The Noise Around AI

COCenk Onur Ozan ·10 Feb 2026Published 10 Feb 2026 ·2 min read

AI has set off a deep change in the technology world and offers new solutions in many areas of our working and personal lives. Our days are full of the AI projects that organisations and employees are running, claim to be running or dream of running. This energy has a shadow too: the unnecessary and misleading elements piling up around AI, which we can call noise. I believe this noise has reached a level where it threatens the technology’s effectiveness, its reliability and its ethical footing.

By noise I mean unnecessary or misleading data, misdirection, bias, and the information clutter that makes it harder to solve real problems. This noise has two faces. On the technical side are errors in training data, biases baked into data sets and poor choices of algorithm. On the social side are misinformation about AI, exaggerated marketing language and promises that go beyond what the technology can really do.

The costs of this noise vary. AI systems shape decisions in many critical areas, from marketing to analysis and from production to the supply chain; errors and biases in training data can lead to wrong analyses and from there to bad decisions. On the ethical side, noise makes it harder to raise awareness of sensitive issues such as data privacy and transparency, and misleading information can pave the way for unethical practices. Then there is trust. Exaggerated claims and unkept promises wear down the trust that organisations, employees and even society place in the technology, and slow down its wider adoption. Finally comes wasted resources; noise can send limited resources to the wrong projects, set the wrong priorities and let important problems slip by.

So what should we watch out for? First of all, data quality. AI is only as good as the data it uses; data needs to be accurate, balanced and as free from bias as possible, and sources need to be reviewed and cleaned regularly. Second, transparency and accountability. How AI systems reach decisions should be understandable, and someone should be answerable for them when needed; especially in public-facing systems, it matters that users can understand how the system works. Third, education and awareness. Making sure organisations and employees can get accurate information about AI is the most effective way to stop misinformation from spreading.

I share the excitement about AI; but to keep that excitement from turning into noise, we need to turn the volume down a little and look at the data and the results. When your organisation talks about AI projects, how do you separate the noise from the signal?

Cenk Onur Ozan COO, Cloud4Feed

Writes about customer experience, behaviour and the decisions that follow from listening.

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