The 9 AI Biases We Don't Talk About
The biggest cause of AI hype is bias, in my opinion.
There’s a great deal of media and influencer coverage of AI, indicating widespread adoption in enterprises. And then there is the fear-mongering about AI replacing everyone’s job, which is somewhat grounded in tech-specific developer layoffs, but also has the power to illustrate the incredible growing power of AI as a cost saver. Much of the hype is grounded in biases.
Most of these hype moments are grounded in a set of specific sources, vendors, consulting firms, nonprofit bodies dedicated to AI, and media and influencers covering the beat. It’s easy to say that consuming news requires weighing the veracity of the source. Surviving today’s digital media environment requires a critical eye.
When it comes to these headlines and the ensuing chatter, one should consider potential biases when interpreting the data. Who is saying this? Are they just amplifying an original source? What is the source of the news? What are their motives? And with that, let’s examine the top biases creating the hype cycle.
1) Vendor Bias
Technology companies like Adobe, Jasper, Open AI and Salesforce have commercial incentives to present optimistic AI adoption narratives and sometimes incredible capability claims. Their surveys and reports may emphasize benefits while downplaying implementation challenges to drive product sales and increase market valuations.
Perhaps this is best seen with demos. The tendency to showcase AI capabilities in controlled demo environments that don't reflect real-world complexity and messiness is often misleading, and creates alarming disconnects between tech companies business users.
2) Consulting Firm Bias
Management consulting firms like McKinsey and EY profit from organizational transformation projects. Furthermore, many of them have significant professional services operations that implement custom AI models. Their research may emphasize transformation opportunities and change requirements to generate consulting demand.
3) Industry Body, Education Organizations and AI Consultant Bias
Usually the reporting and coverage is optimistic to the point of assuming that adoption rates are lower because humans aren’t using AI right. Of course, like the prior two categories, organizations like the Marketing AI Institute, AI consultants (Yes, you can lump CognitivePath into this category), and educational institutions sell training programs, certificates, degrees, and/or consultations. So, the bias tends to lean towards the idea that you need to learn how to use AI correctly.
P.S. Hello, MIT, am I still stupid for using ChatGPT?
4) Media and Influencer Bias
Media and influencers often circulate wild narratives about AI, either dramatic capabilities or doomsaying. While the reasons for this can vary, for example, an engineer emphasizing great capabilities in his blog may genuinely believe in what he is writing, as the prior three cohorts, media, and influencers have a business.
Whether advertising-based, speaking opportunities, book sales, training, or other monetization schemes, this cohort needs attention to drive more people into their various capture funnels. In the case of traditional media, another bias may be fear of being replaced by generative AI solutions.
5) Study Selection Bias
Industry surveys likely oversample early AI adopters and larger organizations more engaged with emerging technologies. I have literally read both Jasper and the MAII marketing AI adoption surveys, and both cited large marketing organizations with 500-1000 people or more having high adoption rates. Well, how many companies have marketing departments that large? Fortune 500 only? Of course they are using AI, duh!
As this example illustrates, oversampling AI users (or members) skews adoption rates higher than the broader market reality, as non-adopters or failed implementers are less likely to participate in AI-focused research. If anything, we’ve learned over the past couple of decades that polling is often skewed toward one cohort.
6) Definitional Bias
"AI usage" definitions vary significantly between sources, ranging from occasional ChatGPT queries to sophisticated operational deployments. This inconsistency makes cross-study comparisons problematic and may dramatically inflate adoption statistics. Making a query a day or a week with ChatGPT is not operationalizing AI into your daily workflows.
7) Survivorship Bias
Published case studies and reported experiences typically come from organizations willing to discuss their AI initiatives publicly. Companies that abandoned AI projects or experienced failures are less likely to share their experiences, creating an overly positive view of implementation success. I don’t see any companies publishing their AI use case failures on public media or in their earnings reports.
8) Temporal Bias
Most surveys and reports reflect recent implementation attempts during a period of significant hype around generative AI. This timing may capture experimental enthusiasm rather than sustainable business impact. We have yet to see demands for proof of ROI enter the conversation consistently. Publishing more revenue gains, more measurable outcomes, and more concrete savings based on specific models and implementations is necessary to fuel long-term adoption trends.
9) Analyst Firm Bias
The inclusion of critical perspectives, failure predictions, and performance limitations helps provide a more balanced assessment of the current AI marketing reality. Independent research organizations, such as Gartner and Forrester, can provide a valuable counterbalance to vendor and consulting narratives, although they also operate within the technology industry ecosystem.
Like consulting firms, they offer services to sell, too, to help facilitate adoption. Plus, they have to push their consistently wonky new nomenclatures for the trend du jour.
Conclusion
So you can see, while not always in play for all parties, there are lots of biases and motives behind creating and amplifying AI-related content. Just because it’s said, does not make it accurate, and we do need to question why an organization or a person of influence takes a particular position.
What do you think of AI hype biases outlined here? Do you have one to add?