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Research: 75 % say AIdriven personalisation is intrusive
UK consumers are becoming more informed, more deliberate and less forgiving when it comes to how companies collect and use their personal data, according to research from Usercentrics.
The State of Digital Trust 2026 report found that four in five( 80 %) UK consumers would stop using a service if they discovered their data was being misused, while almost nine in ten( 88 %) have taken steps to protect their personal data online in the past six months. And at a time when AI is becoming embedded in everyday digital experiences, 61 % of UK consumers say they are uncomfortable with their personal data being used to train AI models.
The shift is being driven by growing awareness. The proportion of UK consumers who are unaware of their privacy rights has fallen from 50 % to 43 % over the past year. This represents the largest year-onyear improvement in privacy rights awareness of any market surveyed.
The findings suggest privacy is moving beyond a passive concern, but an increasingly important factor shaping consumer behaviour, purchasing decisions and brand loyalty.
“ Consumers are making purchasing decisions based on how brands handle their data, and over half are willing to pay more to the ones that get it right. The brands that move first won’ t just earn the premium. They’ ll earn a category position that’ s almost impossible to compete against once it’ s established,” said Tilman Harmeling, Strategy & Market Intelligence, Usercentrics.
The research suggests consumers are becoming more proactive about managing their privacy online. More than two in five( 43 %) have rejected cookies on websites in the past six
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months, while 45 % say they now click‘ accept all’ less often than they did three years ago, up from 40 % in 2025.
At the same time, understanding remains a challenge. Despite taking action to protect their data, 41 % of consumers say they do not have a good understanding of how companies collect and use their personal information.
This growing gap between action and understanding is creating new expectations for brands. Consumers are becoming less willing to accept opaque data practices and increasingly expect organisations to clearly explain how personal information is collected, used and shared.
Analysis: AI assistants inconsistent on answering streaming availability queries
A controlled accuracy analysis of streaming title availability data found that ChatGPT scored 43.76 % and Claude scored 50.21 % when tested against manually verified truth across 100 popular US titles, compared to 96.89 % accuracy from Reelgood, the streaming data and metadata platform. The analysis, conducted by Reelgood, tested each source against the same set of 50 movies and 50 TV shows using identical queries.
The findings arrive as AI assistants are increasingly used for content discovery and recommendation. Both OpenAI and Anthropic have expanded their platforms into media and entertainment partnerships, where accurate‘ where to watch’ data is a baseline requirement for product integrations. When an AI assistant tells a user a title is available on a service where it is not, or fails to list services where it is available, the downstream effects include user frustration, wasted clicks, and erosion of trust in the platform. Large language models weren’ t built to track real-time catalogue changes. The training
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data and retrieval pipelines they draw from were built for a different purpose, and the result is a predictable set of errors when they’ re asked to report what’ s streaming where.
Reelgood’ s analysis identified six distinct error categories that account for the majority of inaccuracies in both ChatGPT’ s and Claude’ s responses. They say these are not random mistakes, they reflect structural gaps in how large language models handle streaming availability data.
Stale Availability. Models confidently report titles as currently streaming on services they’ ve already left. The cause is structural: entertainment press covers new additions to a catalogue extensively but rarely follows up when a title quietly leaves weeks or months later. The training corpus skews heavily toward those announcements, so the model treats outdated positives as current. This is the most pervasive error pattern observed.
Add-On and Bundle Confusion. Models frequently treat titles available through paid add-on channels( such as Starz or Paramount + on Prime Video) as if they were part of the parent service’ s base subscription. Users are told a title is streaming“ on Prime Video” when accessing it actually requires a separate Starz or Paramount + add-on inside Prime Video, creating the false impression that their existing subscriptions cover it.
Long-Tail Service Gaps. Free and ad-supported services like Tubi, Pluto TV, Fawesome, Hoopla, and Kanopy are consistently omitted, even when they’ re valid sources for a given title.
SVoD / TVoD Conflation. Models sometimes list a service as a subscription( SVoD) option when the title is only available there for rent or purchase, misleading users about what their existing subscriptions actually cover.
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TVoD Blindness. Both models almost entirely omit transactional VoD( rent / buy) options from services such as Apple TV and Amazon, affecting the majority of titles tested.
Title Disambiguation Failures. When multiple versions of a title exist( such as One Piece [ pictured ], which has both an anime series and a live-action Netflix adaptation), models conflate availability across different versions.
Research: Brits spend 28 hours a year deciding what to watch
Research from EE TV has laid bare the epidemic of Doombrowsing: the average viewer now burns through a total of 28 hours a year browsing through their TV and movie options.
In an effort to combat this, EE TV launched the Smart Search feature, designed to work the way that viewers actually think and help them find something to watch more efficiently.
Picking what to watch has become a genuine source of stress in British homes. Over half( 51 %) say they feel overwhelmed by the sheer volume of choice, while over a third( 38 %) have thrown in the towel entirely and gone to bed without watching a thing. A further 38 % say the nightly negotiation over what to watch is now a source of household tension, and 34 % would rather just read a book instead.
Two thirds( 66 %) of Brits admit they’ ve fallen asleep mid-browse without ever finding anything, and 54 % admit they often spend longer browsing than actually watching. Additionally, 45 % have stopped trying altogether, defaulting to a familiar favourite they’ ve already seen, simply because it’ s easier than deciding.
It’ s a problem that’ s also driving people to their phones, with 70 % of Brits turning to second screens to look up actors, quotes or plot details mid-browse before giving up.
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10 EUROMEDIA |