Future State Now: A Business Technology Podcast

When AI spending lands in the operating budget, the first thing many companies cut is people. This episode explains why that happens and what to do instead. Show Notes Why AI costs end up as headcount cuts: AI token spend is an operating expense, usually charged to IT, and labor is the easiest line to cut once capital and SaaS commitments are locked in. Cost myopia: cost is right in front of you, and value is further away. Leaders focus on cost because it's easier to control and because cause and effect between investment and value is hard to prove. Value engineering, explained: a practice that began at GE in the 1940s. It breaks a capability down into what its parts cost and the value each part produces. Value is benefits minus cost per unit of time. What chargebacks hide: centralized infrastructure costs left IT spend understated by 40–60%, until a corporate split forced every dollar to land in someone's budget. Avoiding the wrong investment: a value engineering analysis prevented a $20M platform spend and found that the real problem was back orders. Measuring what seems unmeasurable: if you find the right indicator, you can measure things that look intangible. Where to start: change is driven by pain more often than by vision. Fix what's in your control first, then deliver value in 90-day vertical slices that fund the next step. Key Moments  0:00 Cold open: measuring the unmeasurable 1:00 Why does AI cost cutting lead to layoffs instead of smarter IT budget decisions? AI spend, especially tokens, is an operating expense, and it usually lands in IT. Capital spend and SaaS contracts are locked in, so labor is the easiest line left to cut. The real problem is cost myopia: leaders manage the cost in front of them because value is harder to see and harder to prove. 11:23 What is value engineering, and how does it measure business value? It's a discipline that began at GE in the 1940s. You break a capability, process or value stream into what each part costs and the value it contributes. The basic test is simple: value is benefits minus cost per unit of time. 14:02 How do IT chargebacks and cost-only budgeting hide the true cost of technology? Chargebacks move costs around without tying them to value, so nobody owns the real number. In one company, chargebacks hid enough infrastructure spend that IT costs were understated by 40–60%. In another, a value engineering analysis stopped a $20M platform investment and pointed the money at the real problem, which was order fulfillment. 22:27 Can internal teams run a value engineering analysis and measure intangible benefits? Yes. These skills already exist in finance and engineering teams. Almost anything can be measured once you find the right indicator. 26:26 Will value engineering prevent AI-driven layoffs and improve AI ROI? Not on its own, but better math is better than no math. Financial models keep you from making the wrong cuts and the wrong investments, and they keep productivity gains from disappearing without ever turning into value. 30:32 How do leaders move from cost cutting to value-based decision making? Not by starting with culture, because culture is an effect, not a cause. Start where the pain is and fix what's in your own control first. Then deliver value in 90-day slices, and let each one fund the next. Follow Us On Social LinkedIn: https://www.linkedin.com/company/liminalarcgroup Facebook: https://www.facebook.com/liminalarcgroup Instagram: https://www.instagram.com/liminalarcgroup/ X: https://x.com/liminalarcgroup

In this podcast, Stacy Gordon sits down with John Greisner, a management consultant at LiminalArc, to explore why AI doesn't remove your software delivery bottleneck so much as move it. Drawing on Goldratt's theory of constraints from The Goal, John walks through what happens once engineering gets fast and cheap: the real slow point shifts to product management, funding cadence, and how much change your customers can actually absorb.   Stacy and John dig into why flooding your backlog with every new idea only makes that constraint worse, and why deciding better, smaller, and more often works instead. Watch so you can stop chasing engineering speed as the finish line, start treating decision capacity as your real constraint, and finally get the return on AI adoption that an operating model built for a slower era has been quietly blocking.   Follow Us On Social LinkedIn: https://www.linkedin.com/company/liminalarcgroup Facebook: https://www.facebook.com/liminalarcgroup Instagram: https://www.instagram.com/liminalarcgroup/ X: https://x.com/liminalarcgroup

Discover how organizations use business capability heatmaps and domain alignment to make deliberate, data-driven, and defensible modernization decisions that deliver business value at every step of the journey. Follow Us On Social LinkedIn: https://www.linkedin.com/company/liminalarcgroup Facebook: https://www.facebook.com/liminalarcgroup Instagram: https://www.instagram.com/liminalarcgroup/ X: https://x.com/liminalarcgroup

Nearly 80% of enterprise application modernization efforts fail, and the reason has nothing to do with choosing the wrong technology. In this conversation, Melissa Roberts, business architect and value engineer at LiminalArc, explains why modernization programs are designed to produce technology outcomes rather than business results, and why that design flaw kills them. Melissa Roberts and Stacy Gordon walk through the four primary drivers forcing enterprises to modernize, the IT/business alignment problem that stalls most programs before they start, and how a capabilities-driven strategy changes what a modernization program is designed to deliver. If you're leading or influencing a legacy modernization initiative, this is the diagnostic you've been missing. Wanting More? Why Most App Modernization Efforts Fail, and How a Capabilities-Driven Strategy Can Stop the Billion-Dollar Bleed: https://go.liminalarc.co/75j Follow Us On Social LinkedIn: https://www.linkedin.com/company/liminalarcgroup Facebook: https://www.facebook.com/liminalarcgroup Instagram: https://www.instagram.com/liminalarcgroup/ X: https://x.com/liminalarcgroup

A working AI pilot proves less than most executives assume. This episode digs into why AI initiatives clear a demo and then stall in production, and the constraint is rarely the model. It's whether the organization's data, applications, and ownership structure are actually ready to run on it. That readiness gap shows up two ways: AI use turning into the next shadow IT, and teams mistaking individual productivity wins for organizational proof. The conversation also breaks down a framework for sorting what AI can already do inside a business from what it can't do yet, and why context, not model quality, is becoming the real differentiator. Host Stacy Gordon talks with LiminalArc CEO Mike Cottmeyer in the relaunch episode of Future State Now, formerly SoundNotes, tracing the company's shift from LeadingAgile to LiminalArc and what AI transformation means as the next chapter of enterprise change.   Why AI Fails and What You Can Do About It  Get the eBook > https://go.liminalarc.co/jtu   Follow Us On Social LinkedIn: https://www.linkedin.com/company/liminalarcgroup Facebook: https://www.facebook.com/liminalarcgroup Instagram: https://www.instagram.com/liminalarcgroup/ X: https://x.com/liminalarcgroup   Visit Our Website https://www.liminalarc.co