Hello Digizens,

Welcome back to the Dispatch, where we decode the signals from the tech hubs and translate them into a playbook for the rest of us. We have a special version of the Local Edition this week, a great video and in depth discussion of what went on right in our own backyard at the Lancaster AI Symposium 2.0, an event hosted at Millersville University.

Today, we’re tackling the biggest buzzword on the planet: Artificial Intelligence. But we’re going to do it differently. Forget the headlines about sentient robots and world-ending algorithms. We’re going to talk about the real story, the one unfolding not in a futuristic lab, but in conference rooms and factories in places like Central Pennsylvania.

This is the story of what it actually takes to make AI work.

TL;DR;

What if the biggest secret to succeeding with AI has almost nothing to do with the AI itself? It’s a bold question, and one that sends a ripple through the hype-filled narratives coming out of the major tech hubs. While the world is mesmerized by the race to build the next god-like model, a quieter, more practical conversation is happening in the places where real work gets done. This isn’t a story about magic black boxes; it’s a story about messy data, stubborn processes, and the complexities of human psychology. It's the untold story of AI implementation, and it’s a game-changer for every Digizen looking to make a real impact.

This story recently unfolded at the Lancaster AI Symposium 2.0, an event hosted at Millersville University. This wasn't a gathering of Silicon Valley titans, but of the very people on the front lines: business leaders, educators, and technologists from Central Pennsylvania. They are the implementers, the ones tasked with turning the abstract promise of AI into tangible business value. What they revealed was a stark contrast to the polished demos we see online. They spoke of the “last mile” of implementation, where the true value of AI lies not in the algorithm, but in its ability to solve a specific, human problem. They talked about the monumental task of cleaning up “corrupted, bad, missing data,” which, as one expert put it, is still the hardest part of the entire process.

Most importantly, they confirmed a truth many of us have suspected: the biggest barrier to technological progress isn't the technology. It's the people. A staggering 72% of major change projects fail, not because of faulty code, but because of human resistance, fear, and a failure to adapt. The real AI revolution, it turns out, is being fought in process maps and team meetings, not in server farms. It’s a battle to change minds before you can change workflows. The conversation in the tech hubs is about what AI could do. But the conversation in places like Lancaster is about how to actually make it work. So, how do you become part of the 28% that gets it right? The answer isn't in a data center; it's in a playbook. And we're about to open it for you.

What's In It For Me?

Alright, Digizens, let's move from exposure to action. The insights from the Lancaster Symposium aren't just interesting—they are a strategic guide for anyone outside a major tech hub looking to leverage AI effectively. This is your playbook for avoiding the common pitfalls and leading a successful implementation, no matter where you are.

The First Rule of AI Club: Don't Start with AI.

It sounds counterintuitive, but the most successful implementers delivered a unanimous verdict: your AI journey should never begin with an AI tool. It begins with a problem. As the author of the Digizenburg Dispatch, Don Demcsak explained, too many businesses get captivated by a shiny new technology and then desperately search for a problem to solve with it. This is a recipe for failure. The smart move is to reverse the process. Start by mapping your business's "value stream"—the end-to-end process for delivering a product or service. Where are the handoffs between departments? Where does work get stuck? That’s your friction.

One of the most powerful stories from the symposium perfectly illustrates this. A company discovered it took them an astonishing nine months from the day they decided to buy a server to the day it was actually up and running. A nine-month delay! They thought it was a technology problem. It wasn't. After mapping the process, they found the issue: the purchasing department would buy the server but wouldn't notify the data center team when the hardware would physically arrived at the loading dock. The data center team, caught by surprise, had no plan, no space, and no resources ready. The solution wasn't a sophisticated AI orchestration agent. It was a simple rule: the purchasing team had to email the data center team when they placed the order. That one change cut the delay dramatically. No AI required.

Your Actionable Takeaway: Grab a whiteboard or a simple spreadsheet. Pick one process in your organization that everyone complains about. It could be customer onboarding, fulfilling an order, or even hiring a new employee. Map out every single step. Who touches it? Where does it get handed off? Where do delays happen? This "friction map" is your treasure map. On it, you will find the X's that mark the spot for real, valuable improvements—and only then should you ask if AI is the right tool for the job.

The "Last Mile" Is the Only Mile That Matters.

Let's say you've identified a real problem. Now what? The temptation is to find the most powerful, all-encompassing AI model. But as marketing consultant Dennis Sissan pointed out, 80% of the return on investment in AI is found in solving the "last mile problem." This is the final, crucial step where the technology directly helps an end-user with a specific, daily task. A brilliant AI that can analyze the entire global economy is useless to a salesperson if it can't answer their simple, recurring question: "Based on today's events, which three of my clients should I call first?"

Big, generic AI platforms often fail here. They are powerful but not specific. They can't always integrate into the unique "ways of working" that define a company's culture and success. The real wins come from small, customized agents or tools that are designed to solve a handful of high-frequency problems for your team. Think less about a Swiss Army knife and more about a perfectly crafted scalpel. It's about delivering a precise solution to a precise pain point, making someone's job tangibly easier, faster, or more effective.

Your Actionable Takeaway: Go talk to the people who would actually use the AI tool you're imagining. Don't ask them what they want from an AI. Ask them:

1. What is the most repetitive, annoying task you have to do every day?

2. What is one piece of information that, if you had it instantly, would make your job ten times easier?

3. What is the question you have to ask other people most often? The answers to these questions are your "last mile" opportunities. Solving one of them is worth more than a hundred flashy but impractical AI features.

Your Data Is a Mess, and That's the Real Starting Line.

When asked about the hardest part of any AI project, Jeff Pineer of Phoenix Contact didn't hesitate: "The data." It's a universal truth that implementers everywhere are discovering. You can have the most brilliant idea for an AI application, but if your data is a mess, you're dead in the water. The reality for most organizations is that their data is "corrupted, there's bad data, there's missing data, there's gaps in the data."

This is where most aspiring AI projects die a quiet death. But the experts at the symposium offered a crucial reframing of this challenge. Stop thinking of data cleaning as a frustrating chore you have to get through before the "real work" begins. Data collection and cleaning is the real work. It's the foundational first phase of any successful AI initiative. Building a clean, reliable dataset around a specific business problem is a massive accomplishment in itself, one that will pay dividends for years to come, whether you use it for AI, business intelligence, or simple reporting.

Your Actionable Takeaway: Don't try to boil the ocean. Pick one question from your "last mile" discovery (e.g., "What are the top reasons customers call support?"). Now, start a project with the sole goal of creating a small, clean dataset that can answer that one question. You might be pulling from call transcripts, support tickets, or customer emails. Use simple tools like Excel or Google Sheets. The goal isn't to build a model; the goal is to build the fuel for the model. Once you have clean fuel, building the engine is exponentially easier.

Winning the People War: How to Defeat the 72% Failure Rate.

Technology doesn't fail; projects do. And they fail because of people. The fear of job loss, the resistance to changing a comfortable routine, the friction of learning a new system—this is the human element that that 72% failure rate is built on. The speakers at the symposium were clear: you cannot ignore this. You must make it central to your strategy.

So how do you win this war? First, by disarming the primary fear. David Sugars from the WebstaurantStore emphasized that his company is "100% focused on using AI in a way that aids our customers, aids our employees... It's not being used as a human replacement." This message has to be clear, consistent, and authentic. Frame every AI project as a tool to remove drudgery and free up humans to do the creative, strategic work they are best at.

Second, you manage the pace of change. Don Demcsak, drawing from agile principles, warned against both moving too fast ("people just can't handle it, they get burnt out") and too slow ("people feel as though that change isn’t happening"). The sweet spot? An 8-12 week cycle. Introduce a small change, let the team adapt for two to three months, gather feedback, and then iterate. This approach makes change feel manageable rather than like a chaotic, overwhelming revolution.

Your Actionable Takeaway: Forget top-down mandates. Find a small, respected team and invite them to co-create a 90-day AI experiment. Frame it as a low-stakes trial. Say, "We have a hypothesis that this tool could save you five hours a week on reporting. Let's test it for 12 weeks. You are in control. Your feedback will determine if we continue." By giving them ownership, you transform them from potential resistors into engaged partners.

You've Deployed It. Congratulations, You're Now a Babysitter.

Perhaps the most dangerous myth in technology is that of the "fire and forget" solution. You build it, you deploy it, and you walk away as it hums along perfectly forever. This is a fantasy. The reality, as David Sugars noted, is that AI is "definitely not set it and forget it." An AI agent is less like a piece of software and more like a new employee. It needs goals. It needs oversight. It needs to be evaluated for performance and held accountable.

The AI landscape is also changing at a dizzying speed. The model that was state-of-the-art when you launched your project might be obsolete six months later. Part of maintaining your AI solution is keeping an eye on the horizon and being prepared to "start using this a different model to improve and make sure that it's catching up." This requires ongoing engagement and a commitment to continuous improvement.

Your Actionable Takeaway: For any AI tool you implement, create a simple "AI Performance Review" document. Schedule a recurring 30-minute meeting every month or quarter to review it. Your review should answer four questions:

1. ROI Check: Is this tool still saving the time/money or generating the value we expected?

2. Accuracy Check: How often is it right? How often is it wrong? Are its "hallucinations" causing problems?

3. User Feedback: What do the people using it actually think? Is it helping or hindering them?

4. Tech Check: Are there new, better, or cheaper models available now that could do this job more effectively? This simple act of governance turns your AI from a potential liability into a managed, evolving asset.

The Digizen Advantage

The lessons from the Lancaster AI Symposium paint a clear picture. The path to AI success isn't paved with complex algorithms and massive datasets alone. It's built on a foundation of solid business practices: understanding your processes, solving real human problems, cultivating clean data, leading your people through change with empathy, and maintaining what you build.

For years, we've been told that being outside a major tech hub is a disadvantage. This story proves the opposite. Digizens, you are closer to the ground. You are embedded in the real-world businesses that need practical, valuable solutions, not just hype. You have a native understanding of the processes and people that make a company tick. The future of applied AI won't be won by those with the biggest servers in Silicon Valley, but by the implementers with the deepest understanding of their business and their people. And that, Digizens, is a home game for you.

Stay grounded, stay curious, and keep implementing.

Here is the full video:

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