Tech Talks Daily

Why do pet owners so often learn the limits of their insurance when an animal is already ill and the veterinary bill is growing? That trust problem sits at the center of my conversation with Hedda Båverud Olsson, co-founder and CEO of Lassie, a pet insurer that combines coverage with preventative health guidance, rewards, activity tracking, and AI-assisted claims. Hedda's reason for starting Lassie is personal. Her mother is a veterinarian, and Hedda grew up around healthy pets without fully appreciating how much fear and financial pressure many owners experience. After working at McKinsey and EQT, she became absorbed by an idea she describes as putting her mother in every owner's pocket. The aim was to help people understand risks earlier and make better daily choices, rather than waiting until an animal needed treatment. The claims process shows where AI can offer immediate value. Lassie has developed a system called Bark Office that scans an invoice, reads each line, identifies whether the treatment relates to illness or an accident, checks the policy, and decides whether enough information is available. Hedda says that when the system is confident, the money can reach the customer in around six minutes. She reports that approximately 65 percent of claims in Germany follow that route. Automation has limits, especially when a blurry receipt, missing diagnosis code, incomplete journal, unusually expensive treatment, or uncertain policy detail prevents a reliable decision. Those cases can prompt a request for further information or move to a human reviewer. Hedda says customers receive a line-by-line explanation of what was and was not covered, with the option to dispute a result and request another review. She reports an error rate below 2 percent for automated claims and compares it with what she describes as a 5 percent human error rate across insurance. Those are Lassie's figures, but the operating principle is useful across many regulated services: automate clear cases, explain the result, and give uncertain or sensitive cases to a person. We also consider why an insurer should have a role when nothing has gone wrong. Hedda says over 90 percent of Lassie customers use its app and roughly a quarter use it daily. Owners can watch health videos, complete quizzes, follow life-stage guidance, record activity, and earn rewards that can reduce their insurance price. Advice changes according to breed, age, and season, covering subjects such as weight, joint health, toxic foods, nail trimming, and ticks. Lassie also works with Tractive, allowing customers to connect a tracker and bring activity data into the app. Hedda explains that Lassie customers can receive a tracker while paying the Tractive subscription, and existing Tractive users can connect their current device. Owners who do not want a tracker can record activity manually. The feature gives the company another regular point of contact while helping customers follow their pet's routine. That daily relationship has commercial consequences. Hedda says regular app use supports customer loyalty, reduces churn, and raises lifetime value. Preventative actions may also support lower prices for owners. The opportunity is to make insurance useful before a claim, although firms must avoid turning care advice and rewards into confusing conditions or allowing gamification to distract from clear coverage. The conversation moves to the UK, where the supplied briefing estimates that around 20 million pets remain uninsured. Hedda believes culture and distrust may outweigh price alone, comparing the UK with Sweden, where she says approximately 90 percent of dogs and 50 to 60 percent of cats are insured despite higher prices. She also argues that established insurers have been slowed by old systems and disconnected technology, making simple onboarding, mobile service, and automated claims harder to deliver. For Lassie, the test is knowing where automation improves the experience and where it would make a difficult moment worse. Customers may welcome an administrative claim completed in minutes, but few want to speak with a bot when a pet is seriously ill or dying. That distinction between speed and empathy may be the most useful lesson for any business automating emotionally sensitive work. Can insurance become something customers value every day without losing the clarity and human care they need during a crisis? Listen to the episode and share your thoughts with me.  

How much of construction's cost, delay, and waste begins with information that fails to survive the journey from design to delivery? That question runs through my conversation with Julian Geiger, Chief AI Officer at Nemetschek Group, as we look at the practical role of AI across architecture, engineering, construction, and operations. Julian describes what he calls the industry's 90, 40, 20 problem. According to the figures he shares, 90 percent of projects are over budget or over time, the built world accounts for roughly 40 percent of global carbon emissions, and around 20 percent of material is wasted. His argument is that many poor outcomes start as information and decision problems. Each project phase may work reasonably well on its own, but handovers can strip away context. A building information model becomes a PDF, a PDF becomes an email, and a decision may never be recorded against the object it changed. We discuss how AI, building information modeling, and digital twins can identify missing information, scope gaps, clashes, and design choices before they become expensive construction site problems. Julian shares Nemetschek's work bringing Firmus AI into Bluebeam to review two dimensional drawings, then explains the broader goal of feeding lessons from construction back into design and engineering tools. The commercial promise is easy to understand. Finding a mistake while a wall exists only in software costs far less than finding it after workers and materials are waiting on site. The conversation also moves beyond the assumption that every task needs the largest available model. Julian sets out a four tier approach. Deterministic calculations such as structural math should remain deterministic. Stable, high volume checks may be handled by conventional rules. Smaller domain models can classify objects, retrieve data, and interpret geometry close to the source. Frontier models earn their place when the work involves ambiguity, reasoning across documents, or several dependent steps. His test is refreshingly practical: use the least expensive method that is reliably right and fast enough for the person waiting on the answer. That discipline matters when finance teams ask for proof. Time saved on drawing reviews or tender preparation can be measured quickly, while reductions in rework or missed issues require a longer data series. Julian also notes a familiar problem for enterprise AI programs. If a firm never established a baseline, it becomes difficult to show what improved. Usage can indicate that people find a tool useful, but adoption alone does not settle the return on investment question. Data sovereignty adds another layer. Construction files can include valuable designs, commercial information, and details tied to national infrastructure. We discuss where the data is stored, who processes it, which jurisdiction applies, and whether customer material is used for model training. Julian argues for separating genuine intellectual property from routine usage data, then matching controls to the sensitivity of each project rather than treating every data set as identical. Finally, we consider people. In an industry facing a skills shortage, removing junior roles creates a future shortage of experienced professionals. Julian sees AI as a way to shorten the apprenticeship period and reduce repetitive documentation, while preserving a clear line of accountability: AI proposes and a qualified human decides. Could that model help construction professionals spend more of their time on judgment, design, and better buildings, and where should the industry draw the line? Listen to the episode and share your thoughts with me

Why do businesses replace the AI model when the failure may have started somewhere else entirely? In this episode of Tech Talks Daily, I speak with Richard Shaw, Technology General Manager for Databricks in the UK and Ireland. Richard leads the field engineering organization that works closely with customers on data and AI problems, giving him a practical view of what happens when promising agentic AI projects meet production workloads. Richard argues that the model often receives the blame because it is the most visible part of the system. The actual fault may come from stale data, missing business context, inconsistent permissions, an unsuccessful tool call, or another point in the workflow. Replacing the model before tracing the request from start to finish can recreate the same problem in a new place. This is why lineage, end-to-end tracing, and continuous evaluation matter once an agent moves beyond a controlled pilot. We discuss what a production-readiness rehearsal should include. Richard recommends realistic data, realistic user volumes, unauthorized requests, ambiguous questions, failed tool calls, and tests of what the agent should refuse to do. Teams also need agreed standards for quality, security, cost, and auditability, along with a clear decision about which actions an agent can complete independently and where a person must review or approve the result. The conversation also looks at model choice and infrastructure cost. Richard believes the strongest test is performance on the organization's actual work rather than a benchmark leaderboard. A frontier model may suit complex reasoning, while a smaller or open-weight model may perform routine extraction or classification at a lower cost. Access policies, observability, and spend controls need to remain consistent as those model choices change. Conversational analytics creates another governance challenge. Databricks customers such as Virgin Atlantic and Repsol are using natural-language tools to make company data easier for employees to question. Richard says wider access should preserve existing permissions, ownership, definitions, and lineage. An answer becomes far more useful when the user can see where it came from and which team owns the information behind it. We also cover the boundary between historical analytical data and fast operational workloads. Richard describes how Databricks positions the lakehouse for broad enterprise context and Lakebase for immediate reads and writes, such as updating an account, placing an order, or storing agent memory, while keeping both connected to a common data and governance base. Are companies ready to trace and test the whole AI workflow, or are too many treating the model as both the hero and the culprit? Listen to the episode and share your thoughts with me.  

How can businesses turn growing investment in AI infrastructure, cloud capacity and devices into outcomes that employees, customers and finance teams can actually measure? In this episode of Tech Talks Daily, I speak with Neil Sawyer, who manages HP's business across Europe, the Middle East and Africa. Neil works with companies across one of HP's largest global regions as they move from AI experimentation into wider deployment, making him well placed to discuss what happens when early enthusiasm encounters cost, security, governance and the realities of the workforce. We begin with the gap between building AI capacity and applying it to a business problem. Data centers, models and powerful devices provide options, but the investment only becomes useful when a company identifies the workflow it wants to improve. Neil argues that leaders should begin with the outcome, understand where AI can remove friction and decide how they will measure productivity, employee experience and business performance before buying another layer of technology. That raises a difficult question about productivity. If AI helps someone complete a task faster, does the organization use that saved time to improve the work, develop new ideas and give employees room to think, or does it simply add another task to the queue? I share my own experience as a business of one, where every efficiency gain has a habit of becoming extra output rather than a Wednesday afternoon at the cinema. Neil compares the current moment with earlier periods of industrial change and makes the case that automation should release people from repetitive administration so they can contribute creativity, judgment and higher value work. We also discuss why AI costs are becoming a boardroom issue. Token based services and agentic systems can produce growing and unpredictable bills as adoption spreads across a company. Neil explains why every workload does not need the same model or environment. Large language queries may benefit from cloud capacity, while sensitive data, company specific information and some recurring tasks may be better suited to local or on device processing. The decision affects cost, responsiveness, privacy, security, data sovereignty and environmental impact. Neil describes HP's view of hybrid AI, including devices with neural processing units and Z by HP Boost, which can connect available workstation GPU resources. He also explains why device refresh decisions should reflect workforce personas. A data scientist, account manager and office administrator may work for the same company, yet their computing needs can be very different. Mapping technology to the employee's role can help a business spend with greater discipline while giving people the performance they need. The conversation also covers governance and measurement. Informal use of public AI services can be difficult to see, assess or manage. Neil recommends giving employees an approved AI toolkit, using enterprise services that provide telemetry and examining how technology availability and performance affect the employee experience. Adoption figures can show that a tool is being used, but they do not prove that it is improving an outcome. We finish with a practical checklist for leaders. Define the outcome, identify the workflow, determine where each workload should run, calculate the cost, agree the measures of success and put clear controls around data, privacy and cybersecurity. That approach gives cloud and device based AI distinct jobs within the same business strategy. How is your organization deciding which AI workloads belong in the cloud, which should run closer to the employee, and whether the investment is producing measurable value? Share your thoughts with me.

What could your team achieve if a document that previously took two hours could be created in eight minutes? In this episode of Tech Talks Daily, I speak with Oskar Konstantyner, Chief Product Officer at Templafy, about the rapid adoption of AI agents across enterprise document workflows and what those productivity gains mean for knowledge workers. According to Templafy's proprietary usage data, AI agent adoption among its enterprise users grew from virtually zero in October 2025 to 53% by June 2026. Its analysis found that documents created without agents had a median completion time of two hours and an average of 5.6 hours across 16,000 sessions. With AI agents, the median fell to eight minutes and the average to 27 minutes across 14,000 sessions. The most common documents included pitch decks, company communications, sales materials, and product roadmaps. However, Oskar cautions against treating speed as the final measure of AI productivity. We discuss an accounting firm that could not respond to thousands of tenders because it lacked the capacity to create enough proposals. Faster document production could allow that business to participate in additional opportunities while applying its knowledge about what makes a winning submission. The benefit comes from increased commercial capacity and stronger results, rather than counting recovered hours alone. Oskar also explains what happens during those eight minutes. AI can locate relevant information, find approved content, recommend a presentation structure, apply previous lessons, and complete much of the production work. Humans remain responsible for original thinking, client judgment, factual accuracy, and final approval. In many cases, the agent may produce 60% to 90% of the document, but the beginning and end of the process remain human-led. The conversation also considers the growing volume of generic AI documents. A business already has approved slides, company descriptions, brand assets, legal statements, and sales messages. Regenerating all that material wastes tokens and risks inconsistency. Oskar argues that agents should determine when existing content should be reused, when rules should be applied, and when something genuinely new needs to be created. We also discuss why AI adoption improves when agents appear inside PowerPoint, Claude, OpenAI, and Copilot. Most employees are unlikely to abandon familiar workflows every time another AI application arrives. Is your company measuring AI success through minutes saved, or through the additional business those minutes make possible? Listen to the conversation and share your thoughts with me.