Insight On

Insight Enterprises

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.

  1. Gamified AI Training: 93% Completion, Zero Mandates | EP39

    vor 1 Tag

    Gamified AI Training: 93% Completion, Zero Mandates | EP39

    AI 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

    25 Min.

  2. vor 3 Tagen

    You Bought the AI License. Now What? | EP38

    You 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

    23 Min.

  3. vor 6 Tagen

    When Code Is a Commodity, What Do Engineers Own? | EP37

    Building 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

    20 Min.
  4. Cloud Migration for AI — Revionics Beat Their Plan by a Year | EP36

    10. Juni

    Cloud Migration for AI — Revionics Beat Their Plan by a Year | EP36

    Cloud 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

    22 Min.
  5. A Petabyte Migration. Three Months. Zero Customer Impact. Here's How Clari Pulled It Off | EP35

    8. Juni

    A Petabyte Migration. Three Months. Zero Customer Impact. Here's How Clari Pulled It Off | EP35

    Planning a petabyte-scale cloud migration with zero customer impact sounds daunting — and Clari's VP of Engineering Infrastructure 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

    17 Min.
  6. Speed vs. Trust — How To Ship AI Without Sacrificing Either | EP34

    5. Juni

    Speed vs. Trust — How To Ship AI Without Sacrificing Either | EP34

    AI 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

    21 Min.
  7. How HCA Turns Clinical Notes Into Intelligence | EP33

    3. Juni

    How HCA Turns Clinical Notes Into Intelligence | EP33

    Generative 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

    23 Min.

  8. 1. Juni

    AI Didn't Replace These Workers — It Gave Them Their Mission Back | EP32

    AI 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

    23 Min.
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Info

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.

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  • Erstellt von
    Insight Enterprises
  • Jahre aktiv
    2025 - 2026
  • Folgen
    40
  • Bewertung
    Unbedenklich
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    © 2025
  • Sendungswebsite
    Insight On

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