Almost every marketing leader now has an AI goal. Far fewer have an operating model that can deliver it. Gartner's 2026 CMO Spend Survey found that 70% of CMOs consider becoming an AI leader a critical goal for 2026, while a similar share say their internal marketing processes are not yet mature enough to implement and scale AI. The same survey found that 56% of CMOs say their organization lacks the budget to deliver its 2026 strategy.
That gap is rarely closed by another tool. It is closed by a handful of decisions about how the marketing function works. These are the five we see matter most.
1. Decide what AI is for, in business terms
"Use more AI" is not a goal. Pick two or three outcomes the business already cares about, such as shorter time from lead to first meeting, lower cost per qualified lead, or faster campaign launches, and decide where AI should move those numbers. Every pilot should then be judged against one of them.
This also gives the CFO a clear basis for funding: money follows outcomes, not experiments.
2. Fix the data the work depends on
AI systems inherit the quality of the data they use. If lead sources are recorded inconsistently, if the CRM and the ad platforms disagree on conversions, or if nobody owns the customer data model, AI will make those problems faster rather than solving them.
The practical step is an audit of the three or four data flows your priority outcomes depend on, with a named owner for each. It is unglamorous work, and it is usually where the return comes from.
3. Redesign roles around review, not production
When AI takes over first drafts, routine analysis and repetitive operations, the scarce skills shift. Teams need fewer people producing raw output and more people who can brief well, judge quality, check facts and make decisions with the results.
That has consequences for hiring and for agency relationships. Ask whether your team structure still assumes humans do the first draft of everything, and whether your agencies are paid for hours of production or for outcomes.
4. Put approval rules in writing
AI makes it easy to publish, send and change things at scale. Without written rules, it is also easy for unreviewed content, inaccurate claims or off-brand messages to reach customers. Every AI-assisted workflow needs a clear line between what can happen automatically and what needs a named person to approve it, especially anything that is published, spent or sent to a customer.
Content is the most common failure point. Volume is cheap, so teams publish many near-identical articles that compete with each other in search and repeat the same unverified claims. A single editorial owner with the authority to stop publication prevents most of this.
5. Fund foundations and pilots separately
Gartner's survey found that AI-ready organizations allocate 21.3% of their marketing budgets to AI initiatives, against an average of 15.3%, and report total marketing budgets of 8.9% of company revenue against an average of 7.8%. In our reading, the organizations that are ready have funded the foundations, not just the tools.
A workable approach is to split the AI budget into two parts. One funds the foundations above: data, training and governance. The other funds a small number of time-limited pilots with agreed success measures. Pilots that hit their measures earn more budget. The rest stop.
A simple sequence for the next two quarters
| Quarter | Focus | Output |
|---|---|---|
| First | Outcomes, data audit, approval rules | Two or three funded outcomes, named data owners, written approval policy |
| Second | Pilots and role changes | Two pilots measured against baseline, updated job descriptions and agency scopes |
At the end of the second quarter, leadership should be able to see which AI investments changed a business number and which did not. That is the evidence needed for the next budget round.
Where to start this month
Choose the outcomes and name the data owners. Both can be done in a single leadership session, and both make every later AI decision easier. Our strategy team runs these sessions with marketing leadership, and it is often the fastest way to turn an AI goal into a plan the CFO will fund.
Related reading: How to Build a 90-Day AI Marketing Roadmap That Actually Gets Executed | The Retainer vs. Project Debate
Put this into practice
- A clear, data-backed plan that connects channels to business goals. See our digital marketing strategy consulting.
- Technical SEO, content and local search that earn qualified organic traffic. See our AI-powered SEO services.
- Paid search campaigns that are tested, tuned and reported against revenue. See our AI-assisted PPC and search advertising.