Insight On

Data Is My AI Strategy" — How UC Riverside Is Outrunning Organizations With Bigger Budgets | EP31

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 [https://youtube.com/watch?v=rf8MyG22FnA&feature=youtu.be] 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 [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

Data Governance Led MERGE to an Unexpected Product | EP30

Most 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 [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 [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 [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 [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

AI Citizen Development in Construction? | EP29

Construction 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 [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 [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 [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 * 17:28 — Advice for leaders starting with AI in physical industries

Shadow AI Agent Risk: It's Not Just the CISO's Problem | EP28

Shadow 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 [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 [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 [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 [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 [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

AI Agent Governance at Scale: Stagwell's Marketplace Model | EP27

AI 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 [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