AI at Work

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.

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#59
Yesterday · 30 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.
#58
Thursday · 24 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
#57
October 16 · 26 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
#56
October 13 · 28 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?
#55
October 8 · 28 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.
#54
September 29 · 35 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
#53
September 27 · 30 min
Who Controls AI Agents When Software Starts Acting Alone With Oasis Security
What happens when software can reason like a person, move at machine speed, and use every permission it receives? In this episode of AI at Work, I speak with Adam Ochayon, VP of Product Strategy at Oasis Security, about why autonomous AI agents create a different identity and access problem from the software businesses have secured for decades. Adam explains that traditional software is fast but generally deterministic, while people can reason but usually act slowly enough for organizations to intervene. AI agents combine reasoning with machine speed. They are goal-seeking, capable of choosing different routes to complete a task, and likely to use any access available to them. Permissions that might remain unused by an employee can become active risk almost immediately when assigned to an agent. We discuss why static roles are poorly suited to this behavior. An agent may need different permissions from one session or task to the next, depending on the person directing it, the data involved, its recent behavior and the action it is attempting. Adam argues for contextual authorization that can grant narrowly defined access at the right moment, monitor behavior continuously and provide a kill switch when an agent begins operating outside its approved purpose. Ownership presents another problem. Employees can create, modify and reuse agents across several business systems. Some agents act on behalf of a person, while others receive their own autonomous access. In both cases, companies need to know who remains accountable, which credentials the agent holds, what happens when its owner leaves and how its permissions are retired. Adam connects these questions with the wider problem of nonhuman identities, including service accounts, API keys and secrets. Companies that have not brought those identities under control may find AI adoption magnifying weaknesses that already exist. Security teams also face a delicate balance. Excessive friction encourages employees to bypass approved systems, while unrestricted access creates unacceptable exposure. Adam believes identity teams can become advisers to the business by creating controls that permit faster adoption with clearer boundaries. We also discuss Cyera’s announced $1 billion deal to acquire Oasis Security and what the combination of data security and access governance may signal about the direction of enterprise AI security. Adam closes with a practical sequence for leaders: discover which agents exist, understand their access, assign ownership, define policies, monitor activity and enforce controls across cloud and on-premises systems. Who owns your AI agents, and would your organization know when one moves beyond its approved purpose? Share your thoughts with me.
#52
September 12 · 23 min
Measuring Whether AI Is Actually Working With Nexthink
How can a business tell whether workplace AI is producing a meaningful result rather than another encouraging adoption chart? In this episode of AI at Work, I speak with Scott Pope, Director of Value Advisory at Nexthink, about a problem facing many technology leaders. AI tools are reaching employees quickly, but deployment, usage, and business value are often treated as though they describe the same thing. They do not. A company can distribute thousands of licenses and report active users without knowing whether work became faster, easier, less expensive, or less frustrating. Scott argues that AI value has to be defined before a rollout begins. Productivity may matter most to a chief executive or HR leader, while a CFO may focus on cost and an IT support manager may watch ticket volumes. Each stakeholder is working with a different currency of value. Without a baseline, the business cannot measure the gap between its starting point and the result it hopes to achieve. That distinction matters because familiar IT measurements can create a misleading picture. Scott says a decline in support tickets does not automatically prove that the employee experience improved. People may have stopped reporting problems, created workarounds, or accepted friction as part of the job. Infrastructure can appear healthy while employees continue to lose time at the device, application, or workflow level. We discuss why digital employee experience, often shortened to DEX, has moved from a specialist IT concern into a wider business conversation. Work happens where employees interact with laptops, virtual desktops, mobile devices, applications, and services. Monitoring servers and cloud platforms remains useful, but it does not reveal every delay, failed interaction, or workaround experienced by the person trying to complete a task. Scott explains how observability can help organizations understand which AI tools employees are using, where adoption is deep or shallow, and which teams may need support. He is also careful to distinguish visibility from proof of value. Knowing that an employee opened an AI application is a starting point. It does not show whether the tool saved time, improved a decision, reduced cost, or produced a better customer result. The conversation also considers why one AI tool will not suit every role. Different teams work with different information, processes, risks, and desired outcomes. A persona based approach can help a business decide which technology fits the work rather than asking every employee to adopt the same product. It can also reveal where people need timely guidance instead of a training session delivered once and quickly forgotten. For Scott, the people question is where many programs become difficult. Providing access to software has become relatively straightforward, but changing established behavior takes communication, evidence, and a reason employees can recognize in their own work. Leaders often explain what AI could do for the business while giving less attention to the personal value for the person expected to use it. The opportunity is a workplace where technology problems are identified earlier, employees receive help at the moment they need it, and AI investments can be connected with measurable results. The risk is that businesses mistake purchasing and activity for progress while adoption becomes uneven and employees quietly carry the cost of poor implementation. Does your organization know what its AI tools are changing for employees, and which measure would give you the clearest answer? Listen to the episode and share your thoughts.
#51
August 26 · 33 min
Redesigning Enterprise Work Around AI Employees With Ema
What happens when companies stop adding isolated AI tools and begin redesigning entire business processes around AI employees? In this episode of AI at Work, I speak with Surojit Chatterjee, founder and CEO of Ema, which stands for Enterprise Machine Assistant. We discuss why the debate about AI replacing jobs often misses the larger business question: how should organizations redesign work when intelligent systems can coordinate tasks, access enterprise knowledge and complete workflows across multiple applications? Surojit describes this as “agentic business transformation.” Instead of giving every employee another chatbot or assistant, organizations can use coordinated AI agents to manage processes that cross departments, systems and approval chains. People remain responsible for setting boundaries, reviewing sensitive decisions and deciding when an agent has earned greater autonomy. He shares the example of Wipro, where an Ema-powered system called WiproNow supports around 240,000 employees across 65 countries. According to Surojit, it covers approximately 70 use cases spanning the employee journey from recruitment to retirement, connecting with over 100 enterprise applications. The reported results show why workflow-level automation matters. Average response times for employee requests reportedly fell from five days to less than five seconds, while employee satisfaction increased by almost 20 percentage points. Surojit also says the number of people needed for this work fell from roughly 1,000 to 550, with employees reassigned to other areas. We also discuss why companies do not need perfect data before beginning. Surojit argues that capable AI systems can identify contradictions, missing information and undocumented processes as they work. This can expose the informal knowledge that organizations often discover only when an experienced employee leaves or goes on vacation. Trust remains the deciding factor. Surojit compares deploying an AI employee with hiring a talented new colleague. Leaders provide context, test performance, review early decisions and gradually increase autonomy. Clear boundaries remain necessary for sensitive issues involving areas such as employee relations, healthcare or financial decisions. The practical lesson is that meaningful AI returns come from redesigning work across teams rather than measuring prompts, tokens or individual productivity gains. Is your organization preparing AI to own complete workflows, or giving employees another tool to manage? Listen to the conversation and share your thoughts with me.
#50
August 24 · 35 min
How SS&C Blue Prism Helps Businesses Escape AI Pilot Purgatory
Why are some businesses generating measurable value from AI while others remain surrounded by pilots, rising costs and impressive demonstrations that never reach daily operations? In this episode of AI at Work, I speak with Brad Hairston, Director of Strategy at SS&C Blue Prism, about the operational and cultural foundations that separate productive AI programs from expensive experimentation. Brad spent 30 years in consulting before joining SS&C Blue Prism around seven and a half years ago. He now works within the company’s Customer Zero program, which deploys SS&C’s automation technology internally before it reaches customers. Brad says the program has helped SS&C grow revenue by approximately one billion dollars without adding headcount. We discuss why AI programs should begin with the business outcome rather than the latest model. Brad explains why companies making progress connect their automation investments with corporate strategy, build on existing robotic process automation and create reusable governance, security, orchestration and measurement practices. Brad also challenges the idea that AI agents will replace every deterministic automation. Rules-based digital workers remain useful for predictable processes, while AI agents can support work that requires reasoning and adaptation. Combining both approaches can also provide greater control over cost. Our conversation examines what should happen before an AI agent receives permission to make payments, update customer records or initiate business processes. Brad recommends defined roles, limited permissions, human approval for higher-risk decisions, complete audit trails and an orchestration layer connecting agents with people, APIs and digital workers. We also discuss how companies can give employees access to no-code automation while maintaining common standards and oversight. Brad describes the federated model used inside SS&C, where individual business units build automations through shared platforms, templates and governance. For leaders feeling overwhelmed by daily announcements from OpenAI, Anthropic, Google and other providers, Brad offers simple advice: take a breath, return to the business problem and begin with a process where the outcome can be measured. Is your AI program building reusable capabilities with every deployment, or simply adding another experiment to the pilot queue? Please share your thoughts with me.
#49
August 21 · 25 min
Keeping Human Intent at the Center of AI Creativity With Freepik
If anyone can produce a professional-looking image or video with AI, what will make audiences care about one piece of content over another? In this episode of AI at Work, I speak with Joaquín Cuenca, co-founder and CEO of Freepik, about how generative AI is changing creative work, business workflows, and access to professional production. Freepik serves over one million paid subscribers, while Joaquín says the platform attracts over 70 million monthly visitors. At that scale, Freepik has seen the difference between an impressive AI demonstration and a tool people can rely on for real creative work. Joaquín argues that generating something attractive is easy. Producing something that reflects a precise idea, maintains consistency, and creates an emotional response requires direction, judgment, and human intent. We also discuss what Joaquín calls the no-collar economy. His view is that lower production costs will allow individuals, smaller companies, and modestly funded creative teams to pursue projects that previously looked too expensive or risky. That could create opportunities for storytellers, photographers, audio specialists, performers, and other creative professionals. Joaquín also acknowledges that some existing roles will be affected as machines take over repeatable production work. For companies adopting creative AI, Joaquín recommends looking past licenses, activity, and content volume. Experimentation has value while teams are learning, but businesses eventually need to connect AI adoption with revenue, costs, brand performance, or another measurable return. We also consider the threat of AI slop. Better tools cannot provide taste, purpose, or a compelling story. As technical production becomes easier, those human qualities may become the greatest source of differentiation. Will easier production produce a new generation of creators, or will businesses fill every channel with forgettable content? Listen to the conversation and share your thoughts with me.
#48
August 17 · 27 min
Keeping Humans Accountable in an AI First Workplace With Nansen
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.
#47
August 14 · 25 min
Rethinking Legal Work Through Agentic Law With Norm AI
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.
#46
August 13 · 28 min
How Bridge Uses AI to Remove Workplace Communication Friction
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.
#45
August 2 · 22 min
How Taxd Is Building AI Tax Automation With Humans in the Loop
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).
#44
July 29 · 26 min
Taking Agentic AI Beyond Chatbots With EliseAI
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.
#43
July 25 · 30 min
Measuring AI ROI Through Expertise Compounding With Kantata
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.
#42
July 18 · 28 min
What Omnissa Learned From a 1000% Rise in Workplace AI Apps
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.
#41
July 9 · 21 min
Why Tomorrow's Leaders Still Need Today's Entry-Level Jobs with ICIMS
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.
#40
July 5 · 27 min
Why Digital Ownership Matters More Than Ever with lilAgents
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.

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