RFQs are among the most important levers in procurement, yet they are often one of the biggest time drains. Manual setup, scattered data, inconsistent quote formats, and complex comparisons mean that benchmarking happens less frequently than it should. In the webinar, Torben Hinrichs and Jakob Hafner show how AI-powered agents simplify the entire RFQ process: from identifying potential to evaluating quotes.

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Starting Point – The Manual RFQ Process Slows Down Procurement

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In many procurement departments, the RFQ process still runs the same way as years ago. Articles must be exported from the ERP, relevant suppliers are gathered individually, and requests prepared in Word, Excel, or email. Quotes come back in completely different formats and must be manually compared.

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Each round costs time. Each adjustment means copying, formatting, and updating again. Often parts of the information are in files, others in inboxes, and still others in personal notes. As a result, it takes a long time before a complete comparison is available and a decision can be made.

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This way of working means that while RFQs are known as an effective tool, they are often used less frequently in daily operations than would be beneficial. The effort is simply too high, especially when multiple suppliers or multiple rounds are planned.

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Many procurement departments would therefore like to generate more competition and benchmark more frequently, but rarely manage to do so in daily operations because the preparation consumes too much capacity. This is exactly where the webinar starts.

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How AI Relieves and Accelerates the RFQ Process

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Once it becomes clear how labor-intensive traditional RFQs are, it is evident where modern procurement can make an impact. Many of the steps that are done manually today follow recurring patterns: selecting articles, checking suppliers, gathering request data, obtaining quotes, and then painstakingly comparing them. AI can support exactly in these areas, ensuring procurement gains more time for evaluation and strategy.

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Instead of starting from scratch every time, the system automatically analyzes relevant price developments, contract data, and article movements. This generates early indications of which products might benefit from benchmarking or which requests are upcoming. Only on this basis do the AI agents come into play: They take over many of the preparatory and comparative tasks that make the RFQ process so time-intensive today.

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The key agents at a glance:

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Automatic alerts - The system independently identifies suitable starting points for RFQs, for example with notable price developments or approaching contract deadlines.

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RFQ Agent - From an identified topic, a complete RFQ is created with a single click. Articles, suppliers, and relevant data points are prepared directly.

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Quote Reading Agent - Suppliers can submit quotes in any format. The AI automatically reads the contents and converts them into a unified schema.

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Comparison Logic Agent - All responses are automatically compared. Differences in prices, quantities, or conditions are clearly highlighted without manual spreadsheet maintenance.

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This creates an RFQ process that is structured, significantly faster, and fully traceable. Procurement gains time for analysis and argumentation while AI handles repetitive tasks in the background.

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From Alert to Decision

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As soon as a relevant price trend or deviation is detected, procurement can run through the entire RFQ process without media breaks. From an alert, a complete request is created with just a few clicks, quotes are automatically read, and presented in a unified structure. If needed, another round can be initiated directly.

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What previously required many tools, spreadsheets, and email loops now happens in one continuous flow. This makes benchmarking not only faster but also easier to scale: more suppliers, clearer comparisons, and decisions based on a clean data foundation.

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Outlook

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The next steps show where the RFQ process is heading. Features like multi-stage quote rounds, structured follow-up questions to suppliers, or integrated savings tracking ensure that not only the creation of RFQs becomes faster, but also the entire follow-up. Going forward, AI agents can provide even stronger support by suggesting suitable times for new requests or automatically prioritizing anomalies. This turns individual benchmarks into a continuous, data-based process.

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Conclusion

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The RFQ process changes significantly when recurring steps are automated and all relevant data is centrally available. Alerts emerge earlier, requests are prepared in seconds, and quotes can be directly compared. This leaves more time for evaluation and strategy, while AI handles repetitive tasks in the background and ensures end-to-end transparency.

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Torben Hinrichs and Jakob Hafner show how companies can execute RFQs faster, more structured, and more scalably with Tacto – and why a higher tendering rate directly leads to better decisions.