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.
29episodes
Episodes
Newest firstAll episodes
AI Didn't Replace These Workers — It Gave Them Their Mission Back | EP32
1w ago00:23:23Tap 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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Data Is My AI Strategy" — How UC Riverside Is Outrunning Organizations With Bigger Budgets | EP31
2w ago00:22:27Tap to summarizeMost 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
Transcribe →Data Governance Led MERGE to an Unexpected Product | EP30
2w ago00:22:26Tap to summarizeMost organizations are sitting on fragmented data from dozens of sources with no fast way to normalize it, query it, or get answers. MERGE had the same problem — and what they built to fix it internally became a product their clients needed too. In this conversation, Jason Dittmer, SVP of TechOps at MERGE, explains how his team built an automated pipeline on Google Cloud (BigQuery, Looker, Gemini, Google SecOps) to solve their own data fragmentation problem — cutting normalization time from weeks to minutes. Then they heard the same pain from clients and shipped it as a marketplace product. One healthcare client now runs 30 disparate data sources through the same pipeline. If you're thinking about how to productize your own internal AI work, this breakdown of moving from efficiency to revenue is worth reading alongside the episode: https://www.insight.com/en_US/content-and-resources/blog/from-efficiency-to-revenue-productizing-enterprise-ai.html Jason also breaks down MERGE's "Drink Your Own Champagne" philosophy — the idea that you should prove a solution internally before ever bringing it to a client. He shares what it takes to move past the pilot phase, and how MERGE's Humanity Suite puts the human factor at the center of AI-powered marketing. If you're still sorting out what AI agents can actually do in this context, the AI agent cheat sheet is a good companion: https://www.insight.com/en_US/content-and-resources/guide/the-ai-agent-cheat-sheet.html This is the second episode in a series on organizations building AI solutions from the inside out. In the first, Joseph Schultz at JE Dunn Construction explains how field workers with no coding background are building their own AI-powered tools: https://youtu.be/rf8MyG22FnA?si=bs2jyJ2u_1_PO66N Book an Insight AI Readiness and Governance Workshop because you'll get a clear framework for moving your AI projects from pilot to production: https://www.insight.com/en_US/content-and-resources/solution-briefs/ai-adoption-with-ai-readiness-governance-workshop.html Subscribe to Insight On for more conversations with the leaders building what's next. Chapters (5–12) 00:00 — Welcome and introduction 01:32 — What MERGE does and what's on Jason's desk 03:17 — Drink Your Own Champagne explained 05:06 — The internal data problem that started it all 08:27 — What made this solvable on Google Cloud 10:50 — Three months from idea to internal product 12:14 — Surprising insights from contextually aware data 13:13 — The Humanity Suite and infinite individualism 16:07 — Could this have happened a year ago 17:30 — How AI perception changed inside MERGE 18:30 — What's next for MERGE 19:22 — Advice for leaders stuck in the pilot phase #AIDataPipeline #GoogleCloud #EnterpriseAI #AIPilotToProduction #InsightOn
Transcribe →AI Citizen Development in Construction? | EP29
2w ago00:21:59Tap to summarizeConstruction delays cost money. An accidental utility strike during excavation can derail project timelines. Daily safety conversations that become checkbox exercises put crews at risk. These are problems construction teams have dealt with for years — but until recently, the people closest to them had no way to build their own solutions. AI changed that. In this episode, Joseph Schultz, VP of mission critical at JE Dunn Construction, explains how his team's citizen development program gives field workers — people with no coding background — the ability to build AI-powered tools that address the specific problems they face every day. A superintendent built a dig permit app that ensures no crew breaks ground without the right information. AI now listens to daily safety conversations and flags when a crew is going through the motions instead of genuinely assessing risk. This isn't a story about one company. It's a model for any organization where the people doing the work know exactly what's broken — and just need the tools to fix it. See how other organizations are putting AI into practice: The Sherlock Company automates creative content at scale: 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 Insight's own AI playbook for internal transformation: https://www.insight.com/en_US/content-and-resources/case-studies/case-study-the-ai-playbook-ai-transformation.html Ready to move from AI hype to AI problem-solving? Request a Prism workshop because you'll get a prioritized AI roadmap in less than two weeks — no months of discovery, no generic advice: https://www.insight.com/en_US/what-we-do/methodology/insight-prism.html Subscribe for more conversations with the leaders putting technology to work. #AIinConstruction #CitizenDevelopment #DataCenterConstruction #InsightOn #ConstructionTech Chapters (5–12) 00:00 — Welcome and episode introduction 01:20 — Meet Joseph Schultz of JE Dunn Construction 02:46 — What JE Dunn does and the hyperscaler relationship 04:00 — How generative AI enhances construction communication 05:01 — AI reducing meetings and improving field engagement 07:10 — Finding blind spots before they become problems 07:41 — Limitations of AI in construction 09:13 — What citizen development looks like on a job site 10:50 — The dig permit app that prevents utility strikes 13:06 — AI for job safety analysis and crew conversations 16:18 — More time on people and less on documentation <li class="OutlineElement Ltr SCXW3911539 BCX0" role="listitem" aria-setsize="-1" data-leveltext="" data-font="Symbol" data-listid="2" data-list-defn-props= "{"335552541":1,"335559685":720,"335559991":360,"469769226":"Symbol","469769242":[8226],"46977780...
Transcribe →Shadow AI Agent Risk: It's Not Just the CISO's Problem | EP28
3w ago00:23:39Tap to summarizeShadow AI agent risk has moved from information risk to operational risk in less than six months — and that shift means accountability no longer sits with the CISO alone. Vivek Menon, CISO and Head of Enterprise Data at Digital Turbine, explains why the COO, CMO, and CFO are now on the hook when an agent acts without human review. In this conversation, you'll learn how shadow agent risk differs from shadow AI and shadow IT, why Vivek builds governance to the EU AI Act as his North Star even for US operations, and what "survivable, auditable, explainable" actually looks like when an incident reaches auditors at a public company. If you're still getting up to speed on what agents actually are, the AI Agent Cheat Sheet breaks it down: https://www.insight.com/en_US/content-and-resources/guide/the-ai-agent-cheat-sheet.html Vivek also shares the one hiring metric that tells you whether AI adoption is working — and why zero friction in AI tools is a red flag, not a feature. For more on the questions executives are asking behind closed doors about agents, check out our companion episode: https://www.insight.com/en_US/content-and-resources/insight-on/what-executives-are-too-embarrassed-to-ask-about-ai-agents-answered.html This episode wraps our series on the agent economy. If you're building an AI transformation playbook, see how one organization approached it: https://www.insight.com/en_US/content-and-resources/case-studies/case-study-the-ai-playbook-ai-transformation.html — and learn more about Insight's full AI services and capabilities here: https://www.insight.com/en_US/what-we-do/expertise/data-and-ai.html Book a Radius strategy workshop because you'll get a structured path to AI governance and agent readiness tailored to your environment: https://www.insight.com/en_US/what-we-do/methodology/radius-business-strategy-workshops-and-planning.html Chapters (5–12) 00:00 — Welcome and introduction 01:35 — What Digital Turbine does 02:18 — What CISOs admit to each other behind closed doors 03:01 — Shadow IT to shadow AI to shadow agent risk 04:33 — Why AI agent risk is now an operational risk 05:32 — What a survivable AI incident looks like 07:03 — Pressure on CISOs to not be the department of no 08:45 — Red flags when new AI capabilities launch 10:04 — How a dual mandate in security and data helps 12:35 — How business units get green-lit to build agents 15:38 — Managing AI governance across 10 regulators 17:36 — Biggest productivity gain from AI so far 20:53 — How to detect shadow agent activity in your team #AIAgents #ShadowAI #CISO #EnterpriseAI #AIGovernance
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AI Agent Governance at Scale: Stagwell's Marketplace Model | EP27
3w ago00:19:26Tap to summarizeAI agent sprawl is one of the fastest-growing governance challenges for any organization with multiple teams building agents — and Stagwell solved it by building an internal marketplace. In this episode, Merrill Raman, Global CTO of Stagwell, explains how the company's network of 70+ marketing and advertising agencies build, publish, share, and license AI agents through a centralized Agent Cloud — with real financial incentives for the teams that create them. Merrill explains how Stagwell applies the 80/20 rule to technology standardization, why agent discoverability is the antidote to agent sprawl, and what the emotional journey of AI adoption actually looks like for leaders and their teams. He also shares why you should redesign your workflow before applying AI — not after. Download our AI terminology cheat sheet: [AI Terminology Cheat Sheet link] Talk to an Insight AI specialist because you'll get a direct conversation about how to govern and scale AI agents inside your organization: https://www.insight.com/en_US/what-we-do/expertise/data-and-ai/generative-ai.html Subscribe to Insight On for more conversations on the agent economy. Chapters (5–12) 00:00 — Welcome and introduction to Stagwell's agent story 01:12 — How Stagwell started building AI agents 02:09 — Why 70 agencies need a shared agent foundation 04:03 — What the Agent Cloud is and how it works 06:22 — How agencies publish and discover agents 07:46 — The internal licensing marketplace model 09:01 — Turning agents into SaaS revenue streams 11:30 — Agent examples: AI moderator for market research 13:13 — Keeping humans in the loop at agent scale 14:00 — AI-driven content personalization workflows 15:20 — Biggest lessons from the agent development journey 16:44 — The emotional arc of AI adoption for leaders #AIAgents #AgentGovernance #GenerativeAI #InsightOn
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AI Agents Explained: What You Might Be Getting Wrong | EP26
3w ago00:24:27Tap to summarizeAI agents are in every pitch deck and boardroom conversation — but the definition shifts depending on who's talking. In this episode, Miles Ward, CTO of AI at Insight, draws a hard line between chatbots and agents, explains what agent orchestration actually looks like in production, and gives you the three things you're "out of your mind" not doing right now. You'll learn the two-word test for whether something is actually an agent (persist and act), why headless agents cut cost and latency by eliminating the tokens you don't need, and how Mattel uses agent orchestration to move from quality control signals to supplier corrective action without human handoffs. Download the AI Agent Vocabulary Cheat Sheet: https://www.insight.com/en_US/content-and-resources/guide/the-ai-agent-cheat-sheet.html Explore Insight's Data and AI solutions because you'll see exactly how enterprises are building, governing, and scaling agentic AI in production: https://www.insight.com/en_US/what-we-do/expertise/data-and-ai.html Subscribe to Insight On for weekly conversations with the leaders building what's next. Chapters (5–12) 00:00 — Welcome and episode overview 00:57 — What is an AI agent vs a chatbot 04:33 — Where the line between chat and agent actually falls 05:34 — AI agent orchestration explained 08:12 — Mattel's agent orchestration in production 10:41 — What is an AI harness 13:07 — Headless agents and why they matter 15:31 — Which terms executives need to know now 18:10 — The question clients are too embarrassed to ask 18:30 — Three things every leader must do with AI right now 21:50 — The single most important thing to go do today #AIAgents #GenerativeAI #EnterpriseAI #InsightOn
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Lessons from GTT: Essential Moves to Learn Before Scaling Your AI Infrastructure | EP25
4w ago00:28:36Tap to summarizeMost enterprise AI factory deployments fail before they produce a single result — not because of the technology, but because of what companies skip before they build. James Karimi, CIO and CISO at GTT, shares the governance-first playbook that helped GTT stand up three AI factories across the US, EU, and UK in two months. The results? A two-week finance close reduced to one hour. Recruiters reclaimed 60% of their time in a given week. And that was just the beginning. Karimi breaks down the deliberate six-month pause GTT took after procuring infrastructure — before installing anything — and why that decision is the reason their AI operators are delivering real results today. He also gets into the combined CIO-CISO role, how GTT handled employee fear around AI job displacement with radical transparency, and what edge GPU deployment will look like in 2026 for enterprise network services. If you're evaluating AI factory infrastructure, trying to prioritize use cases, or figuring out how to get your organization to actually adopt AI — this conversation has the specifics. Take the IT Infrastructure Readiness Assessment to see where your organization stands before you build: https://www.insight.com/en_US/what-we-do/expertise/modern-infrastructure/it-infrastructure-readiness-assessment.html Explore Insight's modern infrastructure expertise: https://www.insight.com/en_US/what-we-do/expertise/modern-infrastructure.html Subscribe to Insight On for new episodes every week. Jump to… 00:00 — Introduction and the combined CIO-CISO role 02:09 — What GTT does and its global network scale 02:58 — How generative AI disrupted GTT's business 03:48 — What an AI factory actually is 05:44 — Early wins from three AI factory deployments 07:24 — The six-month data governance pause 09:58 — How GTT prioritizes AI use cases
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Stop Replacing Humans With Agents. It Doesn't Work That Way | EP24
Apr 2200:38:26Tap to summarizeMost companies are asking the wrong question about AI — and a Stanford researcher's field work shows exactly where the returns break down. Melissa Valentine, professor at Stanford University, senior fellow at the Stanford Institute for Human-Centered AI, and co-author of Flash Teams, makes the case that AI workflow integration — not better prompting — is what separates real ROI from endless experimentation. In this conversation, Valentine shares findings from her research on why high-impact AI users think like product managers, why the org chart isn't dying but needs to be understood differently in an AI-powered organization, and why the efficiency narrative around AI may be leading organizations straight into the Turing Trap. Subscribe to Insight On for conversations with researchers, practitioners, and business leaders on the technology decisions that shape outcomes. 📖 Read Melissa's research: To Drive AI Adoption, Build Your Team's Product Management Skills: https://hbr.org/2026/02/to-drive-ai-adoption-build-your-teams-product-management-skills How to Make Enterprise Gen AI Work: https://hbr.org/2025/09/how-to-make-enterprise-gen-ai-work Flash Teams (book): https://www.amazon.com/Flash-Teams-Leading-AI-Enhanced-Demand/dp/0262049848 Jump to… 00:00 — Welcome and guest introduction 03:54 — The real reason companies aren't seeing AI ROI 05:18 — The copy-paste trap and integrated workflow problem 09:12 — The product manager mindset for AI adoption 14:18 — Overcoming resistance to workflow change 18:08 — Levels of AI maturity across organizations 25:29 — Is the org chart dying or just changing? 28:29 — Why one-to-one agent replacement is the wrong model 32:11 — What a working agentic system actually looks like 33:36 — Cognitive load and the hidden cost of AI-accelerated work 35:38 — How to bring a CFO along on AI transformation
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Why 'Good Enough' Infrastructure Fails in the AI Era | EP 23
Apr 800:18:15Tap to summarizeAs AI shifts from experimentation to execution, the margin for "good enough" infrastructure disappears fast. In this episode of Insight On, Jillian Viner and Insight CMO Hilary Kerner check in on how AI sentiment has shifted over the past six months — from early experimentation to real pressure on teams, budgets, and expectations. Then, Hilary sits down with Cisco SVP Tim Coogan for a deeper conversation on infrastructure. They unpack why strategies that felt "good enough" just 18 months ago are already risky, how AI changes what scalable really means, and why infrastructure planning has become less forgiving. If you're making technology decisions that need to hold up under AI at scale, this episode is for you. Also… Is your infrastructure strategy prepared for AI? Answer these 6 questions and find out. Move your infrastructure from 'good enough for now' to 'ready for what comes next' with Insight. Jump to: 01:26 – Six months later — how AI sentiment has shifted 01:57 – From experimentation to real-world pressure 03:30 – Why optimism around AI is getting tested 05:10 – Transition to infrastructure and scale 05:40 – Why "good enough" no longer works 09:15 – AI and cloud at scale — either it works or it doesn't 14:30 – How AI changes infrastructure planning assumptions 18:50 – What leaders should rethink going forward
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