The AI Intervention: From Data Audit to Education

You’ve heard it, read it, maybe even said it dozens of times.

AI will either transform your brand or destroy it.

But whatever you do with AI, you have to act now or suffer the consequences!

In reality, most brands are nowhere near ready to introduce AI. First, they need to ask and answer: Which AI? Because not all AI tools are relevant to all problems. Second, they need to decide what challenge they’re trying to solve. And third, and this is the big one, they need to ask, “Where does our first-party customer data live?” I’ve seen this one result in panic. One team member says Salesforce; another says a data lake; another says the CDP. And someone says it’s all in a spreadsheet that was maintained by a former contractor — and it hasn’t been updated since the contract ended in 2021.

They aren’t ready for AI. What they are ready for is a long and tedious audit. Because what they have isn’t really a data infrastructure; it’s a collection of inherited systems that were never designed to work together.

But the state of their data is rarely the only roadblock these brands face. We commonly see the following issues limiting the success of their AI projects:

Lack of appropriate staff and training

It’s impossible to create an AI project that delivers hyper-personalization at scale with a single data engineer. Even well-staffed organizations need to ensure their people are trained on the tools and models.

Too many tools, not enough strategy

As hyped-up tools enter the market, it’s easy to get swept up in the fever and start buying. Without a strategy, clean data, a connected infrastructure, training and governance, these tools will not deliver a measurable ROI.

Poorly designed use case

A lot of pilot programs stall without measurable results. Some use cases just aren’t a good fit. They’re difficult to track and measure, or they don’t address a relevant problem. It’s impossible to test the value of AI on a use case that resolves nothing and creates no business value.

What AI Actually Is

Part of the alignment problem is that these brands aren’t quite sure what AI is (and what it isn’t).

AI is not a strategy.

AI is not a rebrand.

AI is a set of tools that take inputs, find patterns and produce outputs. The quality of those outputs is a direct, unforgiving reflection of your inputs. There is no version of AI in which you feed a large language model chaotic, siloed, unlabeled, unconsented customer data, and it comes back with a masterpiece.

AI will reflect your mess at you, at scale – unless you first implement an…

AI Intervention

Do the following, in this order, to get your team, stack and data ready for AI.

1. Do a real data audit

Not a presentation about doing a data audit. An actual data audit. Where does your data live, who owns it, what does it say, what kind of permissions are involved and how many spreadsheets are we talking about? (There are always spreadsheets.)

2. Define everything

What does the word customer mean to your organization? What does the word conversion mean? If these terms mean different things to different people (and they do), your AI system will inherit that confusion.

3. Connect your data infrastructure

Make sure your data is connected before you connect it to any AI tools. The CDP, the CRM, the analytics platform and the ad tech stack all need to talk to each other before any of them talk to an AI model.

4. Pick a single, valuable use case

Not twelve. And not a lot of “this one might be interesting” use cases. Pick a single use case that is genuinely high value, properly resourced and achievable in the next quarter. Once you prove the model works, you can scale.

5. Build your governance framework

And do so before you go live. Who reviews model outputs? Who owns errors? What happens when the AI confidently says something wrong to a customer at two in the morning on a Saturday? Have an answer before that happens.

6. Educate your team

Everyone who will touch the system, brief the system, interpret the system or sell the system’s outputs must understand what the system is. “The AI decided” is not a strategy. It is an abdication of accountability.

Now you’re likely thinking, “But won’t all this take too long? We can’t afford to be left behind.”

No. The brands that will thrive in an AI-enabled future won’t be the ones that launched the most pilots.

The brands that will win are the ones that are doing the boring, invisible, deeply un-press-release-worthy work of building their foundations. They’re cleaning their data. They’re educating their team. They’re locking IT and Marketing in the same room until they agree on something. They’re writing and implementing governance. They’re picking their single best, most impactful use case and making it work.

Get AI-Ready with Material

With AI, the competitive advantage isn’t about being first. It’s about getting it right. And getting it right requires a foundation. Don’t publish a press release about your penthouse before you’ve poured that foundation. The path to AI is unglamorous, and it takes time, but it’s the only route that produces measurable and sustainable value.

This is the work we do with clients. Material can help you build that foundation; we can clean and connect your data, get your pilot off the ground and make your efforts scalable. If you’re ready to get started, reach out. We’ll bring the map, you bring the spreadsheets; we’ll sort it out together.