Tech Talks Daily

Can electricity grids built for an earlier era support AI data centers, expanding manufacturing, electric vehicles, severe weather, and rising customer expectations at the same time?
In this episode of Tech Talks Daily, I speak with Mark Hollis, utility executive advisor at SAP, about the pressures reshaping the utility industry and the practical choices available to leaders today. Mark spent over 15 years at Duke Energy before moving to SAP, where his work gives him visibility into utility organizations across North America.
Mark describes a combination of load growth, disruption, long construction timelines, and regulation. Data centers are receiving much of the attention because of the electricity required by AI, but he argues that they are only part of the story. Manufacturing is returning to parts of North America, transport and heating are becoming increasingly electrified, and utilities must prepare for wildfires, hurricanes, winter storms, and other events that affect generation and delivery.
The obvious response is to produce additional electricity, but every option comes with physical and commercial limits. Wind and solar contribute to the generation mix, although output depends on conditions. Small modular nuclear reactors could support future demand, but commercial deployment takes time. Batteries can store electricity and release it later, but they do not generate the power they hold. Customer programs can also reduce pressure at busy periods, including arrangements that allow a utility to adjust connected thermostats by a few degrees.
This makes modernization a portfolio of decisions rather than a single bet. Utilities must decide how to divide capital among generation, transmission, resilience, customer systems, and new technology. The people who understand existing processes are often the same employees needed to design and implement replacements. At the same time, information technology and operational technology are becoming increasingly connected, forcing companies to reconsider how business functions share information and how technology decisions support outcomes across the enterprise.
AI creates another tension because it contributes to electricity demand while also offering utilities new ways to work. Mark says most utilities he meets are cautiously optimistic. Their questions include where to begin, whether the value is proven, how long adoption will take, and whether poor data must be fixed before useful work can start. He warns against choosing the hardest problem first or judging a business process by the forgiving standards people accept from consumer AI tools.
His advice begins with the business problem. Automating an inefficient process can make it more expensive and harder to correct. Utilities should define what they need to improve and why, establish connected data with the right business context, set clear quality requirements, and retain human review where errors could affect customers, safety, finance, or regulatory obligations.
Mark brings this to life with several utility AI use cases. AI could summarize customer interactions across field service and contact center systems, allowing the next employee to understand what happened previously. It could review billing exceptions during unusually hot or cold periods and support earlier customer communications when consumption is likely to produce a much higher bill.
He also discusses using AI to summarize lengthy rate-case rulings before approved changes enter billing systems. During outages, an AI system could help dispatch crews by considering skills, equipment, parts, certifications, location, safety, and customers with medical needs. A human dispatcher could then review and approve the recommendation rather than building the complete schedule manually.
The opportunity is real, but so are the limits. Utilities operate regulated infrastructure where reliability, safety, auditability, and public trust cannot be treated as optional features. Where should the industry begin, and which use case offers the right combination of low effort, meaningful impact, and manageable risk? Listen to the conversation and share your thoughts with me.

What happens when an AI system moves beyond recommending the next sales action and begins running a connected revenue workflow?
In this episode of Tech Talks Daily, I speak with Abhijit Mitra, CEO of Outreach, about the operational work required to turn agentic AI into measurable revenue outcomes. Abhijit argues that adding another AI tool can create extra complexity when customer data remains fragmented and applications cannot share context. The starting point is the business process: what problem is being solved, which data supports it, what agents may do, and where human judgment remains necessary.
We discuss the difference between a recommendation and an autonomous action. Revenue teams may begin with supervised spot checks while an agent researches accounts, identifies prospects, drafts messages, and runs targeted campaigns. Once the data and results earn confidence, parts of that process can operate continuously. Multi-step work adds another requirement because the output of one agent must become useful input for the next. Research, outreach, coaching, forecasting, and expansion cannot deliver their full value as isolated tasks.
Context runs through the entire conversation. Abhijit describes the customer history, product usage, prior interactions, industry signals, buyer priorities, and organizational memory that can turn a generic model response into a commercially useful action. He says access to a frontier model alone does not create a revenue platform because each business still needs its own context layer and controls.
We also discuss Outreach's MCP Server and Client, which allow agents to receive information from surrounding systems and return their work to platforms such as Salesforce Agentforce, OpenAI, Anthropic, and Microsoft. That interoperability creates a governance question. Abhijit's advice is to give an agent the same roles, permissions, and data access as the person or team it supports. If the platform cannot control access at that level, he advises companies to wait.
The conversation then turns to the changing software model. Abhijit describes a move from assigning SaaS licenses to employees toward deploying agents with particular skills and a defined capacity. That makes workflow design and measurement increasingly important. He recommends establishing a baseline before rollout and tracking revenue against cost through indicators such as win rate, deal size, quota attainment, pipeline movement, forecast accuracy, and seller productivity.
Can revenue teams use agents to remove administrative work while protecting customer trust and keeping relationship building human? Listen to the episode and share your thoughts with me.

What would happen if specialty insurance underwriters and risk capital providers could work from the same timely, detailed information?
In this episode of Tech Talks Daily, I speak with Jeff Radke, CEO and cofounder of Accelerant, about the infrastructure behind specialty insurance and why his team chose to rebuild it around a data-driven risk exchange. Jeff has spent decades in reinsurance broking, reinsurance underwriting, and specialty insurance across New York, Bermuda, and London. That experience gave him a direct view of a process he describes as expensive, slow, and supported by weak data flows.
Jeff explains that Accelerant backs independent underwriting specialists who focus on narrow areas of risk, from pickleball courts to New York brownstones. These teams need the regulatory ability to issue policies and the capital required to support them. Accelerant connects those needs through a shared platform that routes risks to insurance companies and distributes them across a group of capital providers.
The economic argument is striking. Jeff says the traditional chain can consume about 40 cents of each premium dollar in expenses and overhead. Accelerant instead seeks portfolio-level solutions across a diverse book of business, reducing repeated negotiations and transfers between intermediaries. He also addresses the tradeoff created by concentrating information in one exchange, including the need to protect data and cash flows when participants depend on a shared platform.
Data quality sits at the center of the conversation. Jeff says older policy administration systems often retain only eight to twelve exposure characteristics for each policy, while Accelerant captures over 60 on average. That fuller record gives underwriters and capital providers more information when selecting risk, reviewing performance, or investigating a problem.
We also discuss why smaller underwriting organizations may hold an advantage over established insurers. New teams can begin with data at the center of their operating model, while larger companies must change processes built around older systems. Jeff argues that the biggest barrier is often mindset rather than budget.
AI has a defined role in that model. Accelerant uses agentic AI to organize varied incoming data, identify products whose performance needs attention, support portfolio construction, and improve internal operations. Jeff draws a firm boundary around responsibility: underwriters remain accountable for underwriting outcomes. His father's advice captures the principle neatly: do not blame the wheelbarrow; responsibility belongs to the person driving it.
Could shared data and lower operating expense return more value to policyholders while preserving human judgment? Listen to the conversation and share your thoughts with me.

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" Putcha, 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.