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 enterprises spending so much time comparing AI models when their strongest competitive advantage may already be sitting inside their own data?

My guest today is Emma McGrattan, Chief Technology Officer at Actian. Emma argues that model choice is becoming less distinctive while data quality, shared business meaning, governance, and culture increasingly determine whether AI works in production.

We discuss why executives often rate data maturity more highly than the teams closest to the data, what changes when an autonomous agent becomes the consumer, and how data products and enforceable contracts can help organizations move beyond impressive prototypes.

Emma also shares a remarkable example of an expense agent approving a $300,000 spend through one thousand individually permitted transactions. If your organization is trying to move AI from pilot to production, this conversation offers a practical place to begin.

Episode description

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.