Insight On

Frontier AI labs have disclosed something security leaders need to pay attention to: Their models are escaping test environments and reaching real companies — without anyone directing them to. For security leaders, these are alarm bells for where the AI-powered threat landscape is heading.

Insight's Chief Information Security Officer for North America, Jeremy Nelson, explains how autonomous AI attacks differ from everything that came before them — the speed, the scale, the way they chain zero-day vulnerabilities together faster than any human team could — and why the exploit window has effectively gone negative. By the time you're alerted to a vulnerability, a model may have already found it and begun exploiting it. That changes what "patching in time" means entirely.

You'll also hear why red teaming looks fundamentally different now that a single operator can coordinate an entire team of AI agents with one prompt, why you need a diversity of models on both sides of the fight, and the one phrase that tells a CISO immediately that AI adoption has outpaced security governance.

Want to go deeper? Read Insight's take on the unexpected challenge of AI success: https://www.insight.com/en_US/content-and-resources/blog/the-unexpected-challenge-of-ai-success.html

Get Insight's cybersecurity services because your defense needs to move at the same speed as the threat: insight.com/cybersecurity

Subscribe for more conversations with the leaders shaping enterprise technology strategy.

#AISecurity #CyberSecurity #GenerativeAI #CISO #InsightOn

Chapters

00:00 — Welcome and intro

01:22 — How autonomous AI attacks work without human direction

03:47 — Becoming a victim without touching a frontier model

07:05 — The Hugging Face guardrail paradox — when AI blocks defenders

10:42 — Why you need a diversity of models to fight AI attacks

14:24 — How red teaming has fundamentally changed with AI agents

21:46 — The red flag that AI adoption has outpaced security

22:54 — The negative exploit window and what it means for your defense

More than two years ago, Insight made a bold call: give every one of our 14,000 teammates access to AI tools and tell them to use it everywhere. This episode is the full story of what that journey built, broke, and taught us — the 90% adoption rate, the 8,500 agents, the 49,000 hours of meetings summarized in a single month, and the things our CTO Mark Hughes would do differently if we started over.

In this conversation, Mark walks through the real arc of our AI transformation — from the early "use it everywhere" mandate to the hard pivot toward cost discipline, governance, and smarter deployment. You'll hear why solving the AI problem is really about solving the data problem first, how a 10X overnight cost increase forced a company-wide rethink of token spending, and what "human in the loop" actually means when AI confidently makes things up.

Whether you're building your AI operating model, wrestling with ROI conversations, or trying to get your organization past the pilot stage, this episode gives you a practitioner's view of what AI adoption actually takes — data readiness, governance, change management, and all.

Get Insight's AI and data solutions because it will help you move past pilots and into AI that actually sticks: insight.com/data-and-ai

Also worth exploring:

• Radius AI: insight.com/radius-ai

• AI Flight Academy: insight.com/ai-flight-academy

• Enterprise AI Hub: insight.com/enterprise-ai-hub

• Getting AI Unstuck — From Strategy to Scale: insight.com/getting-ai-unstuck

Subscribe to Insight On for more conversations with practitioners building real AI programs inside real organizations.

#AIAdoption #AITransformation #GenerativeAI #AIStrategy

Chapters

00:00 — Welcome and intro

02:41 — How our earliest AI adopters got started

03:09 — The AI ROI question nobody has a clean answer to

04:06 — When token costs jumped 10X overnight

07:21 — What 49,000 hours of meeting summaries actually means

09:13 — Managing 8,500 agents and the duplication problem

14:00 — Solving the AI problem by solving the data problem first

18:44 — What Flight Academy did for AI training at scale

21:19 — Human in the loop — and why we're thinking about it wrong

29:11 — The Horizon platform: from chat interface to agentic orchestration

As AI labs shift to usage-based consumption pricing, the math on per-seat licensing is getting harder to defend.

In this episode, Shana Meyers, Director of Global Business Solutions and Distinguished Technologist at Insight, breaks down how Insight built its Enterprise AI Hub — a platform that gives every employee access to multiple AI models through API-based token consumption, at roughly one-tenth the cost of individual licenses. You'll hear how a rejected — but successful — pilot became the foundation for a company-wide platform, why curated data beats more data every time, and how solution development and project management is evolving with greater collaboration and front-line leadership.

Get Insight's Enterprise AI Hub because it gives your whole workforce secure AI access without the per-seat price tag:https://www.insight.com/en_US/what-we-do/expertise/enterprise-ai-hub.html

Want to build the AI skills to go with the platform? Learn about AI Flight Academy: https://www.insight.com/en_US/what-we-do/expertise/ai-flight-academy.html

Read more on scaling AI from strategy to production: https://www.insight.com/en_US/content-and-resources/article/getting-ai-unstuck-from-strategy-to-scale.html

Subscribe to Insight On for more conversations on enterprise AI, technology strategy, and what it actually takes to scale.

#EnterpriseAI #AITokenomics #InsightOn #AIAdoption #GenerativeAI

Chapters:

00:00 — Welcome and episode intro

01:00 — How the Enterprise AI Hub got started

03:15 — The failed pilot that changed everything

06:36 — Token-based access vs. per-seat licensing cost

10:30 — Adoption numbers and Flight Academy's role

14:11 — Why measuring ROI in time saved falls short

22:12 — How AskHR works as a persona-based agent

AI token costs just changed — and the invoice is the least of your problems. Juan Orlandini, CTO of North America at Insight, breaks down what metered AI pricing is actually exposing about the structural assumptions your organization made when tokens were cheap or free.

You'll walk away knowing how to read the real damage — the hiring decisions, workflow expectations, and delivery timelines that were built on subsidized pricing — and what to do about it before it compounds. Juan covers model routing, on-premises inferencing, token attribution, and why the shift to metered pricing is, counterintuitively, good news for organizations willing to act now.

Read Juan's companion piece, "The Bill Was Never the Real Cost," for a deeper look at the structural risks hiding beneath your AI spend: https://www.insight.com/en_US/content-and-resources/blog/the-bill-was-never-the-real-cost.html

Get Insight's AI and data expertise because you'll see exactly how to build a cost-disciplined AI strategy that compounds value across every project: https://www.insight.com/en_US/what-we-do/expertise/data-and-ai.html

Subscribe to Insight On for more conversations with the technologists and leaders shaping enterprise AI.

#AITokenomics #EnterpriseAI #AIStrategy

Chapters

• 02:02 — The driveway analogy: when the bill hides the real damage

• 07:16 — How metered pricing exposed broken AI assumptions

• 08:22 — Model selection and the 4,500x price difference

• 09:21 — On-premises and on-device inferencing as a cost lever

• 11:09 — How to explain token economics to business leaders

• 12:03 — Token attribution: knowing if your AI spend is returning value

• 12:24 — What to do first when the AI bill shocks you

• 18:53 — Lessons learned for organizations building agentic systems today

• 24:28 — Red light, green light: five AI spending behaviors rated

AI is reshaping entry-level work fast — and the generation walking into your workforce has already formed opinions about how you're handling it. Four college interns share what they actually think about AI, career development, and the skills that matter most when the task layer disappears.

In this conversation, you'll hear why a company that doesn't use AI is a red flag to this generation, how AI freed up intern time for human connection rather than replacing it, and what one intern learned spending a summer as a formal test subject for AI-augmented entry-level work. If you're making decisions about headcount, hiring, or how to build AI confidence across your workforce, this one is worth your time.

Check out Insight AI Flight Academy — the same gamified AI enablement program these interns experienced firsthand — because you'll see exactly how to build real AI adoption across your workforce: insight.com/en_US/what-we-do/expertise/ai-flight-academy.html

Want to go deeper on building an AI-ready team? "The AI Skills Playbook" walks through a five-step strategy for integrating AI into daily workflows: insight.com/en_US/content-and-resources/gated/the-ai-skills-playbook-ac1402.html

And if adoption is stalling in your organization, "AI Starts With Us" makes the case that AI success is a leadership challenge, not a technology one: insight.com/en_US/content-and-resources/gated/ai-starts-with-us-drive-meaningful-ai-adoption-ac1393.html

Subscribe to Insight On for more conversations with the people shaping how technology gets used at work.

#AI #FutureOfWork #GenerativeAI #AIadoption #InsightOn Chapters

02:07 — How AI shaped their college experience 04:09 — Would you work for a company that doesn't use AI? 07:19 — AI skills coming into the internship 09:42 — Tasks AI made faster — real examples 11:12 — The validation problem — catching AI errors 12:44 — Did you learn more from AI or from people? 16:38 — Are you afraid AI will take your job? 21:48 — Advice for younger siblings entering the workforce 28:32 — What employers should do to support AI adoption 31:11 — Make the business case — why hire humans over AI?

What does it actually take to move from AI adoption to AI transformation? It starts with a structural decision most organizations haven't made: giving someone AI transformation as their only job.

Alex Pugh, director of finance AI operations at Insight, sits inside exactly that model — a dedicated AI ops team embedded within a single business function.

This conversation gets into the accountability structures, the hard-won use case lessons, and the one metric that tells you transformation has worked (it's not what you think).

Request a meeting with an Insight AI specialist to get the right framework for accountability structures that turn AI investment into measurable business outcomes: https://www.insight.com/en_US/what-we-do/expertise/data-and-ai.html

Also worth your time: How To Shift From Reactive to Proactive IT Operations: https://www.insight.com/en_US/content-and-resources/blog/how-to-shift-from-reactive-to-proactive-it-operations.html

And, why prompting isn't the path to AI ROI: https://www.insight.com/en_US/content-and-resources/insight-on/the-product-manager-mindset-why-prompting-isnt-the-path-to-ai-roi.html

If you're still in adoption mode and feeling stuck, here's how BlackLine successfully hit 99% Gemini adoption: https://www.insight.com/en_US/content-and-resources/case-studies/blackline-hits-99-adoption-of-gemini-enterprise-with-insight.html

Subscribe for more conversations on AI strategy, IT modernization, and the decisions that separate leaders from the rest.

#AItransformation #EnterpriseAI #InsightOn Chapters

00:00 — Welcome and introduction

05:36 — Why domain expertise beats pure AI expertise

08:02 — Dedicated AI team vs. distributed champion model

12:56 — Meet the Thundercats — Insight's dedicated finance AI ops team

16:07 — AI adoption vs. AI transformation — there's a difference

22:17 — The CFO's mission and the case for going all-in

25:20 — The bitter lesson and why waiting on AI is not a strategy

28:39 — How to find real AI use cases inside your organization

34:03 — Nine out of 10 use cases are not AI opportunities

46:26 — Rapid fire — worst advice, best metrics, and how you know it's working

GitHub Copilot's billing model changed on June 1 — and most enterprise teams are about to see their first full bill under the new system. For many organizations, it'll be the first real look at what AI adoption actually costs at scale.

The shift from flat-rate to usage-based billing sounds straightforward. Pay for what you use. But what your team is "using" turns out to be a lot more complicated than anyone expected — and the habits that felt like strong AI adoption six months ago are the same ones quietly running up the tab today.

Sumner Ohye works with GitHub partners and enterprise customers across the Americas and APAC. And since June 1, he's been fielding a lot of calls about billing. Parker Johnston is Insight's Agentic Field CTO — he's in the field with engineering teams every week. Together they get into what's happening on the ground, the common — and costly — mistakes people are making with token usage, and how teams are changing habits to keep token costs in check.

There's a reason Parker opens with "you can no longer YOLO mode it" — and it's not just about prompting.

Get GitHub Copilot solutions, available from Insight, because having the right partner means your billing controls, usage dashboards, and workflow guidance are in place before the charges hit — not after: https://www.insight.com/en_US/shop/partner/microsoft/github-copilot.html

Want to go deeper on GitHub? Check out these resources from Insight:

• Burning Questions: Hot Takes on Developer Tools, AI, & What's Next: https://www.insight.com/en_US/content-and-resources/webinars/burning-questions-hot-takes-on-developer-tools-ai-whats-next.html

• What's New in GitHub Advanced Security: https://www.insight.com/en_US/content-and-resources/webinars/whats-new-in-github-advanced-security-key-updates-explained.html

• Maximizing Developer Efficiency With GitHub Enterprise: https://www.insight.com/en_US/content-and-resources/webinars/maximizing-developer-efficiency-a-guide-to-github-enterprise.html

• Strategies for GitHub Actions Optimization: https://www.insight.com/en_US/content-and-resources/webinars/strategies-for-github-actions-optimization.html

• Maximizing Team Productivity With GitHub Copilot: https://www.insight.com/en_US/content-and-resources/webinars/maximizing-team-productivity-with-github-copilot.html

Subscribe for more episodes of Insight On: new conversations every week on AI, technology strategy, and what's actually working in enterprise tech.

#GitHubCopilot #UsageBasedBilling #AgenticCoding

Chapters:

• 03:25 — Is a 24x bill increase normal? What actually changed June 1st

• 05:38 — The transition period and what enterprises need to do now

• 07:14 — Agentic coding: when to use AI vs. doing it yourself

• 09:27 — Governing AI agents inside your codebase

• 11:24 — How to set up billing thresholds and avoid access shutdowns

• 13:28 — Why a third-party partner beats going direct to GitHub

• 17:11 — What the GitHub billing shift means for software development's future

• 18:20 — Running AI models locally vs. cloud: the real cost comparison

• 20:25 — How GitHub Copilot adoption has shifted since June 1st

• 21:43 — GitHub's evolution from source control to full agentic platform

• 25:34 — Building trust in agentic workflows: cost, security, and code quality

• 28:07 — The biggest mistakes teams are making with model selection and context

• 31:53 — Best practices for developers new to the GitHub Copilot environment

• 35:58 — How to measure ROI on agentic AI: DORA metrics, retention, and churn

• 37:54 — Real-world agentic use cases where the ROI still holds

• 40:57 — Code review automation: understanding the double billing reality

The 90-day patch cycle was never perfect. But it worked because the window between a vulnerability being disclosed and an attacker building a working exploit was sufficient time for most organizations to respond. Frontier AI models like Anthropic's Mythos have closed that window. Now we're talking hours. Possibly minutes.

That's not a headline. That's the operating reality two of Insight's CISOs are already building security programs around.

In this episode, Jeremy Nelson explains how frontier AI models don't just find vulnerabilities; they build the exploit in the same process. He walks through the risk tolerance slider bar, a framework for understanding why the old patch cycle used to make sense but no longer does. And he shares the five questions every security leader should be able to answer right now (the same ones your CEO and board are about to start asking).

Jason Rader picks up where Jeremy leaves off. He's been having CISO-to-CISO conversations with clients who want to know how Insight is handling this. Why? Not because everything is going smoothly, but because the honest account of what's working and what's not is more useful than a polished pitch. His answer to mid-market organizations facing the same threat surface as Fortune 500 companies — with fractions of the resources — is direct: Leave behind security operations that aren't core to what you do and get visibility over everything else.

Jeremy and Jason are operating with Insight as client zero, meaning Insight's own environment is where tools and programs are being proven before they're offered to anyone else.

Get Insight's Managed Exposure Defense because it's the same program built and battle-tested on Insight's own infrastructure — and it's available to organizations from one seat up: https://www.insight.com/en_US/what-we-do/expertise/managed-exposure-defense.html

Also worth your time: The CISO Mythos Readiness Checklist: https://www.insight.com/en_US/content-and-resources/guide/the-ciso-mythos-readiness-checklist.html 5 Questions Every CISO Will Be Asked About Mythos: https://www.insight.com/en_US/content-and-resources/blog/5-questions-every-ciso-will-be-asked-about-mythos-and-how-to-answer-them.html Don't Hit Later on That Update Notification: https://www.insight.com/en_US/content-and-resources/article/dont-hit-later-on-that-update-notification.html

Subscribe to Insight On for more conversations with the practitioners shaping enterprise technology strategy.

#FrontierAI #Cybersecurity #CISO

Chapters

• 00:00 — Welcome and episode overview

• 00:37 — Why the weaponization window has collapsed

• 03:54 — How frontier AI models discover and create exploits

• 08:18 — What to do when the threat feels overwhelming

• 10:23 — Why your CMDB is your biggest security liability

• 12:20 — Treating patch updates as security incidents

• 14:22 — How AI fits into vulnerability management and defense

• 16:48 — Inside Insight's managed exposure defense program

• 18:42 — Extending protection to clients of all sizes

• 20:14 — The risk tolerance slider — patching vs. business disruption

• 24:16 — Why patch maintenance is now a permanent priority

• 26:19 — Five questions every security leader must answer now

• 38:12 — Jason Rader on being client zero and managed security services

• 40:29 — What every business leader needs to know about Mythos

• 44:13 — How frontier models operate inside your network

• 47:04 — How Insight's security priorities have shifted

• 48:07 — Mid-market security and the managed SOC advantage

• 55:25 — Phishing risk when vulnerabilities go public

• 57:43 — Zero days, patch ecosystems, and the hacker economy

• 01:02:56 — The one thing every security leader must do tomorrow

WestJet 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

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