YOUR BUSINESS AUTHORITY
Springfield, MO
YOUR BUSINESS AUTHORITY
Springfield, MO
Jonathan Hodges is the Vice President of Technology at Classy Llama, bringing over 15 years of experience in web development, software engineering, and leadership within the ecommerce and technology sectors. With a strong foundation in mathematics and computer science from Evangel University, Jonathan has a proven track record of forming and leading teams, implementing Agile methodologies, and optimizing business operations to deliver impactful results.
Jonathan has held several key roles at Classy Llama, including Director of Development and Chief Operating Officer, where he demonstrated expertise in technical services, key performance indicators, and collaborative problem-solving. His skill set spans web applications, PHP, JavaScript, AI tooling, and product strategy, bolstered by certifications like the Magento Developer Certification Plus and Databricks Lakehouse Fundamentals.
A dedicated leader and innovative thinker, Jonathan is passionate about leveraging emerging technology to create value-driven solutions, improve operational efficiency, and foster community engagement in the tech industry.
I've spent most of my career building custom software, so you'd probably expect me to say build. My default is still buy, and buy almost every time.
The test I use is pretty simple. If the job you want AI to do is something every business does, like answering email, scheduling, or chasing invoices, somebody has already built a tool for it and priced it at a fraction of what custom would cost. Buy it, and be willing to bend your process a little to fit the tool. In my experience most of the "we do it differently" in those areas is habit, and it isn't worth paying to preserve.
Build when the thing you want automated is the reason customers pick you over the shop down the road. Maybe it's how you quote a job, or the way you decide which leads get a call back. Nobody sells that off the shelf, because nobody else does it your way, and if you try to buy it you'll spend months fighting a tool that was designed around somebody else's business.
The cost side has changed. With AI writing most of the code, a focused build that would have cost six figures a few years ago now lands closer to half or better. That lowers the bar for building. What hasn't changed is that you have to keep custom software alive once you own it: hosting, updates, security patches, and somebody who understands it when it breaks. Budget for that before you build.
A practical way to settle it: try to buy first. Spend two weeks with a couple of off-the-shelf tools and see where they rub. If you're fighting the tool over the part of the work customers actually pay you for, that's your build signal. Often that ends up as a hybrid: keep the tool for the generic parts and build only the one piece it can't do. If you're fighting it over something generic, change the process instead. Most businesses I talk to end up buying, and that's generally the right answer.
Every morning AI goes through my inbox before I do. It reads each thread all the way through, files the receipts and newsletters, and for the repetitive stuff (scheduling, pricing questions, the follow-up I've answered some version of twenty times) it leaves a draft reply sitting in my drafts folder, written pretty close to how I'd write it myself.
Then I do my part. I read each draft, fix what's off, and hit send. Nothing goes out without me looking at it. Every so often a draft misses, maybe it offers a meeting time I don't actually have open, and catching that is still my job. Most mornings the whole inbox takes ten minutes of my time instead of the hour it used to.
You don't need my setup to get most of this. Take the five emails you answer the same way every week, paste a couple of your real past replies into a tool like Claude or ChatGPT, and ask it to draft the next one. Give it actual sent messages, since it picks up your voice from examples far better than from you describing it. In my experience the drafts get usable within 1-2 weeks of correcting them. Two cautions. Keep sensitive information out of consumer AI tools: customer financials, health info, and anything else you wouldn't want leaving the building. Business-grade accounts handle privacy a lot better, and it's worth an hour to get those settings right. And don't let it send on its own. The drafting is where the time savings are anyway, and I'd rather spend thirty seconds reviewing than have to explain an email I never read. Email turns out to be a pretty good first test of AI in general, since you already know what a good reply looks like and can check its work in seconds. Most people I've walked through this see the payoff in the first week.
Recently I sat down with a folder of recorded sales calls from a local business here in southwest Missouri. The owner wanted to know why some customers stay and others leave. The old way to answer that is somebody listening to hours of calls, taking notes, and burning a week they didn't have. Instead, I handed the folder to AI and told it what I was after. It transcribed every call, went looking for patterns, hit a few dead ends, backed up and tried different angles, and checked its conclusions against the calls before showing me anything.
When I came back, a finished analysis was waiting. The customers who stayed weren't the ones whose rep followed the script best. They were the ones whose rep sounded like they actually cared. That's something the owner can coach to and hire for. This kind of AI is called an agent.
Instead of answering one question and stopping, an agent keeps working toward a goal: it makes a plan, uses tools, tests its own progress, and gets itself unstuck along the way. If you've tried AI and gotten nothing like this, there's a reason. Two things drive the quality of the result, and most people only know about one of them.
The first is the model, the AI brain itself. The second is the harness, the tool wrapped around that brain that lets it read your files, run software, and check its own work. The same question asked in a free chatbot and in a good harness will give you night-and-day different results.
For the analysis above, my setup was Anthropic's Claude Opus model running inside Claude Code, a developer tool. That combination is the best I've used. If you're not technical, Claude Cowork gives you the same kind of power without the setup: point it at a folder of documents on your computer, give it instructions and permission to work, and check back when it's done. The models keep improving too. Anthropic's newest,Fable, runs longer, gets itself unstuck better, and keeps testing its work as it goes.
OpenAI and Google both have capable models and agent tools as well, and they're worth trying if you're already in those ecosystems. Claude with a good harness is simply what works best for me right now. My advice: give AI one real project this month. A stack of customer feedback, a messy spreadsheet, a pile of old proposals. Spell out what "done" looks like. Then review the work like you'd review a new hire's first project, because it can still take a confident wrong turn, and catching that is still your job. Most business owners I meet aren't overusing AI. They're asking it for far too little.
AI is moving fast, and it is easy to feel behind. The key is combining broad awareness with a few focused deep dives and ongoing usage.
Use newsletters like The Rundown AI, TLDR AI, and Bay Area Times. Spend about 10 minutes a day scanning for what is worth exploring.
Then go deeper 1 to 4 times a week with videos, articles, or blog posts that matter to you.
Finally, get hands-on. Try tools as you learn, follow along, and stay curious. This often pays for itself in time saved.
The key is consistency. Keep learning and stay intentional.
While ROI can vary, I’ve seen two types of initiatives consistently deliver strong returns. First, focused upskilling of interested or high-performing team members. If someone is eager to use AI, teach them how and give them access to the tools they want. You’ll see productivity gains that far outweigh the cost. Second, targeted programs. Look for repetitive or time-consuming work that AI can handle well. Bring in an experienced AI implementer with a clear ROI focus. These programs often pay for themselves within a year or less.
Most people have used ChatGPT or Gemini, but a growing number are discovering Anthropic's Claude, especially for on-computer work. Claude Code took the programming world by storm, and to extend that beyond coding, Anthropic built Claude Cowork. It reads and writes from a folder on your computer, handling Excel sheets, PDFs, Word documents, and more. Drop in your documents, give it instructions, and walk away. It's the force multiplier people have been hoping for. If you haven't tried it, sign up for Claude and give it a spin.
To get real value from AI, curiosity matters most. Experimenting unlocks learning and ideas, creating a cycle of discovery.
Working with AI is like working with a team. Simple questions or shallow delegation produce limited results. Clear communication, strong leadership, and strategic thinking lead to better outcomes.
Instead of asking, “How can I make a lot of money?” try, “What are five businesses I could start that would succeed today? Use research, what you know about me, and interview me to understand my skills.”
Yes! AI is most powerful when it’s deeply integrated into your company’s workflows. Many modern CRMs and ERPs now include native AI capabilities, though their quality varies widely.
I work directly with companies to integrate AI into existing systems, automating workflows in part or end-to-end to reduce slow, manual processes. The most successful integrations start with human oversight and evolve as confidence grows, gradually reducing manual involvement to only the most complex cases.
This installment of Springfield Business Journal’s Architects & Engineers Project Report showcases 26 endeavors by area design and engineering professionals.
