Chatbots in Ecommerce
Introduction
Chatbots. Today, the word instantly brings to mind the three leading LLM-enabled AI assistants ChatGPT, Claude, and Perplexity. But it’s taken now 60 years for the chatbot to reach where it is, along a rocky path filled with sporadic bursts of progress, and though the chatbot can bring users the most heartfelt delight when it’s done right, it is the most harshly criticised tech when it’s not. The highly personal nature of the chatbot saw its early beginnings take place in the field of psychology, psychotherapy, and psychiatry, before becoming a simple tool for entertainment as personal computing gained popularity, until the Internet tapped into its commercial potential as a basic shopping aide, then generative AI emerged, agentic ecommerce became a serious proposition and history, it seems, is still being made in this area, with its full capabilities not yet even imagined and issues of trust, privacy, and security yet to be adequately resolved.
Beginnings
“I think it is probable, for instance, that at the end of the century it will be possible to program a machine to answer questions in such a way that it will be extremely difficult to guess whether the answers are being given by a man or by the machine.”
Alan Turing, 15 May 1951.
Seventy-five years ago, the renowned mathematician and computer science pioneer, Alan Turing, gave a lecture titled, ‘Can digital computers think?’, where he articulated this vision of the ‘emerging science of computing’ (re-recorded by the BBC, 2024). A year earlier he had released his seminal paper ‘Computing Machinery and Intelligence’ (Turing, 1950) that described the ‘imitation game’, now known as the Turing test, as a way to measure the perceived intelligence of a computer program or ‘machine’ designed to communicate in written language with the user by how difficult it might be for an observer to identify which is the human. Through his writings, vision, and ideas about the potential of computing, Turing became an intellectual catalyst for researchers to consider machine intelligence as a serious possibility, and for curious academics in other fields to ponder the promises of closer human-machine interaction.
Fifteen years later, in 1966, one such academic, Dr Joseph Weizenbaum, who held a PhD in computer science, finalised several years of work on a program called ELIZA (see CHM, 2025a; and Adamopoulos and Moussiades, 2020), using his own SLIP list-processing language, designed to simulate a Rogerian psychotherapist by the use of simple rule-based scripting and pattern matching to reflect the user’s own words back as questions (see IEEE Intelligent Systems, 2008). Dr Weizenbaum had only wanted to find out more about how humans and computers might communicate with each other, creating the first ever chatbot in the process, and was not only surprised by how easily its users ascribed human qualities and behaviour to it, the scientist was so alarmed at the compelling effects of such a simple program that he turned his attention to writing a book about it. Computer Power and Human Reason: From Judgement to Calculation was published in 1976, in which Dr Weizenbaum urged caution on the risks of delegating empathy or judgement to technology (MIT News, 2008; and Smithsonian Magazine, 2026).
In 1972, in the last major milestone marking progress of the chatbot for at least another decade, due to a long AI winter that would see research funding scarce for further innovation until the 1980s, a similarly curious and inventive computer scientist and psychiatrist Kenneth Colby wanted to find ways that computing science could be used to improve treatment of patients experiencing various psychiatric conditions. He designed an interactive educational tool for psychiatry students and practitioners, PARRY, programmed to model the cognitive functions and communication of a person diagnosed with paranoid schizophrenia (see Adamopoulos and Moussiades, 2020). PARRY was the first chatbot considered to successfully pass the Turing test (Imperial College London, 2014; and Chatbots.org, n.d.), with 48% of users mistaking it for a human under fairly strict conditions, and Colby later went on to develop an AI version of cognitive behavioural therapy (CBT) for people with depression which was acquired by the US Navy and Dept. of Veterans’ Affairs (Wikipedia, 2025).
Development
The era of personal computing from the mid-1970s meant lack of industry funding posed no barrier to highly driven home lab researchers like self-taught 17-year-old programmer, Rollo Carpenter, who hardcoded a chatbot on a Sinclair ZX8 in 1982 (see BBC News, 2003). Carpenter kept working on his ideas for conversational computing, figuring out that he could get the program to learn from users by use of a feedback loop (The Guardian, 2004). He started an AI learning project called ‘Thoughts’, before creating his own spreadsheet-based programming language, Cleverscript, using it to build a chatbot he called Jabberwacky in 1988 (Jabberwacky.com, 2005; Adamopoulos and Moussiades, 2020; and India AI, 2022). Carpenter intended for the chatbot to just be a means for light entertainment, and a more humorous and playful take on the chatbot concept. Jabberwacky was eventually launched online in 1997, winning the Loebner Prize for most human-like dialogue in 2005 and 2006, before evolving into Cleverbot in 2008, which scored highly (59%) in a Turing test in 2011 (see Cleverbot, 2014; and Business Insider, 2014).
The Loebner Prize was established in 1990 by American inventor, Hugh Loebner (BBC News, 2004). It offered $100,000 and a gold medal to the first person who could write a program that was unable to be distinguished from a human, by a human. Similar to the Turing test, but looser and a little less scientific, it successfully aimed to garner broader participation and interest in the pursuit of machine intelligence. It certainly ignited the interest of computer science PhD, Richard Wallace, who saw how the debut of the Loebner Prize in 1990 failed to find a winner, and set about writing his own open-source programming language, AIML (AI Markup Language), to create a serious contender, ALICE (Artificial Linguistic Internet Computer Entity) (Pysh, 2025). ALICE was launched in 1995, and went on to win the Loebner Prize in 2000, 2001, and 2004 (see Shawar and Atwell, 2015; and BBC News, 2004). Where ELIZA had only 200 keywords and rules, ALICE had over 41,000 templates and patterns at the time, growing to nearly 50,000 by 2015 (Adamopoulos and Moussiades, 2020).
In 1996 while Carpenter was preparing to debut Jabberwacky, and Wallace’s ALICE was in its exciting early stages of being live online, seasoned software systems engineers Garrett Gruener and David Warthen founded, developed, and released Ask Jeeves, a search engine programmed to receive queries and respond in natural language (The Register, 2026). It was named after the butler in the P.G. Wodehouse comic novel series, and described as “scrappy but inventive” (The New York Times, 2026), the search engine was able to run natural language queries - how search engines work today - without use of AI or LLMs. For that reason it is considered an early predecessor to today’s AI chatbots (TechCrunch, 2026) and though, after surviving the dot-com bubble in the early 2000s, it struggled to compete with Google and was acquired by InterActiveCorp (IAC) in 2006 before ultimately being discontinued on 1 May, 2026, its achievements form an important part of the history of chatbot development (see Tom’s Hardware, 2026; and PCMag Australia, 2026).
In the early 2000s instant messaging (IM) became a popular way pre-social media to indicate availability status, create a digital persona, maintain a contact list, and communicate in real-time, and three entrepreneurs, Timothy Kay, Robert Hoffer, and Peter Levitan, set up a company called ActiveBuddy to build chatbots for IM platforms (CHM, 2025b). Kay believed that natural language would become the new user interface for computers and the trio created SmarterChild as the first chatbot to interact with other information systems and provide practical assistance to users by retrieving data like stock prices, weather, sports scores, dictionary definitions, movie times, and news (Adamopoulos and Moussiades, 2020). SmarterChild was available on America Online (AOL) and Microsoft (MSN), and though it struggled to commercialise, it is considered a major breakthrough in conversational technology (CHM, 2025b), inspiring further advancements by others in the field later, perhaps even the creators of Siri.
Steve Jobs is famously remembered for making technology feel more human, and back in 1983 he delivered a speech at the Aspen International Design Conference in which he foresaw the ability of users to interact with information systems through natural language within the next half-century, among other shorter-term predictions that proved somewhat accurate (Steve Jobs Archive, 2025). Then just a few years after Apple launched the iPhone in 2007, Jobs introduced Siri to the world (Apple Newsroom, 2011). Its arrival in October 2011, only a day before Jobs’ passing, gave the moment an unavoidable poignancy, not because Siri completed his vision, but because it pointed so clearly toward one he had been describing for decades. Despite ongoing criticism (see The Verge, 2025), it could be said that Siri led the popularisation of voice assistant technology as a mainstream personal device feature, from other smartphones with Google Now/Assistant (2012), to the PC OS with Microsoft Windows Cortana (2014), and Amazon’s Alexa (2014) for the smart home.
Around the mid-2010’s social media giants like Facebook started to become messaging platforms as well as publishing platforms. Already operating Messenger, Facebook integrated messaging more deeply into its ecosystem and in 2016 it allowed merchants to create their own chatbots with tools like Pandorabots, co-founded by Richard Wallace, for customer service, product discovery, and transactional support inside the same conversational spaces where users already spent their time (see TechCrunch, 2016a). Ebay was one of the first to build a chatbot on Facebook Messenger, called Shopbot (TechCrunch, 2016b). Shopbot was designed to help consumers find what they’re looking for faster on eBay, using AI to improve its responses and ML to learn over time, Other marketplaces like Alibaba built their own customer service chatbots, like AliMe in 2015 (Digital Data Design Institute at Harvard, 2018), and Amazon released Lex (Amazon, 2017) which enabled sellers to create applications that support lifelike interactions, or ‘Lex bots’.
State of the Art
The launch of conversational AI platforms ChatGPT, then Perplexity, and Claude in 2022 and 2023 stirred the imaginations of consumers and merchants about what might be possible with AI in retail, resale, and leisure. Each platform has been following its own path to create a unique and valuable offering for both. Claude incorporated ‘computer use’ in 2024 which could navigate interfaces, click, type, and complete multi-step tasks, as a foundation for agentic commerce (Anthropic, 2024). Perplexity introduced its Buy with Pro feature in 2024 which evolved into Instant Buy by November 2025, allowing US users to search and purchase products directly through the platform (Perplexity, 2025; and PayPal, 2025). ChatGPT moved from being a general conversational assistant to incorporating commercial plugins in 2023 from major brands like Instacart, Klarna, Shopify, Expedia, and OpenTable, shifting to agentic commerce in 2025, with its Agentic Commerce Protocol, dedicated shopping research, and partnering with Shopify for its Instant Checkout (OpenAI, 2025), before stepping back in March 2026 in a humble reappraisal of the complexity involved in agentic transactions (Forrester, 2026).
Microsoft and Google have been furthering their AI features through Copilot and Gemini, respectively, originally named Bing and Bard, since 2023. Microsoft introduced the Copilot Merchant Program in April 2025 (Microsoft, 2025), empowering businesses to offer an integrated shopping experience for customers, before adding Copilot Checkout and Brand Agents in January 2026, so each step of the purchase journey is branded, “from curiosity to checkout” (Microsoft, 2026). In the same month Google launched its Business Agent, Universal Commerce Protocol (UCP), Gemini Enterprise CX, Shopping Agent, and Direct Offers (Google, 2026a). Gemini CX is promoted as pre-built agents which can independently plan, reason, and act on behalf of a brand, managing the customer lifecycle from product discovery to autonomous post-purchase resolution (Google, 2026b). Its Shopping agent is a proactive digital concierge that can process text, voice, and images to autonomously build carts and execute consented actions.
For marketplaces, AWS released Rufus the AI assistant in February 2024, before boosting its capabilities in November 2025 with advanced and custom-built LLMs to offer next-gen AI experiences like predictive discovery and better personalisation (Amazon, 2025), and it is expected to deliver $10 billion in additional annual sales FY26 (Yahoo! Finance, 2025). In October 2025 Meta announced Business AI (Meta, 2025), a sales concierge and AI agent for ecommerce websites, WhatsApp and Messenger chats, Facebook and Instagram ads, to provide personalised customer service 24/7 and at March 2026 was facilitating about 10 million conversations per week (TechCrunch, 2026a). In November 2025, Alibaba launched its AI assistant, Qwen (Alibaba Cloud, 2025), described by Forbes (2026) as “AI that acts as an operating system for commerce” by automating consumer decision execution, and eBay continues to capitalise on its extensive data built over 30 years to create value with AI through, for example, its merchant AI assistant and newer shopping agent (see CNN, 2025).
Shopify has been active in AI since the mid 2010s when it initially launched its Facebook Messenger chatbot in 2016 (Business Insider, 2016), which is thought to have been the first chatbot on that platform specifically for businesses. In 2019 it released Inbox as a way for merchants and customers to message each other directly from a Shopify store (Shopify, 2019). In 2023 it created Sidekick to assist sellers with marketing and store management (Reuters, 2023), and then in 2025 Shopify co-developed the UCP with Google as an open standard for AI-driven transactions - which increased by a multiple of 15 in 2025, as well as introducing Agentic Storefronts so that merchants can access AI channels and their in-app checkouts such as ChatGPT, Microsoft Copilot, and Google Search AI Mode and Gemini, without needing to set up an actual Shopify store (Shopify, 2025). Agentic Storefronts are active by default for eligible Shopify stores and managed with Shopify Admin.
Beyond the fast-moving developments among major ecommerce platforms and marketplaces, major retailers, luxury brands, and leisure services are enthusiastically adopting conversational technology with applications of generative AI and agentic AI. Possibly the most talked about area of conversational AI and its potential is travel, and Expedia Group, with its 30 years of expertise in travel technology, is leading the way. It has been researching applications of AI in improving the travel experience for consumers since 2018, and continues to develop innovations from Smart Mix in 2018 to personalise and optimise search results, to the Partner Central Chatbot on its Centralised Conversational AI Platform (CCAIP) in 2019, through to launching Romie the AI travel assistant in 2024 (see Expedia Group, 2024, 2025a, and 2025b). At 2025, Romie had conducted 143 million conversations and was helping more than 50% of travellers to self-serve their queries without making a call (CX Dive, 2025).
Possibilities
After the long, slow, clumsy crawl of chatbot development from the 1950s to 2010s, the release of common-use generative AI technology from 2022 has seen advancements in this area accelerate at light speed, and the trajectory continues to gain momentum. Ideas that seemed conjecture in only 2023 are the reality today, with agentic commerce already in our midst. The main barriers to it becoming as popularised as other technological advancements over the past half-century include our ability to trust its accuracy, to know that our data is secure when we transact with it, that our privacy will be maintained, that we are getting real value in return for sharing data, and that the AI will not be used to further obscure and evade accountability for poor service. These matters are less to do with the state of the technology itself than how it is architected, implemented, and maintained by those deploying it to provide goods and services, towards which there has been much research.
Expedia (2026) found that while almost half of travellers trust generative AI in planning travel (40-53%), two-thirds (66%) would not trust AI to transact on their behalf due to loss of control (57%), data/payment privacy (57%), and misuse of personal data (56%). Further, over 40% worried about poor customer service if something goes wrong after an AI-driven purchase. This largely mirrors earlier findings by Bain & Company (2025), who identified that while 30-45% of US consumers use genAI in shopping research, a third trust retail-agents more than third-party agents, and about 50% wouldn’t allow AI to handle a transaction end-to-end on its own. These results support those of Accenture (2024), where fewer than one-fifth of 7000 global customers said AI technology had improved their customer experience, instead feeling disgruntled with having to do more work and share more data in return for seemingly less value.
In response, Expedia (2026), Bain & Company (2025), and Accenture (2024) all recommend that organisations treat trust, control, and service quality as design requirements rather than afterthoughts. That means making AI assistance optional, clearly bounded, transparent about how recommendations are produced, and supported by secure payment and data-handling practices. Expedia (2026) explain that critical to trust is accountability end-to-end across the purchase journey. Bain & Company (2024) also emphasises clear data policies, customer feedback loops, and fast correction of inaccurate or unwanted AI outputs, while Accenture (2024) argues that AI should reduce effort by personalising support, anticipating needs, and connecting service insights across the business. The shared lesson is simple: customers may accept more automation, but only when it gives them more value, not less control.
For business, the risk lies in becoming invisible in the purchase process, where conversational AI becomes the new storefront while the brand itself fades into the background. This can even happen when the consumer doesn’t intentionally use genAI in search, rather, inadvertently surfacing products, suppliers, and prices through ‘how-to’ and similar queries (BCG, 2026). BCG (2025) warns that retailers risk being reduced to “background utilities in agent-controlled marketplaces” if they do not act, while Bain & Company (2026) argues that third-party agents could weaken direct customer connections, brand loyalty, and unit economics. McKinsey (2025) similarly notes that, in an agentic world, the customer may no longer be “a human with a browser” but an autonomous agent acting on that person’s behalf. In that world, brands may lose traffic, first-party data, pricing power, cross-selling opportunities, and the chance to shape the customer’s experience directly.
The recommended response is not to resist agentic commerce, but to become visible, useful, and trusted inside it. BCG (2025) suggests optimising for third-party AI platforms, developing proprietary AI-driven customer experiences, and building strong operational foundations for AI integration. Bain & Company (2026) recommends choosing a deliberate posture toward third-party agents: embracing them, building proprietary agents, or fortifying the home-site value proposition with exclusive products, services, support, warranties, and data-sharing arrangements. Further, McKinsey (2025) adds that businesses will need agent-readable product data, agent-ready APIs, authenticated interfaces, and renewed approaches to loyalty, payments, fulfilment, and returns. The new approach is therefore to design for both the customer and the customer’s agent, in order to see beyond the risks of disruption to the possibilities of reinvention (McKinsey, 2025).
Summary
Chatbots? The word now seems slightly outdated, an oversimplification and understatement of the technology we have today. The advances we’ve achieved in human-machine interaction over just the past 75 years have been phenomenal and continue at a fast pace, expanding from scripted exchanges into conversational assistants, voice interfaces, ecommerce agents, and increasingly autonomous systems. Yet the question posed by those who pioneered the chatbot in the 1950s remains the same: What happens when we can talk to machines as we can to another human? The benefits could be substantial, but only if trust, privacy, security, and human judgement remain central to its development. Perhaps Turing can still offer the most fitting summary: “The whole thinking process is still rather mysterious to me, but I believe that the attempt to make a thinking machine will help us greatly in finding out how we think, ourselves” (Alan Turing, 15 May 1951).
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