What's Up with Tech?

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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.
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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

Interested in being a guest? Email us at [email protected]
AI is finally forcing a scoreboard moment in telecom: some organizations are seeing massive productivity gains, while others are stuck between fear of missing out and fear of getting it wrong. From the floor at IBM Think in Boston, we sit down with IBM’s global CTO for telecom, media, and entertainment Eoin Coughlan to get practical about what separates “AI pilots” from AI that actually lands in production and earns trust.

We start with the hard truth that hasn’t changed for decades: data is the bottleneck. Clean, timely, governed data determines whether AI helps you run a network or quietly amplifies bad decisions. From there we move into telecom operations where fragmented observability makes it hard to see what’s really happening. We talk about pulling signals into a unified view, using AI to correlate root causes, and keeping control as you introduce agentic AI. Autonomous networks come up as a real journey, not a magic switch: time series models for network telemetry, multiple agents that can read tickets and vendor manuals, and then automation that begins with humans in the loop and expands as trust grows.

Then we zoom out to the ecosystem: hyperscaler dependence, rising sovereignty requirements, and what it means to run compliant, air-gapped platforms that enterprises can rely on. One of the biggest opportunities may be hiding in plain sight: SMEs often trust their telecom provider more than software vendors or hyperscalers, opening the door for CSPs to deliver packaged AI assistants and managed platforms. We also hit legacy modernization and 5G monetization realities, and finish with what might surprise us next, including early quantum computing use cases. If you found this valuable, subscribe, share it with a telecom leader on your team, and leave a review with the AI or automation challenge you’re tackling right now.
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AI didn’t just change how enterprises innovate, it changed how they get breached. One month you’re racing to deploy new copilots and agentic workflows; the next you’re asking a harder question: did we build any of this to match our risk posture?

We talk with Chris Bonavita, Vice President of Strategy and Technology Adoption at GTT, about what he’s hearing from enterprise security leaders right now and why the mood has shifted from excitement to panic. We dig into the real-world convergence of CIO and CISO responsibilities, and how a unified data view across network operations and security operations can replace the “swivel chair” handoff between teams. When netflow, logs, identity, device posture, and edge behavior get correlated in one place, you can finally decide faster whether you’re looking at a performance issue, a resiliency gap, an optimization opportunity, or a malicious actor.

Chris also shares a sneak peek at GTT’s direction with AI factories, GPU-enabled capabilities, and AI-driven correlation that can shrink vulnerability and CVE matching from weeks to near real time. The bottom line is simple and practical: security wins on time to recognition, time to categorization, and time to action. We close with grounded advice for leaders who feel overwhelmed by the pace of change: stay curious, keep learning, and keep the human conversation alive alongside the machines.

If you found this useful, subscribe, share the episode with a colleague, and leave a review with the one security metric you’re trying to improve most.
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