What does an AI first workplace look like when every employee has an agent but every person remains responsible for the outcome?

In this episode of AI at Work, I speak with Alex Svanevik, co-founder and CEO of Nansen, about how his company is integrating AI agents into daily operations while retaining human judgment, security boundaries, and quality control.

Nansen has around 80 employees, and Alex says each person has been given an AI agent. His own agent, Winnie, prepares draft agendas using previous meetings, company objectives, strategy, and cultural context. Alex then works with the agent to improve the agenda before the meeting begins.

His use of AI extends beyond routine administration. Alex describes building the first version of a Nansen product through Telegram while walking with his daughter. By the time he returned home, the agent had created a working product that later became a command line interface used by thousands of people.

There is also a lighter side to this deeply connected life. Alex and his wife occasionally use their respective agents to broker disagreements. As someone who has been married long enough to appreciate the commercial possibilities of automated diplomacy, I suspect this could become an unexpectedly popular category.

The workplace message is serious. Nansen expects employees to use AI across much of their work, but Alex says the human must own the quality, output, and result. Employees cannot blame the tool for inaccurate, generic, or poorly reviewed work.

Alex compares the review process with sending a disappointing meal back to the kitchen. The first output may be acceptable, but reaching a high standard often requires several rounds of feedback. He believes judgment and taste will become strong sources of differentiation as average quality becomes easier to produce.

We also discuss the security tension surrounding workplace AI. Alex argues that companies must consider the risk of avoiding AI because attackers and competitors are using it. His preference is to provide employees with approved tools and safe environments rather than leave them to assemble uncontrolled alternatives.

One of his most practical recommendations concerns machine readable information. Documents, code, designs, spreadsheets, and diagrams must be accessible to both employees and agents. Nansen has moved internal work toward GitHub repositories, Markdown documents, CSV files, and other formats agents can process.

Making everything readable only by machines would create a different problem. People must retain the ability to inspect, understand, and approve the work. The aim is shared accessibility rather than transferring complete control to an agent.

Evaluation becomes especially important when agents influence financial decisions. Nansen tests trading agents through backtesting, measuring whether they can interpret data, judge the significance of news, and produce profitable decisions. A separate optimizer or coach then recommends improvements to each agent’s strategy.

Alex closes with four human traits he believes will matter in an AI first workplace: high agency, good problem selection, judgment and taste, and clear communication. Experimentation amplifies those qualities, provided people avoid unnecessary risk and retain ownership of the result.

Could giving every employee an AI agent increase productivity while making personal accountability even more important? Listen to the episode and share your thoughts with me.

In this episode of Tech Talks Daily, I speak with John Nay, founder and CEO of Norm Ai, about Agentic Law, AI native legal services, outcome based pricing, and the proposed legal framework for companies managed by AI agents.

John has worked on the application of AI to law and public policy for around 14 years. His research predates the current generative AI era and includes GovDeVec, an early attempt to train neural networks on legal and government text so they could identify concepts embedded across large bodies of policy information.

The arrival of frontier language models opened a different category of legal automation. Deterministic systems can complete forms and apply fixed rules, but language models can also examine precedent and guidance before applying it to a new situation.

John separates this work into three layers. The first covers deterministic rules and repeatable automation. The second uses model based analysis to interpret documents and apply legal guidance. The third preserves human supervision for legal advice, consequential decisions, client communication, and final approval.

We discuss how this structure works inside an enterprise. An AI agent could conduct an initial compliance review of marketing communications against SEC or FINRA rules. A human professional would then review the findings and complete the determination.

Norm Law applies a similar model to legal services. Documents received during a transaction can be processed immediately by AI agents, with the results presented to an experienced attorney. The attorney decides whether to contact the client, negotiate with the counterparty, request additional information, or move the matter forward.

For John, the value includes time savings and broader coverage. A legal team conducting due diligence may lack the time or economic incentive to inspect and cross reference every document in a data room. AI agents can examine a wider set of material and identify inconsistencies that could otherwise remain unnoticed.

Outcome based pricing changes the incentive structure. A law firm charging a fixed price can use AI to review additional evidence without adding hourly fees to the client. John acknowledges the limitations. Predictable transactions can be priced around outcomes more easily than litigation where scope, duration, and strategy may change dramatically.

The operating model also creates new roles. Norm brings together practicing attorneys, legal engineers, and AI engineers. Legal engineers translate professional knowledge and client preferences into agent behavior, while AI engineers build production systems and connect agents with live workflows.

Another part of the conversation concerns supervisory AI. As companies deploy agents that advise customers or take commercial actions, human reviewers may be unable to inspect every decision at machine speed. Norm Ai is developing agents that monitor other agents for compliance with laws, regulations, and company policies.

We also discuss Delaware’s proposed Artificial Intelligence Company initiative. The regulatory sandbox would test a legal entity managed by an AI agent while retaining human involvement, capitalization requirements, disclosure obligations, and government oversight.

John argues that autonomous agents will increasingly take consequential economic actions. The policy question is whether this activity develops within established legal systems or moves toward jurisdictions and technical environments offering fewer controls.

The supplied episode brief also provides significant company context. Norm Ai recently announced a $120 million Series C at a reported $1.2 billion valuation, bringing total funding above $260 million. Norm says organizations representing over $30 trillion in assets under management use its technology for legal and compliance work.

How much productive time disappears because somebody misheard an instruction, missed part of a meeting, or could not fully express an idea?

In this episode of AI at Work, I speak with Paul Lee, CEO of Bridge and InnoCaption, about communication friction and why it deserves greater attention in the workplace AI conversation.

Bridge provides AI-powered real-time captioning, transcription, translation, meeting summaries, and meeting intelligence. InnoCaption provides AI and human-powered telephone captioning for eligible Americans who are deaf, hard of hearing, or have a speech disability. Paul explains how the experience gained from captioning over 30 million calls is informing Bridge’s approach to workplace communication.

Research shared by Bridge says one in six working-age adults experiences hearing loss. It also reports that 37% of employees with hearing loss lose over five hours each week because of communication gaps, while nearly 20% lose over ten hours. Those losses can appear through repeated conversations, missed context, reworked tasks, and weaker decisions.

Paul introduces the curb cut effect, named after the sidewalk ramps created for wheelchair users that also help parents with strollers, cyclists, and travelers carrying luggage. He believes workplace captions can produce a similar result. Technology designed for people facing the greatest communication barriers can improve comprehension, attention, and recall across a much wider workforce.

We also discuss how accurate transcription can turn meetings into searchable company knowledge. Paul shares how his own team uses AI to consolidate brainstorming notes and reduce 100 ideas to a manageable set of choices. The system organizes the information, while people remain responsible for deciding what happens next.

Paul also considers multilingual collaboration, AI translation that preserves meaning and nuance, and why AI ROI should include decision quality, participation, knowledge retention, and product development speed alongside immediate time savings.

For business leaders, his advice is to understand work at the department, team, and individual levels before choosing a tool. Setting an arbitrary AI adoption target can create poor incentives, while studying repetitive tasks and employee frustrations can reveal where AI will offer genuine value.

Where is communication friction quietly consuming time inside your company, and could accessibility technology help everyone participate more fully? Listen to the conversation and share your thoughts with me.

Would you trust an AI system to prepare your taxes if it could not reliably tell HMRC guidance from information published by the IRS?

In this episode of AI at Work, I speak with Arjun Kumar, cofounder of Taxd, about AI tax automation, digital tax filing, and the continuing role of human judgment in regulated financial services.

Arjun began his career at PwC after joining through a school-leaver program. While working in expat tax, he and his cofounder saw how professional services firms often relied on offshoring and annual cost reductions rather than sustained investment in technology. Their attempt to promote a different approach internally eventually led them to create Taxd during the pandemic.

We discuss Arjun’s prediction that routine tax compliance will become increasingly autonomous. When the required data already exists across tax portals, bank accounts, payroll systems, investment platforms, and brokerages, software can connect those sources and complete much of the repetitive work. AI can also help review hundreds of transactions for landlords, sole traders, and small business owners.

However, tax advice often depends on jurisdiction, personal circumstances, and overlapping rules. Arjun recalls seeing customers use AI as a tax advisor, only to receive guidance drawn from the wrong country. A confident answer from a chatbot can become expensive when HMRC and the IRS are discussing entirely different tax systems.

Arjun explains why Taxd combines software and AI with access to human accountants. We also discuss real-time tax reporting, Making Tax Digital, privacy, anonymized data, and how patterns across tax filings can help customers identify relevant deductions and questions.

For founders, Arjun shares why specialist edge cases can provide a strong opening. Taxd began with expat tax, using its founders’ existing knowledge to serve customers whose needs were often poorly covered by general accounting services.

Could your business automate routine compliance while preserving human responsibility for the decisions that carry real consequences? Listen to the episode and share your thoughts with me.

**The Team at TAXD have kindly offered a discount code for listeners of the podcast. Use TECHTALKS to get 10% off any tax filing services (please note, this applies to filing only and excludes our advisory services).

What separates an AI agent that becomes part of everyday operations from one that remains trapped inside an impressive demonstration?

In this episode of AI at Work, I speak with Jacob Kosior, who leads client strategy at EliseAI. The company builds vertical AI agents for the housing industry, handling property management workflows such as answering leasing inquiries, scheduling tours, processing renewals, collecting rent, and coordinating maintenance.

EliseAI says its technology is live across over six million housing units in the United States and Canada. Jacob brings an unusual perspective because he spent over a decade working in multifamily housing operations and was previously an EliseAI customer. He has experienced these systems from both sides of the relationship and works regularly with the operators using them.

We discuss why the agentic AI debate often becomes trapped between exaggerated expectations and deep skepticism. Some people believe agents can already perform almost any task, while others see them as chatbots with a new label. Jacob describes a narrower and far more useful reality: agents completing repetitive workflows from start to finish, provided they have access to the right systems, operational context, and escalation routes.

Housing provides several valuable examples. A conversation about unpaid rent may reveal that a resident is withholding payment because of an unresolved maintenance problem. Handling the complete situation requires an agent that can understand both workflows and connect the relevant information. EliseAI says the experience behind its agents includes over one billion conversations, helping the system account for edge cases it has previously encountered.

Jacob also discusses what separates production deployments from AI pilots that never progress. Adding a chatbot to an existing technology stack may answer basic questions, but it rarely changes how work gets done. An operational agent needs access to the systems, data, and context required to resolve a problem. It must also recognize when it has reached the limit of its ability and pass the customer to the person best equipped to help.

One of the most interesting lessons concerns AI acceptance. According to Jacob, residents generally prioritize a fast, accurate resolution over whether the response comes from a person or an AI agent. EliseAI also found that introducing familiar regional voices to its voice AI increased conversations and conversions. This suggests acceptance can depend on familiarity, responsiveness, and outcomes rather than the technology label.

We also consider how leaders can choose suitable workflows, why agents should be tested with difficult customer questions, and how automation could support heavily manual areas such as affordable housing administration.

Is your business testing whether an AI agent can sound intelligent, or whether it can genuinely resolve the customer’s problem? Listen to the conversation and share your thoughts with me.

How do you know whether AI is making your company smarter rather than simply filling dashboards with impressive activity?

In this episode of AI at Work, I speak with Michael Speranza, CEO of Kantata, about why familiar productivity metrics may be giving business leaders an incomplete picture of AI ROI. Companies can measure time saved, tasks completed, and documents generated, but those figures say little about whether AI is improving commercial decisions, creating revenue, or producing better client outcomes.

Michael introduces the idea of the expertise compounding rate. This measures how effectively a company captures, synthesizes, shares, and builds upon the knowledge created through its projects and people. For professional services firms, that knowledge can include client conversations, previous deliverables, staffing decisions, financial performance, project outcomes, and relationships between colleagues.

We discuss how AI can connect that information through a business specific knowledge graph. A team beginning a new project could identify similar work, locate colleagues with relevant experience, understand previous outcomes, and make better staffing or pricing decisions. Institutional knowledge that previously sat inside documents, meeting transcripts, or an employee’s memory can become available at the point of decision.

Michael also shares an example of a services company using AI to change its project economics. By reducing delivery costs, the firm could offer projects at prices that created a viable business case for clients who previously would have postponed the work. That suggests AI ROI could be measured through sales conversion, opportunity close times, revenue growth, and the ability to expand without adding headcount at the same rate.

Kantata frames the wider market around a revealing paradox. AI adoption across professional services reportedly increased by 40 percent last year, while executive confidence in real time visibility declined and revenue growth slowed to roughly half the industry’s historical benchmark. Greater adoption alone clearly does not guarantee stronger results.

Michael argues that efficiency has become the price of admission. The commercial advantage comes from making each project more informed, predictable, and valuable than the one before it. We consider what leaders should measure, how human expertise and AI resources may influence future pricing models, and why clients care far more about outcomes than invisible automation behind the scenes.

If every project created knowledge that improved the next one, how would that change the way your company measures AI ROI? Listen to the conversation and share your thoughts with me.

What should IT leaders do when employees adopt AI tools faster than their organization can evaluate or approve them?

In this episode of AI at Work, I speak with Hemant Sahani, Vice President of Product Management for Workspace ONE at Omnissa, about the rapid growth of unsanctioned AI applications across the digital workplace.

Omnissa’s State of Digital Workspace 2026 research found that workplace use of AI assistant applications grew by nearly 1000% during 2025. Hemant describes this period as AI’s iPhone moment, with employees choosing the tools that help them work faster instead of waiting for an official corporate rollout.

We discuss why blocking every unapproved application can leave IT blind to what employees need. Hemant explains how observability can reveal where people are finding value, why approved tools may be falling short and which applications deserve a proper security, legal and procurement review.

Our conversation also examines Omnissa’s vision for the autonomous workspace. Hemant imagines an environment that can configure, secure and repair itself while identifying digital experience problems before employees need to raise a support ticket.

We also consider how AI is changing the responsibilities of enterprise IT. As device management, security and employee experience converge, IT teams increasingly need data skills, commercial awareness and closer relationships with HR, finance, security and legal teams.

Could shadow AI become a valuable source of workforce intelligence, and how should organizations balance employee freedom with their responsibility to protect company and customer data? Please share your thoughts with me.

Is artificial intelligence really eliminating entry-level jobs, or is something much bigger happening beneath the surface? As businesses race to improve productivity and invest in AI, many graduates and early-career professionals are wondering whether the first rung of the career ladder is quietly disappearing.

In this episode of AI at Work, I welcome Trent Cotton, Head of Talent Insights at iCIMS, for a data-driven conversation about how AI is changing hiring, workforce development, and the future of careers. Drawing on decades of HR experience and the latest workforce research, Trent separates headlines from reality and explains why the story is far more complex than many people assume.

We begin by examining one of the biggest concerns surrounding AI. Is the technology actually replacing entry-level jobs? Trent argues that the evidence tells a more nuanced story. Rather than AI directly removing roles, many organizations are redirecting investment toward AI infrastructure while failing to rethink how entry-level positions create long-term value. The result is a hiring market where junior candidates increasingly feel employers expect mid-level experience before offering someone their first opportunity.

Our conversation explores why that should concern every business leader. Entry-level employees don't simply fill today's vacancies. They become tomorrow's managers, specialists, and senior leaders. If organizations weaken that pipeline, they risk creating a leadership gap that may not become obvious for years.

We also discuss how AI presents an opportunity rather than simply a challenge. Instead of replacing early-career employees, Trent believes organizations should use AI to reduce repetitive work, accelerate learning, and shorten the time it takes for new hires to become productive contributors. That requires rethinking learning and development, coaching, and career progression instead of simply automating existing processes.

Another fascinating part of our discussion focuses on where technology talent is actually going. While many headlines concentrate on layoffs across large technology companies, Trent explains why skilled professionals are increasingly finding opportunities in healthcare, manufacturing, and other industries that are embracing AI to solve longstanding workforce shortages and operational challenges.

We also examine the skills that are becoming increasingly valuable regardless of how AI develops. Critical thinking, communication, sound judgment, and the ability to orchestrate people, processes, and technology remain difficult to automate. These capabilities, combined with technical literacy and continuous learning, are becoming the qualities that employers value most.

One of the biggest surprises from the conversation comes from changing attitudes among younger job seekers. Where previous generations often resisted assessments during the hiring process, many Gen Z candidates are now actively asking for opportunities to demonstrate their abilities through practical exercises rather than relying solely on a resume. As AI makes resumes easier to generate, proving genuine capability is becoming far more valuable than simply listing experience.

We also discuss responsible AI in recruitment and why governance cannot become an afterthought. Trent explains why organizations need clear policies, transparency, and accountability before introducing AI into hiring decisions if they hope to maintain trust with candidates and employees alike.

Is AI really closing the door on the next generation of workers, or is it giving businesses an opportunity to completely rethink how talent is developed? And as hiring continues to change, are we placing enough value on the human skills that technology still cannot replicate? I'd love to hear your thoughts after listening.

What if your business doesn't actually own its website, customer data, or digital marketing infrastructure? It's an uncomfortable question, but one that many founders never ask until they try to switch providers and discover just how difficult it is to leave.

In this episode of AI at Work, I welcome David V. Kimball, Co-Founder and CEO of lilAgents, for a conversation that challenges many of the assumptions businesses have made over the last decade about websites, software subscriptions, AI, and digital ownership. David argues that convenience often comes at a hidden cost, with businesses gradually handing control of their most valuable digital assets to platforms that make it increasingly difficult to move elsewhere.

We begin by exploring how so many organizations found themselves locked into ecosystems that seemed like the simplest option at the time. Website builders, ecommerce platforms, marketing suites, hosting providers, and CRM systems all promise convenience, yet many businesses only discover the downside when prices increase, features disappear, or they attempt to migrate to something better.

The conversation then turns to artificial intelligence and where it is genuinely making a difference today. Rather than focusing on AI chatbots that have been added to almost every product, David explains why AI agents are becoming far more interesting. These systems can perform real work, connect different applications, automate repetitive processes, and solve practical business problems while people focus on higher-value work.

One example that stood out involved a Shopify store with thousands of products that had accumulated years of inconsistent metadata. Using AI agents connected directly to Shopify's APIs, David was able to automate work that would have taken weeks by hand, helping improve search visibility and delivering measurable growth in organic revenue. It serves as a practical reminder that AI delivers the greatest value when solving real operational challenges rather than simply generating content.

We also spend time discussing the hidden costs many businesses overlook. From paying for CRM contacts that no longer engage to running websites on platforms with far more functionality than they actually need, David explains why simplifying technology stacks can often reduce costs while improving flexibility at the same time. The objective isn't simply spending less. It's building systems that businesses genuinely own and can adapt as their needs change.

Another theme running throughout our discussion is portability. Whether we're talking about websites, marketing platforms, AI models, or business data, David believes organizations should avoid becoming dependent on any single vendor. As AI continues to develop, he argues that businesses should think carefully about building modular systems that make it easy to change providers instead of finding themselves trapped by the next generation of platform lock-in.

This episode offers a refreshing perspective on AI by moving beyond the hype and focusing on practical outcomes. It also raises an important question about the future of digital business. Are companies investing in technology they truly control, or are they simply renting increasingly expensive pieces of someone else's platform?

How much of your digital business do you genuinely own today? And if one of your technology providers disappeared tomorrow, how easily could you move somewhere else? I'd love to hear your thoughts after listening.

What if the next competitive advantage in business isn't working faster with AI, but making better decisions because of it? As organizations rush to become AI-native, many conversations still focus on productivity, automation, and shipping work more quickly. But is speed really the outcome that matters most?

In this episode of AI at Work, I welcome Jorge Valdivia, Chief Technology Officer at Fleetio, for a thoughtful discussion about what AI is actually changing inside modern organizations. Rather than adding another voice to the growing hype around artificial intelligence, Jorge offers a refreshingly practical perspective on why the future belongs to businesses that combine trusted expertise with intelligent technology.

We begin by exploring how enterprise software has evolved over the past decade. For years, success meant becoming the system of record, collecting information in one central place and serving as the trusted source of truth. Today, however, customers expect something more. They want software that helps them produce measurable business outcomes, save money, improve operations, and clearly demonstrate return on investment.

That shift naturally leads us into one of the most interesting parts of our conversation. Jorge challenges the common belief that AI automatically turns average performers into exceptional ones. Instead, he argues that the people gaining the greatest advantage from AI were already deeply curious about their customers, understood their industry, and knew how to solve meaningful problems. AI doesn't replace those qualities. It amplifies them.

Throughout our discussion we examine what separates productive work from valuable work. While AI can certainly automate repetitive tasks and reduce time spent on administration, Jorge believes its greatest contribution comes from helping teams make better decisions. By bringing together customer feedback, product information, engineering data, and business context, AI becomes another source of insight that helps organizations identify the right opportunities instead of simply executing more tasks.

We also discuss what it really means to become an AI-native leader. Rather than chasing every new tool or trend, Jorge explains why successful leaders focus on understanding where AI genuinely creates value for customers. That often means balancing experimentation with discipline, embracing automation where it removes friction, while keeping people responsible for the strategic decisions that still depend on judgment, context, and experience.

One example that stood out involved Fleetio's own product development process. Faced with defining its long-term AI vision, the team used AI to synthesize customer conversations, product feedback, engineering insights, and design concepts into a shared understanding that had previously taken months of discussion without resolution. The technology didn't replace human thinking. It accelerated collective understanding so better decisions could be made.

As our conversation draws to a close, Jorge shares advice for anyone building products or developing their career in an AI-powered workplace. Learning to use AI tools is rapidly becoming an expected part of the job, but lasting success still depends on becoming a trusted expert who understands customers, business problems, and the context behind every decision.

Is the biggest opportunity with AI really about doing more work? Or is it about making smarter decisions that create better outcomes for customers, employees, and the business itself? I'd love to hear where you stand after listening.