A lot of marketing leaders are wondering whether AI is really bringing incremental value to their business. Teams are moving faster than ever. Campaign briefs, social copy, first-pass creative and competitive research can now be produced in a fraction of the time it used to take. Yet it’s not clear if the work is actually better or if the business is seeing improved results with the same resources.
This uncertainty shows where AI is today. Many companies use it, but few can demonstrate real value. MIT found that about 95% of enterprise AI pilots don’t clearly impact financial results. McKinsey’s latest State of AI survey found the same thing: Most companies use AI, but few can prove it helps profits. Marketing is no exception. Plenty of teams can point to a content calendar that’s fuller than ever, but few can tie that output to pipeline, conversion or brand lift. Organizations focus on speed without checking if the technology brings real benefits.
AI makes tasks that happen early in the process, like drafting or summarizing, seem quick and finished. But there’s a big difference between work that looks done and work that’s truly complete. This gap often appears later, during review, fact-checking, brand checks and the final tweaks needed to meet professional standards. In marketing, that gap is especially costly: a generic AI-written email or an off-brand social post doesn’t just cost editing time; it can quietly erode the voice and trust a brand has spent years building.
One key thing often missed in ROI calculations is that the person reviewing and improving AI drafts is usually more senior and costs more than the person who started the task. If a senior team member spends a lot of time fixing average work done using AI, the process can actually lower productivity. This issue is easy to overlook because most teams don’t track the whole workflow.
To see real value, set up a way to measure your AI process. Here are some steps that helped me.
1. Set clear goals. Before you begin, decide what you want AI to achieve by year-end. For example, you might want to cut content production costs by 20% or increase output by 50% without hiring more people. Use measurable targets like time to launch or cost per asset to see real progress instead of just staying busy.
2. Measure your starting point before automating. Record how long each task takes at every step: brief, first draft, brand review, legal review, final polish. It might not be fun, but it gives you the data you need. Without a baseline, you can’t show if things improve later.
3. Automate what already works well. Focus on the most repetitive and time-consuming tasks, not the flashy or untested ones – things like first-draft social variations or routine performance summaries, not brand-defining creative. Only automate processes that are already running smoothly. If you automate messy workflows, it just makes things worse. Make sure your AI has the right, up-to-date data, like your brand guidelines, tone of voice or past campaign performance, to get better results.
4. Run both AI and human processes. Before you switch a task fully to AI, try doing it once with AI and once the usual human way. Compare the time, quality and total cost. Often, AI is faster but might require more work overall, especially when brand voice or nuanced positioning is on the line.
5. Look at the full cost of the whole process. Include everything: doing the work, reviewing it, making changes and how much you spend on tokens. Reviewing and improving drafts takes time and effort from real people and that has its own price.
6. Create your source of truth. Often, problems arise due to poor instructions or missing context, which leads to poor results at the end. Give the AI detailed information about your company, product and project, have people check the work and keep feedback going in every cycle.
AI alone won’t make your team more effective, but good management and measuring your results will. Speed is easy to notice and appealing, but it isn’t everything. Real value only appears when you look at the full cost of the whole process. In marketing, where brand and trust are the real currency, the full cost matters more than most teams realize.
“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
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