Artificial Intelligence is rapidly becoming the engine room of modern business. From personalised recommendations to predictive analytics, its ability to decode and act on consumer behaviour is transforming industries. But as AI grows smarter, businesses must ask: are we crossing the line from insight to intrusion?
by Kasun Illankoon, Editor in Chief at Tech Revolt
Privacy is no longer a peripheral issue, it’s a commercial imperative. For companies leveraging AI to gain competitive advantage, the temptation to exploit behavioural data is powerful. Yet with that power comes an escalating responsibility to protect consumer trust, or risk losing it altogether.
The uncomfortable truth is that many businesses are building AI on a foundation of borrowed privacy. Data that once seemed innocuous, location tracking, app usage patterns, even typing speed, is now fuel for algorithmic decision-making. Consumers, meanwhile, are often unaware of how deeply their digital selves are being mined, categorised and monetised.
Monetising Trust: A Risky Proposition
Businesses have long touted the benefits of AI-driven personalisation. But in the post-GDPR world, personalisation without transparency is a risk few can afford. Just ask Meta. The company faced scrutiny in 2023 when the Irish Data Protection Commission fined it over US$1.3 billion for transferring EU user data to the US. The backlash was swift, not just from regulators but from users and partners alike.
The lesson? Building AI tools that push the limits of privacy can have direct, measurable financial consequences. In the short term, regulatory penalties hurt. In the long term, loss of consumer trust is even more damaging.
From Data Extraction to Data Ethics
The market is maturing, and so are its expectations. Investors are now probing AI firms about data ethics in due diligence processes. Enterprise customers are including AI usage clauses in contracts. Consumers are deleting apps that feel invasive. Privacy has shifted from a legal checkbox to a brand differentiator.
Consider Apple’s strategic pivot. By branding itself as a privacy-first company, it reframed customer data protection not as a constraint, but as a competitive strength. In doing so, it forced rivals like Google and Meta to follow suit, at least on the surface. Privacy is becoming a feature businesses must build in, not bolt on.
And while consumer-facing tech gets most of the spotlight, B2B businesses are just as exposed. AI tools used in HR, healthcare, insurance and logistics often handle sensitive personal information. The danger lies not only in malicious intent, but in system bias and poorly defined data handling practices. The more complex the model, the harder it is to explain, or defend, how it reached a decision.
Regulatory Lag, Commercial Fallout
Regulation always lags behind innovation. But the gap is narrowing. The European Union’s AI Act is set to create the world’s first comprehensive legal framework for AI, classifying systems based on risk and mandating transparency, human oversight, and data quality standards.
For businesses operating globally, this means designing AI solutions that can navigate a fragmented privacy landscape. What’s permissible in one market may be prohibited in another. Multinationals must adopt a privacy-by-design mindset or face constant compliance headaches.
In the UAE, for example, national AI strategies encourage innovation, but with guardrails. Smart Dubai’s Data Ethics Framework and the ADGM’s Data Protection Regulations are early signals that Gulf markets are aligning with global norms. The message to businesses is clear: ethical AI isn’t optional, it’s expected.
The Cost of Getting It Wrong
In 2022, a major UK retailer was called out for using facial recognition software in its stores without clearly informing customers. The resulting media storm damaged its reputation and sparked legal reviews. No data breach had occurred, only a perceived overreach.
That incident underlines a key truth: when it comes to AI, perception is reality. If users feel their privacy has been violated, they’ll vote with their wallets, or worse, in the press.
We’re entering an era where AI literacy will be a strategic differentiator for leadership. Boards need to understand not just what their algorithms do, but how they’re trained, what data they consume, and what risks they expose the business to. Ignorance is no longer a defence.
Finding the Balance
The question is not whether businesses should use AI, it’s how. Companies that lean too far into surveillance risk public backlash. Those that avoid AI altogether risk falling behind. The middle ground is intelligent design, AI that is powerful, but also explainable, accountable and respectful of individual privacy.
That may sound idealistic. But consider the trajectory: consumers are becoming more aware, regulators more aggressive, and the media more critical. Smart companies are getting ahead of the curve, not by doing less, but by doing better.
We are moving from a data economy to a trust economy. And in that economy, the winners will be businesses who don’t just know their customers, but respect them.
