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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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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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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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Agentic AI sounds like magic until you have to ship it inside a real enterprise contact center where every mistake becomes a compliance problem and every bad interaction hits customer trust. From Enterprise Connect, we sit down with Dialpad to unpack how they’re building agentic customer service on top of an end-to-end UCaaS and CCaaS platform, and why having AI deeply integrated into the communications stack matters more than flashy demos.

We dig into the hardest first step: figuring out what to automate. Dialpad explains “skill mining,” a way to analyze your past customer conversations and surface a practical hit list of workflows that are both common and realistically automatable. Instead of guessing which user flows will deliver ROI, you start with the data you already have, then keep refining as new conversations reveal new opportunities.

Then we get into the part that makes or breaks adoption in regulated industries like healthcare and financial services: safety. Dialpad walks through Guardian, a live conversation analyzer designed to protect agentic voice and chat with layers for security and compliance, scope and intent alignment, and frustration detection, plus a built-in path to hand off to human agents when the customer needs it. If you’re evaluating AI for contact centers, customer experience automation, or enterprise-ready agentic AI, this is a practical look at what “trust” really requires. Subscribe, share this with your ops team, and leave a review with the workflow you most want AI to handle next.
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