{"id":75107,"date":"2024-02-08T18:36:55","date_gmt":"2024-02-08T17:36:55","guid":{"rendered":"https:\/\/aton.com\/ia-per-inserire-gli-ordini\/"},"modified":"2025-09-08T14:50:11","modified_gmt":"2025-09-08T12:50:11","slug":"ai-powered-order-entry","status":"publish","type":"post","link":"https:\/\/aton.com\/en\/ai-powered-order-entry\/","title":{"rendered":"Order entry? Let AI handle it!"},"content":{"rendered":"<h2><strong>Understanding Order Entry<\/strong><\/h2>\n<p>In a world moving at unimaginable speeds, where<strong> process optimisation<\/strong> is the foundation of improved business performance, errors are increasingly less tolerated, and manual input is becoming more and more challenging, there&#8217;s still a group of workers left waiting for support. I&#8217;m talking about those users who<strong> work on the move<\/strong>, or in conditions where having access to a computer and a fixed workstation isn\u2019t always possible \u2014 especially when it comes to <strong>entering orders<\/strong>.<\/p>\n<p>Take, for example, those managing kitchens or working in fresh produce and fish markets \u2014 environments where products change daily, and <strong>there\u2019s simply no time to sit at a computer, log in, and complete an order form<\/strong>.<\/p>\n<p>These users often don\u2019t fit into structured order entry interfaces and don\u2019t have the time to figure out which product variant is currently available. What\u2019s more, they tend to have a close, personal relationship with their supplier \u2014 and they expect to be recognised immediately, relying on the history of their purchases and established habits.<\/p>\n<h2><strong>The breakthrough we were waiting for: AI-driven order entry<\/strong><\/h2>\n<p>An order placed under the conditions described above often:<\/p>\n<ul>\n<li>is submitted through <strong>various channels<\/strong> (e.g. email or voice messages)<\/li>\n<li><strong>lacks a defined structure<\/strong><\/li>\n<li>is written in <strong>natural language<\/strong><\/li>\n<li>may contain <strong>errors<\/strong><\/li>\n<li>may include <strong>different information<\/strong> each time<\/li>\n<\/ul>\n<p>The scenario in which these categories of workers operate has made the <strong>order entry process a manual task, entirely dependent on the experience of the operator processing it<\/strong>.<br \/>\nBecause of its complexity, all the underlying logic \u2014 from recognising the customer to selecting the most appropriate product \u2014 could not easily be translated into a standard application.<\/p>\n<p>Now, however, <strong>we can offer real support<\/strong> to these operators \u2013 and to their companies \u2013 <strong>by automating the order recognition process<\/strong>, leaving only the final validation to the human operator.<\/p>\n<p>The <strong>benefits for companies<\/strong> are substantial, as it gives structure to a process that was previously highly unstructured. So how can it be done?<\/p>\n<p>At Aton, we\u2019ve always placed a strong focus on <strong>innovation<\/strong> \u2014 and it\u2019s our clients who are often the first to push us towards research and development. By starting from <a href=\"https:\/\/aton.com\/en\/ai-aton-7-use-cases\/\" target=\"_blank\" rel=\"noopener\">real market experiences<\/a>, we design solutions that address needs already emerging among users. This is exactly how, with the support of LLMs (<a href=\"https:\/\/en.wikipedia.org\/wiki\/Large_language_model\" target=\"_blank\" rel=\"noopener\">Large Language Models<\/a>) and the introduction of <strong>vector databases<\/strong>, we\u2019ve been able to tackle this challenge: starting from an unstructured order, we can now automatically identify the customer, the products, and any order notes \u2014 and return to the operator with a pre-filled order proposal ready for validation.<\/p>\n<p>This process also brings an additional implication: the need to design <strong>new interfaces for interacting<\/strong> with data. Current interfaces are built around a user-driven data entry model. However, such rigidity doesn\u2019t align with emerging processes, where the combination of automation and validation calls for a different user experience (<a href=\"https:\/\/aton.com\/en\/design-thinking-journey-co-design-ux-team\/\" target=\"_blank\" rel=\"noopener\">UX<\/a>).<\/p>\n<p>Let\u2019s take an order as an example: how can we manage it \u2014 and support the user within the company who receives it?<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"wp-image-76228 alignleft\" src=\"https:\/\/aton.com\/wp-content\/uploads\/2024\/02\/Cliente-xyz-3.jpg\" alt=\"\" width=\"561\" height=\"561\" srcset=\"https:\/\/aton.com\/wp-content\/uploads\/2024\/02\/Cliente-xyz-3.jpg 1920w, https:\/\/aton.com\/wp-content\/uploads\/2024\/02\/Cliente-xyz-3-1536x1536.jpg 1536w\" sizes=\"auto, (max-width: 561px) 100vw, 561px\" \/><\/p>\n<p>Let\u2019s first look at the <strong>critical issues<\/strong> within this order:<\/p>\n<ul>\n<li><strong>Customer xxyy<\/strong> &#8211; the customer may write a name that is recognised by the supplier but is not the full legal name, or may even omit the information altogether.<\/li>\n<li><strong>7kg of nw potatoes (as usual) <\/strong>&#8211; the unit of measure is written in natural language and attached to the quantity; the product is generic and contains a spelling error; there\u2019s also a user-added comment.<\/li>\n<li><strong>2 crates of navel oranges<\/strong> \u2013 the unit of measure may not match the one used for sales; the product description is generic and not clearly linked to a specific item in the catalogue.<\/li>\n<li><strong data-start=\"789\" data-end=\"809\">Deliver as usual<\/strong> \u2013 this is an order note that doesn\u2019t correspond to any standard field in a traditional order form.<\/li>\n<\/ul>\n<h2><\/h2>\n<h2><strong>Aton has the solution: AI for order entry<\/strong><\/h2>\n<p>As we\u2019ve seen, the sample order contains several <strong>natural language elements<\/strong> that only a human would normally be able to understand \u2014 and even then, only an operator already familiar with the customer would be able to process it, still running the risk of misinterpretation or typing errors.<\/p>\n<p>By contrast, <strong>with the use of AI, we can now identify the various components of an order<\/strong> and represent them in a structured format.<\/p>\n<p>In this way, the line \u201c7kg of nw potatoes\u201d becomes:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone  wp-image-76235\" src=\"https:\/\/aton.com\/wp-content\/uploads\/2024\/02\/Grafiche-articoli-1.png\" alt=\"\" width=\"455\" height=\"341\" srcset=\"https:\/\/aton.com\/wp-content\/uploads\/2024\/02\/Grafiche-articoli-1.png 1920w, https:\/\/aton.com\/wp-content\/uploads\/2024\/02\/Grafiche-articoli-1-1536x1152.png 1536w\" sizes=\"auto, (max-width: 455px) 100vw, 455px\" \/><\/p>\n<p>Great \u2014 we\u2019ve structured the original line, and now comes the interesting part: <strong>how do we recognise the specific product that the customer usually buys<\/strong>?<\/p>\n<p>Some might argue that traditional database search techniques could handle the task \u2014 and they\u2019re not wrong. But at what level of accuracy?<\/p>\n<p>With <strong>semantic search in a vector database<\/strong>, everything changes: we can identify the product that is statistically most similar to the one requested \u2014 and, why not, even suggest relevant alternatives.<\/p>\n<p>Here\u2019s how our order line is now transformed:<a href=\"https:\/\/aton.com\/wp-content\/uploads\/2024\/02\/Grafiche-articoli.svg\"><img decoding=\"async\" class=\"alignnone size-full wp-image-76245\" src=\"https:\/\/aton.com\/wp-content\/uploads\/2024\/02\/Grafiche-articoli.svg\" alt=\"\" \/><\/a><\/p>\n<p>We\u2019ve worked <strong>a bit of magic<\/strong> \u2014 and now we\u2019re able to pass the result through a more traditional process, which can refine it further. For example, the identified product may be unavailable, or the quantity might not match the standard packaging. At this stage, <strong>an operator steps in to validate the result and send the order for fulfilment<\/strong>.<\/p>\n<p>So how far can we scale this process now? <strong>Starting from an email<\/strong> \u2014 <strong>or even a voice message<\/strong> (yes, we\u2019re now talking about voice message interpretation too) \u2014 we can achieve an incredible result in just a few seconds.<\/p>\n<p>That\u2019s right: we\u2019ve used AI to enter orders.<\/p>\n<p>We\u2019ve read and interpreted natural language, and we\u2019ve embedded part of the customer\u2019s knowledge and habits into an automated workflow. Not bad, right?<\/p>\n<h2><strong>Thoughts on AI<\/strong><\/h2>\n<p><strong>Bringing new technologies into the right context and using them appropriately is essential<\/strong>. Referring to <em>Artificial Intelligence<\/em> just to ride the trend or make headlines risks trivialising its true potential.<\/p>\n<p>A technology that is easily accessible is not necessarily easy to adopt \u2014 which is why Aton invests in<strong> training people<\/strong>. This enables them to experiment, learn, and take solutions to the next level.<\/p>\n<p><strong><div class=\"custom_button\"><a href=\"https:\/\/aton.com\/en\/contact\/\" class=\"dropbtn mx-auto _slim\" title=\"Get in touch to learn more\" target=\"\" ><div class=\"dropbtn_content\">Get in touch to learn more<\/div><div class=\"dropbtn_drop\"><i class=\"sn_sprite _button_drop \"><svg viewBox=\"0 0 147 64\"><use xlink:href=\"\/wp-content\/themes\/aton\/assets\/images\/icons.svg#button-drop\" \/><\/svg><\/i><\/div><div class=\"dropbtn_spacer\"><\/div><\/a><\/div><\/strong><\/p>\n<p>Curious about the new technologies Aton is bringing to life? Stay tuned.<\/p>\n<p><a href=\"https:\/\/www.linkedin.com\/company\/aton-s.p.a.\/\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-49188\" src=\"https:\/\/aton.com\/wp-content\/uploads\/2023\/05\/linkedin-1-1.png\" alt=\"linkedin-icon-img\" width=\"30\" height=\"30\" \/><\/a> <a href=\"https:\/\/www.instagram.com\/atonspasocietabenefit\/\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-49191\" src=\"https:\/\/aton.com\/wp-content\/uploads\/2023\/05\/instagram-2-1.png\" alt=\"instagram-icon-img\" width=\"30\" height=\"30\" \/><\/a> <a href=\"https:\/\/www.facebook.com\/aton.spa\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-49194\" src=\"https:\/\/aton.com\/wp-content\/uploads\/2023\/05\/facebook-1-1.png\" alt=\"facebook-icon-img\" width=\"30\" height=\"30\" \/><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Understanding Order Entry In a world moving at unimaginable speeds, where process optimisation is the foundation of improved business performance, errors are increasingly less tolerated, and manual input is becoming more and more challenging, there&#8217;s still a group of workers left waiting for support. I&#8217;m talking about those users who work on the move, or [&hellip;]<\/p>\n","protected":false},"author":15,"featured_media":70871,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false},"class_list":["post-75107","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","keyword-sales-en","keyword-tech-en"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Order entry? Let AI handle it! - Aton<\/title>\n<meta name=\"description\" content=\"Tired of processing orders manually? Discover how AI is transforming the way orders are placed and processed.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/aton.com\/en\/ai-powered-order-entry\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Order entry? Let AI handle it! - Aton\" \/>\n<meta property=\"og:description\" content=\"Tired of processing orders manually? 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