The conversation about AI in marketing often begins with productivity. How much faster can we create content? Which tasks can we automate? How can we analyze information more efficiently or reduce the time spent on reporting?
These are relevant questions, and many of the possibilities are genuinely exciting. AI is already changing how marketing professionals approach research, content development, data analysis and everyday workflows. For organizations facing increasing expectations and limited resources, the ability to work faster and more efficiently is understandably attractive.
But I've become increasingly interested in another question: what happens when we introduce AI into a marketing organization that isn't functioning as effectively as it could?
If strategic priorities are unclear, teams operate independently or marketing and sales aren't properly aligned, faster execution won't automatically lead to better outcomes. In some cases, it may simply allow organizations to produce more work without addressing the reasons why that work isn't delivering the expected results.
To understand where AI can genuinely add value, we need to look beyond individual tools and consider the broader marketing operating model.
Why the way marketing operates matters
A marketing operating model describes how an organization connects its business strategy to the people, processes, capabilities and activities required to deliver marketing results. It encompasses everything from strategic planning and decision-making to collaboration, execution and performance measurement.
In a well-functioning marketing organization, these elements reinforce one another. Teams understand the commercial priorities, responsibilities are clear and marketing activities follow a shared direction. Information flows between departments, and performance insights help people make better decisions.
When those connections are weak, technology alone is unlikely to resolve the underlying problems.
Consider a company that wants to improve its content marketing. By introducing AI, the team can suddenly produce articles, social posts and campaign materials considerably faster. Productivity increases, and the organization may initially regard this as a success.
But imagine that the company's positioning is unclear or that different departments communicate inconsistent messages about its services. Increasing the volume of content won't necessarily improve how potential customers understand the business. It may even amplify the inconsistency.
The same applies to campaign reporting. AI can help consolidate data, identify trends and generate summaries, but if marketing and sales don't agree on what constitutes a qualified opportunity or which commercial outcomes matter most, faster reports won't automatically lead to better decisions.
These examples illustrate why understanding the problem should come before selecting the technology.
Finding opportunities where AI creates real value
I believe organizations benefit from taking a practical approach to AI adoption, starting with the work their marketing teams perform and the challenges they encounter.
Where do people spend time on repetitive activities? Which processes create unnecessary delays? Where could better information improve the quality of decisions? And which parts of the work would benefit from more time for strategic thinking, creativity or customer interaction?
Looking at marketing in this way often reveals opportunities that are more relevant than adopting a tool simply because it is new or widely discussed.
Research is one example. AI can help marketers process large amounts of information, compare sources and identify patterns that deserve further investigation. This can reduce the time required to prepare an initial analysis, allowing experienced professionals to concentrate on interpreting what the findings mean for their organization.
Content development offers similar possibilities. AI can assist with structuring ideas, preparing drafts and adapting existing material for different audiences or channels. However, the value of the content still depends on the quality of the underlying thinking, the organization's expertise and how well the message connects with the people it wants to reach.
Campaign analysis is another useful application. Reducing the manual effort involved in bringing performance data together can be valuable in itself, but the greater opportunity lies in helping marketing teams recognize patterns earlier and make more informed decisions about targeting, messaging and investment.
In each of these situations, AI supports an existing business objective. Its usefulness depends on whether the improvement is meaningful, not simply on whether a task can be automated.
Moving beyond individual experimentation
Many marketing professionals have started exploring AI independently, discovering tools that make their daily work easier or help them approach familiar tasks differently. This experimentation is valuable because it encourages curiosity, builds experience and creates opportunities to improve existing ways of working.
The challenge changes when an organization wants to move from individual use to a more consistent, team-wide approach.
At that point, questions about leadership, responsibilities and collaboration become increasingly important. Which applications should the organization prioritize? How will they fit into existing processes? Who is responsible for checking the quality of the results? What skills do people need, and how will the organization evaluate whether the new approach is actually working?
There are also practical considerations around information security, data protection and the appropriate use of company or customer information. These require clear guidelines and, where necessary, involvement from technical, legal or security specialists.
Marketingleaders don't need to become AI engineers to navigate these questions. They do, however, need to understand the opportunities well enough to identify relevant business applications, involve the right expertise and guide their teams through changes in how work is organized.
As with any organizational improvement, adoption is more likely to succeed when people understand why something is changing and how it will help them do better work.
Measuring progress beyond productivity
One of the risks of adopting new technology is measuring success primarily through its use. The number of AI tools available, the volume of content produced or the percentage of employees experimenting with AI may provide useful information, but they say relatively little about the business value being created.
A more meaningful approach begins by establishing what the organization wants to improve and how the process currently performs.
If AI is introduced to support reporting, for example, the team could compare preparation time, accuracy, review effort and the usefulness of the resulting analysis. If the objective is to improve content workflows, the evaluation might include production time, consistency, quality and how effectively the content supports its intended purpose.
It's also important to account for the effort required to review and correct AI-generated output. A process that appears faster initially may deliver little benefit if significant additional work is needed to make the results reliable.
Over time, organizations can use these observations to decide which applications deserve further investment, which processes need adjustment and where conventional improvements may be more effective than automation.
The objective should be to make marketing more effective, not merely more automated.
Keeping the bigger picture in focus
With more than twenty years of experience in marketing, I've seen how important it is to connect strategy, people and execution. Technology has always played a role in enabling better marketing, but its value ultimately depends on the decisions organizations make and how effectively they put those decisions into practice.
I see AI as another powerful addition to that toolkit. I use it to support research, analysis, content development and practical marketing activities, while remaining conscious that experience, creativity, commercial understanding and human judgment are essential to the quality of the work.
For some organizations, AI will create opportunities to improve efficiency and free up valuable capacity. Others may first need to address more fundamental challenges involving positioning, priorities, collaboration or the way their marketing function operates.
At VMA360, my starting point is always to understand the business and identify what will make the greatest difference. AI can certainly be part of that solution, but it should serve the marketing strategy rather than determine it.