As generative AI becomes increasingly commoditized, the next competitive battleground is AI personalization; according to Boston Consulting Group, it represents a $2 trillion opportunity. It’s no wonder, then, that companies are racing to build AI systems that go beyond generic outputs, tailoring expert feedback, health guidance, and marketing copy to individual users and brands.

Specific; Not Statistically Average

GPTZero , initially known for its AI detection platform, is developing ‘digital twins’ of verified industry experts. These AI models are trained on the specific knowledge, judgment and working styles of professionals ranging from Emmy-winning producers and legal experts to romance writers.

The aim is to create AI-powered reviewers that deliver highly specific feedback, rather than statistically average responses.

“When you ask a generic chatbot to assess a script or a research paper, it aims to make your writing as statistically average as possible,” says CEO Edward Tian. “When Greg Altman, an Emmy-nominated TV producer, builds a digital twin trained on his own rubric - what makes a good cold open, how he approaches dialogue, and the notes he’d give in a writers’ room - you get more specific feedback.”

The concept was recently demonstrated when a news editor at The Wrap created a digital version of himself trained to edit feature stories in his own style, focusing on headlines, introductions, angles and concise language. The AI was used as a ‘first pass’ tool to streamline lengthy copy.

Tian believes the same approach could eventually support areas where expert judgment is a bottleneck, including academic peer review, industry-standard document editing and patent application checks.

“Wherever a person has spent years honing their craft and developing a rubric for evaluating work, there’s an opportunity to capture those insights and make them available to those still developing their skills to improve quality across the board,” he says.

The quality of a digital twin, however, depends entirely on the quality of its training data. Experts must upload documents, demonstrate edits and explain the reasoning behind their feedback. “It follows the golden rule in computer science: ‘garbage in, garbage out’,” adds Tian.

GPTZero has also introduced guardrails to ensure experts remain in control. Digital twins can be switched on or off and provide suggestions only, rather than generating original content under the expert’s name. “After all, says Tian. “We’re trying to preserve authenticity in the age of AI, not proliferate synthetic authors.”

Precision Guidance For Child Gut Health

Alba Health is applying personalization to another area where generic advice often falls short: child gut health. Its in-app AI advisor is trained on the company’s proprietary library of clinical studies and published research, offering guidance tailored to a child’s microbiome profile.

“The personalization question is really a data question,” says CEO and co-founder Nora Cavani. “Any AI can generate health advice; the limiting factor is what it actually knows about the person asking, and about what the right solution is.”

As Cavani points out, generic recommendations often fail to account for major biological differences between individuals.

“Most health apps are working from population-level research on what tends to help people in general,” she says. “For example, they might recommend eating more fiber. That's useful for some people, but it’s not always the right advice for them. If people’s test results show they are missing an entire class of bacteria, which is increasingly common, they need tailored advice on nutrition and probiotics that goes beyond advising a better diet or more fiber. A Google search won’t be able to address that need in the way that the right AI can.”

The Alba AI advisor combines microbiome sequencing results with information on diet, symptoms, medical history, lifestyle and preferences.

“Our company has been tracking hundreds of families longitudinally, which means we have real data on how children's gut health changes over time, and which specific probiotic strains and brands actually produce results in children with different profiles,” adds Cavani. “That combination is what makes the difference between advice that sounds relevant and advice that actually is.”

Cavani’s own experience overcoming allergies and eczema linked to gut bacteria imbalance helped inspire the business. She believes that personalized AI could help close the gap between scientific knowledge and practical healthcare guidance.

Crafting Brand-Specific Marketing Voices

In marketing, personalization has long been a priority but achieving it consistently at scale has remained difficult. Jacquard uses AI to generate marketing copy tailored not only to target audiences, but also to a brand’s specific tone of voice. Its clients include Sephora, TUI and Sainsbury’s.

“We work with each client to define how their brand sounds, not in vague terms, but specifically. Is it formal or casual? Does it use humor, and if so, what kind? explains chief product and growth officer Toby Coulthard. “A brand might want to be warm but not chatty, or confident but not brash. Those distinctions matter, and they're difficult to maintain consistently across a large team, let alone at scale.”

The company found that different sectors required very different approaches. For Confused.com, direct and confident language consistently outperformed more creative or urgent messaging. P&O Cruises, meanwhile, required a more emotional and considered style. Jacquard’s AI analysis identified new messaging styles and copy lengths that outperformed human-written versions.

Enhanced Performance And Faster Scale

The most significant benefits brands derive from Jacquard’s personalized AI marketing copy include performance and scale. Coulthard says: “Marketers have known for years that they should be testing more, personalizing more, covering more channels, but there aren't enough hours to write the volume of copy that real personalization demands, and that’s where AI comes in.”

AI also helps companies maintain consistency across markets, agencies and internal teams, reducing the risk of brand voice drifting over time. Coulthard argues that avoiding generic AI output depends less on prompting techniques and more on understanding how brands use language.

As AI evolves, the emphasis is shifting from simple content generation toward systems capable of adapting to individual expertise, biology and brand identity.

Whether it is replicating the judgment of industry specialists, delivering tailored healthcare guidance or crafting marketing copy that reflects a company’s voice, the next wave of AI personalization innovation will depend on how effectively these systems understand the people they are designed to serve.