Inspire AI: Transforming RVA Through Technology and Automation
Our mission is to cultivate AI literacy in the Greater Richmond Region through awareness, community engagement, education, and advocacy. In this podcast, we spotlight companies and individuals in the region who are pioneering the development and use of AI.
Episodes
Middle managers have been “written off” for years, especially whenever a new wave of technology promises faster communication and smarter decisions. We disagree. When AI can summarize meetings, generate reports, and answer routine questions, the manager’s job does not disappear. It finally becomes what great management was always meant to be: helping people grow, making strategy usable, and turning...
Most teams don’t fail with AI because they picked the wrong software. They fail because they drop powerful AI tools into workflows that were already overloaded, slow, and full of bottlenecks. That’s like building a race car and then driving it on a road full of potholes. The tool isn’t the problem. The system is.
We dig into the difference between automation and transformation, and why speed...
AI is moving into every corner of work, and it’s creating a quiet trap for leaders: the belief that we have to become deeply technical before we can lead. We push back on that idea and argue for something far more useful: AI literacy. For us, AI literacy isn’t about Python, model building, or chasing the newest tool. It’s the ability to make good decisions when AI is generating information faster t...
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AI is moving into every workflow, but most teams are still treating it like a faster search bar. We take a different stance: the winners aren’t the organizations that simply adopt AI tools, they’re the ones that redesign work so humans and AI complement each other. If you lead people, run meetings, or make high-stakes calls, this matters because speed alone isn’t the goal. Better judgment, better a...
AI is moving into your workplace whether leadership feels ready or not, and that reality creates a dangerous gap: employees are already experimenting while executives are still debating permission. We start a new Inspire AI series by naming the real challenge for leaders in the age of AI: learning how to lead while everything around you changes, and giving people clarity before certainty exists.
We unpa...
AI headlines love extremes: total automation or sci-fi superintelligence. We take a different angle and ask a more personal, more practical question: can AI help us become better versions of ourselves? When you stop treating AI like a shortcut machine and start using it like a coach, tutor, analyst, and thought partner, the real value shows up in how you think, how you learn, and how you decide.
We dig into t...
Prompt tricks used to feel like the whole game. Now AI coding agents can run commands, inspect logs, edit multiple files, and stay on task for hours, and that changes what “good” looks like in AI-enabled software development. We dig into loop engineering, the idea that the biggest gains come from designing the system around the model: clear objectives, objective verification, structured feedback, smart r...
AI doesn’t fail in enterprises because the model isn’t impressive. It fails because nobody can answer the uncomfortable questions: who owns the data, who carries the liability, and what “trust” even means when software can hallucinate with confidence. We sit down with Ben Hawkins, a technology transactions lawyer working at the intersection of AI commercialization, enterprise software, and go...
The biggest risk in an AI-powered organization isn’t a lack of intelligence, it’s a lack of shared meaning. As tools get faster and output gets cheaper, teams can still stall, ship the wrong thing, or quietly lose trust because the human communication system can’t keep up with the speed of automation. That’s why I sat down with Andrea Gulet, founder of Debugging Human Communication and a long...
Software is slipping from “hard to produce” to “easy to generate,” and that single change forces a rethink of how we build companies, teams, and careers. When AI compresses planning, implementation, and iteration, the bottleneck moves away from writing code and toward directing intelligence. We zoom out on what happens when creation becomes abundant and the economics of software engineering s...
If you’re an engineer staring at AI code generation and wondering where you fit, the uncomfortable truth is also the freeing one: trying to “outproduce” AI on repetitive implementation is not a durable plan. We talk through a calmer, more useful strategy for building a resilient software engineering career as coding becomes increasingly automated and teams move toward AI-native workflows.
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AI is quietly rewriting the org chart, and it’s not because everyone suddenly works faster. The real shift is structural: teams are becoming blended systems of humans, AI agents, orchestration layers, evaluation pipelines, and continuous automation workflows. That changes what leadership even means. We’re no longer just managing people, projects, and process. We’re learning to manage systems of int...
AI can generate code, analysis, and recommendations faster than any team in history, but there’s a catch: verification doesn’t scale the same way. When intelligence becomes abundant, judgment becomes scarce, and that scarcity reshapes what “good engineering” and “good leadership” actually mean.
We walk through the hidden asymmetry behind modern generative AI: organizations ...
AI can write code faster than any team on earth, so why does it still feel like shipping software is hard? The uncomfortable answer is that speed is not the same as progress, and generation is not the same as judgment. We challenge the tired question “Will AI replace programmers?” and replace it with a more useful one: at what layer does human judgment become most valuable as AI absorbs more of implement...
The scariest part of AI in software is not that it writes code. It is that it changes what “being an engineer” even means. When generative AI can scaffold applications, spin up infrastructure configs, draft tests, refactor modules, and debug common failures in minutes, the act of typing implementation stops being the limiting factor. That is a fundamental shift in software engineering, and it is bigger t...
AI agents are showing up everywhere, but most enterprises are discovering a frustrating truth: getting an agent to “work” in a demo is easy, getting it to deliver measurable value in production is brutally hard. We dig into why the bottleneck is shifting away from model performance and toward the fundamentals leaders control: data foundations, data governance, and organizational design that can support a...
AI is everywhere right now: copilots, automated workflows, faster analytics, better dashboards. And yet a lot of leaders still feel the same uneasy question underneath the hype: if AI is so powerful, why aren’t we seeing truly transformational business outcomes everywhere? We dig into the uncomfortable answer: many organizations are solving the wrong problem by treating AI as an efficiency upgrade instead of a...
The biggest problem with modern technology is not that it moves fast, it’s that it makes us feel like we’re failing to keep up. We keep adding AI copilots, new platforms, new dashboards, and new workflows, and somehow the payoff is often cognitive fatigue, decision exhaustion, and a persistent sense of digital overwhelm. That experience isn’t random. It has a name in the research: technostress.
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“We’ll learn AI once we understand it” sounds responsible, but it’s one of the fastest ways to fall behind. We sit down with Matt to argue for a different approach: learn AI by building with it, in small scopes, with real users, and with the humility to let the work teach you what the strategy can’t. The result is faster AI adoption, better judgment about what models can and cannot do, ...
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