After stepping off stage from moderating a panel, a Senior Frontend Developer from Capgemini waited to say hello. She asked for a quick photo, and within minutes, we were deep in conversation about hackathons, women in tech, mentoring, and the pride she felt watching Egypt host a platform of this scale.

Her name is Alaa Ali Kortoma, and what began as a quick introduction turned into her very first podcast appearance.

In today's episode, you will hear directly from someone on the ground in Cairo about what AI Everywhere means to her, to Egypt, and to a generation of more than 750,000 graduates entering the workforce each year. We talk about bridging the gap between academia and industry, shrinking the distance between startups and investors, and why she believes AI represents opportunity rather than replacement.

If AI really is everywhere, it should look like a possibility. It should look like inclusion. It should look like young women mentoring at hackathons. It should look like national strategies focused on responsible adoption and skills development.

So let me beam your ears to Cairo and introduce you to Alaa Ali Kortoma.

And after spending three days at AI Everything MEA, what does AI Everywhere mean to me?

It is not hype. It is not a headline. It is policymakers embedding AI into public services. It is engineers building Arabic language models tailored to local needs. It is healthcare systems using AI to detect disease earlier. It is investors listening to founders. It is young professionals investing in themselves.

One phrase from this conversation will stay with me long after the microphones were turned off. Proud and full of possibility.

Over the last decade, I have seen technology stories unfold across continents, but Cairo reminded me why I started this podcast in the first place. Technology becomes powerful when it connects people. When it builds confidence. When it proves that innovation is not reserved for a select few regions.

AI is often framed as a Silicon Valley or East Asia story. What I witnessed in Egypt suggests something broader is taking shape. Capital is flowing differently. Partnerships are forming across Africa and the Middle East. Talent is visible. Voices are confident.

So if AI can thrive beside the Nile, if it can empower graduates in Cairo to see opportunity rather than threat, then perhaps AI really is everywhere.

The final question is this. What does AI Everywhere look like where you are, and what role are you playing in shaping it?

Wherever you are listening from, I would love to hear your story too.

In this episode, I'm joined by Bill Briggs, CTO at Deloitte, for a straight-talking conversation about why so many organizations get stuck in what he calls "pilot purgatory," and what it takes to move from impressive demos to measurable outcomes.

Bill has spent nearly three decades helping leaders translate the "what" of new technology into the "so what," and the "now what," and he brings that lens to everything from GenAI to agentic systems, core modernization, and the messy reality of technical debt.

We start with a moment of real-world context, Bill calling in from San Francisco with Super Bowl week chaos nearby, and the funny way Waymo selfies quickly turn into "oh, another Waymo" once the novelty fades. That same pattern shows up in enterprise tech, where shiny tools can grab attention fast, while the harder work, data foundations, APIs, governance, and process redesign, gets pushed to the side.

Bill breaks down why layering AI on top of old workflows can backfire, including the idea that you can "weaponize inefficiency" and end up paying for it twice, once in complexity and again in compute costs. From there, we get into his "innovation flywheel" view, where progress depends on getting AI into the hands of everyday teams, building trust beyond the C-suite, and embedding guardrails into engineering pipelines so safety and discipline do not rely on wishful thinking.

We also dig into technical debt with a framing I suspect will stick with a lot of listeners. Bill explains three types, malfeasance, misfeasance, and non-feasance, and why most debt comes from understandable trade-offs, not bad intent. It leads into a practical discussion on how to prioritize modernization without falling for simplistic "cloud good, mainframe bad" narratives.

We finish with a myth-busting riff on infrastructure choices, a quick look at what he sees coming next in physical AI and robotics, and a human ending that somehow lands on Beach Boys songs and pinball machines, because tech leadership is still leadership, and leaders are still people.

So after hearing Bill's take, where do you think your organization is right now, measurable outcomes, success theater, or somewhere in between, and what would you change first, and please share your thoughts?

• Connect With Bill Briggs
• Deloitte Tech Trends 2026 report
• Deloitte The State of AI in the Enterprise report

Perform 2026 felt like a turning point for Dynatrace, and when Steve Tack joined me for his fourth appearance on the show, it was clear this was not business as usual.

We began with a little Perform nostalgia, from Dave Anderson's unforgettable "Full Stack Baby" moment to the debut of AI Rick on the keynote stage. But the humor quickly gave way to substance. Because beneath the spectacle, Dynatrace introduced something that signals a broader shift in observability: Dynatrace Intelligence.

Steve was candid about the problem they set out to solve. Too much focus on ingesting data. Too much time spent stitching tools together. Too many dashboards. Too many alerts. The real opportunity, he argued, is turning telemetry into trusted, automated action. And that means blending deterministic AI with agentic systems in a way enterprises can actually trust.

We unpacked what that looks like in practice. From United Airlines using a digital cockpit to improve operational performance, to TELUS and Vodafone demonstrating measurable ROI on stage, the emphasis at Perform was firmly on production outcomes rather than pilot projects. As Steve put it, the industry has spent long enough in "pilot purgatory." The next phase demands real-world deployment and real return.

A big part of that confidence comes from the foundations Dynatrace has laid with Grail and Smartscape. By combining unified telemetry in its data lakehouse with real-time topology mapping and causal AI, Dynatrace is positioning itself as the engine behind explainable, trustworthy automation. When hyperscaler agents from AWS, Azure, or Google Cloud call Dynatrace Intelligence, they are expected to receive answers grounded in causal context rather than probabilistic guesswork.

We also explored what this means for developers, who often carry the burden of alert fatigue and fragmented tooling. New integrations into VS Code, Slack, Atlassian, and ServiceNow aim to bring observability directly into the developer workflow. The goal is simple in theory and complex in execution: keep engineers in their flow, reduce toil, and amplify human decision-making rather than replace it.

Of course, autonomy raises questions about risk. Steve acknowledged that for now, humans remain firmly in the loop, with most agentic interactions still requiring checkpoints. But as trust grows, so will the willingness to let systems self-optimize, self-heal, and remediate issues automatically.

We closed by zooming out. In a market saturated with AI claims, Steve encouraged listeners to bet on change rather than cling to the status quo. There will be hype. There will be agent washing. But there is also real value emerging for those prepared to experiment, learn, and scale responsibly.

If you want to understand where AI observability is heading, and how deterministic and agentic intelligence can coexist inside enterprise operations, this episode offers a grounded, practical perspective straight from the Perform show floor.