Enterprise 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

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

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

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

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

Planning 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

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

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

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

Most organizations spend months gathering requirements before a single line of code gets written. UC Riverside's CIO Matthew Gunkel is compressing that entire cycle into a single day — running live design sessions where stakeholders walk in with a problem and walk out with a working application spec built in real time using AI.

In this conversation, Gunkel explains why his AI strategy starts and ends with data — not models, not agents — and how UC Riverside's lack of legacy data infrastructure became an unexpected advantage, letting them skip traditional data warehouses entirely and move straight to vector databases and graph knowledge. He also shares how the university is deploying AI agents for student wellness outreach and procurement, using Notebook LM as a classroom RAG tool, and why the most practical AI skill you can learn right now has nothing to do with code.

This is the final episode in our citizen development series. Catch up on earlier episodes with Joseph Schultz and Jason Dittmer [link pending] for the full picture of how individuals and organizations are building their own solutions.

Contact an Insight AI specialist because you'll get solutions tailored to your data maturity, infrastructure, and use cases — not a generic platform pitch: https://www.insight.com/en_US/what-we-do/expertise/data-and-ai.html

Subscribe and follow Insight On for new episodes every week.

#AI #DataStrategy #HigherEducation #CitizenDevelopment #InsightOn

Chapters (8)

• 00:00 — Welcome and introduction

• 00:38 — Citizen development at institutional scale

• 03:06 — Change management at the speed of AI

• 04:00 — Live design sessions that replace months of planning

• 06:05 — Procurement and student wellness AI use cases

• 10:14 — Authentic assessment and AI in education

• 13:28 — Why data is the AI strategy

• 18:25 — The case for learning folders and markdown