AI at Work

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.
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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.