Pops and Steele tackle a problem most IT and finance leaders haven't caught up to yet: AI subscriptions and API spend are becoming the new shadow IT. Teams are subscribing to AI tools on corporate cards with zero governance or intake process, token-based consumption is replacing predictable licensing, and nobody's tracking the "blast radius" of what these tools can access. They walk through why AI spend breaks the old software asset management playbook (or doesn't — they debate this), how to treat AI subscriptions like governed IT assets, what review cadence makes sense (monthly until you understand it, then quarterly), and how to open the budget conversation with finance before finance opens it with you. Along the way: a $2,000-in-24-hours cautionary tale, a Lexis Nexis printer-page analogy, and Steele's line that becomes the episode's thesis — "whoever ignores the budget conversation loses the seat at the table."

Key Takeaways

• Shadow AI hides wherever there's no intake and governance process. If you don't have a formal way to bring AI tools in, someone already brought one in without you.
• AI spend is jagged and consumption-based, not predictable like traditional per-seat licensing — auto-approval settings and "set it and walk away" agent usage can generate runaway bills.
• Wrong tool, wrong job costs money. The $2K/24-hour example: using a frontier model for basic tasks is "driving a Ferrari to the grocery store."
• Treat AI subscriptions like governed IT assets — track what models, datasets, skills, and prompts are in use, ideally surfaced first in a spreadsheet, then a CMDB.
• Ownership is shared, but accountability isn't optional — the business owns the risk, but the team that brought a tool in without process owns the consequences.
• Review cadence: monthly until you fully understand a new tool's usage pattern; only then step back to quarterly/biannual. New capabilities may need daily/weekly checks at rollout.
• Bring numbers to finance before they ask. Go in with an annual spend estimate, a value narrative, and a straight answer — don't let finance find out from the invoice.
• Read the fine print on new model releases — usage multipliers (like a new model consuming 50% more of your quota) can quietly blow through limits.

Keywords / Tags
AI spend management, shadow AI, shadow IT, AI governance, ServiceNow ITAM, IT asset management, AI subscription tracking, token consumption, CMDB, AI budget, finance and IT alignment, agentic AI risk, AI cost governance, enterprise AI adoption, frontier models, AI ROI, IT leadership, CAB governance, AI sprawl, security risk AI tools, AI procurement

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Pops and Steele dig into a problem hiding in plain sight: uncontrolled AI subscription spend. As teams experiment freely with AI tools — often on personal corporate cards with zero IT or finance visibility — costs are quietly compounding into what could become a governance crisis. The two draw parallels to the early days of cloud computing and Shadow IT, arguing that AI subscriptions, tokens, and usage-based billing need to be tracked like any other IT asset, ideally landing in a CMDB. They walk through real-world cautionary tales (a $2K/day AI bill, a Meta token-spend anecdote), debate who actually owns AI risk within an organization, and lay out a practical cadence for reviewing AI spend — monthly until you understand it, then scaling back. The episode closes with concrete advice: read the fine print on your AI tool's usage limits, bring a real cost forecast to finance early, and don't wait for the invoice to start the conversation.
Key Takeaways

Shadow AI hides wherever there's no intake and governance process — not in one department, but across every team running its own point solutions.

Usage-based billing breaks traditional software asset tracking. Unlike flat licensing, token/consumption-based spend is jagged and hard to forecast without active monitoring.

Treat AI subscriptions like governed IT assets — track what models, datasets, and prompts are in use, ideally inside a CMDB, the same way you'd track any other asset with blast-radius risk.

Review cadence should match maturity, not comfort: monthly (or even daily/weekly for new capabilities) until the org actually understands its usage pattern — then it can stretch to quarterly.

Ownership of AI risk is shared, but accountability isn't. The team that brings a tool in without going through proper process still owns the consequences.

Bring a number to finance before they ask for one. Proactive cost forecasting protects the relationship — and the budget.

"Ferrari to the grocery store" problem: using frontier/premium models for simple tasks is where a lot of runaway spend comes from — match the model to the job.

AI spend management, Shadow AI, Shadow IT, AI asset management, CMDB, IT asset management, ITAM, AI governance, token-based billing, usage-based billing, AI budget, finance and IT alignment, AI subscription tracking, consumption-based licensing, AI cost governance, enterprise AI adoption, CAB governance, AI risk management
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Your company doesn't have one front door — it has six. IT has one. HR has one. Facilities has one. Legal ops has one. And employees hate every single one of them.

In this episode, Pops and Steele pop the hood on ServiceNow Employee Center — not as another tech deployment, but as an enterprise transformation play. They tackle the governance question every organization avoids (centralized vs. federated vs. product-led ownership), why portal fragmentation is a measurable business risk and not just a UX nitpick, and how to build an executive case using real pain points and testimonials instead of another feature-list slide.

The back half gets tactical: who owns which KPI, chargeback vs. showback cost models, and the cadence for reviewing metrics with stakeholders — weekly at the platform level, monthly red/yellow/green rollups to execs, and quarterly strategic moves through CAB.

A single employee front door isn't a technology rollout — it's an organizational commitment. Product-led ownership with domain accountability is the "Goldilocks" governance model: centralized ownership becomes a bottleneck at scale, federated governance drifts without a brand standard, but product-led ownership with strong stakeholder engagement holds the line while still giving each domain room to serve its users well.

If you're an IT leader, ServiceNow product owner, or anyone fighting portal sprawl inside a large or regulated organization (yes, including law firms), this one's for you.

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ServiceNow Employee Center, Employee Experience, Portal Fragmentation, Business Transformation, Governance Model, Product-Led Ownership, Federated Governance, Shadow IT, Chargeback vs Showback, KPI Ownership, Executive Sponsorship, CAB, OCM (Organizational Change Management), Digital Front Door, ROI Metrics, Legal Industry IT

If you're staring down your own portal sprawl, drop a comment — how many front doors does your org actually have? Subscribe for more under-the-hood conversations on ServiceNow, AI, and IT leadership, and we'll see ya later.

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Most IT organizations aren't behind on technology — they're behind on thinking. In this episode, Pops and Steele sit down with Matt Coatney, CIO of a large national law firm, to break down persona-based agentic AI: virtual agents that adapt to *who* they're talking to, not just *what* they're asked. Matt shares how a law firm environment — high-touch partners, urgent deadlines, wildly different workflows across practice groups — makes generic self-service fall flat, and what it actually takes to build role-aware automation that works. They dig into data readiness, the Big Brother problem, tier-zero service desk automation, and where the real ROI conversation with a skeptical CFO needs to start. Matt also gets candid about the risks of over-permissioned AI agents digging up things "security by obscurity" used to hide.

Key Takeaways
- Persona-based AI isn't mainstream yet — the technology exists, but adoption is gated by change management, privacy comfort, and governance, not capability.
- Generic self-service fails high-touch environments. A law firm with hundreds of partner "entrepreneurs" each running practices their own way can't be served with one-size-fits-all automation — urgency and workflow context matter enormously.
- Data exhaust is the unlock. Ticket history, assets, and system usage are already-known data that can power personalization without requiring people to hand over new personal information.
- Big Brother concerns are real but manageable. Personalization lands well when it's baked into an expected workflow (like a service desk that already has ticket history) rather than feeling like surveillance.
- Agentic AI raises new data-handling risks — attachments containing PII/PHI, agents that could store or forward sensitive data, and tools like Copilot surfacing improperly secured internal information that "security by obscurity" used to hide.
- ROI is often about noise reduction. In flat organizational structures, faster, better, more personalized service reduces escalations straight to the CIO/COO — a compelling case even without hard automation-cost metrics.
- The tier-zero cleanup loop: using AI-assisted human agents today (suggested KB articles, predictive closure notes) trains and cleans the knowledge base, setting up cleaner true self-service later.
- Personal accountability doesn't disappear with AI. Ceding too much judgment to an agent ("just read my email and tell me what matters") risks missing what actually counts — and someone still owns the outcome.
- Advice for leaders starting out: network with peers (ILTA, vendor conversations), and personally use the tools — you can't lead AI adoption you haven't internalized yourself.

Keywords / Tags
persona-based AI, agentic AI, ServiceNow, IT service desk, self-service automation, tier zero support, law firm technology, legal IT, CIO leadership, AI governance, data privacy AI, Now Assist, ITSM automation, enterprise AI agents, Copilot security, change management AI, CMDB, knowledge management, virtual agents, AI adoption

If you're an IT leader wrestling with self-service that feels generic, this episode is your blueprint for making it personal — subscribe to The Wired Garage with Pops so you don't miss the next conversation, drop a comment on where your organization stands on the "mainstream vs. science fiction" scale, and share this with a fellow CIO who's still fighting the one-size-fits-all portal.

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The chatbot era is winding down — and what's replacing it doesn't wait to be asked. In this episode of The Wired Garage with Pops, Pops and co-host Steele sit down with Matt Coatney, a technology leader operating at the intersection of enterprise AI and legal industry practice. Matt breaks down the real difference between a chatbot and an autonomous AI agent, shares what multi-agent systems actually look like in production today (not the sales pitch version), and offers a clear-eyed take on governance, accountability, and responsible adoption. From his own experiments building with Claude Code at home, to running AI workshops inside a major law firm, to advising on where to move fast and where to pump the brakes — this conversation is grounded, practical, and a little bit urgent. The Internet of Agents isn't a concept on a roadmap. It's being wired up right now, one workflow at a time.

KEY TAKEAWAYS
- Agents act. Chatbots answer. A chatbot waits for your question. An agent has knowledge, skills, guardrails, and can be proactive — more like a coworker than an advisor.
- Multi-agent systems are real, but still maturing. Most enterprise deployments today are the same capability wearing different hats. The leap to agents filling entire job roles — not just tasks — is where the real shift happens.
- Move fast on stable infrastructure — not on everything. Target high-repetition, high-cost, low-risk tasks first. In regulated environments (law, health, finance), some things in the value stream should never be automated, regardless of capability.
- When an agent makes a mistake, accountability still sits with you. If you didn't set up the right guardrails, that's on the human who deployed the system — the same way a manager owns the outcomes of the people they supervise.
- Governance for agents isn't new — it's just scaling fast. Test harnesses, simulation, failure mode analysis, escalation paths. The questions are the same ones any good manager asks. The challenge is applying them at speed and at scale.
- Skill atrophy and over-reliance are real risks. After ninety-nine good AI outputs, you stop checking the hundredth. That's fine for low-stakes work — dangerous for skills that still matter when the tool goes down.
- "AI powered" is a marketing claim, not a fact. Get the technologists in the room. The gap between vendors who've embraced AI in a mature way and those who've just applied the label is already showing up in product quality and stability.


KEYWORDS
AI agents, autonomous agents, multi-agent systems, internet of agents, AI governance, AI accountability, enterprise AI, AI adoption, AI in legal, IT leadership, AI vs chatbot, agentic AI, AI guardrails, AI risk, AI washing, ServiceNow AI, Claude Code, MCP protocol, AI productivity, future of IT, IT service desk AI, AI skill atrophy, AI in enterprise, responsible AI, tech leadership, future of work, AI tools, wired garage


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Jeremy Duncan is a cloud platform solution architect with 20+ years of experience, Fortune 500 engagements, and a reputation that precedes him — green glowing shoes and all. But behind the accolades is a story built on grit. Raised by a single mom working three jobs, Jeremy grew up watching hustle from a bar stool at a Nashville watering hole and turned that into fuel. In this episode, Jeremy takes us from a maraschino-cherry childhood to a 10-year run as a reserve police officer — all while building a career at the top of the ServiceNow ecosystem.

We get into his work connecting Ukrainian war refugees to American sponsors through the Goldman Sachs-backed welcome.us platform (later the subject of a Tribeca film), his unsanctioned mentorship cohort turning nurses and veterans into tech professionals, and his honest, grounded take on AI, workforce transformation, and how leaders should navigate the noise. He closes with two words that say it all: Choose joy.

✅ KEY TAKEAWAYS

• Grit is inherited — Jeremy's drive traces directly to watching his mom hustle across three jobs. The foundation of his work ethic wasn't a college campus, it was a bar stool.
• Intangibles over credentials — When mentoring, Jeremy doesn't look for degrees or certifications. He looks for people who are already the "go-to" for computers, who lean in naturally, who want to sit behind a screen.
• Technology with a human center — His most meaningful career moment wasn't a Fortune 500 deployment. It was connecting Ukrainian refugees to American families, one platform, one family at a time.
• Imposter syndrome is universal — Even the most decorated architects feel it. The answer isn't to ignore the change — it's to ride it.
• AI will change IT, not destroy it — Marketing is ahead of engineering. The pendulum will correct. The skill of the 21st century is prompt engineering, not just tool mastery.
• Don't let leaders swing the pendulum too far — The C-suite mistake Jeremy sees repeatedly: wholesale pivots instead of bite-sized, thoughtful AI adoption that starts with the soul-crushing work nobody wants anyway.
• Faith and family are the real grounding agents — When the stakes are highest, Jeremy doesn't look at the spreadsheet. He looks up.

🔑 KEYWORDS / TAGS
ServiceNow, Cloud Architecture, AI and the Future of Work, Mentorship, Workforce Transformation, Human-Centered Design, Prompt Engineering, Imposter Syndrome, Tech Leadership, Faith and Career, Origin Story, Reserve Police Officer, Ukrainian Refugees, welcome.us, Grit and Resilience, Choose Joy

CHAPTERS

• Jeremy Duncan's Origin Story
• Mentorship and Paying It Forward
• Meaningful Projects and Humanitarian Impact
• The Future of Technology and AI
• The Economic Impact of AI on Employment
• Trust and Security in Technology
• Navigating Change in Leadership
• Personal Grounding in a Tech-Driven World
• The Human Element in Technology

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s1e33 Who's Building Your Software Now? IT, the Business, or the AI?

This episode of The Wired Garage with Pops digs into how software delivery is being fundamentally restructured — not just accelerated. Pops and Steele walk through the evolution from traditional IT-led development to a multi-persona development model, where business users (citizen developers), professional developers, platform teams, and AI agents all share responsibility across the app lifecycle.

The conversation starts with the "why" behind citizen development — IT backlogs, understaffed teams, frustrated users waiting months for simple solutions — then moves into how governed, low-code platforms let business users get in the game without blowing up the architecture. They draw sharp lines between citizen development (process-governed, platform-scoped) and Shadow IT (your cousin's 99-cent app running on Bill's laptop).

From there they transition into agentic AI — what separates a chatbot from an agent, how AI agents plan and execute autonomously or with a human in the loop, and why governance applies to machine personas just like human ones. The episode wraps with a live screen share walkthrough of ServiceNow Studio and App Engine Studio as real-world examples of governed sit-dev platforms, plus a broader call to action for teams to start experimenting now.

Keywords: citizen development, multi-persona development, agentic AI, low-code no-code, Shadow IT, ServiceNow App Engine, ServiceNow Studio, governance, blast radius, human in the loop, AI agents, workflow automation, platform ROI, digital transformation, pro-code vs low-code, guardrails, two-lane highway, delivery velocity, compliance by design, enterprise AI

Key Takeaways

• Multi-persona development is the new operating model — business users, pro devs, admins, and AI agents all contribute on the same governed platform
• Citizen dev ≠ Shadow IT — the difference is process: a pipeline from idea to production vs. winging it with whoever knows somebody
• The "Two-Lane Highway" principle — how much breadth you give someone reflects trust, character, and risk tolerance; guardrails build confidence, not restriction
• Risk, Complexity, and Blast Radius — three filters to determine whether a task belongs to a citizen dev, a pro dev, or an AI agent
• Agentic AI is a governed persona on the platform, not a side experiment — it plans, executes, and can operate autonomously or human-in-loop depending on stakes
• Executives want ROI proof before scaling AI — excitement is real, but accountability, decision rights, and governance have to come with it
• Pro devs should be advancing the ball, not building email management workflows — free them up for what actually moves the business
• The workforce shift is already in motion — IT pros have always had to evolve; this is just the next lane change, and language/prompting skills are now table stakes
• Low-code tools like Make.com, Zapier, and AI assistants are accessible entry points for anyone wanting to get started today

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