In the final part of this three-part conversation, Mark Kavanagh and I close things out with where AI actually fits into RTM today ... and where it might be headed.

Mark walks through how he actually uses tools like Claude in his day-to-day: not to make decisions, but as a way to surface and present data so the real thinking still happens between people. We get into how far he trusts it, whether there's a point where AI starts overriding human judgment rather than just informing it, and what RTM might look like in five years if the industry's AI predictions hold up. Along the way, Mark pushes back on some of the hype, including his skepticism about how much of the current AI push is genuinely value-driven versus share-price-driven, before we wrap with why, twenty years in, he's still not bored of RTM.

That's the full three-part conversation ... thanks for listening.

In part two of this three-part conversation, Mark Kavanagh and I get into what happens when the plan meets reality mid-week, and the two competing philosophies on what to do about it.

Philosophy A: the plan is the plan, RTM reacts and mitigates, and any gaps get folded into next week's forecast. Philosophy B: adjust in real time as the variance shows up. We dig into why Mark comes down firmly on one side, what gets lost when you "move the goalposts" mid-cycle, and why nobody ever quite says "the forecast was wrong." From there we get into the politics of blame between RTM and forecasting, the "big picture" deflection tactic used to wave away real problems, and finally.... should RTM have a seat at the table when the plan is actually being built?

Part 3 : where AI actually fits into RTM decision-making ... is coming soon.

In part one of a three-part conversation, Mark Kavanagh joins to dig into what "good" Real-Time Management actually looks like, and why that's a harder question than it sounds.

What happens when the data you're using to make important business decisions isn't quite telling you the truth?

For Episode 10 of The Brád-Cast, I’m joined by someone I’ve known and worked with for many years, Fran Healy, to explore a subject that sits underneath almost every modern business decision: data.

Fran brings decades of experience across reporting, business intelligence, analytics and data management, and together we get into a much bigger conversation than simply asking whether data is "accurate".

We talk about data integrity, data quality, reporting, business intelligence, technology, AI and decision-making, but also about something much more fundamental:

Do organisations actually understand the data they are using?

Because having more data doesn't necessarily mean having better information. And having sophisticated technology doesn't necessarily mean you're making better decisions.

We explore how seemingly small problems with definitions, ownership, processes and data quality can ripple through an organisation and ultimately influence the decisions people make.

We also get into the human side of the equation: how people interpret information, how organisations can become overly dependent on dashboards and reports, and why sometimes the most important question isn't "What does the data say?" but:

"Why does the data say that?"

And, naturally, we couldn't have a conversation about data in 2026 without talking about AI and what happens when we start giving machines access to increasingly large amounts of information that may or may not be reliable in the first place.

This is a deliberately long-form conversation. No corporate presentation. No script. Just two people who have spent a considerable amount of their careers working with data, talking about what it means, where it goes wrong, and why getting it right matters.

If you work in WFM, operations, analytics, reporting, business intelligence, technology or leadership, there should be plenty here to get you thinking.

And perhaps the biggest takeaway of all is a simple one:

Bad data doesn't just produce bad reports. It produces bad decisions.

🎙️ The Brád-Cast — Episode 10
Guest: Fran Healy
Topic: Data, Decisions & the Danger of Getting It Wrong

Shrinkage. It’s one of those WFM terms that gets thrown around constantly, but what does it actually mean?

In this episode of The Brád-Cast, we take a practical look at shrinkage and why it matters so much when forecasting, capacity planning and scheduling your workforce.

We’ll look at the difference between planned and unplanned shrinkage, why shrinkage isn't necessarily a bad thing, how it affects your staffing requirements, and the important distinction between gross and net shrinkage.

Most importantly, we'll look at why WFM shouldn't be trying to eliminate shrinkage. People need holidays, training, coaching, meetings and time away from the customer. The real challenge is understanding it, forecasting it and making sure your staffing model reflects reality.

Because having 100 people on the payroll doesn't necessarily mean you have 100 people available to handle the workload.

That's shrinkage.

Artificial Intelligence is one of the biggest topics in business today, but what does it really mean for Workforce Management?

In this episode of The Brád-Cast, I share insights from my MBA dissertation, where I explored the adoption of AI in Workforce Management and discovered that the biggest challenge isn't the technology itself... it's the people, the processes and the leadership behind it.

We explore:

• Why AI won't replace Workforce Managers any time soon.
• The surprising 1950s mining study that still holds lessons for AI adoption today.
• Why trust, governance and explainable AI matter just as much as sophisticated algorithms.
• How organisations can use AI to augment human expertise rather than replace it.
• What skills the next generation of WFM professionals will need to stay relevant.

Whether you're a Workforce Planner, Resource Manager, Contact Centre Leader or simply curious about how AI is changing the workplace, this episode cuts through the hype to focus on what really matters.

Because the future of Workforce Management isn't Human vs AI. It's Human + AI.

Reporting is one of the most overlooked parts of Workforce Management—right up until the moment something goes wrong. Then suddenly everyone becomes a data analyst, scrutinising forecasts, service levels, occupancy, adherence and every report they can get their hands on.

In this episode, I explore why reporting should be about far more than producing numbers. We discuss the purpose of WFM reporting, the difference between information and insight, and why the best reports are the ones that help people make better decisions.

I also tackle a question many WFM teams face: should every report request be fulfilled simply because someone asked for it, or should there be a process to challenge whether a report is genuinely needed? Is WFM the gatekeeper of reporting, or should it be enabling curiosity and discovery?

Whether you're a Workforce Management professional, an operations leader, or simply interested in how organisations use data to make decisions, this episode offers practical insights and plenty of food for thought.

After all, if nobody would notice a report disappearing tomorrow… why are we still producing it today?

Real Time Management is often described as the “entry level” role within Workforce Management. In this episode of The Brád-Cast, I explain exactly why I believe that view is completely wrong.

RTM sits at the heart of every contact centre operation. The people doing it make hundreds of decisions every day, balancing customer demand, service levels, staffing pressures and operational reality in real time. When things go right, they often go unnoticed. When things go wrong, everyone suddenly remembers they exist.

I explore the personalities and traits that make great RTM professionals, why certain people thrive in the role while others struggle, and why these teams are frequently the unsung heroes of the operation.

If you've ever worked in Workforce Management, Operations, Planning, or Real Time Management itself, this episode shines a light on one of the most misunderstood roles in the contact centre.