Advertise on podcast: AI at Work

If your organization switched off its AI systems tomorrow, would customers, employees or the finance team notice any meaningful difference? In this episode of AI at Work, I speak with Frank Böhmer, founder and CEO of Ninox, about the growing gap between AI activity and measurable business value. Companies can count tokens, prompts, users, copilots and prototypes, but those numbers mainly reveal participation. They do not show whether a customer issue was resolved faster, an audit passed, an error rate fell or a team handled additional work. Frank argues that token consumption is a particularly weak proxy for success. An organization would never judge an employee by the number of meetings attended or the number of words typed into email. Paying attention to how many tokens people consume creates a similar incentive. The CFO wants an operational result, not proof that a model stayed busy. That distinction becomes important as natural language tools make software creation available to people who could not previously build applications. Frank sees genuine value in vibe coding because it reduces the cost of experimentation. Someone can describe an idea and create a convincing prototype in hours. The difficulty begins when that prototype meets the operational world. A production application needs reliable data, authentication, role based permissions, testing, integrations, audit logs and clear ownership. It must continue working as the organization grows, regulations change, employees leave and processes evolve. Frank suggests a more demanding question than asking whether an application can be built in an afternoon: can the company trust it three years later? We discuss where developers fit when AI can generate interfaces and basic code. Frank believes experienced developers move toward architecture, security, integration, data modeling and the consequences of design decisions. Plausible output can conceal incorrect assumptions and forgotten edge cases. As the volume of generated software grows, businesses need people who can distinguish a durable design from a shortcut that only works today. The same reasoning applies to process automation. Automating each step of an inefficient workflow can create a faster inefficient workflow. Frank encourages teams to reconsider the process using the capabilities now available. If AI can bring the right information to the employee immediately, some handoffs, searches and approval steps may no longer be required. For leaders assessing an AI project, Frank offers both a rigorous method and a simple test. The rigorous version begins with a baseline covering process time, cost, errors, delays, people involved and the final outcome. The company then compares those measures with the AI supported version. The simpler test asks what would happen if the agent were switched off tomorrow. If the business barely notices, the project may be an experiment rather than an operating capability. We also revisit shadow IT. Business teams create their own solutions because central IT can never satisfy every request within the available time and budget. Banning creation will not remove that demand. Frank recommends an approved environment where employees can build quickly while identity management, security, backups, auditability and deployment remain under organizational control. This changes IT from the team expected to build everything into the team that provides the safe foundation. Should leaders celebrate wider AI adoption, or reserve the word success for the moment a process becomes measurably better? Listen to the episode and share your thoughts with me.

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.

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?

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.

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

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

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?

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.

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

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.

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.

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.

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.

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.

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.