Introduction

Personalisation has been the holy grail of functional wellness for years. In 2026 that ambition is meeting practical capability: artificial intelligence and machine learning (AI/ML) are finally making tailored CBD solutions scalable. From bespoke oil blends to context-aware product recommendations, AI is being used to match ingredients with user needs and the scientific literature — changing how British brands design, position and sell CBD.

What's trending: AI-driven personalisation goes mainstream

Several converging forces explain why personalisation is suddenly moving from boutique to mainstream:

• Strategic partnerships are accelerating capability. BusinessOfCannabis (2026) reports that Kanabo has partnered with Pomicell to deploy the POMI AI platform, which analyses formulations and cross-references more than 40 million clinical and scientific data resources to match active ingredients to stated indications.
• Industry expectation that AI will scale personalisation. Coverage in BeverageDaily predicts AI will finally make true personalisation scalable across functional wellness categories — surfacing brands and recommending products based on clinical evidence rather than influencer reach alone.
• Market trends and consumer appetite. Market commentary from GetQuantic lists personalised CBD products as a top 2026 trend, noting that AI and data analytics are driving bespoke oils, edibles and hybrid formulations targeted at needs like sleep, stress and pain relief. Trend summaries also cite a 2023 survey finding that roughly 70% of consumers would pay more for customised health products, signalling real commercial appetite.
• Technical transfer from pharma to wellness. Pharma-focused analysis in the European Pharmaceutical Review shows AI/ML is already being used to predict formulation properties such as solubility, stability and exposure — capabilities that translate directly to CBD formulation development and speed up R&D.

Why it matters

This shift matters for three reasons. First, credibility: AI lets brands base product suggestions on a wider body of evidence and formulation science rather than solely on marketing narratives. Second, efficiency: predictive models reduce the experimental burden in formulation development, lowering cost and time to market. Third, customer experience: AI enables dynamic, context-aware recommendations — adapting suggestions to a customer’s preferences, prior purchases and even time of day — which can improve relevance and conversion, a point emphasised by Forbes and other marketing analysts.

Implications for UK CBD brands

• Product development moves from art to evidence-informed science, with data guiding cannabinoid ratios, terpene matches and delivery formats.
• Retail experiences become personalised: online quizzes, in-app recommendations and subscription offerings can be powered by models that suggest finely tuned oils, gummies or vapes.
• Compliance and transparency become differentiators: brands that pair AI recommendations with accessible Certificates of Analysis and up‑to‑date advertising compliance will build more trust.

Examples in practice

The Kanabo × Pomicell story crystallises the trend. According to BusinessOfCannabis (2026), the POMI platform ingests millions of clinical and scientific data points to identify ingredient–indication alignments and to suggest formulation approaches. That capability — matching evidence to product design — is precisely what GetQuantic and BeverageDaily describe as the next wave of functional-wellness personalisation.

On the product side, brands are already using AI-informed approaches to diversify formats and dosing. In the UK market you'll see tailored offerings across traditional drops and newer formats. For consumers who prefer classic tinctures, higher‑strength laboratory-verified options such as the Wylde Natural Cold Pressed Drops 1000mg CBD Oil 10ml are the sort of SKU a recommendation engine might propose for a user seeking a flexible sublingual regimen. For people who favour measured doses and ritual, microdosed edibles like Wylde CBD Gummy Bears 30x (10mg per bear) Full Spectrum can be surfaced by an AI system trained to match flavour preferences and intended use times. Meanwhile, formulators experimenting with inhalable formats have higher-potency e-liquids such as Canavape Blue Dream Complete CBD E-liquid 1800mg 50ml appearing in portfolio mixes where rapid onset is a priority.

High‑strength tinctures used by experienced customers are also part of the picture; some platforms will recommend concentrated options such as the CBD Living Tincture 30ml 4500mg (0% THC) when a user profile indicates a preference for microdosing from a low-volume product.

Regulatory and marketing guardrails

AI enables richer recommendations, but the regulatory landscape remains complex. Advertising and platform rules (search and social) continue to evolve: guidance compiled on cannabisregulations.ai in 2026 warns that Google, Meta and TikTok maintain nuanced restrictions for CBD-related content and ads. That means AI-powered recommendation engines and dynamic messaging must be built with compliance by design — using legal copy templates, avoiding medical claims and ensuring regional variations are respected.

Operationally this requires cross-functional teams: data scientists, legal/compliance specialists, and product formulators working together so models prioritise evidence and compliant phrasing (for example, using language such as "may support" or "many users find" rather than therapeutic claims).

Future outlook: five things to watch

• Evidence-first discovery — consumers will increasingly expect product suggestions that reference research or clear formulation rationale rather than celebrity endorsements alone.
• Model-driven R&D — predictive tools will shorten iteration cycles and enable smaller brands to offer bespoke products without huge lab overheads.
• Context-aware commerce — recommendations that account for time of day, recent purchases and lifestyle signals will become standard in apps and retail sites.
• Compliance-first personalisation — brand trust will hinge on transparent labelling, COAs and conservative, non-medical marketing approaches that align with platform policies.
• Ethics and data privacy — successful personalisation will depend on clear consent flows and privacy-preserving modelling, especially as more sensitive health-adjacent data is incorporated.

Conclusion

AI-powered personalisation is transforming how UK brands create and recommend CBD products. The Kanabo × Pomicell partnership exemplifies a new generation of data-driven formulation work, while industry commentators predict wider adoption across oils, edibles and inhalables. For brands, the opportunity is twofold: deliver more relevant, evidence-informed products to consumers, and do so within a compliance and privacy framework that preserves trust. For shoppers, that means better-matched options — but also a need for clear labelling and transparent sourcing so choices can be made with confidence.

As the market matures, expect a steady stream of AI-assisted innovations — provided brands prioritise evidence, compliance and humane data practices over hype. The result should be a more tailored, credible and accessible CBD landscape for UK consumers in 2026 and beyond.