How Thoughtly Is Turning AI Voice Into A Competitive Advantage

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?

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:

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.