It’s late in the year, Christmas is approaching fast, and for many people the annual gift-buying scramble is reaching its peak. Consumers juggle gift preferences, budgets, purchase channels and delivery-date guarantees. For both retailers and shoppers, it’s the busiest, most pressured time of the year. 

All the while, around the world, the retail sector is undergoing a revolution – driven not by customers but by machines. As shoppers increasingly lean on AI for discovery and purchase decisions, inbound traffic from generative-AI assistants to UK retail sites has ballooned by 

. Research shows that globally around half of all web traffic now originates from bots rather than humans (2), while surveys of US consumers found that  

  plan to use AI to assist with shopping in the future (3). 

For those of us at True who spend our time advising retailers on the latest technology, these facts raised an obvious question: 

Can a bot do all of my Christmas shopping for me? 

And if so, what implications would that have for retailers and brands? 

and gave each of them a profile consisting of demographic features, Christmas values, and gift preferences. The idea was to provide sufficient information to allow an agent to guess what these personas might like for Christmas. 

We agreed some guardrails for the search: the gifts must all fall within a total budget (£250), be purchased at their lowest costs, sourced from unique retailers, and be guaranteed to arrive before 23

Finally, the experiment would test some differing levels of human involvement in the shopping process, ranging from user-led to fully autonomous:

At the lowest level of automation, the AI behaved most like a capable shopping assistant. It asked sensible clarifying questions, quickly built a picture of taste and constraints, and translated vague preferences into concrete product directions. 

. It surfaced products, explained why they might suit a person, and spoke to some trade-offs around price, delivery, reviews, and sustainability. Great. 

. The AI often narrowed too quickly, moving to specific products and next steps before fully exploring the category or getting my broad feedback. When deeper research was triggered, it sometimes swung too far the other way — taking a long time to return results that were thoughtful and well-justified but not always proportionate to the time spent. 

Execution was mixed. Multi-retailer discovery worked well, but product imagery and links were not always reliable. 

Though the agent was able to identify the potential for discount codes, search for those codes, and test them on the checkout page, which looked, frankly unsettlingly conscious activity. 

Overall, Level 1 worked well as a thinking partner. It reduced cognitive load and got me to a good recommendation quickly, but still required a human to manage pacing, verify details, and sequence the task effectively. 

Level 2: Semi-Autonomous Agentic Commerce 

Level 2 was surprisingly effective. With richer context upfront, a few more clarifying instructions around preferences, and fewer interruptions, the AI handled end-to-end reasoning much more coherently. Broad product category selection, shortlisting, and comparison were generally strong, and it comfortably worked across multiple retailers rather than defaulting to a single platform. 

This was also where the system began to show its value as a 

, not just a recommender. It reduced noise, surfaced credible options, and explained trade-offs like it had done in the more conversational-style of the previous experiment. In several cases, the final shortlist was good enough that the human role became one of approval rather than decision-making. 

Checkout automation varied by retailer. Links broke. Images didn’t always represent the actual product. 

Most interesting to me was the fact that, despite a clear instruction to find the cheapest retailer to buy the recommended product, the agent ignored this request and prompted to purchase from a more ‘reliable’ retailer (£2+ than the cheapest option, for the exact same product). The implications of this for retailers are huge and are explored in the implications section below.  

Level 2 felt close to something useful, but not yet reliable. The intelligence was there. The slick execution layer wasn’t.

Level 3: Fully Autonomous, End-to-End Agentic Commerce 

At Level 3, the experiment removed the safety net of requiring the agent to confirm with me before progressing. The AI was authorised to choose, proceed, and push as far into purchase as possible without asking for any confirmation. 

What stood out was how confidently it made decisions. It concocted an elaborate assessment framework which quantitatively assessed products against elements it thought the persona might value. Category choice, product selection, and retailer preference were sensible and conservative, favouring established brands, clear descriptions, and predictable delivery. This wasn’t creative thinking, it was structured risk minimisation. 

With my payments and delivery information preloaded into the prompt, the agent was able to partially complete the check-out fields, but completing the payment transaction still remains the hard boundary. 

Identity checks, checkout UX assumptions, and payment authorisation are, for now, still a human-activity. The AI didn’t lack the intent, it knew the ask, but the technical systems it was navigating weren’t designed for non-human actors. Yet. 

Level 3 failed in its main purpose; the ‘single prompt purchase’. This is because the surrounding infrastructure still isn’t ready for autonomous transactions. Many of the technical, regulatory, and platform foundations needed to make this possible are being developed rapidly, but they haven’t yet converged into something reliable enough for true end-to-end autonomy. We are confident this will be possible before next year’s festive frenzy.  

Gift: Lefrik Scout Stripes Backpack - Olive Green from Lefrik, bought from KJ Beckett

Gift: Friday Night Pottery workshop from Belfast Ceramics Studio

Gift: Hermione (Harry Potter) Fancy Dress bought from 

Gift: Luxury Chocolate Selection Box from Fortnum & Mason

Given only light context, the AI was consistently good at inferring taste, lifestyle, and intent, and often explained 

 something would suit a person, not just 

Its biggest practical win was filtering. A long-list of messy options quickly collapsed into short, decision-ready lists and recommendations, saving time even when a human still made the final call. 

The AI naturally searched across lots of brands and retailers, often surfacing credible options beyond the obvious names. Notably, the first results weren’t always the cheapest, suggesting clarity and metadata matter more than price in agent-led discovery. 

Unless explicitly guided, the AI tended to converge too quickly, optimising early choices rather than fully exploring the category landscape. 

Broken links, misleading imagery, inconsistent delivery information, and discount-code loops regularly interrupted otherwise strong decision-making. 

No matter the level of autonomy, payment and identity remained the point where human intervention is unavoidable, for now. 

Agentic shopping works best when treated as a 

, not a single request. Let the AI help you explore, narrow, and compare, but the experiment highlighted that the best results come from being deliberate about the order in which you want to do those things and the ranking of your trade-off preferences. Early framing strongly influences what gets surfaced later. 

For now, the sweet spot is partnership. Use AI to reduce effort and improve decision quality, but expect to stay involved along the way (verifying details, sanity-checking claims, and completing checkout). Full automation is coming, but it isn’t the most reliable mode yet. 

How products get discovered will change before where they’re sold

having product/service information legible to machines matters as much as being compelling to humans

. Products with clearer descriptions, better imagery, and richer contextual signals were consistently easier for AI to reason about and recommend. 

More importantly, intent matters more and more. Brands that clearly articulate 

 — and for whom — are more likely to be surfaced when an agent is trying to complete a mission, not just match keywords. Over time, structured, mission-based product information may become a meaningful source of advantage, even in channels brands don’t own. 

Execution, not intelligence, is the new battleground

The experiments showed that AI reasoning is already strong, but retail infrastructure is patchy. Checkout complexity, brittle discount logic, inconsistent delivery metadata, and poor or inconsistent product imagery regularly broke otherwise good journeys. 

As agents take on more of the shopping process, retailers with 

simpler, more predictable, and more machine-readable purchase flows

 will increasingly become default endpoints for AI-driven demand. In this world, being “agent-friendly” may become as important as being mobile-friendly once was. 

Agentic shopping already works well to take some of the stress out of exploring and can help us find gifts we otherwise might not have considered, especially when time is short and Christmas stress is high.  

But gift-giving shouldn’t be about optimisation. The value lies in intent, thought, and care – things AI can support with, but hopefully not replace. 

The technology will keep improving. As it does, the more interesting question isn’t what bots can buy for us, but what activities we choose to keep human. Christmas feels like a good place to start drawing that line.  

Experiments in Agentic commerce: implications for customers, brands and retailers.

Can A Bot Buy My Entire Christmas Shopping List?

Neurodiversity is a broad term to describe the natural variation in how we think, learn and process the world around us. It encompasses conditions such as autism, ADHD, dyslexia and dyspraxia, amongst many others, and whilst neurodiverse people often have unique strengths and talents, they can also face challenges in daily life and the workplace.  

 of the global population are neurodiverse, meaning a potential global market of 

 consumers that builders in the space can tap into. Despite this huge opportunity, the neurodiverse have been historically marginalised, particularly within the tech community. Some key reasons for this are:  

Existing solutions often fall short; tools are either specialised but expensive, or cheap but lacking adaptation to individual needs. Solutions are also often built around assumptions rather than lived experience, resulting in poor usability and efficacy.  

However, it’s not all doom and gloom. Rising awareness, tech advances and a growing digital movement are opening up opportunities for innovation and greater inclusivity.   

So, why does this long-ignored segment now present fertile ground for innovation? We think there are three primary drivers:  

Awareness has exploded. For instance, mentions of ‘neurodiversity’ in UK job postings have nearly quadrupled since 2018 and posts with #ADHD on TikTok have surpassed 11 billion total views.  

Beyond traditional social media, platforms like Reddit have given rise to deeply engaged peer networks. For instance, communities such as r/ADHD (1.2 million subscribers), r/autism (390,000 subscribers), and r/neurodiversity (110,000 subscribers) highlight just how large and active neurodivergent online communities have become.  

The result of all of this is a highly engaged, self-advocating community that openly share their frustrations and favourite solutions. For entrepreneurs, this represents a powerful, organic distribution channel with inherent virality potential.  

As mentioned previously, the heterogeneity of neurodivergence has made the market historically difficult to serve. Needs differ widely not just between conditions, but between individuals and across contexts, meaning products rarely fit everyone, and ‘one-size-fits-all’ tools struggle to gain traction. 

AI has fundamentally changed this. Systems can now adapt and tailor interfaces, workflows, sensory environments and support in real time, learning from each user’s patterns and preferences. Combined with wearables and biometrics, this allows products to deliver highly individualised experiences at low cost.  

Governments are paying attention. In the UK, initiatives like the 

Partnerships for Inclusion of Neurodiversity in Schools (2025)

 guidance for neurodiversity at work signal a shift from awareness to action. Institutional buy-in has legitimised the market and lowered barriers for innovators. 

Below are some of the subsectors within which startups are emerging:  

Neurodiversity represents more than a market opportunity - products designed for neurodiverse users can create better experiences for everyone, with clearer interfaces, adaptive communication and flexible functionalities.  

The next generation of founders who treat neurodiversity not as a subset but as a starting point will define the future of human-centred tech. What began as assistive is becoming increasingly essential. 

At True, we back mission-driven entrepreneurs building a better, more sustainable future within the consumer space. If you’re a Pre-Seed to Series A founder building tech for neurodiversity, or an investor and would like to exchange ideas, we would love to hear from you. Please get in touch at 

VC's latest blog by Amai Ganjam dives into how neurodiversity is no longer a niche consideration in tech — it’s a rapidly growing movement that’s reshaping how products are built and experienced.

From Assistive to Essential: Tech for the Neurodiverse 

 we spent three days at Web Summit in Lisbon — speaking with founders, operators and investors, and seeing hundreds of demos, workshops and main-stage sessions. Across it all, five themes kept resurfacing. Each theme points to a deeper shift in how retailers, brands and enterprise organisations will build, operate and compete over the next 18–36 months. 

Below are the ones we believe matter most: 

1. Agentic Commerce Moves from Theory to Deployment

Agentic commerce was everywhere this year — and not as a thought experiment. AI-driven traffic to ecommerce sites reportedly grew more than 4,700% year-on-year in July 2025, and retailers like Walmart and Etsy are already enabling transactions directly inside AI applications like ChatGPT and Perplexity. It’s a clear signal: a growing share of shopping journeys will soon begin with an agent, not a human. 

That shift has consequences. If the primary “customer” becomes an AI agent optimising for structured data — price, stock accuracy, delivery terms, returns clarity — then the traditional discovery journey compresses. The battleground becomes data quality, not merchandising; clarity, not persuasion. 

Dana Lawson’s session on Zero-UI engineering captured this well: retailers will need to design for AX (agent experience), not UX. That means cleaner schemas, machine-readable policies, real-time availability and well-structured APIs that let agents interpret and compare products confidently. 

A wave of early infrastructure is emerging. 

 lets brands create a dedicated AI-readable web layer. 

 provides a real-time MCP/API bridge so agents can fetch accurate availability, pricing and policy data — reducing the risk of hallucinated recommendations, out-of-date stock, or unsanctioned purchases. 

But major questions remain. Agents still recommend out-of-stock or unsuitable products, largely because most aren’t yet integrated into live commerce systems. And as agents transact independently, intent, liability and dispute resolution become non-trivial. Retailers will need strong recourse models before this scales. 

The Missing Layer in Enterprise AI: Context, Not Capability

Enterprise AI surfaced repeatedly, but the mood was clear: adoption is rising; ROI is not. Speakers cited MIT’s finding that roughly 95% of AI projects fail to scale, alongside McKinsey’s estimate that nearly 80% of companies using AI see no material bottom-line impact. 

The message was the same across the board: the bottleneck isn’t model capability — it’s context. 

Many enterprise deployments still sit on top of weak foundations: fragmented knowledge stores, shallow retrieval layers, inconsistent metadata and limited orchestration across systems. Without a shared, structured source of truth, even highly capable agents behave unpredictably, generating confident answers that collapse under scrutiny. In other words: enterprises don’t have a model problem, they have a memory and meaning problem. 

A growing set of startups is positioning against this gap. 

 each focus on strengthening the knowledge layer itself — building the contextual scaffolding that lets agents reason, retrieve and respond with reliability rather than surface-level fluency. 

Until that layer matures, experimentation will continue to outpace impact.  

Cybersecurity Becomes a Front-Line Commercial Risk

Cybersecurity felt unusually front-and-centre this year — especially for retailers, where the financial and reputational stakes are rising sharply. Recent breaches at Louis Vuitton, Dior and M&S illustrate how quickly incidents now escalate, with losses reaching tens or hundreds of millions. Cyber risk has now moved well beyond IT. It’s now a commercial issue. 

The newest wave of startups reflects that reframing. 

 takes a human-centred approach, using AI-powered simulation and personalised training across email, SMS, voice, chat and deepfakes — acknowledging that most breaches originate with people, not perimeter failures. Whereas 

 covers the technical heavy lifting — automated pen-testing, round-the-clock monitoring and dark-web intelligence — aimed at organisations without a full internal security function. 

A recurring theme: tools alone aren’t enough. Resilience — clear roles, practiced response plans and decision-making discipline — now matters as much as detection. In sectors built on trust, a breach doesn’t just compromise systems; it compromises brand credibility.  

4. The Creator Economy Becomes a Performance Channel

The creator economy had a visible presence at Web Summit — not just in startup pitches, but across the headline speakers. Talks from Grace Beverley and Josh Richards underscored how far the space has moved: influence is no longer the product; it is the distribution engine for IP, brands and communities. Creators aren’t just producing content anymore — they’re building brands. 

In parallel, we saw a new layer of tooling emerge to help brands run creator partnerships with more structure and less guesswork. 

 is targeting Shopify-powered brands with automated creator matching, briefing and outreach. 

 is pushing into enterprise-level campaign governance with workflow control, audience authenticity and analytics. 

 is taking a different angle, offering performance-based pricing where brands pay only for verified commercial outcomes. 

What we’re seeing is a shift toward creators becoming a structured, performance-driven commercial channel. Brands that treat this as an ongoing, measurable capability — supported by the right platforms — are the ones unlocking repeatable value rather than sporadic campaign wins. 

5. Robotics and Embodied AI Move Toward Practicality

Robotics and embodied AI were highly visible this year, from roaming floor demos to full humanoid models capable of shaking hands and holding conversations — including a striking blue-haired humanoid showcased by the Artificial Intelligence Association. But after the novelty wore off, the real question felt more practical than futuristic: what design, environment and ROI case will make robots useful, not just impressive? 

Two of the strongest examples came from companies focused on operational efficiency rather than anthropomorphic design. 

 demonstrated how hospital-grade cleaning and workflow automation can unlock real clinical value, while 

 showcased multi-purpose cleaning, delivery and building automation robotics for hospitality, logistics and smart infrastructure. Both prioritise safety, uptime, and integration over human-like interaction — and that felt far closer to deploy-ready reality. 

At this stage, the robots gaining real traction are the ones built for a specific job and a clear business case — not humanoids designed to feel like co-workers. The bigger open question remains: as hardware capability accelerates, where will commercial value justify the investment, and what form factor will end-users truly trust — tool-like, human-like, or something in between? 

Web Summit gave a clear view of where the industry sits today. Curiosity has met capability, and the focus is shifting toward what creates repeatable operational value. Clean data, strong architecture, governance and clear ROI are becoming the real differentiators. 

The takeaway wasn’t that everything is ready. It’s that the centre of gravity has moved. The next wave belongs to operators who can turn promising technology into measurable outcomes. 

In our latest blog, we break down five themes we saw at Web Summit 2025, from agentic commerce to enterprise AI, cybersecurity, creators and robotics, and what they signal about how retailers and brands will build and compete over the next 18–36 months. 

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Can A Bot Buy My Entire Christmas Shopping List?

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From Assistive to Essential: Tech for the Neurodiverse

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Web Summit 2025: Five Themes Shaping the Next Wave of Tech