Tech Talks Daily

How do you govern AI when the technology, its users, and the work it performs keep changing after you approve it?
On Tech Talks Daily, I'm joined by Alex Harland, co-founder and CEO of AI Score, to discuss the distance between an AI policy and the reality of everyday operations. A company may approve a platform and complete its initial review, yet employees discover new applications, vendors introduce features, and agents begin working with different information. Alex argues that governance has to keep following those changes if it is going to remain useful.
What interested me was his emphasis on individual use cases. An inventory of product names can tell you what a business has purchased, but it may tell you very little about what those products are allowed to do. We discuss why an organization needs to connect its AI assets with approved activities, named owners, and continuing evidence of how they operate. The same platform can create very different risks depending on the information it handles and the decisions it supports.
We also look at vendor assurance. Adding AI questions to a supplier questionnaire is a reasonable start, but the answers need evidence behind them. Alex describes AI Score research involving approximately 250 organizations using AI. He says 92% were beyond pilots, while only 16% had an inventory. These are company-reported findings, and the conversation does not establish the survey methodology. They provide a starting point for a practical question rather than a universal measure of enterprise readiness. Can your supplier explain what AI it uses, for which activities, and how it knows when that picture changes?
AI agents add another challenge. Existing identity, permission, and network controls remain relevant, while autonomous behavior creates a need to notice changes after deployment. Alex discusses monitoring whether an agent starts accessing external information when its approved use was limited to internal sources. We talk about the people responsible for those systems and how operational accountability should connect with board oversight.
There is also a commercial argument for governance. Alex sees it as a way to pursue business objectives within an understood risk appetite. Controls still need to justify the work they create, and the interview does not provide a measured customer return or a product demonstration. The useful lesson for listeners is to ask whether governance is helping a business make better decisions as its AI usage changes.
We close with the company's growth following the $5.4 million seed round referenced in the supplied guest notes and our conversation. Alex describes the challenge of keeping pace with customer needs and a rapidly changing market. For listeners responsible for AI adoption, security, procurement, or board reporting, the episode offers a practical way to connect written rules with observable activity.
I'd like to hear your thoughts. What would help your organization keep its AI approvals, ownership, and day-to-day use aligned as the technology changes?

If an AI pilot works, what evidence would persuade you to fund it across the business? Three years after our conversation about Digital Transformation Payday, I welcome Tim Bottke back to Tech Talks Daily to discuss PREVAIL, his book on the decisions leaders make before anyone selects a model. Tim's argument is that by the time a project disappoints, the questions that might have changed its direction can be months behind it.
We talk about what happens when the CTO wants room to experiment, the commercial team wants a better customer experience, and the CFO expects lower costs. Those are all reasonable goals, but they lead to different projects and different ways of measuring success. Tim describes how he used company filings to study patterns in what businesses say about AI over time. The filings can show how the story changes. They can't tell us on their own whether a quiet company abandoned a project or simply stopped talking about it.
I also asked Tim what a CFO should expect to see before approving an AI budget. His answer includes a baseline, a comparison with simpler options and the costs that appear once a pilot is running across the business, including model usage, maintenance and retraining. We discuss his idea of "latte time," a reminder that saving a few minutes for each employee leaves a separate question about what the business or its customers gain from the time saved.
The conversation turns to AI agents and what meaningful human control looks like. Tim argues that leaders should decide where an agent can act, which cases should be escalated and who has enough time and information to intervene. We finish with three decisions a team can make before its next funding meeting: define the problem, name the person responsible and decide what will happen after launch. I'd love to hear your experience. Which of those decisions has made the biggest difference to an AI project in your business?

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 details. The person arriving may need experience drilling into a brick wall, the appropriate tools, availability at the right time, and a price that works for the customer. Ania describes how Taskrabbit uses data and AI to improve matching across those factors. She also discusses tests designed to ask customers better questions about what they need before recommending a tasker.
Trust runs through the conversation. Inviting someone into your home involves a different decision from clicking on a product listing. Ania explains how the platform learns from taskers' experience, the jobs they complete, and the work they do well. The aim she describes is a useful match and a completed job that gives the customer confidence to return. We also hear what she says taskers want from technology, including flexibility, transparency, and a clearer understanding of how to find work and price their time.
I ask how the team decides whether an AI feature is making a difference. Ania describes testing new features with control and treatment groups, then examining whether they make matching easier and whether the job actually takes place. She acknowledges that introducing AI can sometimes add friction. I found that a useful management lesson because it ties investment to the experience of the people using the service, rather than assuming that another AI feature must represent progress.
One of the most revealing moments comes from a conversation Ania had with a customer in Brooklyn. Asked about an AI-related booking idea, the customer expressed strong opposition to AI. Ania uses the story to reflect on the assumptions people can make inside San Francisco's technology industry. It is one person's view, rather than a survey of customer attitudes, but it makes the case for continuing to ask questions when a product team thinks it already knows the answer.
We also discuss practical skills, careers, and the value of judgement, empathy, and relationships. Ania believes the unpredictable work involved in home services remains difficult to automate. Her comments are a perspective on the work Taskrabbit sees, rather than a guarantee about the future of robotics or employment. The useful question for leaders is how technology can help people bring those skills to the work customers need done.
For me, the conversation returns to a familiar principle: technology earns its place when it helps people. In a marketplace, that requires listening to both the customer booking a task and the person accepting it, then testing what changes for each of them.
I would love to hear your experiences, so please share your thoughts. Where has AI made a service easier to use, and where would you prefer a company to focus on the human relationship?

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 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?

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 technical sequence. During a demonstration using those plans, he says the business identified a different priority: restoring its online presence before concentrating resources on stores that had some manual alternatives. The lesson is about the assumptions inside the plan. This was a planning example, rather than evidence of a measured recovery during an actual outage.
What interested me was the distance between knowing which applications sit on a server and knowing which business activities depend on them. That connection affects how teams judge financial impact, customer consequences and the order in which work should happen. It also explains why operational resilience is a boardroom discussion, with choices about suppliers, investment and acceptable disruption sitting alongside technical decisions.
We consider the role of simulation and testing across different scenarios. Mike describes how Fusion's platform can help teams compare variations and identify recurring weaknesses. Those findings depend on the information and assumptions behind the model, and the conversation doesn't establish that a simulation can guarantee what will happen in a real incident. It does offer a useful way to question whether a single rehearsal has covered enough possibilities.
There is also a very practical communications example. If the usual email service becomes unavailable, teams need a way to coordinate that doesn't depend on the same failed services. Mike argues that deciding this while normal communications still work is much easier than trying to assemble an alternative during disruption.
Finally, we discuss people as potential single points of failure and the decisions involved in relying on one supplier. Resilience becomes something leaders consider when designing the business, supported by testing and an understanding of the consequences of different choices. Please share your thoughts with me. Which dependency would you examine first to find out whether your recovery plan matches the business your customers rely on?