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Your carrier has more data than almost any company you interact with, yet most telcos still struggle to turn that advantage into growth. We sit down with Miguel Carames, the Chief Product Officer at Mobileum to sort out what’s real, what’s next, and what’s pure hype when it comes to AI in telecom, 5G monetization, and the future of operators as intelligence-driven businesses. Along the way, we get honest about why “we invested billions” doesn’t automatically translate to new revenue and why regulation and privacy expectations reshape every AI roadmap.

We also challenge the idea that AI only arrived with generative tools. Telecom has used machine learning for years in automation, anomaly detection, and capacity planning, but the story hasn’t been told well. Miguel shares concrete, production-minded examples: using LLM-style interfaces to make deeply technical testing platforms usable for roaming managers and analysts, moving toward automated root cause analysis, and deploying agent workflows in fraud and revenue assurance so cases arrive pre-analyzed with evidence and a human still making the final call.

From there we go into customer experience, where proactive network intelligence can prevent tickets before customers ever feel the pain, and into churn reduction, where the opportunity is huge but the privacy line is delicate. We wrap with fraud and security, the whack-a-mole reality of bad actors, and what it takes to escape pilot purgatory so telecom can move at AI speed. If you found this useful, subscribe, share it with a telecom leader, and leave a review. What’s the best AI use case you’ve seen a telco actually scale?
Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
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If your SOC is buried under alert noise, another flashy AI demo won’t save you. We go deeper into what actually works: starting with data strategy and detection quality so automation has real signal to work with, not chaos to summarize. Our guest CEO and Founder Karthik Kannan from Anvilogic explains what “agentic SecOps” looks like in practice, from data onboarding and normalization to detection engineering, hunting, triage, investigation, and the integrations that move outcomes into your ticketing or case management systems.

We talk through why many AI security operations tools jump straight to alert triage and why that can turn into a band aid. The more durable path is end-to-end context: knowing exactly which data sources fed a detection, what logic fired, and how the alert was produced. That lineage supports higher accuracy, cleaner investigations, and consistent mapping to frameworks like MITRE ATT&CK. We also dig into “show your work” explainability, why black box answers stall adoption, and how a decision trace helps teams build trust step by step.

On the architecture side, we explore federated security operations across the tools enterprises already run, including Splunk, Microsoft Sentinel, Snowflake, and Databricks. Instead of forcing every byte into a monolithic SIEM, federated queries and data lake strategies let teams correlate where the data lives while controlling cost and complexity. We close with a grounded take on whether AI replaces security analysts and why the real win is reducing burnout and up-leveling people into higher judgment work.

If this helped you rethink SOC automation, subscribe, share the episode with your team, and leave a review with the biggest bottleneck you want AI to tackle next.
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Cloud was supposed to simplify everything. Instead, a lot of CIOs are staring at bills that are far higher than anyone forecast, feeling locked into hyperscalers, and wondering where the business value went. I sit down with David Linthicum, former Deloitte chief cloud strategy officer turned tech influencer, to give an unvarnished reality check on cloud computing costs, cloud repatriation, and what “pragmatic architecture” looks like when budgets are real and timelines are slow.

We also get blunt about enterprise AI. David explains why so many AI-driven transformations stall out on two constraints: money and talent. We dig into why AI can cost 10 to 20 times more than traditional software, why “AI-first enterprise” messaging can be dangerous, and how leaders can pick high-impact use cases instead of trying to bolt generative AI onto everything. Along the way, we talk about how AI is reshaping SaaS economics as agents start using systems on behalf of humans, and what that means for vendors and buyers.

Then we tackle the loudest buzzword of the moment: agentic AI. Where does it shine as a productivity force multiplier, and where is it mostly hype when you try to deploy it at enterprise scale? We round out with underhyped edge computing opportunities and the growing backlash around data centers, power, and the grid. If you care about enterprise architecture, cloud strategy, generative AI, and what’s actually deployable right now, you’ll get a clear set of takeaways you can use this week. Subscribe, share this with a CIO or architect, and leave a review with the most overrated tech trend you want us to challenge next.
Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
Listen on: Apple Podcasts Spotify
Support the show
More at https://linktr.ee/EvanKirstel

Interested in being a guest? Email us at [email protected]
Your backup system is sitting on a gold mine and most companies are treating it like a fire extinguisher behind glass. From the floor of Dell Technologies World, we talk with Simon Taylor CEO of HYCU about their next chapter: HYCU Air, an AI resiliency platform designed to turn SaaS backup data into something you can actually interrogate, learn from, and use to stay ahead of cyber risk. 

We dig into the big idea that the most valuable asset is not the LLM itself, but the unique datasets inside your systems of record. HYCU Air pairs a knowledge graph and context engine with an LLM so you can ask natural-language questions of your backup history, the same way you would investigate a security camera recording. That reframes data protection from “pay for recovery” to “use backup data every day” across modern cloud applications, collaboration tools, and enterprise SaaS sprawl. 

Then we get practical: cybersecurity posture management when AI agents and integrations are “running amok,” spotting policy drift, and using data classification to find sensitive data like PII that never should have been where it ended up. We also share what we’re hearing from customers, why demand is accelerating, and how this approach starts to look like the “brain of an organization” by connecting corporate memory across dozens of SaaS services. 

If you want to see where AI resiliency is heading, hit play, share this with a security or IT leader, and leave a review with the one question you wish your backups could answer.
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The fastest way to break a modern network isn’t your Netflix download, it’s the quiet, constant upload from sensors, logs, and telemetry that nobody ever reads. We sit down with Julien Dersey from AtomBeam to unpack why data efficiency is suddenly a front-line issue for IoT networking, edge computing, and cloud operations, even in a world with 5G and new satellite options like Starlink. The uncomfortable reality is that bandwidth grows, then data expands to fill it, especially once cybersecurity teams demand near real-time visibility into who connected to what, from where, and when.

We get concrete about the uplink bottleneck that hits IoT deployments first, and why “just filter the data” is a risky workaround. Julian shares a field deployment with an oil and gas fracking operator transmitting over Starlink, where compaction reduced traffic dramatically and kept gigabytes per day flowing reliably for months, while also helping identify odd behavior coming from a sensor. From there, we explore how AtomBeam’s lossless “compaction tunnel” differs from traditional compression, how it can run with extremely low CPU and memory, and why keeping applications unchanged is a big deal for real teams.

We also dig into enterprise and operator integrations: testing with Ericsson over a 5G router and SD-WAN style network bonding, the latency and performance questions engineers always ask, and the security posture using TLS 1.3 with an added obfuscation effect. Finally, we widen the lens to point-of-sale receipt transmission at scale, disaster recovery replication speedups, and what’s coming as connected vehicles, smart meters, and smart grid AMI 2.0 generate even more machine data.

If you care about IoT bandwidth, edge efficiency, secure data transport, and the future of connected devices, subscribe, share this with a colleague, and leave a review. What’s the single noisiest data stream on your network right now?
Everyday AI: Your daily guide to grown with Generative AI
Can't keep up with AI? We've got you. Everyday AI helps you keep up and get ahead.
Listen on: Apple Podcasts Spotify
Support the show
More at https://linktr.ee/EvanKirstel