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Everyone is obsessed with bigger models and faster GPUs, but the part that decides whether AI actually works in the real world is the data behind it. We talk with Paul Speciale CMO at Scality, about what happens when enterprises try to store, protect, and serve AI data at petabyte and exabyte scale, and why the rise of huge context windows turns “just a chat” into a massive storage and latency problem.

We walk through the full enterprise AI pipeline, from data collection and cleansing to inference and the long archive tail most teams never plan for. Paul breaks down which AI phases truly need the fastest storage, why inference can demand microsecond access, and how tiered designs (hot, warm, cool) align to modern GPU stacks. We also dig into what’s changing in the market right now: flash price spikes, constrained supply, and the reality that power per rack often matters more than raw capacity.

From there, we get practical about operations and risk. We explore autonomous infrastructure as “tell the system what you want, not how to do it,” including human-in-the-loop recommendations that can move data to cheaper tiers and cut power bills. We also cover cyber resilient storage, immutability for ransomware defense, and the growing pressure around data sovereignty and even code transparency. If you’re building an AI infrastructure plan, this is the blueprint mindset that keeps GPUs busy and data trustworthy. Subscribe, share this with your infrastructure team, and leave a review with the biggest AI data challenge you’re facing.
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AI is cranking out more code than ever, but that doesn’t mean teams are shipping the right outcomes. From PegaWorld, I sat down with Steph Lewis, Senior Director of Community Developer Programs at Pega Systems, to unpack what’s actually changing for developers, architects, and business stakeholders as AI, agents, and LLMs reshape enterprise delivery. 

We get specific about the Solution Designer role Steph helped build and why it’s showing up now. We talk about Pega Blueprint as a practical way to turn business ideas into a live application earlier, and how that changes the skills teams need across low-code development, workflow automation, and digital transformation. Steph explains why the goal never changes (delivering value), but the day-to-day work does, especially as “hands-on keyboard” tasks shrink and the need to challenge AI outputs grows. 

A big thread is the human edge: judgment, creativity, and the discipline to align what you build with what stakeholders truly need. We dig into the skills gap Steph sees most often, why workshops and real conversations beat passive training, and what sits behind eye-catching outcomes like 80% of projects going live in 90 days. We also zoom out to what Steph wants for the developer community over the next year: guiding architecture decisions, placing Pega in the right spots, and incorporating other agents only when it makes sense. 

If you care about modern software delivery, developer enablement, and the real-world impact of AI on building systems, this one’s for you. Subscribe, share this with a teammate, and leave a review with your take: what “human skill” will matter most as AI becomes default?
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Your CRM knows a lot about your customers, so why does it still feel like a place you go to do homework? We talk with SugarAI CEO David Roberts about the hard truth many sellers live every week: CRM has mostly evolved into a management tool, not a system that makes reps better in the moment that matters, right before a call, a meeting, or a critical follow-up.

We dig into why the “360-degree view of the customer” often backfires. More dashboards and more reports can create more overwhelm, especially when a rep is managing hundreds of accounts, dozens of deals, and a product catalog with thousands of SKUs. David lays out a more practical promise for AI in CRM: move from data straight to action. Instead of forcing sellers through a rigid process, AI can interpret unstructured sales work like emails and meetings, spot gaps like missing executive sponsors, and recommend the next best step that improves retention, expansion, or new business.

We also explore contextual intelligence and “precision selling” and why generic AI advice is easy to ignore. The best guidance comes from combining customer data with the real selling context: sales methodology, pitch decks, competitive battle cards, and objection handling. For manufacturing and distribution teams, connecting front-office CRM with back-office ERP data like inventory and shipping can surface reorder opportunities without sending reps into endless spreadsheets.

If you care about sales productivity, revenue team performance, AI CRM, and faster sales onboarding, you’ll get a lot from this conversation. Subscribe, share this with a sales leader or RevOps partner, and leave a review with the biggest “next step” you wish your CRM would tell you.
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Your AI demo is easy. Shipping AI inside a regulated insurance workflow is the hard part and that’s where the real advantage gets built. We sit down with Doug Marquis, CTO at ZyWave, to get specific about what’s changing in insurance technology and why “agentic AI” only matters when it reliably drives growth outcomes for agencies and brokers.

We dig into ZyWave’s front office focus from lead identification and marketing to quoting, service, and renewals and why that matters as the insurance workforce ages and capacity tightens. Doug breaks down the two inputs that separate winners from copycats: access to high-quality insurance data and the discipline to encode domain knowledge and real producer workflows into artifacts that AI agents can actually use. We also talk through how automation can cut the administrative load so producers spend more time being consultative with customers.

Then we get practical about enterprise AI: compliance and security up front, licensing and data access controls, and the “AI harness” you need around agents, including observability, testing, cost management, and explainability. Doug also explains ZyWave’s insurance MCP server and how plugging insurance-specific data into LLM tools turns generic answers into specialized, contextual guidance. If you’re a CIO, CTO, or product leader trying to move from pilots to production, this is a clear look at what to prioritize next.

If this helped, subscribe, share it with a teammate building enterprise AI, and leave a review with the biggest challenge you’re facing right now.
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AI isn’t just changing cybersecurity tools, it’s changing the pace of the entire fight. When attackers can scale campaigns with automation and operate at machine speed, the old rhythm of annual pen tests and quarterly reviews stops making sense. We sit down with Hom Bahmanyar Global Enablement Officer at Ridge Security, to talk about what security teams are up against right now and how continuous security validation can turn an endless backlog of findings into a short list of exposures that truly matter.

We dig into the problem every enterprise security team feels: information overload. Vulnerability scanners generate massive reports, but severity scores don’t reliably predict what gets exploited in the wild. Our focus shifts to a more practical approach to vulnerability management and exposure management, prioritizing issues based on exploitability in your specific environment and whether the weakness is actually visible to a threat actor. That perspective helps reduce alert fatigue and aligns remediation work with real-world risk.

From there, we explore how agentic AI and autonomous security testing change the workflow. Hom explains why being model agnostic matters, especially for organizations that must run in air-gapped environments and rely on open source or open weight models. We also talk about why remediation is the bottleneck, how compensating controls like firewalls can buy time while patching catches up, and what early results can look like when teams start validating continuously, including uncovering previously missed SQL injection.

If you care about continuous security validation, AI-powered penetration testing, and keeping pace with modern cyber threats, subscribe, share this episode with a teammate, and leave a review. What part of vulnerability triage or remediation slows your team down the most?
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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