BMW’s bruising third quarter in 2024, when net profit fell 83.8% to €476 million, has become a useful illustration of why some industrial groups are now turning generative AI to procurement rather than to more glamorous front-office uses. The German carmaker’s profits were hit by weak demand in China, delivery stoppages tied to its integrated braking system and warranty provisions linked to the fault, according to company filings and industry reports. By the third quarter of 202...
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5, BMW had largely moved past that slump, reporting net profit of €1.697 billion and saying it was on track to meet full-year targets. But the episode underlines the pressure facing manufacturers to find faster, more reliable ways to protect margins.
For many companies, procurement offers the clearest route. A small reduction in buying costs can matter more than an equivalent rise in sales because purchasing typically accounts for the bulk of manufacturing expenditure. In sectors where operating margins are thin, even a modest improvement in procurement can translate quickly into a meaningful lift in profit. That is one reason executives are increasingly viewing AI not as a customer-facing novelty, but as a lever for spending discipline.
It is also a practical choice. Procurement systems generate large volumes of messy, unstructured information: contracts, supplier offers, specification sheets, email trails and internal notes. Generative AI is well suited to reading, comparing and extracting meaning from such material, especially when human teams would otherwise need to spend hours reconciling different formats and terminology. The technology is less about replacing buyers than about allowing them to process more information, more consistently, in less time.
BMW’s own experience shows how that can work. In procurement, the company has used AI to draft requests for proposals from historical data, standardise supplier submissions, search internal knowledge bases and generate negotiation support for both major vendors and smaller suppliers that had previously received less attention. The point is not merely speed. According to the approach described by industry observers, AI helps procurement teams compare all bids against the same criteria, uncover hidden contractual risks and enter negotiations with better information.
That shift can improve outcomes beyond simple efficiency. Hidden liabilities in indemnity, penalty or renewal clauses are easier to spot before they become costly disputes. Better visibility into market prices and supplier conditions can narrow the information gap between buyer and seller. And by extending scrutiny to smaller and indirect suppliers, companies can identify savings that were previously overlooked. BCG has argued that, in global automotive and manufacturing procurement projects, the potential impact can be substantial, with cost reductions in the range of 7% to 10% of procurement spend, although actual gains depend on data quality, internal readiness and leadership commitment.
The broader lesson is that companies do not necessarily need perfect data before introducing AI. In practice, the technology can help impose order on fragmented records while delivering savings along the way. For Korean manufacturers, that may be especially relevant: many already have relatively mature ERP and e-procurement systems, which means the bottleneck is less about infrastructure than execution. As BMW’s recent results suggest, the race to protect profitability may increasingly be won not only on the factory floor or in the showroom, but in the purchasing department.
Source: Noah Wire Services
