The Wired Garage with Pops | Digital Innovation

Cost of Doing Business with David CramDavid Cram, founder of ValSync, joins Pops and Steele to take aim at one phrase: "that's just the cost of doing business." He argues that organizations exaggerate the cost of change until it becomes a kind of prison — and that phrase becomes the excuse for staying trapped in it, at the expense of productivity, profitability, and digital transformation.The conversation digs into where costs quietly slip through the cracks — auto-renewals, shelfware, unused licenses, scope creep — and why most organizations' inventory management is still stuck on spreadsheets rather than tools like ServiceNow's SAM module. David breaks down how to negotiate vendor contracts without breaking the supplier's revenue recognition (the thing that actually keeps them motivated to deal with you), then turns to AI: why so many companies are chasing ROI without a real plan, why "token" is a moving target nobody's pinned down, and why token cost surprises are shaping up to be the next cloud sticker-shock crisis. He closes with a challenge to the audience — before calling something "just the cost of doing business," ask why change actually feels scary, and go pull your renewal dates and license utilization this week.Keywords / TagsIT cost optimization, ValSync, David Cram, cost of doing business, digital transformation, contract negotiation, auto-renewal, shelfware, unused licenses, scope creep, software asset management, ServiceNow SAM, revenue recognition, RevRec, vendor negotiation, AI ROI, AI governance, token costs, AI observability, cloud cost management, IT leadership, enterprise technology spendChapters00:00 – Intro / David returns to the garage00:20 – Why "cost of doing business" is a trap01:08 – Hidden contract costs: auto-renewal, shelfware, scope creep01:44 – Why inventory management still fails at most orgs02:30 – Negotiating without killing the supplier's RevRec04:19 – How AI is changing tech spend conversations04:36 – The AI ROI blind spot06:45 – Late adopters, fast followers, and the token cost crisis09:30 – Final words: ask why change is actually scary10:27 – Closing challenge and where to find DavidTake one thing from this episode — pull your renewal dates and license utilization this week. You might be surprised what you find.Subscribe and drop a comment with one line item in your budget that no one's ever questioned.Support the show

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 TakeawaysShadow 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 / TagsAI 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 procurementIs AI spend already a line item at your shop — or are you still finding out from the invoice? Drop a comment.If you're the one who has to explain the AI bill to finance, hit subscribe — this is the show for you.Tag the person on your team who needs to see this before the next invoice lands.Want the one-sheeter framework Pops uses to pitch new AI tools to finance? Link in bio.Support the show

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 TakeawaysShadow 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 managementSuggested CTAsIs AI spend already a line item at your shop — or are you still finding out about it from the invoice? Drop a comment and let us know.If you're wrestling with AI governance at your org, hit subscribe — we're covering this space every week.Tag someone in IT or Finance who needs to hear this before the next invoice lands.Support the show

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.🍺 New episodes every week 🚗 Subscribe for more under-the-hood conversations on ServiceNow, AI, and IT leadership.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 ITIf 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.Support the show

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 / Tagspersona-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 adoptionIf 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.Support the show