In today’s technology landscape, it’s almost impossible to find a cloud service, hardware device, or marketing app that doesn’t claim some form of “AI-powered” capability. From deep learning to cognitive AI and new buzzwords like MCPs (multi-component protocols designed for AI models), the software industry is obsessed with slapping an “AI” label on every product.
Yet according to industry observers, this explosion of AI branding raises critical questions: How much of it is legitimate innovation, and how much is simply marketing snake oil? The problem is compounded by AI’s diverse modalities; it can process everything from text to images and video and an ever-expanding list of poorly defined subcategories like agentic AI or generative AI, making it harder than ever for marketers and buyers to cut through the noise.
Marketing’s Blind Spot: Technology Literacy
Digital marketers are experts in analysing consumer behaviour, crafting messaging, and optimising campaign performance. But many lack specialised training in computer science, data architecture, or algorithmic design areas critical to understanding the real capabilities behind AI-powered platforms.
This knowledge gap makes marketers vulnerable to unprovable claims. For instance, consider a hypothetical SaaS platform that lets marketing teams track digital campaigns, analyze competitor performance, and forecast e-commerce revenue. If the software allows users to ask in plain language, “Show me the best-performing campaigns on mainland Europe,” and then delivers an answer, a large language model is likely parsing that query. While this is technically an AI application, does it truly justify positioning the entire platform as “AI-powered”?
The “Black Box” Challenge
A central issue is the proprietary nature of commercial software. Marketers and even IT staff have no way to peer inside the “black box” of AI tools to verify what’s really going on. Are advanced neural networks driving insights, or is it basic statistical modelling in a slick dashboard?
This opacity means businesses must rely on vendors’ marketing claims rather than technical evidence. As the article notes, “Almost without exception, [marketing tools] claim AI elements, and none of those claims are open to outside scrutiny to a level that would satisfy a specialist in IT.”
Even features like competitor analysis or predictive revenue forecasting may depend on classical algorithms rather than true machine learning. Time-series regressions, for example, can extrapolate future sales but are closer to traditional statistical models than to advanced “deep AI.” That nuance is critical because platforms might hype basic predictive features as cutting-edge AI without meaningful differences from established analytics techniques.
Transparency: The Key to Trust in AI Marketing
If vendors want their AI claims to stand out, they need to offer proof beyond vague buzzwords. Publishing technical details such as model architectures, hyperparameters, or training data sources could help validate AI functionality. While this information might be dense for the average marketer, it would enable qualified data scientists to assess whether a platform’s AI claims are genuine or exaggerated.
“Verification by means of some degree of openness would let objective third parties…be sure they weren’t being sold snake oil,” the article argues. Without this transparency, buyers are left guessing whether the tools they invest in will truly enhance performance or just add another layer of marketing hype.
Investor Frenzy Obscures Product Quality
The current funding climate only muddies the waters further. Investors are rushing to back any startup with “AI” in its pitch, regardless of whether the underlying technology provides meaningful value. As the article points out, “Investors are throwing money at any company with an AI element in its (self-published) description.”
This frenzy means marketers can’t rely on investment rounds or big-name backers as reliable indicators of a platform’s quality or effectiveness. Amid the hype, experience, strategic thinking, and industry knowledge remain a marketing team’s most valuable assets.
Real Value Lies Beyond the Buzz
Traditional software tools, built on solid, time-tested algorithms, have delivered immense benefits to marketers for decades. While machine learning can undoubtedly enhance some aspects of these platforms, vendors owe it to their customers and the integrity of the industry to clearly articulate exactly how AI improves outcomes beyond what classic software can do.
Otherwise, the risk is that AI’s transformative potential in marketing will be undermined by a flood of empty promises, leaving buyers sceptical and stalling genuine progress. The future of AI in marketing isn’t in the buzzwords; it’s in transparency, measurable impact, and provable results.
