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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.
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Interested in being a guest? Email us at [email protected]
ANZ moves fast, and that speed exposes the difference between “cloud by default” and communications that actually hold up under pressure. We sit down with Mitel and Ethan Global to unpack what it takes to deliver unified communications, contact center, and customer experience platforms across Australia, New Zealand, and the South Pacific where geography is huge, budgets demand efficiency, and resilience is non-negotiable.

We talk about why Australia and New Zealand are early adopter markets, how hybrid work has shifted to a role-based model, and why complex verticals like government, healthcare, education, emergency services, mining, and transport can’t rely on one-size-fits-all deployments. You’ll hear what customers are asking for right now: redundancy, tight integration into core business systems, managed services, and clear answers on data sovereignty and regulatory expectations.

Then we get into the AI reality check. Instead of vague hype, we focus on what’s delivering immediate value in CX and contact centers, including agent assist, conversation summarization, quality monitoring, and AI that improves IT operations through faster issue resolution and smarter provisioning. We also explore cloud-first mandates, the surprising rise of cloud repatriation when organizations move too quickly, and why the partner ecosystem now drives innovation as much as the platform itself.

If you’re planning a UCaaS or CCaaS modernization, building an AI roadmap for customer experience, or supporting a hybrid workforce at scale, this conversation will help you pressure-test your strategy. Subscribe, share with a colleague, and leave a review with your biggest question about cloud, AI, or enterprise communications.
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 AI assistant is only as smart as the mess behind your dashboards. When business data lives across CRM, accounting, HR, and job systems, “connect ChatGPT to our data” quickly turns into rate limits, broken joins, confusing IDs, and answers nobody trusts.

We sit down with Craig Morrall, co-founder of Tugger, to unpack a practical architecture for enterprise AI that actually holds up in the real world: pulling data from many platforms into a warehouse, then layering on a semantic model that explains what the data means and how records connect across systems. That extra context is what turns a chatbot into something you can rely on for revenue questions, profitability analysis, and cross-platform reporting without spending months on custom pipelines.

Craig also shares what customers are doing once the foundation is in place, including building interactive dashboards in minutes and generating repeatable board packs that used to take finance teams hours. We dig into time to value, early ROI stories, and how Tugger approaches security and governance with ring-fenced data storage, ISO 27001 certification, and guidance on using business-grade LLM plans to reduce training risk.

If you’re evaluating enterprise AI, data warehousing, semantic layers, or secure analytics with Claude or ChatGPT, this conversation will help you separate real capability from hype. Subscribe for more practical AI stories, share this with a friend building on enterprise data, and leave a review with the biggest data problem you want AI to solve.
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]
AI agents are starting to do real work inside real companies and they often do it by acting as us. That’s exciting, and it’s also a security wake-up call. We sit down with Matthew Immler Regional CSO, Americas at Okta, to unpack why identity security has become the primary battleground and why attackers increasingly prefer impersonation over breaking through a “front door” with zero-days.

We get concrete about what “non-human identities” actually means in plain English, and how agentic AI changes the rules. When employees connect new tools and click consent, an AI agent can gain access not just to a calendar, but to email, files, and other sensitive systems through broad OAuth scopes. From the security team’s perspective, the activity can look like normal user behavior, which creates a visibility problem at the exact moment enterprises are being pushed to adopt AI faster than their controls can mature.

We also talk solutions: treating AI agents as first-class identities with owners, managers, and access reviews; spotting non-human behavior through signals like abnormal client secret flows and extreme refresh token patterns; and why blocking AI outright can drive “shadow AI” instead of safety. Matt shares how standards work like cross-app access can shift control from end-user consent to IT policy so teams can approve tools, lock scopes down, and keep tight governance.

If you care about AI security, identity and access management, OAuth risk, and practical guardrails for agentic AI, this conversation will help you think clearly and act faster. Subscribe, share this with your security or IT team, and leave a review with the one control you think every AI agent should have.
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Healthcare keeps getting more expensive, yet most of us feel like we’re doing more work just to get the same care: more portals, more forms, more phone calls, and more confusing lab results. We sit down with Greg Brady the founder and CEO of Connect4Patients to dig into the root cause he’s spent decades solving in other industries: fragmented data. His claim is direct and a little startling. If we can’t assemble a complete, real-time medical record, we can’t reliably reduce errors, we can’t simplify administration, and we can’t move the system upstream toward prevention. 

We talk about what a patient-centric system actually looks like in practice: one unified “single version of the truth” for your health record that can be shared across providers, while still working with existing EMR/EHR systems. Greg explains how an AI-based network can fuse and cleanse records in a HIPAA-compliant way, then translate medical jargon into plain English so patients can understand what their numbers mean and what actions to take. That shift is bigger than convenience. It’s the foundation for catching trends early, like rising glucose before prediabetes, and for preventing dangerous mistakes, like prescriptions that conflict with other meds a patient is already taking. 

We also get into the uncomfortable incentives that keep healthcare stuck in a treatment loop: more tests, more procedures, more friction in prior authorization, and a system where insurers can delay care through manual workflows. Greg shares a view of what could change if large employers, cities, or states act as self-insured organizations and reward preventive behaviors directly, using data and personalized guidance to lower chronic disease rates over time. 

If you’ve ever wondered why healthcare feels “designed” to be hard, this conversation offers a concrete infrastructure-level answer and a practical path forward. Subscribe, share this with someone who’s tired of managing their care across multiple portals, and leave a review with the biggest healthcare friction you want fixed 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