Using AI to Support Human Skills With Taskrabbit
Tech Talks Daily
What does useful AI look like when the work still needs someone to arrive at your home with the right skills and tools? In this episode of Tech Talks Daily, I speak with Ania Smith, CEO of Taskrabbit, about the relationship between digital technology and practical work. We discuss furniture assembly, TV mounting, and the decisions involved in connecting a customer with someone who can complete the job. For Ania, the opportunity is to make that connection easier while keeping the people doing the work central to the experience. A TV mounting request sounds straightforward until you consider the...
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How Deloitte Measures the Cost of Enterprise AI
Tech Talks Daily
Could you explain why your AI bill rose and what the business got in return? I asked Jim Rowan, US Head of AI at Deloitte, because a total spend figure tells a finance team how much it paid, but gives it little help deciding which work to expand. In Deloitte's State of AI in the Enterprise survey, 71 percent tracked total AI interface and API spend, 51 percent tracked token volume, and 44 percent tracked the cost per unit. Jim suggests looking at what it costs to finish a task to an acceptable standard. We talk about what surrounds the model: the tools it can call, the information it can...
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Recovering what matters first with Fusion Risk Management
Tech Talks Daily
If your business lost several systems at once, would the recovery order reflect what your customers need most? In this episode of Tech Talks Daily, I speak with Mike Campbell, CEO of Fusion Risk Management, about operational resilience and the connections that can turn one disruption into a wider business problem. We discuss why application recovery, supplier continuity, employee knowledge and customer service need to be understood together when leaders decide what to restore first. Mike shares an example involving an unnamed retailer whose existing IT recovery plans followed a logical...
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Understanding the Data Costs Behind Enterprise AI With Acceldata
Tech Talks Daily
What happens to your AI budget when one business question becomes a series of data queries across several systems? In this episode of Tech Talks Daily, I speak with Rohit Choudhary, founder and CEO of Acceldata, about the work taking place behind an AI response. An agent may need to find information, call several tools, and repeat parts of that process before it can return an answer. Each step can consume resources, and the person asking the question may have little visibility into the infrastructure activity it creates. Rohit uses the example of looking for jewelry without knowing which vault...
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Securing AI Generated Code Without Losing Speed With Checkmarx
Tech Talks Daily
If AI can help anyone create an application, who checks whether the result is secure enough to put into production? In this episode of Tech Talks Daily, I speak with Frank Emery, VP of Product Management at Checkmarx, about the skills and security practices needed as AI becomes part of software development. We begin with developers entering the workforce alongside coding assistants and the experience they still need to build. Frank argues that architecture, review and an understanding of what code actually does become increasingly valuable when generating that code takes less time. We...
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Controlling AI Agent Access and Accountability With Ekco
Tech Talks Daily
What happens when an AI agent pursues an approved task in a way nobody expected, using access that reaches further than it needs? On Tech Talks Daily, I'm joined by Jonathan Trayers, a director at Ekco, to discuss the security decisions behind agentic AI. The conversation focuses on what changes when a system can act with credentials, connect to business applications, and trigger workflows. A useful answer in a chat window and an automated action inside a company create different responsibilities. Jonathan describes working with both deterministic workflows, where rules specify what should...
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Bringing enterprise AI inference on premises with Scality
Tech Talks Daily
What should a business consider before moving AI inference onto infrastructure it owns? In this episode of Tech Talks Daily, I speak with Paul Speciale, Chief Marketing Officer at Scality, about the decisions behind enterprise AI infrastructure. Following the launch of Scality AI Inference Factory, we discuss why organizations are assessing on-premises inference alongside cloud services, with attention to costs, model control and the location of sensitive data. Paul expects a hybrid approach, with different workloads running in the environments that suit their requirements. The conversation...
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Why AI Agents Need Better Data With Steffen Hoellinger of Confluent
Tech Talks Daily
What if many of the problems businesses blame on AI are actually problems with their data? It's a question I wanted to examine when I sat down with Steffen Hoellinger, Field CTO EMEA at Confluent, during Current 2026 in San Francisco. After a year dominated by conversations about AI agents, I'm increasingly interested in the less glamorous work companies need to get right before these technologies can deliver anything useful. Steffen works with enterprises across Europe, helping them manage real-time data and develop AI applications. Our conversation begins with the difficulties businesses...
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Current 2026: Why Confluent's Streamhouse Could Solve AI's Real-Time Data Problem
Tech Talks Daily
What happens when an AI agent makes a business decision using information that's already out of date? It's a question I wanted to put to Stephen Deasy, CTO of Confluent, when we met at Current 2026 in San Francisco. After attending more than 20 technology conferences this year, I've heard countless promises about what AI agents will eventually achieve. But there's a growing problem behind those ambitions. Businesses are still struggling to get accurate, current information out of the systems they already own. Confluent has introduced Streamhouse, a data architecture designed to address that...
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Electrifying Collection Fleets and Improving Driver Safety With Republic Services
Tech Talks Daily
How do you introduce electric vehicles and AI-assisted safety technology into a collection fleet that customers depend on every day? On Tech Talks Daily, I'm joined by Brett Rogers, Vice President of Operations Technology at Republic Services, to discuss the engineering, infrastructure, and human decisions behind modern waste and recycling operations. Brett's background includes work at Toyota, General Motors, and Cruise. He describes arriving from the automotive sector and discovering how much complexity sits behind a service most people expect to happen without thinking about it. We give...
info_outlineCould you explain why your AI bill rose and what the business got in return? I asked Jim Rowan, US Head of AI at Deloitte, because a total spend figure tells a finance team how much it paid, but gives it little help deciding which work to expand. In Deloitte's State of AI in the Enterprise survey, 71 percent tracked total AI interface and API spend, 51 percent tracked token volume, and 44 percent tracked the cost per unit. Jim suggests looking at what it costs to finish a task to an acceptable standard.
We talk about what surrounds the model: the tools it can call, the information it can retain, and the permissions it needs to work with company systems. Jim gives a simple example. An email connection can let an agent read messages, draft a reply or send on someone's behalf. Each step carries different consequences. Getting that decision right can take longer than choosing the model, especially when an agent needs access to a system the company has relied on for decades.
Jim also explains why he'd start with a company's existing approval limits before giving an agent authority to act. The same payment might require review at a small company and fall within routine approval limits at a larger one. Deloitte's survey puts that discussion in context: 40 percent reported allowing AI to execute decisions or actions without human approval to at least a moderate degree, while 23 percent reported unclear accountability for negative outcomes. Those are separate findings and tell us nothing about how much the groups overlap.
We finish with a question for anyone tempted to swap models after the next release. Does it do your work better, and is the improvement worth the cost of testing and switching? Deloitte's survey also found that 28 percent reported revenue gains from AI, up from 20 percent in the previous edition. That's encouraging, although reported revenue gains do not establish that a project paid for itself. I'd love to hear your experience. What do you measure to decide whether an AI workflow deserves a bigger budget?