The 4 Key Stages of AI Adoption for your Marketing Team

AI for marketers is what cryptocurrency was to finance in 2017: shiny, promising, but just esoteric enough to keep many people on the sidelines.

Will marketers who fail to adopt AI look back at their decision the same way people look back at their lack of investment in Bitcoin in 2017? Probably. 

While the future of AI may be uncertain (and even slightly intimidating), the real-world use cases that exist are already reshaping marketing—and if you're not actively adopting them, you're falling behind. 

But getting your team on board isn't as simple as sending them the login information for a new tool. Questions and concerns about job security, governance, and strategic control are real. And that’s fine.

Real change won’t happen overnight. It takes intention, a clear strategy, and education.

And most of all, your team needs to know you’re leading them down the right path. That starts by understanding where AI currently fits into your marketing strategy, what it’s truly capable of, and the practical steps you can take together to integrate the tools in a way that sticks.

Let’s break it all down. 

How Marketers View AI Today

Asking marketers what AI means to them is a little like asking someone about abstract art. Everyone might be looking at the same thing, but interpretations vary, in part because AI has found so many use cases so quickly.

So, “AI” means different things to different people. For some, it’s purely for generating creatives. Others use ChatGPT every day, but only for personal things. For others, they associate it with Siri, or Google Search or what’s built into ad platforms like Meta and Google.

Because of the fragmented perspectives, marketers are all over the place when it comes to adoption. I see four basic groups.

1. The Skeptics: They prefer to keep AI at arm’s length due to doubts that AI can truly deliver. They might also have some fears around loss of control or even job security, too.
2. The Onlookers: They’ve heard the AI buzz but don’t see it as relevant to them yet.
3. The Explorers: They know they should be using AI but aren’t sure how.
4. The Evaluators: They’re using AI and embracing its long-term potential.

Where you land in these categories of adoption will depend on your experience and how your company is approaching digital transformation, and each mindset is totally valid.

Everyone deserves time to warm up to new technology, especially something as transformative as AI. I’m here to help marketers peek beyond the first, easiest-to-adopt use-cases and highlight the way I’ve seen AI adoption transform organizations in some unexpected ways.

The pressing question for you is: How do you encourage your teams to adopt it? It’s a journey.

Understanding the AI Adoption Journey  

“By 2005 or so, it will become clear that the Internet’s impact on the economy has been no greater than the fax machine’s.” — Paul Krugman (Economist), 1998. 

Well, that certainly didn’t age well. 

Mainstream adoption took time. It involved trial and error, healthy skepticism, education, and demonstrable value before these tools became the norm. 

AI adoption will follow a similar path. 

Stage 1: Pre-decision (Month 0)

For better or worse, many vendors market AI tools as plug-and-play solutions. You log in, enter a few inputs, and let it run. That’s not the reality. The reality is that adoption should be planned and scaled based on three core essentials:

1. Goals and KPIs: How does success look, and how will you measure it?
2. Reliable Data: Do you have clean, consistent, comprehensive data to generate meaningful outputs? As they say, garbage in, garbage out.
3. Executive Buy-In: Has leadership understood the why and recognized that it's not a magic wand? Long-term adoption will depend on this. 

However, even with the right strategy, adoption relies on something deeper: your team's willingness to learn, make mistakes, and grow.

I talked to Todd about what I’ve seen here and he agreed: "Adoption depends on how teams embrace learning and change. It's a viewpoint on life and how they approach it."

In other words, AI adoption is as much a cultural shift as it is a technical one.

Stage 2: Decision (Month 1)

Goals. Data. Leadership buy-in. 

Check, check, check. 

Now, it's time to start evaluating tools. We know that's exciting, but don't fall into the trap of jumping at the tool with the coolest gradient. Adoption means getting your team to use the right tool that naturally fits into their workflow, not just getting them to use a new tool as quickly as possible.

To do that, focus on a few foundational features:

• Time to Use: Can the AI become part of an existing workflow?
• Security: Is your data (and your customers' data) protected?
• Integrations: Does the AI work with your existing tools or will adding another one make your team roll their eyes?
• Governance: Can you maintain control and oversight?

That last point—governance—is key because, for many marketers, the biggest blocker isn't using the tool itself, but the fear of losing control over the output. That's why it's so important to show them they're still in the driver’s seat..

Our teams guide customers to look at governance in three phases:

1. Observation: Campaigns are tagged, and the AI observes historical and real-time data to determine the best model, like maximizing conversions or reducing customer acquisition cost (CAC), based on your campaign objective.
2. Recommendation: The AI suggests changes with a confidence score, but nothing goes live.
3. Calibration: After alignment with the customers, humans push the changes live, and the AI implements those changes and continues to learn.

Importantly, even though the AI is taking action, there's always human oversight. For example, if Pixis discovers a new high-value audience cohort based on real-time insights and campaign performance data, it won’t automatically add it to your campaigns. Instead, you’ll have the chance to review the recommendation, apply your expertise, and make the final call.

Stage 3: Proof of Concept (Months 2-4)

AI can do a lot, but that doesn't mean you should try to do it all. In the early days of adoption, your smartest move is to pick one focused experiment or use case, lean in, and build trust.

I find myself frequently reminding customers to start small and remember you’re just planning an initial approach. It’ll change as you learn, and that’s just fine.

For example, you can focus on: 

• Budget Allocation: Identify which channels drive the best performance.
• Audience Expansion: Use lookalike modeling to uncover and convert new audiences.
• Creative Optimization: Pinpoint which message or ad format performs the best. 

It's also important to keep an open mind during these experiments because the reality is, not every decision the AI makes will make sense. But the results will compound over time. And that’s the real power of AI. It learns as it goes. It takes what worked and what didn’t, adjusts its approach, and applies those learnings to future campaigns. And it does this repeatedly, so every data point you feed it sharpens the next output.

Stage 4: Adoption (Months 6+)

Then, armed with your insights and initial buy-in, build on the momentum. This might involve exploring new use cases. For instance, shifting from audience targeting to creative optimization or redirecting your focus from budget allocation on Facebook to cross-channel allocation across YouTube, TikTok, and beyond. It may also entail introducing AI to new segments of your marketing team.  

For example, if your Media team was the first to explore the technology, consider how your Creative or Content team could also gain from AI. 

Wherever you choose to go next, adoption is a long-term play. There's no such thing as a "get-rich-quick" scheme. Lasting impact arises from consistent and measurable improvements over time. 

3 Components of a Strong AI Foundation

Rolling out AI to your team is one thing. Making it stick is an entirely different ballgame. To drive durable adoption, you need a strong foundation built on three things: clear goals, agile processes, and intentional automation.

Create Clear, Actionable Goals Tied to Business Outcomes

You wouldn’t frost a cake while it’s baking. You also wouldn't develop your AI strategy while rolling it out.  

To drive meaningful results, you need to be clear on what success looks like. For most teams, their goals fall into one of three buckets:

• Scale without increasing headcount by operating more efficiently
• Reduce customer acquisition cost (CAC)
• Both (the holy grail)

Those goals are a great start, but to build trust in AI in your organization, you’ll need to be even more specific and tie AI to clear, measurable business outcomes. So, instead of “increase efficiency,” say “save X hours a week on bid and budget optimization.”

Then, communicate on progress to the goal frequently. Make it part of a weekly email you send your team, an agenda in your team meeting, give it a home on your dashboard… whatever you need to do to keep it in focus.

That's the kind of clarity and direction that keeps everyone aligned and builds confidence.

Move Fast (But With Intention)

Speed is one of AI's greatest selling points, but speed without direction leads to chaos. Similar to automation, if you scale AI on a poor process, you’re just proliferating poor AI outputs.

The best marketers understand that speed matters only when it is thoughtfully paired with purpose and potential. This means balancing AI's automation with human judgment and acting only when the insights truly help move the needle. 

We help our customers move quickly and strategically by filtering insights and surfacing those that matter, like uncovering lookalike audiences that drive incremental conversion or optimizing budget or bid allocation through day-of-week or hour-of-day analysis.

Focus on What Matters

I mentioned speed as one of AI's biggest selling points. In my eyes, the biggest one isn't what AI can do but what it frees you up to do. It can analyze the data you don’t have time to analyze and will uncover valuable insights you didn’t know you needed (in a fraction of the time).

By automating time-consuming yet essential tasks (ad management, bid and budget allocation, creative asset management, etc.) AI provides your team with the capacity to focus on what drives impact, including strategy, experimentation, and campaign execution.

Todd said it perfectly: "With the time-savings, marketers can focus on higher-value tests and tasks they didn't have time for before. They have the data, the reporting, and the automation to pull it all together, learn from it, and act quickly."

Real Change Takes Time, But That’s the Point

The most successful Marketing teams don't rush into AI or jump at every seller’s email that promises the Earth, moon, and stars.

The best teams take a different approach. They begin with the right mindset, establish clear, measurable goals, and determine where AI fits naturally into their workflows. After that, they scale, but only when it makes sense. 

Because when that foundation is in place, AI does so much more than optimize performance. It unlocks a new ecosystem of tools and capabilities that help teams move faster, make smarter decisions, and strike the perfect balance between long-term gains and immediate results. 

Want to explore how Pixis can improve performance while making your team more efficient? Schedule a demo today.