Tech Talks Daily

Why can an AI pilot produce an impressive result and still fail to create measurable value for the business?
In this episode of Tech Talks Daily, I speak with Jitendra "Jit" Putchea, chief operating officer at Tredence, about what the company calls the last mile of AI. This is the gap between generating an insight and making sure it reaches the person, process, and decision where it can produce a useful result.
Jit argues that many companies are facing an execution problem rather than a shortage of technology. Models are widely available, and teams can build demonstrations at remarkable speed. The harder task is redesigning a complete workflow so that employees can use AI without leaving one system, checking another, and manually carrying information between the two. Trust, explainability, governance, and continuous evaluation also become harder once a pilot moves from a small group into everyday enterprise operations.
We discuss why resistance from employees should not be dismissed as stubbornness. People are trying to understand what AI means for their role, judgment, and future. Jit recommends translating the program into a practical question: how will this make somebody's Monday morning better? He describes human and AI agent teams, along with workshop-based learning that allows employees to solve real problems, test the tools, and understand where human judgment remains necessary.
The conversation then turns to measurement. Jit challenges technology teams to move away from vanity measures such as the number of models built or code interactions recorded. Instead, he recommends examining margin improvement, loss reduction, cycle time, conversion, customer satisfaction, and other measures already understood by the business.
Jit supports the argument with several customer examples. He says one retail workflow reduced analyst effort by 70 percent, while a manufacturing supply chain platform reportedly produced $10 million in first-year savings. He also describes a supermarket forecasting program that reportedly produced close to $200 million in value and replenishment match rates above 90 percent, along with another modernization program associated with a reported $100 million loss reduction. These are Tredence customer examples shared by Jit during the interview and should be presented as attributed company claims.
We also discuss an AI-native operating model built around three layers: foundation, intelligence, and experience. Data infrastructure and governance support the foundation, intelligence turns data into decisions, and the experience layer brings those decisions into human workflows. Jit adds five supporting elements covering human and agent teams, execution rhythm, business measures, the technology ecosystem, and company culture.
His final advice is refreshingly practical. Escape the demo trap, prepare the whole organization for deployment and ongoing operation, consider an internal marketplace for reusable agents, and give the supposedly boring work a larger role. Data hygiene, evaluations, governance, change management, and runbooks help AI continue producing value after the launch presentation has ended.
Is your company measuring the number of AI projects it has created, or the business outcomes those projects have changed? Listen to the conversation and share your thoughts with me.

What if the vehicles already traveling through our towns and cities could report road damage before a pothole becomes dangerous and expensive?
In this episode of Tech Talks Daily, I speak with Jonathan Selbie, CEO of Stockholm-based Univrses, about using computer vision and vehicle sensor data to give road authorities a much clearer picture of the infrastructure they manage. Jonathan's career has taken him from Formula One engineering at Red Bull Racing to unmanned aircraft and autonomous navigation, before bringing those lessons into automotive AI and road monitoring.
Univrses can work with cameras installed by vehicle manufacturers or retrofit cameras and processors to vehicles already operating around a city. Waste collection trucks and taxis can continue their normal routes while gathering information about surface damage, obscured traffic signs, roadworks and deteriorating road markings. The video is processed on the vehicle, and authorities receive mapped findings and recommended actions rather than hours of footage.
Jonathan explains that some Swedish cities moved from road condition surveys every five years to updates every two weeks. According to the figures discussed in our conversation, one council reduced its pothole count from around 3,000 to 900 in six months. He also says repairing damage at an early stage can cost up to 15 times less than waiting for it to become a major pothole. That changes road maintenance from an expensive reaction into a regular process based on current evidence.
We also discuss whether road infrastructure is ready for autonomous vehicles. Waymo uses a broad mix of cameras, radar and lidar alongside detailed maps, while Wayve is pursuing an approach designed to adapt to changing roads without relying on the same level of pre-mapping. Jonathan explains why faded lane markings can reduce the performance of driver-assistance systems, creating a useful feedback loop in which vehicles rely on roads and also provide data to maintain them.
The conversation also covers Pirelli's 30 percent investment in Univrses and the combination of connected tire data with forward-facing cameras. A tire can feel the road surface while a camera sees what lies ahead, giving vehicles and road operators different views of the same conditions. Jonathan also addresses privacy, explaining that Univrses detects and blurs faces and license plates before deleting the original imagery.
This is a practical example of AI producing value through existing fleets, frequent data and earlier decisions rather than another expensive technology project searching for a problem. Could the cars, taxis and service vehicles already using our roads become part of the infrastructure maintenance system, and would you be comfortable with that if privacy protections were clear? Please share your thoughts.

What happens when a one-hour conversation with a financial advisor creates an entire day of paperwork behind the scenes?
In this episode of Tech Talks Daily, I speak with Hardy Michel, Co-Founder of Marloo, about the administrative load limiting how many clients financial advisors can support. Hardy previously helped build retail investing platforms in New Zealand and the UK, where he saw people gain easier access to investments while personal financial advice remained harder to obtain.
Before building Marloo, Hardy and his co-founders spent months inside financial advice firms. They interviewed managing directors, compliance leaders, support teams and advisors, then worked beside them as they moved between inboxes, planning tools, client records and compliance systems. This "go slow to go fast" approach helped the team map the complete advice process before deciding where software could remove friction.
Hardy says a 60-minute client meeting can produce 10 to 14 hours of follow-up work. An advisor may need to document the discussion, demonstrate why the advice was suitable, complete product research and cash-flow modeling, record fees and disclosures, and prepare a client-facing report that can run to dozens of pages. According to Hardy, the cost and time involved have left some advisors unable to accept new clients for several years.
Marloo began as a specialist meeting assistant because note-taking is frequent, painful and driven by regulation. Hardy explains how transcripts created a current source of client context that was often absent from static records. The company then expanded into the work that follows a meeting, including advice documents and presentations, with the longer-term aim of becoming a central working environment for an advice firm.
We also discuss the trust required when AI handles personal and financial information. Hardy describes Marloo's zero-data-retention arrangements for certain model APIs and the security information it provides to firms. He argues that specialist systems need to demonstrate how client data is handled and give advisors language they can use to explain recording and transcription to clients.
Adoption is another major theme. Hardy recommends a focused two-week trial with three to five likely power users, a defined goal and a clear measure of value. Rather than relying on a successful demonstration, firms should examine whether advisors continue using the product and are prepared to recommend it to colleagues.
The strongest business outcome may be what advisors choose to do with the time returned to them. Hardy says some Marloo users have increased client meeting frequency from once or twice a year to five or six times. Should AI in financial advice be measured by the volume of cases completed, the quality of client relationships, or a combination of both? Listen to the episode and share your thoughts with me.

What does it take to move from giving employees AI tools to rebuilding how an organization gets work done?
In this episode of Tech Talks Daily, I speak with Oren Levitzky, VP of R&D at Fiverr. Oren has spent ten years at the company, progressing from backend engineer through a series of leadership roles before taking responsibility for Fiverr's AI program.
That experience gives him a valuable view of AI adoption from inside a global technology marketplace. He has watched engineering teams move from using ChatGPT as a conversational assistant to GitHub Copilot for code completion, Cursor for context-aware development, and an internal agent ecosystem containing Fiverr's code, data, and organizational knowledge.
Oren explains that adding AI to an existing workflow produced useful gains, but it did not completely change how people worked. Becoming AI native required Fiverr to create a dedicated team of engineers, designers, and product managers responsible for building agents around company context and helping employees adopt new working practices.
Fiverr reports that this approach has made some development work three to five times faster. Repetitive coding and design tasks can be passed to agents, allowing employees to concentrate on decisions, validation, and accountability. However, Oren is clear that manual code review remains necessary when AI-generated changes could introduce bugs or destructive operations.
We also discuss what AI fluency means for hiring. Fiverr has redesigned parts of its engineering recruitment process so candidates can use their preferred AI tools to build an application during the interview. Oren says around 80 percent of the assessment focuses on how candidates work with AI, communicate instructions, make decisions, verify changes, and demonstrate that they understand the resulting code.
This creates opportunities for people who can combine technical knowledge with AI fluency, but it also introduces a serious learning problem. Junior engineers may produce work at a speed previously associated with experienced developers without acquiring the knowledge needed to spot errors or question poor recommendations.
Oren argues that regular workshops, practical education, self-directed learning, and continued hands-on work are needed to prevent that loss of understanding. His advice applies to leaders too. Remaining close to the work makes it easier to recognize where AI succeeds, where it struggles, and what employees need from management.
Beyond Fiverr's internal engineering teams, we consider how AI is affecting the global freelance workforce. Businesses increasingly want people who can take an AI-generated draft and turn it into secure, accountable, production-ready work. Oren points to AI video production as one example where independent creators can produce work that previously required a larger studio, while retaining the judgment and creativity customers value.
For leaders hoping to make agentic AI part of daily operations, Oren recommends dedicated resources, structured education, employees who constantly seek better ways to work, and clear measurement. Releasing another tool will achieve little when habits, incentives, and expectations remain unchanged.
As employers place greater value on people who can direct, question, and verify AI, how should we prepare today's workforce without weakening the knowledge tomorrow's experts will need? Listen to the episode and share your thoughts with me.

How much control would you hand to an AI agent when the result is a real flight, a real hotel, and a meeting you cannot afford to miss?
In this episode of Tech Talks Daily, I speak with Evan Konwiser, Chief Product and Strategy Officer at American Express Global Business Travel, about the role AI can play across search, booking, disruption support, expense management, and the wider managed travel experience.
Evan begins with a problem many travelers already recognize. Buying a ticket has become far harder than choosing a departure time and airline. Travelers now face different cabins, fare types, seats, amenities, loyalty benefits, corporate policies, and payment rules. Amex GBT and Ipsos research referenced during the interview also found that four in ten Gen Z business travelers consider arranging work trips too difficult. The challenge for a travel platform is to reduce that complexity while respecting the policies of the employer and the preferences of the person taking the trip.
That is where AI becomes promising, but the consequences of failure are unusually tangible. A wrong answer in a chat window is irritating. A travel tool that sends someone to a closed location or recommends a train that does not stop at the required station can damage confidence immediately. Evan describes trust as the deciding factor and argues that business travel may have an advantage over leisure travel because a managed travel provider already knows the traveler's profile, company policy, payment method, and authority to book.
We discuss what Evan calls trusted transaction authority. Agentic workflows can help arrange a trip, but most travelers still want to confirm the final booking. Disruption may become one of the first situations in which people accept greater autonomy. If a flight is canceled and time is short, an agent could reserve a suitable alternative, provided the action can be reversed and the traveler can reach a human advisor whenever needed.
Evan also describes how AI can identify possible disruption before it happens, prepare alternative routes, and carry the context of a digital conversation to an experienced travel counselor. This matters because automation and human service do not have to operate as separate experiences. Travelers may begin in a self-service channel, move to a person when the situation becomes complicated, and expect the context to follow them.
Expense management provides another practical example. Evan believes much of the manual expense report could eventually disappear as trip data, receipts, virtual cards, risk controls, and exception handling work together behind the scenes. He describes guest travelers, contractors, recruits, and event attendees receiving controlled virtual payment cards so ordinary travel spending can be processed automatically while unusual purchases are blocked or reviewed.
We also look at bringing travel assistance into tools such as Microsoft Teams. The potential benefit goes beyond convenience. An enterprise assistant may already understand a traveler's calendar and meeting commitments, allowing the booking experience to exclude flights that arrive too late. That context may help employees make better choices while increasing policy compliance, although it also raises questions about data access, responsibility, and how results should be measured.
Evan argues that companies should assess AI supported travel at both the program and traveler levels. Time to book and cost matter, but so do satisfaction, policy fit, channel choice, human support, and the quality of the trip itself. He also acknowledges that early agentic chat workflows can take longer than established booking tools, a useful reminder that novelty and improvement are not the same thing.
Would you allow an AI agent to rebook a canceled flight automatically if you could reverse its decision, or would you always want to approve the change first? Listen to the episode and share your thoughts with me.