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AI hype is everywhere at Mobile World Congress, so we went looking for something rarer: real operational proof. From the Ericsson booth in Barcelona, we sit down with Ibrahim Eldeftar, who leads Cognitive Network Solutions product and portfolio for telco AI software, and Claudia Muñiz Garcia, Global Head of Sales for the same unit, to talk about what it actually takes to run autonomous networks.

We break down the autonomy journey in plain language: moving from manual ways of working to systems that can sense, analyze, decide, and execute, with intent-based networking as the layer that helps unlock Level 4 autonomy. Ibrahim shares where carriers really are today (around Level 2 on average) and why many are publicly targeting Level 4 by 2028 to 2030. The driver is network complexity plus rising expectations for “networks for AI” that can support new AI workloads and millions of connected devices without brittle operations.

Claudia explains why Ericsson is being recognized for 5G RAN automation platforms, from standards compliance and security to commercial deployments and scale, and how an open rApp ecosystem accelerates innovation. Then we get into the part operators care about most: outcomes. You’ll hear concrete results from trials and deployments, including major OPEX efficiency gains, fewer issues through anomaly detection and root-cause approaches, spectral efficiency improvements, and a standout uplink story where AI optimization drives meaningful uplink quality and throughput gains.

If you care about network automation, 5G RAN, telco AI, rApps, and the practical road to Level 4 autonomous networks, hit play. Subscribe, share this with a network leader on your team, and leave a review with the one autonomy question you want answered next.
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CRM is about to feel less like a database and more like a conversation. We sit down with Brevo to unpack how a fast-growing customer engagement platform thinks about the next era of CRM, where large language models change the “entry point” for customer data and where the old battle over slick UI starts to disappear.

We walk through Brevo’s evolution from an agency to an email automation tool (many will remember the Sendinblue days) and then into a broader customer engagement and CRM platform built for B2C brands. The big idea is the engagement layer: the system that turns customer data into action across channels, agents, and third-party tools. As LLMs become cheaper and more interchangeable, the winners won’t be the tools with the flashiest interface, they’ll be the platforms that orchestrate workflows, permissions, integrations, and real outcomes.

Then we get practical about ROI. Customer engagement software should be measurable because it exists to move KPIs tied to revenue: subscriber growth, net new customers, repeat purchase, and bigger baskets. We also break down how to evaluate AI inside CRM without hype by looking at efficiency, automation, and output per marketer so you can grow without constantly adding headcount. To round it out, we talk martech consolidation, what M&A looks like as valuations reset, and a smart loyalty and advocacy approach that turns happy customers into demand generators.

Subscribe for more conversations on AI in CRM, customer engagement, marketing automation, and martech strategy and if this sparked a new idea, share it and leave a review. What part of your customer lifecycle would you automate first?
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A check-in tablet passes from one patient to the next, and a single leftover photo of an ID or an auto-filled keyboard suggestion becomes a PHI nightmare. From the Samsung booth at HIMSS, we sit down with Ray to unpack how Samsung SDS approaches healthcare IT with a simple goal: make devices and workflows safer, easier, and more useful for the people on the front line.

We get specific about what Samsung SDS does beyond distribution, including helping health systems get the right Samsung phones and tablets into hospitals and clinics, and even porting key applications from iOS to Android when teams need the same functionality across platforms. We also talk about Samsung DeX and the bigger operational idea behind it: reducing dependence on costly nursing carts and PCs by enabling a one-to-one mobile device model that follows the nurse, supports real mobility, and keeps care more connected to the patient.

Then we dig into privacy, compliance, and practical risk. Ray shares how shared tablets used for patient check-in can accidentally carry over sensitive information, and how an approach like Data Sweeper can reset devices between patients without removing the core app staff depends on. We close with a look at the connected patient room and the potential of Google XR for patient distraction and comfort during difficult moments like burn wound care or long dialysis sessions, plus why Samsung’s broader investment in healthcare shapes this “work backwards from the patient” mindset.

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The fastest way to understand agentic AI is to stop thinking about chatbots and start thinking about outcomes. From Enterprise Connect, we sit down with RingCentral’s John Finch to talk about agentic voice AI that can handle real customer work end to end: answering the call, connecting to back-end systems, completing a transaction, confirming the result, and closing the loop without bouncing the customer between departments.

We also get specific about what makes this hard and why it matters. Voice is still the most demanding channel in the contact center, and RingCentral’s view is that a strong communications layer unlocks everything else: omnichannel customer engagement, smoother handoffs, and higher containment where it actually helps the customer. From there, the CX revolution becomes orchestration. We talk about scheduling AI agents alongside human agents, tracking performance across both, and using signals like CSAT and NPS to continuously improve. The goal is not “AI replaces people,” but “AI removes the repetitive parts so humans can do higher-value work.”

Healthcare raises the bar even further, so we dig into how agentic AI can validate patients, schedule appointments like imaging, and operate with strict guardrails that prevent unsafe medical advice. With templates and deep integrations, teams can deploy faster in complex environments while keeping compliance and safety in view. If you’re evaluating agentic AI, voice AI platforms, or contact center automation, you’ll walk away with a clearer picture of what’s real today and what’s coming next.

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Your phone rings. The screen shows an unknown number or “spam likely.” Do you pick up, or do you protect yourself and ignore it? That split-second decision has become the default for millions of people, and it’s crushing legitimate communication from banks, healthcare providers, insurers, and service teams that genuinely need to reach customers.

From the Enterprise Connect show floor, we talk with Nick Moss from TransUnion’s communications product team about how the voice channel lost trust and how to earn it back. We get into verified caller identity, branded caller ID, and the real-world impact of call authentication built on STIR/SHAKEN principles. The conversation breaks down why spoofing is so easy, what it takes to vet enterprises, and how carriers can help deliver clear identity at scale so a legitimate call looks legitimate when it matters most.

We also explore practical outcomes across industries: fewer missed pharmacy and provider calls, faster resolution for financial services alerts, and stronger defenses against fraudsters who target contact centers as well as consumers. If you care about robocalls, phone spoofing, call labeling, and the future of trusted telephony, this one connects the dots with concrete examples and a clear direction forward.

If you find this helpful, subscribe, share it with someone fighting “spam likely” fatigue, and leave a review with your biggest question about trusted calling and caller ID verification.
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