AI is the making decisions about your purchases using bad data

From where I sit as COO of E-Boost Consulting, I see it every day. Marketers are relying on automated systems to determine which ads you see, what products get recommended, and sometimes even what price you’re shown. But the data feeding those systems is deteriorating. We call it the “signal crisis”, and it’s quietly reshaping the way consumers experience the internet.

In simple terms, a “signal” is just a clue about who you are and what you might want. It could be a website you visited, a product you added to cart, your location, or the device you’re using. For years, platforms vacuumed up these clues through third-party cookies and cross-site tracking. That gave advertisers a fairly detailed map of consumer behavior. Now that map is full of blind spots.

Privacy regulations, browser restrictions, and platform walled gardens have cut off many of the old tracking methods. Apple limits cross-app tracking. Browsers like Safari and Firefox block third-party cookies by default. Even Google is tightening the screws. The result? Marketers have less reliable visibility into who is actually engaging, converting, or returning. But here’s where it gets tricky. The automation hasn’t slowed down.

Artificial intelligence systems still optimise ad placements and bids in real time. Dynamic pricing engines still adjust what you’re charged based on predicted demand and behaviour. Recommendation algorithms still decide which products rise to the top of your feed. They’re just doing it with fuzzier inputs.

Imagine trying to navigate rush-hour traffic with a GPS that’s missing half the roads. You’ll still get directions – they just might be wrong.

When signals are incomplete or distorted, AI fills in the gaps. It makes probabilistic guesses about who you are based on patterns that may or may not apply to you. Maybe you share a device with a family member. Maybe you cleared your cookies. Maybe you browse in incognito mode. To the system, that ambiguity doesn’t register as uncertainty; it becomes an assumption.

That’s how consumers end up seeing irrelevant ads for weeks, getting product recommendations that feel wildly off-base, or encountering price fluctuations that don’t make intuitive sense. From the outside, it can feel manipulative or invasive. From the inside, it’s often just bad data compounded by aggressive automation.

The signal crisis also affects pricing in subtler ways. Many e-commerce platforms use dynamic pricing models that analyse demand, competitor pricing, inventory levels, and behavioural data. When behavioural data becomes less accurate, the models lean more heavily on generalised patterns. That can create price volatility that feels arbitrary to shoppers, even if no one is deliberately targeting them.

And then there’s attribution, the way companies decide which ad or channel “caused” a purchase. In the past, tracking was granular. Today, with fragmented data, marketers rely more on modeled attribution. That means statistical estimation rather than direct measurement. If those models are trained on flawed or incomplete signals, companies may over-invest in channels that appear to work and under-invest in ones that actually drive value. Consumers ultimately feel that in the form of repetitive ads and homogenous digital experiences.

The irony is that automation was supposed to make marketing smarter and more efficient. In many cases, it has. But automation without clean signals is like autopilot without radar. It keeps flying, just with less clarity about what’s ahead.

This isn’t an argument against privacy. Consumers deserve transparency and control. But the industry hasn’t fully adapted to the new environment. Too many brands are still chasing performance metrics as if the data behind them is pristine. It isn’t.

What needs to change is how companies think about data quality and first-party relationships. Brands must invest in consent-based data collection, better measurement frameworks, and a deeper understanding of their own customers rather than renting fragmented insights from platforms. They also need to temper blind faith in AI systems and reintroduce human oversight, asking not just whether the algorithm is optimising, but what it’s optimising on.

The signal crisis isn’t a headline-grabbing scandal. It’s quieter than that. It shows up in the ads that miss the mark, the offers that feel oddly timed, and the shopping journeys that feel slightly off. Consumers may not know the term, but they’re experiencing it every day.

Unless the industry confronts the quality of the data powering its decisions, we risk building a digital marketplace that feels less personal, less fair, and ultimately less trustworthy, even as it becomes more automated than ever.

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