Free Daily Podcast Summary
AI at Work: Daily Summaries Delivered
by Neil C. Hughes
What does AI really mean for the modern workplace, and are we ready for what comes next?AI at Work is a podcast from the Tech Talks Network, the home of conversations that showcase the voices at the heart of enterprise technology. You may know me from Tech Talks Daily, where we explore a different area of innovation in every episode. This show takes a focused look at one of the biggest shifts in business: how artificial intelligence is transforming the way we work.From intelligent automation to agentic AI and from the promise of workplace efficiency to the risks of unintended consequences, we aim to provide a grounded and accessible perspective on how AI is shaping the future of work.If you’re using AI in your business or thinking about how to get started, this podcast is your chance to learn from the people already doing it.
Latest Episodes
The most recent episodes — sign up to get AI-powered summaries of each one.
- 3 days ago29 min
Giving Enterprise AI the Right Business Context With Sphinx AI
In this episode of AI at Work, I speak with Rohan Kodialam, co-founder and CEO of Sphinx AI, about the difference between an AI system that sounds capable and one that understands how a business defines truth. Sphinx AI describes itself as an AI research company building human-interpretable context graphs that can support agents across different models and enterprise systems.Rohan argues that consumer AI can feel impressive because many everyday questions are supported by information in the public domain. The situation changes inside a company. Questions about customer churn, product performance, acquisition costs, or operational efficiency depend on definitions, assumptions, and working practices that belong to that organization. A model may reach the data through an enterprise connector and still lack the knowledge needed to interpret it correctly.The most revealing example comes from a retailer using a metric called revenue per store. One team divided revenue by every store in the estate. Another used only stores that were open on the relevant day. Both definitions were reasonable for their intended users, but an AI relying on retrieved documents could select one almost at random and return an answer that looked convincing. The error might remain hidden until somebody compared it with a manually prepared report.Rohan says the problem can be resolved by making the missing business context explicit. In this case, the AI needed a short explanation describing both definitions and when each should be used. It could then consider whether the request came from a real estate or operations user and select the relevant metric. This is a small example of a much larger enterprise problem: the information that separates a useful answer from a costly mistake often lives in people’s experience rather than a formal system.We discuss why retrieval augmented generation can struggle when company material is raw, contradictory, or unsynthesized. Rohan reports that Sphinx AI customers have seen accuracy increases of roughly 35 to 40 percent on existing enterprise benchmarks after curating context, with larger improvements in some cases. These are company observations rather than independent benchmark results, but they show the size of the gap Rohan believes a dedicated knowledge layer can address.Context also changes. Rohan references an example in which an agent reportedly moved from 95 percent accuracy to 65 percent within a month because business processes and priorities had changed. His argument is that companies need context stewards who can review updates, approve definitions, reject unauthorized changes, and maintain an audit trail. He compares this with code review, where machine-generated work still requires accountable human oversight.Security remains part of the design. Rohan says a context layer should explain what information exists while respecting role-based access controls. It should sit virtually above existing systems of record rather than copying sensitive data into a new repository. An agent might know that customer phone numbers exist while remaining unable to retrieve them. It can then tell the user that permission is required instead of inventing an answer or bypassing an established control.Before allowing an agent to act on company data, Rohan recommends two forms of testing. Business experts should create a small gold-standard set of tasks they know well, while the company can build a broader benchmark from repeated work already visible in its operational history. Together, these tests can show whether the agent understands the decisions people actually make rather than performing well on a generic demonstration.Does your organization have a shared, reviewable account of how it defines its most important metrics, rules, and working assumptions? Listen to the episode and share your thoughts.
- 5 days ago29 min
Building audit ready AI agents in finance with Avalara
What should a finance leader be able to prove before giving an AI agent permission to act?In this episode of AI at Work, I welcome Danny Fields, Chief Technology and Customer Operations Officer at Avalara, for a conversation about the distance between a convincing AI pilot and a finance workflow people can trust. We discuss the appeal of faster execution alongside the evidence, controls and business ownership needed when software begins making decisions or recommending actions.Avalara's research provides the starting point. Its global survey of 1,505 CFOs and senior finance leaders found that 92% felt career pressure to demonstrate AI agent ROI, while half reported limited measurable returns. Respondents came from businesses already deploying, piloting or evaluating agents, so these findings describe a particular group rather than every finance department. What interested me was the tension between pressure to demonstrate progress and the work required to make that progress defensible.Danny argues that copying an existing process into an AI workflow can preserve the problems already inside it. He describes why controlled pilots can struggle when production introduces incomplete records and unpredictable inputs. We consider what it means to redesign the workflow, measure accuracy over time and judge value through risk, audit preparation and business outcomes as well as hours saved.We also discuss accountability. Danny's view is that responsibility stays with the team that owns the business outcome, supported by documented roles across finance, compliance, IT and legal. Permission boundaries, recorded approvals and tested incident response become part of the operating model. A CFO may not need to understand the mathematics behind a language model, but should understand what information an agent uses, what it can do and when a person takes over.The conversation closes with a practical test for expanding autonomy: can the business demonstrate value, sustained accuracy, clear ownership, appropriate access and controls that reflect how agents actually work? These are Danny's recommendations, rather than a guarantee of compliance or financial performance. Please share your thoughts with me. What would your team need to see before trusting an AI agent with a consequential finance decision?
- 2 weeks ago30 min
Earning AI Autonomy Through Governance With Wrike
How much autonomy should an AI agent receive before it has proved that its work can be trusted?In this episode of AI at Work, I speak with Alexey Korotich, Chief Product Officer at Wrike and a former Dell software leader. Wrike supports around two million users, along with a growing number of agents, giving Alexey a close view of how enterprise teams are trying to move from AI experiments into daily work.Alexey challenges two popular measures of progress. A dashboard built in an hour may look impressive, but enterprise software creates lasting value through trust, context, and reliable delivery. The same problem appears in AI adoption metrics. Prompts fired, tokens burned, and tools deployed can show activity without revealing whether cycle time improved, margins changed, customers became happier, or a workflow produced a better result.The conversation then turns to the reason many AI projects stall. Alexey does not believe every process must be rebuilt from the beginning. He sees a more immediate problem in work that remains undocumented, unstructured, or divided across teams and systems. When employees repeatedly explain how work should be done, much of the time saved by AI disappears. Historical work, process artifacts, permissions, and accountability all give an agent the context it needs to produce a useful result.We also discuss how leaders can set boundaries for autonomous AI agents. Alexey compares agent onboarding with employee onboarding. Start with high-frequency, low-risk tasks such as meeting summaries, request triage, status updates, and risk flags. Review the output, build trust, and raise autonomy gradually. Higher-stakes decisions should remain with people, supported by identity controls, limited access, review channels, and a way to recover when an agent makes a mistake.For Wrike, shared context comes through what Alexey calls a work intelligence graph. It records how work moves, who participates, who owns each stage, and what each person or agent can access. That organizational memory can help agents learn from completed work and human feedback instead of treating every request as an isolated prompt.Should companies measure AI adoption by how often employees use it, or by what their workflows deliver? Listen to the episode and share your thoughts with me.
- 2 weeks ago24 min
Governing AI Agents Without Losing Cost Control With Basware
How do you give AI agents greater responsibility without losing control of the decisions they make or the costs they generate?On AI at Work, I’m joined by Jason Kurtz, Chief Executive Officer of Basware, to discuss governed autonomy, financial accountability, and what businesses need to consider as AI begins taking a more active role in their operations. For finance leaders, the challenge extends beyond choosing a tool. They need to understand what an agent is authorized to do, where human judgment remains necessary, and how to establish whether the investment is delivering useful results.Our conversation looks at the relationship between autonomy and oversight. Giving an AI system permission to complete a task creates questions about responsibility, approval boundaries, and intervention. When should an agent proceed independently, and when should a person review its work? These are business decisions that require input from the people responsible for the process, alongside the teams implementing the technology.We also discuss the CFO’s role in assessing AI value. Understanding the bill is only part of the job. Leaders need to connect spending with outcomes, whether that means improving a finance process, reducing repetitive work, or helping employees make better decisions. The conversation considers why cost visibility and evidence of value need to develop together.Token-based pricing adds another complication. As AI usage grows, businesses can face costs that vary with the amount and complexity of the work being performed. An experiment that appears affordable at a small scale may require a different commercial assessment when it becomes part of everyday operations. Jason and I discuss the questions leaders should ask about pricing, usage, and the relationship between greater adoption and greater expense.The people involved deserve equal attention. Working alongside AI agents may create new responsibilities for training, supervision, and governance. Employees need to understand how these systems fit into their work, what they can reasonably expect from them, and when to challenge an output or escalate a decision. Introducing digital workers brings organizational questions as well as technical ones.For anyone considering AI in finance or across the wider enterprise, this episode offers a way to think about adoption through accountability, commercial discipline, and business purpose. The opportunity is to give teams useful support while keeping responsibility for outcomes clear.Please share your thoughts. How is your organization deciding which responsibilities to give AI agents, and what evidence would convince you that their autonomy is delivering value?Learn more about Basware
- 3 weeks ago26 min
Proving the Business Value of AI With Equiteq
What evidence shows that AI is making a business more productive, more resilient, and more valuable to a potential buyer?In this episode of AI At Work, I speak with Robby Riggs, Global Chief Transformation and Operations Officer and Head of Coverage at Equiteq. Robby previously built a consulting business with a cofounder and sold it to North Highland. His experience as an operator informs the way he now works with founders and investors assessing technology services and consulting businesses.Our conversation begins with the gap between AI claims and operating reality. Robby says buyers are increasingly examining AI as part of due diligence, while questioning how widely it is used inside the business. His suggested evidence includes revenue per employee, margins, delivery speed, and the actual work promised in client contracts. He also offers a simple diagnostic question. If AI were removed, would the firm's economics change materially? The answer can reveal whether it supports the operating model or mainly supports the sales story.We discuss how AI could affect the economics of professional services. Robby believes smaller firms can gain capacity through faster analysis and delivery, while larger businesses may retain advantages through proprietary knowledge, customer access, and established relationships. His argument raises a useful question for leaders deciding where to invest. If common tools make certain tasks easier for everyone, what remains distinctive about the firm?Robby also describes investor interest in recurring revenue and payment for outcomes, alongside the possibility of improved margins. He emphasizes the need to demonstrate those improvements over time. A profitable quarter created by reducing headcount tells a different story from a business that uses additional capacity to deliver better work and develop its people. We examine what happens to the talent pipeline when short term financial targets take priority over future capability.The discussion then turns to data rights, dependence on models, security, and client concentration. Robby explains why these issues deserve closer attention when AI becomes part of service delivery. After an acquisition, different tools, controls, and working practices can create problems that reduce the value the buyer expected. His advice is to understand the integration work early and make people responsible for delivering the intended result.For founders, his recommendation is to pursue a consistent plan, demonstrate improvements over successive quarters, and turn existing organizational knowledge into reusable assets. His optimism about deal activity is presented as his outlook, rather than a forecast listeners should treat as certain.Where is AI creating lasting value in your business, and what would you measure to prove it? I'd love you to share your thoughts.Useful Links: Equiteq website: https://www.equiteq.com/Robby’s LinkedIn: https://www.linkedin.com/in/robbyriggs/Robby’s three-part series on AI adoption and value creation, which argues that AI should be treated as a tailwind, rather than a headwind, for services and consulting firms and ties closely to several of the themes discussed today: https://www.equiteq.com/resources/artificial-intelligence-adoption-reality-and-the-future-of-value-creation
- 3 weeks ago28 min
SAP on Why the Autonomous Enterprise Needs a New Kind of Leadership,
Are businesses investing in tomorrow's artificial intelligence while continuing to operate with yesterday's organizational structures? And could that explain why so many enterprise AI initiatives struggle to deliver the results leaders expect?In this episode of AI at Work, Neil C. Hughes is joined by Jesper Schleimann, Head of Business AI for EMEA at SAP, to discuss why adopting AI technology and achieving meaningful business transformation are two very different challenges.Jesper shares what he's seeing across organizations in Europe, the Middle East, and Africa, where AI adoption is accelerating but progress remains uneven. While some businesses are already reporting improvements in productivity and customer service, others are discovering that isolated projects and impressive demonstrations do not automatically translate into business value.The conversation examines why companies need to begin with a business problem rather than a technology solution, and why adding AI to inefficient processes can limit its potential.They also discuss the growing interest in autonomous AI agents and the practical realities of allowing intelligent systems to take action inside an enterprise. Jesper explains why business context, defined authority, reliable data, and human accountability all play an important role in building trust.As businesses progress toward increasingly autonomous operations, what happens to traditional management structures? How should leaders decide when humans need to intervene? And could the greatest opportunity come from redesigning how work happens rather than simply making existing tasks faster?Jesper also introduces ideas from his book, The Intent-Driven Leader, which examines the changes in leadership and organizational thinking required as AI becomes more capable.With companies investing heavily in enterprise AI, this conversation offers a timely reminder that technology alone cannot deliver organizational transformation.Are businesses ready to rethink the way they operate, or will familiar structures and habits continue to limit what AI can achieve?
- 4 weeks ago28 min
Turning Cargo Data Into Five Second Decisions With CargoSeer
How can customs agencies inspect vast volumes of cargo without slowing legitimate trade or handing consequential decisions entirely to an algorithm?In this episode of AI at Work, I speak with David Smason, founder of CargoSeer and Head of Customs AI at BigBear.ai, about a form of workplace AI that operates far from the usual office productivity conversation. CargoSeer was built to help customs teams combine cargo documents, x-ray images, routes, commodity codes, tariffs, and historical patterns before an experienced officer makes the final decision.David explains why traditional customs work can overwhelm even highly trained people. A single shipment may involve dozens of documents, different languages, handwritten information, revenue questions, and national security risks. His vision is one operator overseeing hundreds or eventually thousands of analyses while concentrating attention where human experience matters. He compares the result to a James Bond command center, although the real work involves careful data processing, explainable recommendations, and a clear role for the officer.We discuss CargoSeer’s internal findings that an operator using its supporting technology can assess a shipment in under five seconds, compared with several minutes in some existing processes. That speed comes with an important qualification. False positives may delay legitimate cargo, while false negatives may allow serious risks through. David says each customs agency must define its own risk appetite, operating procedures, success measures, and thresholds rather than accept a universal model.Human feedback remains central to the system. David compares an AI model to a baby that must be taught continuously. When an officer corrects a false alert, the correction can improve later recommendations. He argues that operators value this influence because their knowledge becomes part of the technology instead of being discarded.We also discuss localization and proof. David estimates that CargoSeer’s models are currently about 85 percent standardized, with the remaining work shaped by local data, language, culture, tariffs, agreements, and operating practices. His advice to public agencies is direct: demand evidence that the system works on your data, in your environment, and within your workflow. Test it, try to break it, and define performance before live deployment.Could this model of AI-supported judgment work in other high-consequence roles where speed, evidence, and accountability must coexist? Listen to the conversation and share your thoughts with me.
- Sep 29, 202635 min
Why Model Choice Is the Wrong AI Obsession
Why are so many enterprise AI programs still stuck in pilot mode when powerful models are widely available?In this episode of AI at Work, I speak with Emma McGrattan, Chief Technology Officer at Actian, about why model selection may be receiving more attention than it deserves. Emma argues that models are becoming increasingly interchangeable, while an organization’s proprietary data, business definitions, governance controls, and data culture remain much harder to reproduce.Emma explains why familiar terms such as customer, revenue, and churn can mean different things across sales, finance, tax, and operational teams. AI cannot reliably fill those gaps without context, ownership, lineage, and measurable quality. We discuss why enterprise data designed for dashboards and human interpretation must be treated differently when an AI system or autonomous agent becomes the consumer.The conversation examines Actian’s data governance research, including the finding that 83% of organizations face governance and compliance challenges and that executives rate data maturity 12 percentage points higher than operational managers. Emma describes what this gap looks like in practice, from duplicate customer records and undocumented pipelines to a business field called “Revenue Final Version 7 Verified and Revised.”We also discuss data products, data contracts, executable governance, and BARC research suggesting that organizations using data products and contracts were 3.4 times more likely to report success with AI at scale. Emma recommends beginning with one valuable use case, defining the data product it requires, agreeing the contract around it, and proving that the organization can deliver reliable results before expanding.Agentic AI raises the stakes further because the human who previously handled ambiguity may no longer be present at every step. Emma shares the example of an expense agent that approved one thousand $300 purchases. Every transaction sat below its individual limit, yet the combined spend reached $300,000.Finally, Emma explains what a strong data culture looks like. Employees need permission, access, and skills to question an AI answer, trace its lineage, and recognize when the result does not make sense.Learn
Get AI at Work summaries in your inbox
Free AI-powered daily recaps. Key takeaways, quotes, and mentions — in a 5-minute read.
Get Free Summaries →Free forever for up to 3 podcasts. No credit card required.
You Might Also Like
Listeners also like.
The AI in Business Podcast
Interviews with AI executives reveal practical applications and strategic insights for business leaders adopting AI.
Me, Myself, and AI
Leaders from top companies share real-world strategies for turning artificial intelligence into measurable business results.
What Comes Next with Arun
Explores how data and AI strategy create competitive advantage in business, based on real-world enterprise experience.
The AI Daily Brief: Artificial Intelligence News and Analysis
A daily analysis of artificial intelligence news, exploring its creative potential, industry impacts, and ethical challenges.
Hands-On AI (Video)
Teaches practical ways to understand and use artificial intelligence in daily tasks through clear, hands-on examples.
AI Business Daily
Daily insights into AI, tech trends, and digital innovation for entrepreneurs and professionals navigating the future of business.
How I AI
A practical guide to using AI tools in work and life, featuring guests who share specific, actionable techniques and workflows.
Intentional AI
Two productivity experts explore practical applications of artificial intelligence to improve work efficiency and daily workflows.
AI + a16z
Discussions with AI engineers, founders, and experts on the technological and industry shifts in artificial intelligence.
The Most Interesting Thing in AI
Conversations with leading thinkers on the ethical, economic, and social impacts of artificial intelligence.
This Week in AI
Four AI experts analyze the latest news and trends in artificial intelligence each week.
80,000 Hours Podcast
Discusses artificial intelligence and global catastrophic risks with experts and researchers.
About AI at Work
What does AI really mean for the modern workplace, and are we ready for what comes next?AI at Work is a podcast from the Tech Talks Network, the home of conversations that showcase the voices at the heart of enterprise technology. You may know me from Tech Talks Daily, where we explore a different area of innovation in every episode. This show takes a focused look at one of the biggest shifts in business: how artificial intelligence is transforming the way we work.From intelligent automation to agentic AI and from the promise of workplace efficiency to the risks of unintended consequences, we aim to provide a grounded and accessible perspective on how AI is shaping the future of work.If you’re using AI in your business or thinking about how to get started, this podcast is your chance to learn from the people already doing it.
Customized Recaps
AI-powered recaps with compact key takeaways, quotes, and insights.
Straight to Your Inbox
Get key takeaways from AI at Work in a 5-minute read.
Save Hours Every Week
Stay current on your favorite podcasts without falling behind.
Frequently Asked Questions
What is Podzilla's AI at Work daily summary?
It's a free AI-powered email that summarizes new episodes of AI at Work as soon as they're published. You get the key takeaways, notable quotes, and links & mentions — all in a quick read.
How does the AI at Work podcast summary work?
When a new episode drops, our AI transcribes and analyzes it, then generates a personalized summary tailored to your interests and profession. It's delivered to your inbox every morning.
Is this an official AI at Work product?
No. Podzilla is an independent service that summarizes publicly available podcast content. We're not affiliated with or endorsed by Neil C. Hughes.
Can I get summaries of other podcasts too?
Absolutely! The free plan covers up to 3 podcasts, any in the catalog. Basic raises that to 15, Pro to 40 and Max to 100. Browse our full catalog at /podcasts.
How often does AI at Work release new episodes?
AI at Work publishes weekly. Our AI generates a summary within hours of each new episode.
What topics does AI at Work cover?
AI at Work covers topics including Technology, Business. Our AI identifies the specific themes in each episode and highlights what matters most to you.
Start getting AI at Work summaries tomorrow morning.
Free forever for up to 3 podcasts. No credit card required.
Free forever for up to 3 podcasts. No credit card required.