# AI in logistics: use cases and implementation > AI in logistics: where it holds up today, why projects fail and how we implement it. With references from terminal operations and freight forwarding, GDPR-compliant from Vienna. Canonical: https://www.waterglass.ai/en/ai-in-logistics/ Language: English (Deutsch: https://www.waterglass.ai/de/ki-in-der-logistik.md) Company: Waterglass FlexCo, Börseplatz 1/3/6, 1010 Vienna, Austria · hi@waterglass.ai ## AI in logistics Dispatch, freight documents, investment decisions: we show logistics companies where AI genuinely holds up today, and then build it. Processing in Europe, GDPR-compliant. ## From practice: What AI delivers in logistics today Logistics is the sector we have worked in longest. For the venture builder [HHLA Next](https://www.waterglass.ai/en/portfolio/hhlanext/) we built a complete venture from zero that compares terminal equipment and makes investment decisions traceable through total cost of ownership. The MVP stood in under six months, run with AI agents that handle document processing, investment calculations and research. Before that we built [Heylog](https://www.waterglass.ai/en/portfolio/heylog/), a logistics-tech company from Vienna funded with 3.8 million euros from Schmitz Cargobull, LKW WALTER and 9.5 Ventures. That one was about the gap between the dispatch desk and the cab: WhatsApp messages became structured workflows, from an agreed tour through to a captured document. Both show the same pattern. In logistics, AI rarely fails on the model. It fails because the data sits scattered, because a plan leaving dispatch is overwritten by mid-morning anyway, and because nobody has time to look after a pilot. So we start with the process, not the tool. We work from Vienna across the whole DACH region, on site or remote. [Get in touch](https://www.waterglass.ai/en/contact/) for a free, no-obligation intro call. ## Why Waterglass: What we actually do in logistics ### Documents and paperwork Capture freight papers, delivery notes, customs documents and invoices automatically and write the results back into existing systems. At [InStaff](https://www.waterglass.ai/en/portfolio/instaff/) more than eight endpoints do exactly that, entirely inside the EU. ### Communication along the chain Turn messages from drivers and partners into structured transactions instead of leaving them in a chat log. That was the core of [Heylog](https://www.waterglass.ai/en/portfolio/heylog/). ### Decisions backed by numbers Investment and TCO calculations that pull together scattered data on machines, energy and maintenance. For [HHLA Next](https://www.waterglass.ai/en/portfolio/hhlanext/) that became a product of its own. ### Processing in Europe Shipment and personal data do not leave the EU. On request with European-hosted or open models on [infrastructure we run ourselves](https://www.waterglass.ai/en/products/tide/). ## How it runs: Four steps to the first result A sequence that works alongside day-to-day business: few meetings, clear interim results and a decision after every step. ### Intro call In an intro call we establish your starting point and your goals. Then we go through the options for putting AI to work in your company. Free and without obligation. ### Assessment We review processes, systems and data and talk to dispatch, the warehouse and accounting. The result is a sober picture of where AI holds up today and where the foundations are still missing. ### Workshop and prioritisation One workshop with leadership and team puts the use cases in order by value, effort and risk. Afterwards it is clear what to start with and who owns it internally. ### Pilot and operations The first use case goes into production, with real data and a measurable result. On request we then take on running it, so the solution does not fail for lack of staff. ## Selected references: Successful client projects > "AI agents are a transformative technology that unlocks efficiencies across a wide range of businesses." > > — Silia Traub, Director AI & Strategy, willhaben > "We are using AI agents to radically improve our business efficiency and quality of service for our customers." > > — Oliver Lukesch, CTO, StartMatch ## We integrate seamlessly into your infrastructure Slack, Microsoft Teams, Outlook, Gmail, Notion, Google Drive, Salesforce, HubSpot, OpenAI, Anthropic, Datadog, Jira, Zapier, Stripe, GitHub, Linear, QuickBooks, Zendesk, Asana, Intercom, Google Calendar ## Questions & answers: Common questions about AI in logistics **Which use cases pay off first in logistics?** As a rule, the tasks that recur often and need little judgement: capturing freight papers and delivery notes, checking invoices, answering standard enquiries, moving data between systems. Route optimisation and forecasting come later, because they assume clean data that many operations still have to build up first. **Does this work with our TMS or forwarding software?** Usually yes. What matters is not the vendor but whether data can be pulled out of the system and results written back, through exports or a database connection if need be. Where an interface is missing, we say so in the assessment rather than adding it later as extra scope. **What happens to drivers’ personal data?** Telematics, location and contact data are personal data. We design data protection in from the start: processing inside the EU, data minimisation and clear rules about which fields a model gets to see at all. On request the models run on [European infrastructure we operate ourselves](https://www.waterglass.ai/en/products/tide/). **We are a mid-sized forwarder, not a corporate group. Is it worth it?** There is no minimum size, but there is a sensible precondition: there has to be a task that recurs often enough for automation to pay off. An operation with thirty vehicles and daily recurring paperwork meets that just as well as a terminal does. An intro call is usually enough to judge it in a few minutes. **How long until the first result?** A compact assessment gives you clear first recommendations within a few weeks. For [HHLA Next](https://www.waterglass.ai/en/portfolio/hhlanext/) a complete venture stood in under six months, but that is building an entire company and not the yardstick for a single use case. **Will AI replace our dispatchers?** No, and logistics makes that especially clear. A plan that ignores the exceptions gets overwritten by dispatch anyway. What AI takes on is the groundwork: gathering data, capturing documents, running through options. The decision stays with the people who know the operation. **What does an AI project in logistics cost?** Scope depends on your starting point and the question, from a compact assessment through to full implementation with operations. After a short intro call you get a fixed, no-obligation quote, scope, sequence and cost included. **Who runs the solution afterwards?** We do, if you want. Running it is a separate, clearly priced part of the offer: monitoring, updates and adjustments when your workflows change. If you want to run it in-house, we document accordingly and hand over. ## Let us talk about AI in your logistics operation Tell us where your company stands. We will show you where AI has the greatest leverage and put together a free, no-obligation quote. ### How can we help? Book an intro call and we will look together at which workflows are worth it and which are not.