Marketers: Stop Experimenting with AI and Start Solving Real Problems

From 'Toy' to 'Tool': Marketing's AI Awakening Moment

As nearly every marketing team experiments with ChatGPT for copywriting and Midjourney for image generation, a pointed question has surfaced — what real business problems are these experiments actually solving?

Atlassian's Work Life platform recently published an article that has sparked heated industry discussion. The core message is straightforward: Marketers should stop aimless AI experimentation and instead focus on solving real problems in their workflows.

This is not an argument against AI. Quite the opposite — it's a call to embrace AI with higher standards of quality and purpose.

'Stuck Moments' Are the Best AI Use Cases

The article proposes a highly practical methodology: marketing leaders should identify "stuck moments" in their teams' workflows and transform these bottlenecks into the most valuable AI application scenarios.

"Stuck moments" refer to those points where workflows stall, team members feel frustrated, and time is repeatedly consumed with limited output. Examples include:

• Data consolidation and report generation: Marketing teams spend significant time each week manually aggregating data from different platforms to produce weekly and monthly reports.
• Information gaps in cross-departmental collaboration: Communication between marketing and sales departments involves constant back-and-forth, with information distorted in transit.
• Content localization and multi-channel adaptation: A single piece of content must be repeatedly rewritten and adjusted for different platforms and regions.
• Lengthy creative approval processes: A campaign proposal spends weeks passing through layers of approval.

These are the scenarios where AI should truly intervene — not because "AI can do it," but because "this genuinely needs to be solved."

Why the 'Experimentation Mindset' Is Holding Marketing Teams Back

Many marketing teams have fallen into a classic trap with AI: using AI for the sake of using AI. Team members individually try different AI tools, producing results that look impressive but are difficult to measure in terms of value. Ultimately, these experiments fail to crystallize into systematic organizational capabilities.

This "experimentation mindset" creates several problems:

1. Scattered resources: Team energy is spread across dozens of AI tool trials with no focus.
2. Unmeasurable ROI: Experimental usage is difficult to tie to business metrics, making it hard to demonstrate value to leadership.
3. Non-transferable experience: Individual AI skills remain at the personal level and cannot be converted into team-wide capabilities.
4. Tool fatigue: Frequently switching between and trying new tools actually increases cognitive burden.

Atlassian's perspective points to a more mature AI adoption path: Start from the problem, not from the tool.

Practical Advice: Three Steps to Building AI-Driven Marketing Workflows

Based on the article's core framework, marketing leaders can follow these steps to implement AI applications:

Step One: Identify bottlenecks. Spend one to two weeks having the team document the most time-consuming, least efficient, and most frustrating parts of their daily work. No complex research is needed — a shared document will suffice.

Step Two: Match solutions. For the top-ranked bottlenecks, evaluate which can be improved through AI tools. The focus should not be on finding the "most advanced" AI, but the "best-fit" solution.

Step Three: Standardize the process. Once validated, embed the AI tools into standard workflows, create SOPs, ensure everyone on the team can use them, and continuously iterate and optimize.

Industry Trend: From AI Novelty to AI Infrastructure

The timing of this article's publication is noteworthy. After more than two years of the generative AI boom, the global marketing industry is moving from the "novelty phase" into "deep waters." According to multiple industry surveys, over 80% of marketing teams are already using some form of AI tool, but fewer than 20% have established systematic AI workflows.

In other words, most teams remain in the "individual experimentation" stage and have yet to cross into the "organizational capability" stage.

As a leading enterprise collaboration tool provider, Atlassian's timing in issuing this call also hints at the direction of its product strategy — deeply embedding AI capabilities into team collaboration processes rather than offering them merely as standalone "AI features." This aligns with the current integration path of Atlassian Intelligence within Jira and Confluence.

Looking Ahead: The Metrics for AI Value Are Changing

In the future, the standard for measuring a marketing team's AI maturity will no longer be "how many AI tools are used" or "how much AI-generated content is produced." Instead, it will be "how many real business bottlenecks have been solved" and "how much human capacity has been freed up for higher-value creative work."

For marketing professionals in the Chinese market, this approach is equally applicable. Whether using Tongyi Qianwen, Ernie Bot, or other domestic large language model tools, the core logic remains the same: First find the pain point, then introduce AI, and finally solidify it into a capability.

Stop experimenting and start solving problems — this may be the message marketers most need to hear in 2025.