What Are Machine Customers?
So-called machine customers are non-human customers that behave similarly to humans during the purchasing process: they can decide what and when to buy and autonomously carry out the ordering process. Machine customers, also known as custobots (short for “customer robots”), operate independently with the help of artificial intelligence (AI) and machine learning (ML), interacting with companies or services, such as online platforms like Amazon. They can also respond to inquiries from humans or other machines via chatbots, automated processes, or virtual assistants.
Do machine customers already exist?
Yes, machine customers already exist, but they often still operate under human supervision or with limited decision-making power. They play a role both in large-scale industrial applications, such as supply chain management, and on a smaller scale in smart homes (IoT). Voice assistants like Alexa can already make online purchases on their own when prompted. More advanced examples like Perplexity’s AI shopping assistant are pushing these boundaries further. However, humans still play a significant role in these processes – something that could change in the future.
How do machine customers differ from human customers?
Real people make purchasing decisions based on a variety of factors. Advertising, communication psychology, emotions, and feelings – such as hunger – play a major role in the path to a purchase, and the process is shaped by many subjective perceptions.
An example from the supermarket: We go shopping and put the ten things from our shopping list in the cart. Then we add something on sale that we didn’t plan to buy, and maybe something with eye-catching packaging that grabs our attention. At the very end, a chocolate bar suddenly ends up in the cart – unplanned, but we were having a bit of an energy dip. Machine customers are very different from human customers. They make decisions based on programming and measurable factors like quality or price, not on emotions or spontaneous impulses.
AI-Driven Purchasing Decisions: How Machine Customers Work
Machine customers are programmed with specific, predefined goals and act based on algorithms. For instance, they might aim to buy items at the lowest possible price or maintain a specific inventory level. To do this, they collect data on availability, prices, and consumption behaviour. As soon as a defined condition is met, a low stock, for example, a purchase is triggered automatically. These purchases don’t necessarily have to involve products. Services like booking doctor appointments or maintenance tasks can also be handled by machine customers.
By the way, my colleague Neil Sinclair has written a compelling article on intelligent AI-powered systems, which also play an interesting role in ordering processes: Agentic AI: Creating Value with Intelligent Systems – applydata
Advantages of Machine Customers – Why Machines Are Becoming More Important as Buyers
Time efficiency
Machines have advantages over humans in several areas. For example, machine customers can place a much larger number of orders in much less time. They analyze data in real time, recognize demand immediately, and order automatically without delays or human intervention.
Reducing human error & promoting sustainability
Incomplete or delayed orders are common in daily business, but they can cause financial losses or production delays. AI-powered algorithms ensure orders are precisely aligned with actual needs, avoiding over- or under-ordering. This also contributes to environmental protection and waste reduction, e.g., perishable foods are ordered only in quantities that can be consumed within a certain timeframe.
Scalability
Processes become more efficient, time-saving, and scalable. Machines can operate 24/7, adapt dynamically to demand changes, and, if properly trained and configured, respond to market developments by comparing prices and delivery times in real time to make cost-effective decisions.
Stability through automated purchasing decisions
Successfully integrating machine customers into existing logistics and supplier systems supports the automation and optimization of the entire procurement process. This can lead to lower operating costs, increased resource availability, and a more stable supply chain.
Use Cases for Machine Customers
Machine customers can appear as procurement bots that reorder consumables like printer paper or coffee. Another important area is automated service booking. While we still book our next doctor appointment via platforms like Doctolib because we remember we’re due for our annual dental checkup, a machine customer could autonomously schedule appointments based on availability and urgency, syncing with our calendar.
In the banking and finance sector, automated trading (particularly algorithmic or high‑frequency trading) has held a central position for many years. Market data are evaluated in real time, buy and sell orders are executed within milliseconds, and profit potential is exploited to the fullest. On this basis, a promising field of application is emerging for machine customers that can act on behalf of individual investors.
The automotive industry is another area where this technology could thrive: autonomous vehicles might schedule maintenance appointments on their own, for example, when sensors detect that a part needs replacing soon or the oil level is nearing a critical threshold.
Real-World Example: Perplexity’s AI Shopping Assistant
In November 2024, Perplexity launched its AI shopping assistant, representing one of the most advanced implementations of machine customer technology in the consumer market. It functions as an intelligent intermediary between shoppers and products, fundamentally altering the customer journey. The assistant combines visual search capabilities with AI-powered product recommendations based on user queries. Most notably, its “Buy with Pro” feature enables one-click purchasing directly through Perplexity’s platform, so the system is handling product research, comparison, and checkout logistics in one run.
For merchants, this shift introduces a critical new dependency: their visibility now hinges on algorithm favorability rather than traditional marketing approaches. These AI systems can effectively become powerful digital gatekeepers, potentially reshaping which products succeed in the marketplace.
Thoughts on how it could affect consumer behavior and the necessity of a brand identity
The impact on consumer behavior could unfold in two distinct directions. On one hand, we might see an increase in impulse purchases as friction is removed from the buying process, while simultaneously witnessing a decline in traditional brand loyalty. This would occur as AI assistants prioritize measurable metrics like price and ratings over the emotional connections brands have cultivated over time with their consumers.
Alternatively, we could witness a more nuanced evolution. If shopping assistants begin analyzing not just product specifications but also brand values and identity markers, they might actually strengthen value-based purchasing. Imagine a scenario where the AI recommends products not just because they’re objectively superior but because they align with a consumer’s ethical preferences or lifestyle. This would shift competition from product-level attributes to brand-level values and identity, rewarding companies whose core principles resonate with specific consumer segments, making it even more important for a brand to have a bold and sharp identity.
Challenges for Providers and Users
Automating purchase processes raises questions about data protection, ethical implications, and new business models. How can misuse be prevented? Who is liable when errors occur? How do machine customers affect competition? (High-frequency trading is already a controversial example of potential unfair competition.)
Companies and legislators will need to address these issues. The EU AI Act is already tackling some of these topics and can be explored here: AI Act Explorer | EU Artificial Intelligence Law. However, as these regulations are still in their early stages and technology is advancing faster than the accompanying legislation, the AI Act doesn’t yet provide final answers for all potential and existing AI use cases.
For machine customers to make informed decisions, certain conditions must be met. Since their choices are based on the analysis of product and service data, high data quality is essential. Inaccurate or incomplete datasets can prevent machine customers from performing effectively and achieving their intended goals.
The future of AI-driven shopping
One thing is clear: Commerce will fundamentally change through machine customers, both in B2B and B2C contexts. AI-powered tools, like our intelligent product search Smart Search, are increasingly being integrated into online stores and businesses, enabling smarter shopping processes. How do you see it – ready for your first bot customer?
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