Essay: AI Exposes Bad Growth Strategy
A new essay argues that while AI can accelerate marketing and testing, it can't fix a fundamentally weak product or go-to-market plan. The key takeaway for PMs is to use AI to rapidly expose gaps in a growth thesis, not to generate one from scratch.
The author of the essay, Anthony Neal Macri, argues that in an era of generative search, traditional SEO is becoming less effective. He posits that growth will come from being strategically cited and referenced in media, building "citation momentum" rather than just being indexed. This strategic clarity is critical because a high percentage of AI projects fail to deliver on their promised goals. One Gartner study revealed that as many as 85% of AI initiatives do not meet their objectives, often due to a misalignment with business strategy rather than technical shortcomings. When a solid strategy is in place, AI acts as a powerful accelerator, not a creator. For instance, AI-powered A/B testing can compress experimentation cycles from weeks into days, process a volume of variables unmanageable for humans, and reallocate resources to winning variations in real-time. AI tools can also rapidly analyze vast amounts of unstructured customer feedback from sources like support tickets and social media. Using natural language processing, product managers can identify unmet needs, feature requests, and sentiment patterns that would be impossible to detect manually. However, the output is only as good as the input. A primary pitfall for AI initiatives is low-quality or siloed data. Incomplete or biased training data can lead AI models to produce inaccurate predictions and perpetuate discriminatory outcomes, amplifying flaws in the underlying data infrastructure. McKinsey has outlined five distinct roles for AI in assisting strategy development: Researcher, Interpreter, Thought Partner, Simulator, and Coach. As a "Thought Partner," AI can serve as a check against the cognitive biases of business leaders by testing strategic plans against established frameworks. Ultimately, effective teams treat AI as infrastructure rather than the strategy itself. They use human judgment to define the core message and ideal customer profile, then deploy AI to scale production of variations for ads, landing pages, and email sequences.
