Stop Chasing Smokestacks. Grow What You Have.

Stop Chasing Smokestacks. Grow What You Have.

Most rural communities have a comprehensive plan. Very few have one that changes anything.   John Shepard, AICP, has spent 30 years in planning and economic development across the Midwest and Mountain States, and he has seen more plans collect dust than he cares to count.  They make great doorstops. In this episode, he makes the case that the failure isn't in the strategy, it's in how plans get built, adopted, and forgotten.   Key tensions this episode gets into:   * Why zoning codes written decades ago are quietly blocking the businesses rural towns need most, and why enforcement is often politically impossible even when the code is clear * What made Burwell, Nebraska's award-winning plan actually implementable, and why it came down to one person who had real skin in the game * The difference between a comprehensive plan and a doorstop, and why the thickness of the document is inversely related to the likelihood it gets used * How rural economic development shifted from "smokestack chasing" to "economic gardening," and why most small towns still haven't made the switch * Why the data rural leaders need to measure success often doesn't exist below the county level, and who is trying to fix that   You'll leave this episode with a sharper sense of what separates the communities that execute from the ones that plan and wait.

Economic Gardening: Planting the Seeds for Community Abundance

What if the secret to building a thriving tech economy wasn't tax incentives or recruiting big companies... but growing your own? Brit Fontenot has guided Bozeman, Montana through nearly two decades of explosive growth, a housing crisis, a fiber optic revolution, and a CHIPS Act tech hub designation, all without offering a single incentive package. His philosophy: economic gardening. Grow from within. Invest in people. Build the kind of place talent actually wants to stay. And his take on the boom town paradox? "Not living in the shadow of, but basking in the glow of." #EconomicDevelopment #EDO #Leadership #DynamicDecisions #DecisionIntelligence

When a City Learns to Say "Yes"

What does it look like when a city stops being a bureaucratic wall and starts being a growth partner? Samantha Mlot, Economic Development Advocate for the City of Bloomington, Illinois, is building exactly that model. Bloomington is growing faster than it ever has, and Samantha is at the center of it, serving as the single point of contact between developers, labor unions, commercial brokers, and the internal processes that can either clear a path or create one more obstacle. In this episode, Teasha Cable and Samantha explore: * The "advocate" role: how it differs from traditional economic development titles and why it matters * Coloring outside the lines: when to follow the process and when to adapt it, while documenting everything * Incentive tools in practice: TIF districts, tax abatement, facade grants, enterprise zones, and how to match the right tool to the right project * The GJ Lofts redevelopment: a legacy project navigating phasing, public trust, and a Starbucks LOI in downtown Bloomington * Why building a strong relationship with your planning team is non-negotiable for any economic developer Samantha's closing advice is something every community leader should carry: "Ask what you're doing wrong. You can't effectuate real change if you think everything is already working." If you work in economic development, municipal government, or community investment, this episode will reframe how you think about the role of advocacy inside your own organization.

Bad Pattern Recognition is Breaking the Bank

Most manufacturers aren't short on data. They're short on understanding. In this episode, Teasha Cable sits down with David Craig, industrial engineer, systems architect, AI practitioner, and founder of Eminence Consulting and Prodigy IQ Technologies, to dig into why 70% of industrial AI pilots never reach production and what separates a plant that collects data from one that actually learns from it. David has spent nearly 20 years in manufacturing, including time on the floor with tier one and tier two automotive suppliers. He's seen companies burn through a quarter million dollars a day in scrap while consultants delivered beautiful charts that solved nothing. His answer was to build something different: an AI-native quality platform that traces defects back to root cause automatically, learns from every lesson, and pushes decisions to operators before problems compound. In this conversation, Teasha and David explore: * Why pattern recognition, not data volume, is the real differentiator for smart manufacturers * How David's "quality gates" approach stopped a failing production line that 70 quality alerts couldn't fix * The difference between pulling information reactively and building a system that pushes decisions to you * How to bring veteran operators along when AI sees something their gut doesn't * The human cost of defects: what a single bad shift can do to a family, not just a balance sheet David's core argument is that AI doesn't replace judgment. It exposes what money and people have been quietly covering up. And once you see it, you can't unsee it. If you're leading operations, designing automation projects, or trying to move from reactive firefighting to proactive intelligence, this one's for you.

The Domain Experts are Winning the AI Race

Most AI companies are building the same thing for every industry. Hans Sandhu thinks that's the losing strategy, and he has the data to prove it. Hans Sandhu is the Chief Business Officer of The Agentic, a hardcore tech company that helps domain experts, not fresh-faced founders, launch production-ready vertical AI companies in 8 to 24 weeks across biotech, health tech, cybersecurity, and fintech. He has spent over a decade building revenue from scratch at venture-backed companies, setting records at SLI Systems and SnapLogic, and advising and investing across the ecosystem. In this episode of Dynamic Decisions, Teasha Cable sits down with Hans to unpack what's actually working in applied AI right now, and what's just noise. Topics covered: * Why AI founders almost always price their products too low, and how outcome-based pricing changes the math entirely * The Prometheus model: how The Agentic lets domain experts keep their equity and IP while handling engineering, sales, billing, and contracts * Why accuracy is non-negotiable in regulated industries, and what it really means when an agent makes an error at machine speed * The hidden reason most AI proof-of-concepts never become paying clients: stakeholder management * Hans's contrarian thesis, decoupling reasoning from the frontier models so companies own their intelligence, not OpenAI or Anthropic If you're a business leader trying to figure out where AI actually fits in your organization, this conversation will give you a sharper lens for cutting through the hype.