Podcast
Insight On
Hosted by Insight Enterprises · EN
Insight On is the podcast for leaders who make technology decisions — and need them to deliver. From CEOs and CIOs to the next wave of decision-makers in strategy, operations, and IT, you're invited into the inner circle of business and tech leadership.
Each episode features a candid conversation or focused expert take exploring how real organizations across industries are choosing and using technology to drive results. Whether it's AI adoption, IT investments, or navigating change, Insight On gives you the insight you need before your next big decision.
38episodes
Episodes
Newest firstAll episodes
WestJet's Apple Device Program: A Client Story | EP41
6d ago00:13:56Tap to summarizeWestJet crews used to carry their entire jobs in heavy tote bags — paper manuals, paper shift trades, policy updates inserted page by page. It was tedious, inefficient, and everyone knew it had to change. This is the story of what happened when WestJet went all-in on Apple devices across every cockpit and cabin. It's a story about a former flight attendant from a 500-person town in Newfoundland who became the person responsible for putting an iPad in every crew member's hand. A story about what goes wrong when your device vendor is in another country and keeps shipping the wrong thing. And a story about what "ready for takeoff" actually means when thousands of iPads are expected to work seamlessly before hundreds of daily flights. This episode is told in a narrative format — cinematic, human, and unlike anything we've done before on Insight On. Get Insight Flex for Devices, an AI-enabled Device as a Service (Daas) model because it can lower your total cost of ownership, automate lifecycle workflows, and enhance your employee experience: https://www.insight.com/en_US/what-we-do/expertise/digital-workplace/device-as-a-service.html Learn more about effortless Apple device management for the modern workforce: https://www.insight.com/en_US/content-and-resources/brands/apple/effortless-device-management-for-the-modern-workforce.html #DeviceLifecycleManagement #Apple #DeviceAsAService #WestJet #InsightOn
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Partner Evaluation: What to Demand | EP40
2w ago00:26:25Tap to summarizeEnterprise AI deployment is moving fast — and most organizations feel like they're already behind. This episode breaks down what separates companies making real AI progress from those stuck in strategy mode, and what you should demand from any partner before you commit. You'll find out what leaders across industries are asking about agentic AI, why agent sprawl is the new shadow IT, and the three criteria that matter most when evaluating an AI partner. Ferhan Zaki, SVP of Sales for the Google Cloud Solution Line at Insight, shares what he's seeing on the ground every single week in client conversations across North America. Get Insight's data and AI solutions because you'll see exactly how enterprises are moving from proof of concept to production without getting stuck in pilot purgatory: https://www.insight.com/en_US/what-we-do/expertise/data-and-ai.html Subscribe to Insight On for new episodes every week. #EnterpriseAI #AgenticAI #AIGovernance #GoogleCloud #InsightOn Chapters (5–12) 00:00 — Welcome and intro 01:25 — Common themes from enterprise AI client conversations 03:01 — Why every organization feels months behind 05:17 — How financial services is deploying agentic AI 08:05 — Manufacturing gets real-time sentiment analysis with AI 10:03 — Three layers of AI deployment in organizations 12:12 — What separates fast movers from stuck organizations 14:28 — How to evaluate your AI partner today 16:49 — The agent sprawl governance challenge 18:21 — Why data readiness is the question nobody asks 19:16 — Top reasons organizations aren't getting AI outcomes 23:14 — Why AI licenses become shelfware without adoption strategy 25:01 — What every organization should do right now
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Gamified AI Training: 93% Completion, Zero Mandates | EP39
2w ago00:24:36Tap to summarizeAI training for creative teams fails when it ignores the real barriers: People don't know where to start, and they're afraid they'll automate themselves out of a job. Samuel Archibald, director of AI at the Sherlock Company, built an entire AI enablement program from scratch — gamified learning modules, multi-agent grading systems, prompt auditing tools — and hit a 93% completion rate without making any of it mandatory. In this conversation, Samuel breaks down how he moved a creative agency from AI resistance to AI fluency. You'll hear how he separated LLM capabilities from image and audio models to reduce fear, why non-coders started building Photoshop plugins before the engineering team caught up, and the simple automation rule that changed how his team thinks about repetitive work. See how the Sherlock Company is actively using AI to transform their creative workflow with Insight and Vertex AI (now Gemini Enterprise Agent Platform): https://www.insight.com/en_US/content-and-resources/case-studies/the-sherlock-company-automates-creative-content-at-scale-with-vertex-ai-and-sada-services.html Still facing AI hype? See how Insight is helping organizations build AI programs that stick — from training to production: https://www.insight.com/en_US/what-we-do/expertise/data-and-ai.html Subscribe to Insight On for new episodes every week. #AItraining #AIadoption #GenerativeAI #CreativeAgency #InsightOn Chapters (5–12) 00:00 — Welcome and intro 01:49 — What the Sherlock Company does for studios 05:11 — Building a custom AI learning platform 05:45 — Gamified quest mode and 93% completion 07:23 — Overcoming fear and the empty chat box 09:01 — Finding AI use cases for every role 09:55 — Multi-agent grading and prompt auditing 12:02 — Non-coders building apps and plugins 14:06 — Keeping training current when tools change monthly 16:28 — Blurring roles between creatives and engineers 19:43 — Turning AI-resistant teammates into adopters 22:50 — Reframing AI as "I can do this faster" 23:09 — Why showing failures matters for adoption
Transcribe →You Bought the AI License. Now What? | EP38
2w ago00:23:17Tap to summarizeYou distributed AI licenses across your organization and expected adoption to follow. Instead, employees are using it for basic search — or not at all. The gap between "here's the tool" and "this changed how I work" is where most AI investments quietly die. In this episode, John Veltri — Managing Director of Insight's Google AI go-to-market — explains why that gap exists and what to do about it. He breaks down a launchpad framework that builds AI adoption in concentric layers: foundational user training first, governance and security hardening second, and only then identifying the highest-ROI agentic workflows for your business. You'll hear why prompt engineering — the dominant AI training topic less than a year ago — is already obsolete, why single-engagement deployments can't keep up with how fast AI evolves, and what it looks like when leaders model AI adoption by building tools themselves and failing in front of their teams. See how organizations are putting this into practice: How Blackline hit 99% adoption of Gemini Enterprise with Insight How Freestar sped up sales leads 60x with Gemini Enterprise The AI Playbook for enterprise AI transformation Get Insight's Google AI solutions because you'll see exactly how to move from license to real adoption with a partner who stays with you for the full journey: https://www.insight.com/en_US/shop/partner/google.html Subscribe and follow Insight On for new episodes weekly. #EnterpriseAI #AIAdoption #GeminiEnterprise #ChangeManagement #InsightOn Chapters 00:00 — Welcome and series introduction 03:39 — Getting past the license to real business impact 05:50 — The order of operations for AI adoption 08:12 — When are organizations ready for agentic AI 12:11 — Realistic timelines for AI transformation 14:35 — Why prompt engineering is already obsolete 16:34 — Why utilization stays low and how to fix it 20:31 — What leaders should do to model AI adoption
Transcribe →When Code Is a Commodity, What Do Engineers Own? | EP37
3w ago00:19:45Tap to summarizeBuilding AI agents for production means choosing how much autonomy to give them — and that answer keeps changing. Aakriti Bhargava, VP of product engineering and AI at Revionics, has spent 20+ years in applied AI. When generative AI arrived, the hardest part wasn't building features. It was a security conversation she didn't see coming, and an architecture debate that's still evolving. This conversation covers how Revionics thinks about the balance between deterministic and autonomous agent behavior, why they chose not to fine-tune foundation models and focused engineering effort on the system around them instead, and what happens to engineering value when code generation becomes trivial. Aakriti also shares how generative AI compressed RFP timelines from weeks to days, why code review is the new bottleneck, and her candid take on AI coding tools making junior engineers worse at problem solving. This is the third and final episode in a three-part series on building the infrastructure foundation that makes everything else possible. The first two episodes cover Clari's petabyte-scale migration and Revionics' cloud migration that finished a year ahead of schedule. Get Insight's modern infrastructure solutions because you'll see how enterprises are building AI-ready foundations and shipping agents to production: https://www.insight.com/en_US/what-we-do/expertise/modern-infrastructure.html Subscribe and follow Insight On for new episodes every week. #AIagents #GenerativeAI #ProductEngineering #EnterpriseAI #InsightOn Chapters (5–12) 00:00 — Welcome and introduction 01:28 — What Revionics does and its AI history 02:01 — Why generative AI integration was harder than expected 04:04 — What generative AI makes possible that wasn't before 05:19 — Buy vs. build for generative AI and pricing AI 07:07 — Deterministic vs. autonomous agent architecture 10:00 — Why every leader needs to understand agent decisions 11:10 — How AI coding tools changed engineering productivity 13:17 — Why junior engineers may be hurt by AI tools 15:35 — Rebuilding customer trust for generative AI 17:19 — What engineers need to unlearn in the AI era
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Cloud Migration for AI — Revionics Beat Their Plan by a Year | EP36
3w ago00:22:22Tap to summarizeCloud migration for AI-readiness isn't just about moving workloads — it's about removing the barriers that keep your teams from focusing on business value. Patrick Lea, SVP of global cloud operations at Revionics, led a full infrastructure migration to Google Cloud a year ahead of schedule, consolidating from more than 130 vendors down to about 30 in just over two years. This conversation covers how Revionics chose Google Cloud based on cultural alignment as much as technical fit, why Patrick restructured his teams into tiger teams to separate migration work from daily operations, and how getting infrastructure right before generative AI arrived meant Revionics was ready when the moment came. This is the second episode in a three-part series on building the infrastructure foundation that makes everything else possible. Get Insight's modern infrastructure solutions because you'll see exactly how enterprises are cutting migration timelines and positioning for AI: https://www.insight.com/en_US/what-we-do/expertise/modern-infrastructure.html Subscribe and follow Insight On for new episodes every week. #CloudMigration #GoogleCloud #AIreadiness #EnterpriseInfrastructure #InsightOn Chapters (5–12) 00:00 — Welcome and introduction 04:29 — Signs your infrastructure needs to change 07:05 — How to choose the right cloud provider 09:55 — Migration timeline and vendor consolidation 10:59 — What made the migration finish a year early 13:17 — How cloud migration set up generative AI-readiness 15:19 — Picking where to focus when you can't do everything 16:27 — Speed vs. reliability in AI adoption 18:53 — One piece of advice for leaders starting now
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A Petabyte Migration. Three Months. Zero Customer Impact. Here's How Salesloft Pulled It Off | EP35
3w ago00:17:20Tap to summarizePlanning a petabyte-scale cloud migration with zero customer impact sounds daunting — and Salesloft's VP of Infrastructure Clari/Salesloft did it in three months. Balaji Narayanan breaks down the planning discipline, pre-mortem process, and cutover strategy his team used to migrate 110 databases across three regions to Google Cloud without a single customer feeling it. This conversation covers how Balaji's team categorized every migration step as a one-way or two-way door decision, why he insists on testing the full procedure in a replicated environment before the real cutover, and how global round-the-clock coverage across India, South Africa, and the U.S. kept the project on track. This episode kicks off a three-part series on building the infrastructure foundation that makes everything else possible. Want another story of infrastructure modernization under a tight deadline? Read how the AROYA cruise ship — the largest global cruise ship renovation to date — was modernized end-to-end across three onboard data centers in under 11 months: https://www.insight.com/en_US/content-and-resources/case-studies/it-transformation-of-the-aroya-cruise-ship.html Start with Insight's modern infrastructure solutions: https://www.insight.com/en_US/what-we-do/expertise/modern-infrastructure.html Subscribe and follow Insight On for new episodes every week. #CloudMigration #GoogleCloud #EnterpriseIT #InfrastructureModernization #InsightOn Chapters 00:00 — Welcome and series introduction 02:23 — What Clari and SalesLoft do 04:52 — How to reduce unknowns before migration 06:33 — Managing the cutover moment 09:32 — Lessons learned and reversible migrations 11:31 — Scale: a petabyte, 110 databases, 3 regions 14:07 — The Insight partnership and what made it different
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Speed vs. Trust — How To Ship AI Without Sacrificing Either | EP34
4w ago00:21:24Tap to summarizeAI trust in regulated industries isn't a compliance checkbox — it's an operational discipline you build into every iteration. Abhijeet Gulati, head of AI at Mitchell (an Enlyte company), has spent nine years shipping AI into property and casualty insurance claims management, where accuracy is non-negotiable and a wrong estimate has real financial consequences for real people. In this conversation, Abhijeet introduces the velocity-veracity paradox — the tension between time to market and time to trust — and breaks down how his team resolves it. You'll hear how Mitchell decides when AI is production-ready vs. when to pause, why bias must be operationalized as a routine rather than treated as a one-time audit, and how human-in-the-loop design works in practice when AI confidence meets uncertainty. This is the final episode of our three-part series on AI as an operations force multiplier: EP32: AI Didn't Replace These Workers — It Gave Them Their Mission Back — https://youtu.be/ziCYZtWNzps EP33: How HCA Turns Clinical Notes Into Intelligence — https://youtu.be/XAGaIPnuUhY Book an Insight Prism workshop because you'll get a structured framework to identify where AI can create operational intelligence from your existing data: https://www.insight.com/en_US/what-we-do/methodology/insight-prism.html Subscribe to Insight On for new episodes every week. #AItrust #RegulatedAI #ResponsibleAI #InsightOn Chapters 00:00 — Welcome and introduction 01:57 — What Mitchell does in claims management 03:23 — How AI powers the insurance claims experience 05:22 — The velocity-veracity paradox explained 08:00 — Building and maintaining customer trust with AI 10:18 — Build vs. buy decisions for regulated AI 11:53 — What generative AI makes possible now 13:26 — Framework for deciding when AI is ready to ship 15:29 — Operationalizing bias as a continuous routine 18:09 — The one question every AI leader should ask 20:35 — Closing thoughts
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How HCA Turns Clinical Notes Into Intelligence | EP33
4w ago00:22:40Tap to summarizeGenerative AI is making it possible to process unstructured clinical data at enterprise scale for the first time. HCA Healthcare's AVP of Data Science, Sara Liao-Troth, PhD, MBA, explains how her team is extracting intelligence from doctor's notes, nursing handoffs and free-text records that represent roughly 50% of HCA's patient data across 44 million annual encounters. But the conversation takes a surprising turn when Sara reveals how she's rethinking team composition. Rather than relying solely on senior data scientists, she's pairing experienced practitioners with junior talent and interns who push generative AI tools to their limits precisely because they don't know the old way of doing things. The result: a spine surgery data extraction problem solved by an intern that now informs enterprise-wide benchmarking and supply chain decisions. You'll learn how HCA approaches responsible AI in a zero-tolerance-for-hallucination environment, why Sara argues you just start experimenting with AI rather than planning your strategy, and how targeted teams can build internal capability to adapt as the technology keeps shifting. This is episode two of our three-part series on AI as an operations force multiplier. Catch episode one: AI Didn't Replace These Workers — It Gave Them Their Mission Back | EP32 Book an Insight Prism workshop because you'll get a structured framework to identify where AI can create operational intelligence from your existing data: https://www.insight.com/en_US/what-we-do/methodology/insight-prism.html Subscribe to Insight On for new episodes every week. #HealthcareAI #DataScience #GenerativeAI #InsightOn Chapters 00:00 — Welcome and introduction 00:47 — What is HCA Healthcare and Sara's role 03:17 — 50% of patient data trapped in free text 05:14 — How generative AI changes the patient experience 07:11 — Rethinking data science team composition 09:21 — Will AI eliminate data science roles 12:56 — Giving interns impossible problems to solve 14:29 — The spine surgery use case explained 17:57 — How to plan when AI keeps changing 19:55 — Responsible AI in healthcare 21:22 — One thing leaders should believe
Transcribe →AI Didn't Replace These Workers — It Gave Them Their Mission Back | EP32
Jun 100:23:23Tap to summarizeAI agents for non-emergency calls are solving a problem that policy, process changes, and hiring couldn't fix for nearly a decade. At 911 centers across the United States, the majority of incoming calls are non-emergency inquiries — parking tickets, road obstructions, animal control — handled by operators trained for life-or-death situations. Viiz Communications built conversational AI agents on Google's Contact Center as a Service (CCaaS) platform to intercept those calls, provide full responses, and keep humans focused on emergencies. Chad Brothers, VP of emergency services programs at Viiz, explains how the company recognized a massive problem — skilled 911 operators drowning in work that didn't require their expertise — and built an AI solution that the industry had been waiting years for. You'll hear why one of the most cautious industries in America adopted AI faster than anyone expected, how Viiz proved the concept internally by taking QA coverage from 1.5% to 85% of calls in less than two weeks, and what every organization can learn about protecting skilled workers' time and mental capacity for the work that truly requires human expertise. Talk to an Insight specialist about Insight AI solutions because you'll get a clear path from one operational friction point to measurable AI results — the same approach that worked in this risk-averse industry: https://ips.insight.com/en_US/what-we-do/expertise/data-and-ai.html Subscribe to Insight On for new episodes every week. #AI #AIagents #PublicSafety #ContactCenter #InsightOn Chapters (5–12) 00:00 — Welcome and introduction 02:18 — What Viiz Communications does 03:02 — Why this AI story flips the job displacement narrative 04:32 — The 911 staffing crisis explained 07:32 — 60% of 911 calls aren't emergencies 09:51 — What Viiz built with Google CCaaS 11:12 — How emergency calls still reach humans 12:49 — Why a risk-averse industry adopted AI fast 15:37 — The parallel to every knowledge worker 17:35 — How to start with AI in your organization 19:27 — The QA agent that 3Xed output in days 22:20 — Homework for every listener
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