[{"data":1,"prerenderedAt":338},["ShallowReactive",2],{"navigation-en":3,"language-switcher-\u002Fen\u002Fblog\u002Fcategory\u002Fai":87,"blog-kategoria-en-ai":93},{"services":4,"industries":48,"caseStudies":69},[5,10,14,19,23,27,31,35,39,43],{"slug":6,"navLabel":7,"navGroup":8,"heroHeadline":9},"web-applications","Web applications","build","A web application built around your process",{"slug":11,"navLabel":12,"navGroup":8,"heroHeadline":13},"erp-systems","ERP systems","An ERP system built for your company, not the other way round",{"slug":15,"navLabel":16,"navGroup":17,"heroHeadline":18},"integrations-and-api","Integrations and APIs","improve","Your systems stop being islands",{"slug":20,"navLabel":21,"navGroup":17,"heroHeadline":22},"ai-implementation","AI implementation","AI deployed where it actually saves time",{"slug":24,"navLabel":25,"navGroup":17,"heroHeadline":26},"process-automation","Process automation","Work no one should be doing by hand",{"slug":28,"navLabel":29,"navGroup":8,"heroHeadline":30},"websites","Websites","A site that loads instantly and brings in inquiries",{"slug":32,"navLabel":33,"navGroup":8,"heroHeadline":34},"mobile-applications","Mobile applications","One codebase, both platforms",{"slug":36,"navLabel":37,"navGroup":17,"heroHeadline":38},"code-audit-and-project-takeover","Code audit and takeover","The project stalled. We'll take it over and tell you the truth about its condition",{"slug":40,"navLabel":41,"navGroup":17,"heroHeadline":42},"maintenance-and-support","Maintenance and support","A system needs someone watching it after launch",{"slug":44,"navLabel":45,"navGroup":46,"heroHeadline":47},"discovery-workshop","Discovery workshop","start","Before you decide to build, find out exactly what you're building",[49,53,57,61,65],{"slug":50,"navLabel":51,"heroHeadline":52},"manufacturing","Manufacturing","The works order reaches the floor as a printout from an email",{"slug":54,"navLabel":55,"heroHeadline":56},"automotive","Automotive","The customer exists in three places at once",{"slug":58,"navLabel":59,"heroHeadline":60},"industrial-automation","Industrial automation","You sell competence that doesn't photograph",{"slug":62,"navLabel":63,"heroHeadline":64},"e-commerce","E-commerce","The shop works. What sits behind it comes apart",{"slug":66,"navLabel":67,"heroHeadline":68},"logistics","Transport and logistics","The job arrives by email, the status comes back by phone",[70,77,82],{"slug":71,"client":72,"tag":73,"year":74,"summary":75,"image":76},"dss-by-caredo","Caredo","Product",2025,"We build the whole DSS product together with the Caredo team — from the CRM and planner to the software behind the key lockers and totems.","\u002Fimg\u002Fcase-studies\u002Fdss\u002Fkluczykomat\u002Fframe_0169.webp",{"slug":78,"client":72,"tag":79,"year":74,"summary":80,"image":81},"dss-by-caredo-website","Product website","We built the DSS product website end to end — from design to launch — on Caredo's visual identity.","\u002Fimg\u002Fscreenshots\u002Fdss-desktop.jpg",{"slug":83,"client":83,"tag":84,"year":74,"summary":85,"image":86},"2motion","Brand and website","A whole brand from scratch — logo, colours, business cards — a company website and blog posts that position 2motion in search.","\u002Fimg\u002Fscreenshots\u002F2motion-desktop.jpg",[88],{"code":89,"label":90,"name":91,"to":92},"pl","PL","Polish","\u002Fblog\u002Fkategoria\u002Fai",[94],{"id":95,"title":96,"author":97,"body":98,"category":319,"date":320,"description":321,"draft":322,"editorialNote":323,"extension":324,"image":325,"meta":326,"navigation":327,"path":328,"rawbody":329,"seo":330,"sitemap":331,"stem":332,"tags":333,"translationKey":336,"updated":323,"__hash__":337},"blogEn\u002Fblog\u002Fwdrozenie-ai-w-firmie-od-czego-zaczac.md","AI adoption in your company — where to start","zespol",{"type":99,"value":100,"toc":306},"minimark",[101,118,123,126,133,137,140,146,152,158,161,165,172,175,179,182,188,194,200,206,210,213,235,238,242,268,272,278,284,290,296,300,303],[102,103,104,105,109,110,113,114,117],"p",{},"Start with a process that meets three conditions at once: it's ",[106,107,108],"strong",{},"repetitive",", has a ",[106,111,112],{},"countable time cost",", and ",[106,115,116],{},"tolerates a mistake",". Classifying incoming documents usually meets all three. \"An assistant that knows the whole company\" meets none of them — which is why so many rollouts that start there never finish.",[119,120,122],"h2",{"id":121},"why-pilots-dont-reach-production","Why pilots don't reach production",[102,124,125],{},"A demo shows a model handling a dozen or so examples chosen by the person who prepared them. Production is tens of thousands of examples nobody chose: scans crooked on the glass, documents in a format outside the test set, invoices with a handwritten note in the margin.",[102,127,128,129,132],{},"The difference between the two isn't model quality. It's ",[106,130,131],{},"handling the cases where the model isn't sure"," — and that's exactly what a rollout is actually made of. A pilot that skips it isn't an earlier version of the rollout. It's a different thing entirely.",[119,134,136],{"id":135},"how-to-choose-your-first-use-case","How to choose your first use case",[102,138,139],{},"Make a list of candidates and score each on three dimensions.",[102,141,142,145],{},[106,143,144],{},"Repeatability."," How many times a month does someone do this? Below a hundred, the savings rarely justify a rollout.",[102,147,148,151],{},[106,149,150],{},"Time cost."," How many minutes does one instance take? Multiply by repeatability. That number sets the upper bound on a sensible budget.",[102,153,154,157],{},[106,155,156],{},"Tolerance for error."," What happens if the model gets it wrong? If the answer is \"someone catches it at review\" — good. If it's \"money leaves an account\" — you need a human approval step, which changes the maths.",[102,159,160],{},"The best candidates in a typical company: classifying and routing incoming documents, extracting data from invoices and orders, drafting first responses to repetitive support questions, searching internal documentation.",[119,162,164],{"id":163},"set-a-success-metric-before-you-start","Set a success metric before you start",[102,166,167,168],{},"Before a prototype exists, write down one sentence: ",[169,170,171],"em",{},"\"This rollout makes sense if the model handles at least X% of cases on its own, at accuracy of at least Y%.\"",[102,173,174],{},"Without that number, there's no way to later decide whether the project succeeded. With it, the prototype ends in a clear decision: proceed, or stop. Stopping is also a good outcome — just a cheaper one than finding out a year in.",[119,176,178],{"id":177},"an-architecture-that-survives-production","An architecture that survives production",[102,180,181],{},"A rollout that lasts has four elements a demo doesn't.",[102,183,184,187],{},[106,185,186],{},"A confidence threshold."," The model returns a result along with its own confidence score. Above the threshold, automation runs; below it, the case goes to a human. Setting that threshold is a business decision, not a technical one.",[102,189,190,193],{},[106,191,192],{},"A queue for uncertain cases."," The place where a human resolves cases below the threshold — and its size is the real measure of how well the system is working.",[102,195,196,199],{},[106,197,198],{},"A decision log."," A record of what the model received, what it returned, and with what confidence. Without it you can't diagnose degradation or answer an auditor's question.",[102,201,202,205],{},[106,203,204],{},"Continuous measurement."," Data changes. A model that was 94% accurate in March can be at 80% by November — not because it broke, but because the input stream changed.",[119,207,209],{"id":208},"where-your-data-goes","Where your data goes",[102,211,212],{},"The question that comes up in every first meeting, rightly so. You have three options:",[214,215,216,223,229],"ul",{},[217,218,219,222],"li",{},[106,220,221],{},"A hosted model, no-training agreement."," Data leaves the company, but the provider commits not to use it for training. Cheapest and fastest, sufficient for most use cases.",[217,224,225,228],{},[106,226,227],{},"A hosted model in your own private cloud."," Data never leaves your infrastructure. More expensive, justified for sensitive data.",[217,230,231,234],{},[106,232,233],{},"A model on your own hardware."," Full control, the highest entry and running cost. Makes sense under regulatory requirements that rule out the other options.",[102,236,237],{},"The choice is a decision about risk, not technology. You make it once, and it affects cost for the entire lifetime of the solution.",[119,239,241],{"id":240},"what-it-costs","What it costs",[214,243,244,250,256,262],{},[217,245,246,249],{},[106,247,248],{},"Prototype on your data:"," PLN 15–30k net, 2–3 weeks. Ends with a number you can base a decision on.",[217,251,252,255],{},[106,253,254],{},"Production rollout:"," PLN 60–200k net, depending on the number of integrations and approval requirements.",[217,257,258,261],{},[106,259,260],{},"Running the models:"," from a few hundred to a few thousand zloty a month, roughly proportional to volume.",[217,263,264,267],{},[106,265,266],{},"Maintenance and tuning:"," usually 15% of the rollout's value per year.",[119,269,271],{"id":270},"four-mistakes-that-come-up-most-often","Four mistakes that come up most often",[102,273,274,277],{},[106,275,276],{},"Starting with the most impressive use case."," A conversational assistant impresses the board and is the hardest possible starting point — unbounded scope, no good success metric, high expectations.",[102,279,280,283],{},[106,281,282],{},"No human in the loop."," A system with no path for uncertain cases either performs badly or needs such a high confidence threshold that it only automates a small share of cases.",[102,285,286,289],{},[106,287,288],{},"Measuring accuracy alone."," 95% accuracy sounds great until you work out that at a thousand documents a day, that's fifty errors someone has to catch.",[102,291,292,295],{},[106,293,294],{},"Treating the rollout as a closed project."," A model needs watching, like any other piece of production infrastructure. A rollout with no measurement plan ages quietly.",[119,297,299],{"id":298},"the-first-step-you-can-take-this-week","The first step you can take this week",[102,301,302],{},"Pick one process. Count how many times a month it happens and how many minutes it takes. Write down what happens when someone gets it wrong.",[102,304,305],{},"Those three numbers are enough to judge whether a prototype is worth building — and they're worth more than a month of conversations about AI's possibilities.",{"title":307,"searchDepth":308,"depth":308,"links":309},"",3,[310,312,313,314,315,316,317,318],{"id":121,"depth":311,"text":122},2,{"id":135,"depth":311,"text":136},{"id":163,"depth":311,"text":164},{"id":177,"depth":311,"text":178},{"id":208,"depth":311,"text":209},{"id":240,"depth":311,"text":241},{"id":270,"depth":311,"text":271},{"id":298,"depth":311,"text":299},"ai","2026-09-02","How to choose your first use case, what it costs, and why most corporate AI projects end at the demo instead of reaching production.",false,null,"md","\u002Fimg\u002Fblog\u002Fwdrozenie-ai-w-firmie.jpg",{},true,"\u002Fblog\u002Fwdrozenie-ai-w-firmie-od-czego-zaczac","---\ntitle: AI adoption in your company — where to start\ndescription: How to choose your first use case, what it costs, and why most corporate AI projects end at the demo instead of reaching production.\ndate: 2026-09-02\ncategory: ai\ntags:\n  - ai\n  - automation\n  - rollouts\nauthor: zespol\nimage: \u002Fimg\u002Fblog\u002Fwdrozenie-ai-w-firmie.jpg\ntranslationKey: ai-adoption-where-to-start\ndraft: false\n---\n\nStart with a process that meets three conditions at once: it's **repetitive**, has a **countable time cost**, and **tolerates a mistake**. Classifying incoming documents usually meets all three. \"An assistant that knows the whole company\" meets none of them — which is why so many rollouts that start there never finish.\n\n## Why pilots don't reach production\n\nA demo shows a model handling a dozen or so examples chosen by the person who prepared them. Production is tens of thousands of examples nobody chose: scans crooked on the glass, documents in a format outside the test set, invoices with a handwritten note in the margin.\n\nThe difference between the two isn't model quality. It's **handling the cases where the model isn't sure** — and that's exactly what a rollout is actually made of. A pilot that skips it isn't an earlier version of the rollout. It's a different thing entirely.\n\n## How to choose your first use case\n\nMake a list of candidates and score each on three dimensions.\n\n**Repeatability.** How many times a month does someone do this? Below a hundred, the savings rarely justify a rollout.\n\n**Time cost.** How many minutes does one instance take? Multiply by repeatability. That number sets the upper bound on a sensible budget.\n\n**Tolerance for error.** What happens if the model gets it wrong? If the answer is \"someone catches it at review\" — good. If it's \"money leaves an account\" — you need a human approval step, which changes the maths.\n\nThe best candidates in a typical company: classifying and routing incoming documents, extracting data from invoices and orders, drafting first responses to repetitive support questions, searching internal documentation.\n\n## Set a success metric before you start\n\nBefore a prototype exists, write down one sentence: *\"This rollout makes sense if the model handles at least X% of cases on its own, at accuracy of at least Y%.\"*\n\nWithout that number, there's no way to later decide whether the project succeeded. With it, the prototype ends in a clear decision: proceed, or stop. Stopping is also a good outcome — just a cheaper one than finding out a year in.\n\n## An architecture that survives production\n\nA rollout that lasts has four elements a demo doesn't.\n\n**A confidence threshold.** The model returns a result along with its own confidence score. Above the threshold, automation runs; below it, the case goes to a human. Setting that threshold is a business decision, not a technical one.\n\n**A queue for uncertain cases.** The place where a human resolves cases below the threshold — and its size is the real measure of how well the system is working.\n\n**A decision log.** A record of what the model received, what it returned, and with what confidence. Without it you can't diagnose degradation or answer an auditor's question.\n\n**Continuous measurement.** Data changes. A model that was 94% accurate in March can be at 80% by November — not because it broke, but because the input stream changed.\n\n## Where your data goes\n\nThe question that comes up in every first meeting, rightly so. You have three options:\n\n- **A hosted model, no-training agreement.** Data leaves the company, but the provider commits not to use it for training. Cheapest and fastest, sufficient for most use cases.\n- **A hosted model in your own private cloud.** Data never leaves your infrastructure. More expensive, justified for sensitive data.\n- **A model on your own hardware.** Full control, the highest entry and running cost. Makes sense under regulatory requirements that rule out the other options.\n\nThe choice is a decision about risk, not technology. You make it once, and it affects cost for the entire lifetime of the solution.\n\n## What it costs\n\n- **Prototype on your data:** PLN 15–30k net, 2–3 weeks. Ends with a number you can base a decision on.\n- **Production rollout:** PLN 60–200k net, depending on the number of integrations and approval requirements.\n- **Running the models:** from a few hundred to a few thousand zloty a month, roughly proportional to volume.\n- **Maintenance and tuning:** usually 15% of the rollout's value per year.\n\n## Four mistakes that come up most often\n\n**Starting with the most impressive use case.** A conversational assistant impresses the board and is the hardest possible starting point — unbounded scope, no good success metric, high expectations.\n\n**No human in the loop.** A system with no path for uncertain cases either performs badly or needs such a high confidence threshold that it only automates a small share of cases.\n\n**Measuring accuracy alone.** 95% accuracy sounds great until you work out that at a thousand documents a day, that's fifty errors someone has to catch.\n\n**Treating the rollout as a closed project.** A model needs watching, like any other piece of production infrastructure. A rollout with no measurement plan ages quietly.\n\n## The first step you can take this week\n\nPick one process. Count how many times a month it happens and how many minutes it takes. Write down what happens when someone gets it wrong.\n\nThose three numbers are enough to judge whether a prototype is worth building — and they're worth more than a month of conversations about AI's possibilities.\n",{"title":96,"description":321},{"loc":328},"blog\u002Fwdrozenie-ai-w-firmie-od-czego-zaczac",[319,334,335],"automation","rollouts","ai-adoption-where-to-start","3h3Uyc_r6v-R1oggm3sXVR3s8arJjyy0xQy78uryQSg",1790522546275]