digital skills, connectivity, confidence, or access.
Could your healthcare organization improve patient access and staff capacity by redesigning one familiar process before purchasing another large technology platform? Listen to the episode and share your thoughts with me.

What would change if a customer could return three days later through a different channel and continue the same conversation without repeating a single detail?
In this episode of Tech Talks Daily, I speak with Paul Adams, Chief Product Officer at Fin, the company previously known as Intercom. Paul has spent almost 13 years with the business and provides a candid account of how it abandoned its previous roadmap, placed a company-wide bet on AI, and rebuilt its products and working practices around AI agents.
Our conversation begins with Fin's move from a customer service agent toward what Paul calls a single customer agent. The idea is that customers do not care whether their request belongs to sales, service, or customer success. They want the company to understand their situation and help them complete the task.
Paul explains how AI agents can bring customer history, company knowledge, operational data, and business goals into the same conversation. This could allow an agent to resolve an issue, support a purchase, recognize a valuable customer, or transfer the conversation to a human without losing the context already provided.
We also examine the economics behind poor customer service. Many companies are not ignoring customers through a lack of concern. They are receiving volumes of requests that cannot economically be handled by adding people alone. Paul says some Fin customers are resolving between 70 and 90 percent of customer queries through AI. Rather than seeing entire teams disappear, he is observing employees move into customer success, knowledge management, AI supervision, and higher-touch services.
The episode also provides an unusually candid account of what it took to rebuild an established SaaS company around AI. Paul describes the process as brutal. Strategies were discarded, familiar processes were removed, and some people decided the new direction was not for them. His advice is to prioritize speed, place smaller experiments in front of real customers, and learn from evidence rather than waiting for every internal condition to become perfect.
Paul also recalls working on early versions of mobile YouTube and Gmail when colleagues questioned whether anyone would watch video or answer email on a phone. Those stories provide a timely warning about judging new technology by its early limitations.
Could AI agents finally give customers continuity across sales, service, and support, or will internal company structures remain the greater obstacle? Listen to the conversation and share your thoughts with me.

Can organizations trust an AI recommendation when they cannot understand the evidence, relationships, and previous decisions behind it?
In this episode of Tech Talks Daily, I welcome back Jim Webber, Chief Scientist at Neo4j, to discuss the company's acquisition of GraphAware and its move from graph database provider to graph intelligence platform.
GraphAware has worked with Neo4j for many years and developed Hume, an intelligence analysis platform used to connect and examine complex information. Bringing the two companies together gives Neo4j a direct role in applications serving police forces, governments, intelligence agencies, and other organizations handling connected data.
Jim explains why context has become one of the biggest requirements for dependable AI. Enterprises already possess enormous volumes of data, but facts alone provide endpoints rather than the complete path leading to a decision.
An agent needs to understand the knowledge available, the conversation taking place, and the record of previous decisions. It also needs to know which actions produced good outcomes and which produced poor ones.
Jim compares these information layers to SimCity. Each can be viewed separately, but their greater value appears when they are combined. Knowledge, conversations, and decision traces can then help an agent understand why something happened and learn from the result.
This introduces an interesting lesson from scientific research. Positive outcomes are frequently published, while failed experiments receive less attention. An AI agent needs both. Recording the breadcrumbs behind good and bad decisions provides the material required to improve its future behavior.
We also discuss why large language models cannot understand every organization by themselves. Jim describes a model as a lossy compression of the internet. It can generate impressive natural language, but it does not automatically understand a company's policies, customers, history, evidence, or operating environment.
Retrieval augmented generation can introduce relevant organizational information into the process. Graph RAG adds relationships between facts, helping the system understand how people, events, products, accounts, and other entities connect.
According to research Jim references from the National Innovation Centre for Data, Graph RAG can improve accuracy while reducing costs by using fewer, higher-quality tokens.
Explainability becomes especially important when AI supports decisions across policing, cyber defense, taxation, intelligence, banking, and government. A fluent answer may sound authoritative while containing a serious technical mistake.
Jim shares an example from his own work where an agent confidently warned him about a "committed minority" inside a fault-tolerant computing protocol. The statement sounded plausible, but only a majority could commit within that protocol. Someone without Jim's technical knowledge might have accepted the recommendation and removed working code.
This leads us to human oversight. Jim argues that the correct level depends on the consequences of the action. Automating a routine banking process with monitoring and safeguards may improve the customer experience. Ordering someone's arrest based solely on an agent's conclusion demands human involvement.
We also consider digital sovereignty and why control over data has become a strategic concern for governments and large enterprises. Geopolitical instability, overseas technology dependencies, privacy requirements, and changing national policies are forcing leaders to ask where their data resides and whether they can retrieve or move it.
Jim explains how Neo4j intends to offer organizations flexibility over where their information is stored and how it is deployed. The discussion also examines the opportunity for Neo4j and Hume to provide an alternative within a market where Palantir has held a powerful position.
Looking ahead, Jim imagines intelligence analysts directing swarms of digital agents. Those agents could search data, connect evidence, identify relevant patterns, and present findings while humans retain responsibility for consequential decisions.
If AI can connect information at machine speed, how do we ensure the person making the final decision can inspect the evidence and challenge the conclusion? Listen to the episode and share your thoughts with me.

What does an AI agent need to understand about your business before you allow it to make decisions and take action without waiting for human approval?
In this episode of Tech Talks Daily, I speak with Kash Mehdi, Field CTO at Reltio, about the move from analytical AI that supports decisions to agentic AI that can execute them.
Kash argues that leaders should begin treating AI agents as a workforce rather than another collection of software tools. A digital workforce needs training, boundaries, oversight, trusted information, and clear permissions before it can act safely.
He uses the analogy of raising a puppy. When the puppy misbehaves, the problem may be inadequate training or poorly defined boundaries. AI agents present a similar leadership challenge. Organizations must ask what the agent has learned about the business and what authority it has been given.
We discuss why model selection may be receiving too much executive attention. Kash describes four components of an agentic system: the model, tools, data, and context. Models are improving rapidly and tools are increasingly available, but business context remains incomplete across many enterprises.
Data tells an agent a fact. Context helps it understand what the fact means within a particular customer relationship, geography, policy, or business process.
Kash illustrates the difference with a pizza order. The data may confirm that someone is logged in, the model can interpret the request, and a tool can place the order. Context tells the system that it is Friday night, the customer is watching television, and they usually order pineapple and cheese pizza.
The same principle becomes far more serious when an agent is dealing with medical equipment, supply chains, financial customers, or regulated information. It must understand which entities exist, how they relate, what information it may access, and which actions it has authority to complete.
Kash identifies three requirements for safer autonomy: a governed source of truth, a live feedback loop, and enforceable permission boundaries. Trust must be built into the data and operating rules before the agent acts because the familiar human review step may no longer exist.
We also discuss how governance changes when AI can execute decisions at machine speed. A poor decision made by one employee can usually be reviewed and corrected. A poor decision repeated automatically across thousands or millions of transactions can become a business incident before anyone intervenes.
Kash shares examples involving restaurant menu launches, medical equipment deliveries, and call center offers. Each depends on current information and the relationships connecting customers, products, suppliers, locations, and previous interactions.
For CIOs preparing today, Kash recommends building context around reusable entities rather than constructing an isolated data project for every AI use case. He points to Schneider Electric as an example where one unified foundation supported sales, shipping, operations, and marketing use cases.
The conversation ends with a warning about slow data. Autonomous agents need current context because information that arrives after a decision has been made may no longer carry much business value. Kash predicts that the half-life of enterprise data will become a board-level measure.
If a smarter agent can make a poor decision faster and with greater confidence, is your organization investing enough in the context, governance, and feedback needed to keep it on course? Listen to the conversation and share your thoughts with me.

 
 
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