From AI Hype to Meaningful Marketing Results: Start With These Three Key Changes

As a journalist who frequently meets with marketers in small groups called "Marketing Therapy," I've noticed that lately, everyone wants to talk about AI. While it's great to see people pushing themselves to try new things and learn while in motion, the motivation behind their AI adoption seems to be driven by fear of falling behind rather than confidence in its opportunities.

The Gap Between AI Adoption and Value

Most members of my group can't articulate their AI strategy, instead reacting to demands for "more AI" or feeling like others are doing more with it. This has resulted in a lot of "spaghetti method" use of AI that might look innovative but doesn't actually move the needle.

Sun Tzu once said, "Tactics without strategy is the noise before defeat." I've fallen into this trap myself, and there isn't one path to AI-adoption maturity that works for every marketing leader. However, I can tell you how to find a way for AI to work for you without spending time spinning your wheels.

The first step is to slow down and think critically about where AI fits into your goals. Don't rush into using an LLM or any other AI tool without a plan in place. Take the time to identify what success looks like, map out your current processes and goals, and determine which AI tools will help you achieve those results.

Three Key Changes for Meaningful Marketing Results

To generate true impact with AI in marketing, you need to make three fundamental changes:

1. **The AI Road Map Starts With Your Goals**

When it comes to AI adoption, many marketers start by identifying pain points and desired business outcomes rather than starting with their goals. This is a big mistake.

Before inserting AI into any stage of your process, you need to know what success looks like. This enables you to map out your current processes and goals to the best AI tools for the results you want to achieve.

Here are two examples of realistic goals that can help build momentum and confidence in your AI-adoption journey:

* Mapping a complete AI campaign lifecycle integration * Treat AI as a collaborator, not just a tool

The first goal involves integrating AI across the entire campaign lifecycle, not in isolated pockets. To do this, you'll need to use multiple types of AI and not rely on one solution that fixes all your problems.

Take GroupM, for example. The company noticed bid and budget inefficiencies across many client campaigns but couldn't solve them with just one solution. Instead, it used machine learning to analyze customer behavior, uncover trends, and segment audiences more effectively. With those insights, the team used optimization AI to fine-tune media spend in real-time, maximizing each client's budget and driving stronger engagement.

Once the entire process is dialed in, agentic AI can act on some of the steps without needing manual intervention.

2. **Treat AI as a Collaborator, Not a Speedy Automator**

The true value realized from AI comes when it makes us sharper, more creative, and more strategic, not just more efficient.

Don't think of your AI (especially LLMs) as tools you use but as partners you collaborate with.

Imagine telling ChatGPT that your brand's Meta ads are underperforming. Give the AI a data set showing where metrics are falling flat, ask for how to troubleshoot the issue, and get a response. This might not be correct or what you need.

However, when you engage in conversation with AI, giving it context on targeting techniques, creatives in rotation, and past campaigns that have been winners, you work through the problem together. AI identifies the root of the problem – creative fatigue – and goes into action to generate new creative to A/B-test and see what resonates.

A hack I like to use is the Socratic method: tell AI not to give you the answer but ask questions that will help you uncover your own answer.

You're still in the driver's seat, and I guarantee you'll walk away with better insights.

3. **Move From Reactive to Proactive**

For years, AI in marketing was equated with glorified automation. This mindset has led marketers to select AI tools based on how much they can fix problems rather than capitalizing on opportunities.

To win, brands need to focus on anticipating and pre-empting changes that would otherwise leave them flat-footed. AI tools use probabilistic and causative models trained on marketing-specific data, making them well-suited for this approach.

You can't design a playbook for every event, but you can create a system that learns in real-time and constantly predicts new likely outcomes to drive a proactive marketing strategy primed for resilience.

The Shift From AI-Ready to AI-Native

I'm often asked by digital marketers how they can realize the value of AI quickly. It's possible, but speed without strategy only leads to failure.

AI should be integrated into your marketing efforts in a way that feels organic and natural, not forced or tacked on for the sake of it.

Remember, treating AI adoption as a living experiment allows you to learn along the way and move closer to your objectives. Focus on achieving outcomes today and learning from them so you can tackle more ambitious goals tomorrow.

By adopting these three key changes, you'll be well on your way to harnessing the power of AI in marketing and achieving meaningful results that drive real business impact.

More Resources

* "Human-Ready Marketing": The Power of Human-AI Synergy * Why Marketers Shouldn't Wait for the Perfect AI: Lessons From Apple's Delay * AI Belongs in Your Marketing Toolkit, but Save Space for Humanity, Too