This insights post is a summary of the blog post published by Mahlab. View the full insight at: Beyond the AI bubble: Why marcomms teams need to take a step back to speed up.
The AI bubble has swelled faster than most marketing and communications teams can adapt. With more than 5,000 tools now flooding the market, the promise of transformation has given way to a scramble through the noise — and Worldcom partner Mahlab argues it’s time to step back before moving forward.
What began as experimentation has rapidly escalated into an industry-wide expectation that every team should have an “AI strategy.” The pressure to adopt the latest platforms, plugins, and features has widened the gap between hype and reality. Adoption has become messy, uneven, and often directionless.
The messy middle
AI itself isn’t new. Most communications professionals have relied on it quietly for years — through predictive text, recommendation engines, and automation built into everyday apps. But generative AI changed the dynamic entirely. Suddenly, AI wasn’t running quietly in the background; it was producing ideas, content, and visuals in seconds, pushing teams into reactive mode.
Many organizations were asked to outline an AI strategy before they had the basics in place: clear workflows, governance, data standards, risk frameworks, or even defined use cases. The result is what Mahlab calls the “messy middle” — disconnected pilots, fragmented tools, no shared standards, no clear ownership.
The consequences are measurable. An MIT Media Lab study found that 95% of AI pilots deliver zero measurable ROI, and that 50–70% of AI investment flows into sales and marketing pilots — the areas with the least return. AI placed on top of broken systems doesn’t fix them. It accelerates the wrong things faster.
That’s not failure. It’s where organizations land when a powerful new technology arrives faster than teams can absorb it. But it is a signal: slow down long enough to repair foundations before layering on more tools.
4 Ways to Create a Path Forward
To get the most from AI tools, it is important to remember where AI excels: speed, pattern recognition, summarization, recall of vast amounts of information, and the ability to analyze or translate in seconds. The real opportunity to implement AI systems lies in the space where human intelligence and artificial intelligence overlap.
Getting to that middle requires rethinking how things are done. AI should not be a shortcut nor a tacked-on step at the end of a workflow. It becomes part of the system – but the system still needs human direction, clarity and structure to work.
- Start with one real problem: Clearly define a pain point. If AI cannot solve a real business problem, don’t apply AI for the sake of it.
- Design roles, workflow, and measures: Instead of bolting AI onto an existing process, it is worth rethinking who owns it, where AI sits in the workflow, and how success is measured.
- Set guardrails for data and ethics: Human oversight is a non-negotiable. Guardrails ensure tools don’t introduce errors, bias, or copyright risks.
- Build a simple learning loop: AI systems improve through iteration. AI systems take ongoing support and refinement to get the best results.
By using this guidelines, the redesigned processes allow both AI and Humans to do what they do best.
To get examples and more insights, read the full post at Beyond the AI bubble: Why marcomms teams need to take a step back to speed up.
