by Tyler Kelley
After spending nearly two years warning you about AI’s business impact, I’m changing gears.
This is my 20th column, and frankly, I’m tired of theory.
If you’re still just “thinking about” generative AI while your competitors are building custom GPTs and deploying AI agents, you’re already behind.
The generative AI revolution isn’t coming. It landed, unpacked, and is currently redecorating your industry while you’re still reading whitepapers.
So starting now, this column is pivoting hard from predictions to practical applications.
No more philosophical debates about AI’s future impact. We’re talking implementation, i.e. specific tools, techniques, and tactics you can deploy this quarter.
Here are five concrete ways to start enabling generative AI across your ecosystem:
1. Build Custom GPT Libraries
Stop using generic AI.
Every organization should be developing custom GPTs tailored to their specific needs.
The difference between generic ChatGPT and a finely-tuned custom GPT is like comparing a Swiss Army knife to a surgical instrument.
What does this look like in practice? A collection of purpose-built GPTs for different functions – one for generating marketing copy that matches your brand voice, another for analyzing financial data with your metrics, a third for customer service that knows your policies.
Then share these custom GPTs across your network to multiply their impact.
2. Deploy AI Agents for Repetitive Tasks
While everyone’s obsessing over chatbots, the real revolution will happen with autonomous AI agents.
These systems don’t just respond. They take action. They monitor, analyze, decide, and execute without constant human guidance.
Start with something simple: An agent that monitors your competitor’s pricing and automatically adjusts yours based on parameters you set. Or one that scans incoming customer inquiries, categorizes them, drafts responses, and routes them to the right department.
3. Implement Prompt Engineering Boot Camps
Effective prompting is the new SQL. It is a fundamental skill your team needs yesterday.
Host intensive two-day boot camps where participants bring actual work challenges and learn to craft sophisticated prompts that deliver results.
Cover advanced techniques that turn mediocre results into exceptional ones. Then create a shared repository of proven prompts that everyone can access and improve.
Remember: Small improvements in how you talk to AI yield massive differences in what you get back.
4. Create AI Output Testing Frameworks
Generic AI evaluation won’t cut it anymore.
You need testing frameworks specific to your use cases and standards. Develop rubrics for systematically evaluating AI outputs against your quality benchmarks
.
What percentage of AI-generated email responses need human editing? How often does your custom GPT provide information that aligns with company policy?
5. Establish Cross-Functional AI Teams
Siloed AI initiatives fail. Period.
Form teams that cut across IT, operations, legal, and business units to rapidly prototype AI applications.
Give these teams actual decision-making authority and concrete goals: “Reduce customer service response time by 40% using generative AI by end of quarter” or “Implement three autonomous AI agents that each save 20+ hours of staff time weekly.”
The Next Chapter
In the coming months, I’ll break down exactly how to implement these strategies with practical examples anyone can follow, regardless of technical background.
The warnings are over. Either you’re building your AI future, or you’re becoming obsolete. Which side of that divide do you want to be on?
Tyler Kelley is the Co-founder and Chief Strategist of SLAM! Agency, a marketing execution and creative operations agency. He advises businesses on leveraging AI to drive growth and innovation. For questions or to explore these predictions further, email Tyler at tyler@slamagency.com.