Everyone is talking about AI. Fewer are talking about the data it runs on

In South Africa's automotive and logistics sectors, discussions about AI have dominated strategy sessions, but many businesses lack confidence in their data accuracy and completeness. The conventional wisdom is to fix data first and then consider AI, yet this often results in endless clean-up projects that fail to address the root cause – the data's inherent issues.

Data and AI are interconnected; AI requires high-quality data for value, while AI can also be a powerful tool for improving data quality. Therefore, a better approach is to leverage AI to tackle data issues directly. In this industry, data resides in various systems such as dealer systems, ERPs, finance tools, email inboxes, and numerous spreadsheets.

In logistics, much of the daily operations occur through WhatsApp, where drivers post delivery notes and dispatchers confirm loads via chat. However, these informal systems are not reportable until someone manually transfers the information into a spreadsheet. This leads to fragmented data sources, manual data entry, and processes that are slow and prone to human error.

To address these challenges, a three-pronged solution is needed: combine data sources, clean the data, and automate manual processes. A test drive trial at a dealership generates data, which is then categorized as genuine or placeholder records. For a global vehicle manufacturer, an AI agent built on CloudSmith's ObjectiveFoundation assesses retailer records upon arrival.

Valid records are stored in a trusted golden record, while invalid records are directed to a quarantine queue for data engineers to address, providing a clear list of issues that need fixing. A South African automotive components group consolidated data from subsidiaries with varying reporting methods into a single data lakehouse, utilizing AI agents within their team messaging workspace to explain financial variances, flag exceptions, consolidate risk registers, generate ESG reports, and detect payroll discrepancies.

For a motor retail and logistics group, CloudSmith's solution automated the handling of quote requests, filtering out irrelevant emails and processing over 8,200 in two months with 100% accuracy. Meanwhile, a fuel distributor uses an AI agent to manage WhatsApp group notifications, filling in its master sheet with relevant data.

A delivery platform automates payout processes for outsourced driver partners, reducing the workload from three to four people to just one and shortening the time from days to mere hours. In each scenario, data was initially scattered and messy. Cleaning the data is now an integral part of the process, rather than a prerequisite.

CFOs in automotive businesses now prioritize technology investments based on tangible benefits, such as time saved, volume handled without additional staff, reduced reporting cycles, and minimized errors. By starting with one process, demonstrating the savings, and using this success to fund further improvements, organizations can unlock the true value of AI and data.

The key takeaway is that before committing to an AI pilot, businesses should ask a fundamental question: can they confidently answer how many real customers they have? CloudSmith, a South African technology consultancy, specializes in helping organizations transform their potential into performance through cloud, data, and AI solutions.

With partnerships across major cloud platforms, they assist clients in transitioning from AI pilots to production-ready, governed environments. To learn more, visit cloudsmiths.ai.