What we learned at our latest Outdoor Speaker Series

The July heat forced Mindgrub’s Outdoor Speaker Series inside, but the conversation that followed burned hotter than the Baltimore summer. Marketing and tech leaders packed the room to tackle the question keeping everyone awake: How do you actually make AI work for your team?

What happened next cut through the usual conference fluff. Three professionals who’ve been in the AI trenches for years shared the real story, including the failures, the breakthroughs, and the hard-earned lessons that separate successful AI adoption from expensive experiments.

Todd Marks, Mindgrub’s CEO, brought together a panel that knew their stuff:

Keisha Clarke-English runs project management at Exelon and leads the American Marketing Association’s Baltimore chapter. She’s spent two years rolling out AI tools across an enterprise with thousands of employees—and lived to tell about it.

Shashi Bellamkonda analyzes technology decisions for C-level executives at Info-Tech Research Group. He started working with AI seven years ago, back when people called it machine learning and ChatGPT was just a twinkle in OpenAI’s eye.

Branddy Spence directs marketing at Mindgrub, where she’s turned a small team into a content-creation machine using AI tools that actually work. She’s been marketing AI solutions since before ChatGPT, so she knows what works versus what just sounds impressive.

These weren’t theoretical discussions. Every insight came from real-world application.

Stop Chasing Shiny Objects, Start Solving Real Problems

Clarke-English dropped the first truth bomb: “Everyone wants to say they’re using AI, but just because a tool uses AI doesn’t mean it’s right for your team.”

At Exelon, they’ve mastered the art of saying no. Every AI tool faces a myriad of questions before it gets budget approval. Does it solve a specific problem we can measure? Will it work with our existing systems? Can we track its impact on productivity?

“You need to know what problem you’re solving,” Clarke-English said, “or you’ll just end up with digital clutter.”

Spence runs her own version of AI bootcamp at Mindgrub. “We’re constantly testing, but if something doesn’t actually make our work better, we don’t keep it.” Her team treats AI tools like startup experiments—quick tests, clear success metrics, and brutal elimination of anything that doesn’t deliver.

The result? Spence built a custom GPT that handles content rewriting with her company’s exact voice and style guidelines baked in. What used to take hours now takes minutes, and the quality stays consistent across her entire team.

“It’s not about adopting AI for the sake of it,” she explained. “It’s about saving time, boosting quality, or simplifying complexity. If it doesn’t check those boxes, we move on.”

Building Buy-In Without the Buzzwords

Here’s where most AI initiatives crash and burn: People hate being told their jobs are about to change, especially by tools they don’t understand.

Bellamkonda has watched this movie too many times. “It’s not enough to say ‘this is cool.’ You have to show how it connects to what people care about: saving time, hitting goals, reducing stress.”

He’s developed a translation strategy that works. Finance teams hear about cost savings and efficiency gains. Creative departments see enhanced output and expanded possibilities. Operations teams focus on process improvements and fewer errors.

“When people can see how it makes their job easier, adoption follows,” Bellamkonda said.

Spence took a different approach at Mindgrub. Instead of mandating AI use, she created what she calls “AI infrastructure” with guides, checklists, and FAQs that make tools approachable for everyone, not just the tech-savvy early adopters.

“We create a lot of internal documentation,” she said. “It helps people use tools consistently and actually trust the process.”

The takeaway: People need to feel prepared, not replaced.

Learning Out Loud Beats Perfect Planning

All three panelists agreed on one thing: You don’t need a master plan to start making progress.

“There’s no one-size-fits-all roadmap,” Clarke-English said. “But there’s a lot of power in learning out loud, staying curious, and involving people early.”

At Exelon, they run pilots on everything. Got an idea for an AI tool? Pitch it to the innovation forum. If it gets funding, they test it small before rolling it out wide. Their chatbot started as an experiment last August. Now it gets smarter every day, helping employees find information faster than ever.

Spence shared a similar story about Mindgrub’s ambassador program. Instead of the CEO telling everyone to use AI, they let peers teach peers. The reception was completely different.

“If it’s peer-to-peer, it works,” she said. “If it’s top down, not so much.”

What Actually Works: Four Rules for Marketing Teams

The panel’s collective scars taught them these principles:

Start with real needs, not cool demos. Don’t chase tools. Find the gaps in your current workflows and work backward from there.

Get people involved early. The more buy-in you build upfront, the smoother your rollout becomes. Resistance kills more AI projects than technical problems.

Test fast and document everything. Short pilots plus clear guides build confidence and consistency. People need proof that new tools improve their work.

Keep expectations grounded. AI enhances your work, not replaces your brain. The best implementations treat it like a really smart assistant.

The Tools That Actually Matter

Beyond the philosophy, the panelists shared specific tools that changed how they work:

Branddy’s Custom GPT: She built an “expert rewriter” that knows Mindgrub’s voice inside and out. Feed it any content, and it rewrites it to match their brand guidelines perfectly.

Keisha’s Voice Revolution: Exelon invested in WellSaid Labs for AI voiceovers. What used to require studio time and talent booking now happens in 30 seconds. They’ve created commercials, training videos, and tutorials at a fraction of the old cost.

Shashi’s Research Secret: He swears by Notebook LM for digesting complex information. Upload documents, and it creates podcast-style conversations that make dense material digestible.

The pattern? These tools solve specific, measurable problems instead of trying to do everything.

Why This Matters Right Now

Clarke-English put it best: “AI can enhance your work, but it’s not a magic fix. Keep goals people-centered and results-focused.”

The companies winning with AI share three traits: They experiment without fear, they support their people through change, and they measure everything that matters.

The losers? They either avoid AI completely or chase every new tool without strategy.

Spence summed it up with a reality check that closed the night: “You can lead the horse to water, but you can’t make them drink. Those that drink are going to thrive. The writing is on the wall.”

Events like this remind us that the future of marketing is being shaped by people willing to share both their wins and their missteps. The AI shift is underway. The question is: Will your team be ready?

Watch the full event recording here for the complete conversation, including audience Q&A and additional tool recommendations that didn’t make it into this recap.