I’m hearing a familiar theme from a lot of our customers. Marketing teams feel pressured by leadership to use AI, but they have no idea where to start, how to show progress, or how to know if their AI tools have a positive impact. That’s not an AI problem. It’s a methodology problem.
AI in some form has been in use since the 1940s. It’s why no matter what playlist I listen to on Spotify, I’ll eventually hear Mazzy Star’s “Fade into You,” and why you don’t know that I’m not a great speller. AI is data. That’s it. What most teams are missing isn’t just access to better tools, but rather a repeatable process for deploying them.
Three ways organizations respond to AI — only one leads to ROI
Some teams ban AI entirely, but that’s a fast lane to falling behind. Others go full wild west adopting every tool for every role with no guardrails. The third group builds deliberately, with a defined process and clear success criteria. Those are the teams that see returns. At DCG ONE, we apply the same data-led methodology to our AI program that we use in our analytics practice. Novelty is not the goal. Measurable impact is. Here’s the framework I share with clients who ask where to start.
Start with a goal, not a tool
Before evaluating anything, name the one thing you most need to achieve. Common goals I hear include lower cost per acquisition, generate content at scale, improve brand compliance, and create more actionable reporting. Resist the temptation to pick a shiny tool and make up a problem for it to solve. Sometimes the right solution isn’t even AI, and that’s a completely legitimate outcome.
Define success in measurable terms before you build anything
With a goal in hand, research what can actually get you there. That might be a custom agent, an out-of-the-box platform, or a partner already building the right toolset (ahem). Then write a hypothesis, which includes one primary metric tied to your goal. “If Kristen builds me an agent to automate this data cleaning, it will save me 16 hours a week and $20K in therapy bills” is a hypothesis. “I feel like this will help” is not.
Why the testing phase is where most AI initiatives quietly die
I’ve seen so many people skip this step. One good-looking output is not proof a tool works. Some disbarred lawyers can tell you why blindly trusting an AI output is a problematic approach. Before you go live, you need a repeatable accuracy score, a benchmark or control group (so you don’t accidentally credit a Taylor Swift album drop for your AI’s win), tested guardrails (including what it will say if someone asks it if it’s a Swiftie), fully documented inputs, and success criteria defined before you see the numbers. It’s the boring part. It’s also the difference between a tool that compounds in value and one that gets quietly shut off at the next budget review.
AI tools degrade over time — measurement is how you catch it
Every time someone says “I feel like it isn’t working,” an analyst gets another grey hair. Don’t just feel. Know. Mid-market marketing teams that launch AI tools without a measurement framework almost always end up cutting what they can’t defend. Build a simple one with primary KPIs, metrics from a reliable data source, a defined reporting cadence, and a plan for feeding results back into the tool. Because, AI is data, and that’s what you do with data.
The most important step is just starting
Don’t expect a 50% efficiency improvement on day one. Give yourself and your tools a little grace. It takes time to learn something new. Pick one goal, one tool, one metric. Test it, measure it, iterate. The teams that uncover a material positive impact build toward them systematically, brick by brick.
If you’re not sure where to start, find someone already elbow-deep in this work (AHEM). DCG ONE helps mid-market marketing teams build and measure AI programs using the same rigorous, data-led methodology we apply to every analytics engagement.