Quick answer: Before you consider an AI tool, know the specific business goals you’re addressing, know which use cases actually fit those priorities, and prioritize for impact and feasibility, not just time saved.

Leadership’s Gap Between AI Ambition and Execution

Let’s start with the uncomfortable part: 70% of CMOs call AI leadership a critical goal for 2026. At the same time, an equal 70% admit their internal AI processes are still immature.

That’s not a coincidence. That’s what happens when ambition gets ahead of strategy. Everyone knows AI matters. Fewer have actually been able to figure out where it matters most for their specific business, in what order, and why. But if you’re there, you’re still early in the game and there’s a way forward. And, if you’re just getting started, here’s the path forward.

Start With the Goal, Not the Tool

If you can’t connect an AI initiative to a real end goal, you have an AI project without a real purpose. Before considering any tool, get specific: what business or customer priority or challenge are you trying to address?

Identify concrete goals or challenges: response times on renewal inquiries are too slow. Content production can’t keep pace with campaign volume. Customer feedback is getting lost in support tickets and we need to synthesize it quickly.

Know Your Use Cases, Not Just Priorities

The next step is identifying the actual use cases involved in addressing the priority. Marketers report being most involved in content marketing (79%), social media (65%), email (64%), analytics (62%), and advertising (58%), with SEO, comms/PR, and customer experience close behind. Chances are, your use cases can be found in these buckets.

And when marketers are asked what AI trend will matter most over the next year, the answers cluster around a few categories: AI agents and autonomous workflows (27%), generative content across text, image, and video (17%), and predictive analytics and data insights (7%), with AI-powered search and SEO close behind.

Translate that into your own priorities, and you’ll usually land on one of these:

• Speed: content, campaigns, or reporting that take too long
• Scale: personalization or outreach beyond what your team can hand-craft
• Insight: data you already have, but no time to turn into a decision or action
• Visibility: do AI search and answer engines cite and represent you accurately

List your specific use cases that map to your priorities. That’s the real output of this step.

Prioritize for Outcomes and Feasibility, Not Just Efficiency

81% of marketing leaders using AI still evaluate its effectiveness based on time saved.

That’s an easy trap, because time saved is simple to measure but the far better move is to prioritize AI initiatives that impact revenue growth and strategic decision-making, not just tasks that get done faster. A tool that saves your team three hours a week on reporting is nice. Automating a use case that improves conversion, shortens a sales cycle, or catches churn risk earlier delivers a much better business outcome.

Next, you’ll need to prioritize your use cases by assessing how feasible it is to automate each one. Some you won’t be able to, at least not fully, like for instance the final review of new positioning during a rebrand.

You’ll end up with a list of prioritized use cases that will deliver the most business impact that can also be automated intelligently.

Keep a Human in the Loop

As your AI use cases scale and get more autonomous, you’ll need to decide early what parts stays human-owned and what gets automated. The safest default is human-in-the-loop: a person signs off on brand voice, assets, ABM segments, customer outreach emails and any decision with real consequences, and, as trust in the system is earned you can loosen that oversight deliberately.

This isn’t about mistrusting AI. It’s about training the AI and knowing, before you scale, how much oversight each use case actually needs, and ensuring your team applies it consistently. There should always be some level of human oversight.

Coming Next

Once you’ve finalized your list of priority use cases for AI, the next question is whether your organization is set up to execute on them. In our next blog we’ll share how to map the capability, data, compliance, and talent gaps that can sink AI initiatives before they start.

Want help building an AI-forward marketing strategy that starts with your priorities? Let’s talk.

Next up: Step 2, Mind the Gaps

Data sourced from Gartner’s Q3 2026 CMO Report and the 2025 State of Marketing AI Report.