Procurement maturity key to unlocking spend visibility and strategic potential

Procurement teams are still struggling with a problem that sounds simple but proves stubborn in practice: they cannot see their spend clearly enough to manage it confidently. The root cause is usually not a lack of effort, but a patchwork of systems, inconsistent supplier naming and classification rules that vary by business unit, region or platform. Sievo argues that those weaknesses are best understood as a maturity issue, not a master-data problem to be solved in isolation.

...

The company says the instinct to wait for perfect data before investing in analytics is one of the costliest mistakes procurement leaders can make. In its view, modern spend analytics tools are designed to work with imperfect inputs, cleansing, normalising and classifying data as part of the process rather than demanding a flawless starting point. That matters because manual workarounds such as spreadsheet reconciliations, ad hoc extracts and quarterly clean-ups may keep reports moving, but they do little to create a durable analytical base.

Other industry commentators make a similar point. Simfoni says disconnected procurement systems still prevent organisations from operating procurement as a single intelligence layer, while Atamis warns that the hidden cost of fragmentation is not just inefficiency but weaker supplier leverage, more risk and repeated duplication of effort. OappsNet, meanwhile, says ERP systems on their own do not guarantee visibility if the underlying data remains inconsistent or delayed.

Sievo frames the issue through a four-stage maturity model. At the earliest stage, procurement is reactive and reliant on manual reporting from raw, disconnected sources. The next phase introduces basic spend reporting and some supplier tracking, but classification remains incomplete. By the optimisation stage, reporting is trusted, insights are more automated and internal data is supplemented with outside sources. The most advanced level is AI-native, where predictive tools and market benchmarks are built into day-to-day decision-making.

The distinction is important because, according to Sievo, many procurement functions overestimate where they are on that curve. The company says its assessment tool is designed to identify the gap between perceived and actual maturity, then point to the next most useful intervention. That sequence matters because applying advanced analytics to a weak data foundation can create expensive complexity without improving trust in the numbers.

Classification remains one of the biggest pressure points. Sievo says that when accuracy falls below 80%, a significant share of spend sits outside meaningful analysis. For a large enterprise, that can mean hundreds of millions of pounds’ worth of purchasing activity is effectively invisible to category strategy, sourcing decisions and savings analysis. The company also points to customer examples in which targeted work on supplier normalisation and classification coverage materially improved accuracy and visibility within months.

Sievo says AI becomes genuinely valuable only once spend is unified, supplier records are normalised, taxonomy is sufficiently granular and data refreshes are automated. Until then, AI-generated outputs still need manual checking, which erodes much of the efficiency gain. In that sense, the company’s message is less about technology than sequence: first build visibility, then trust, then automation.

For procurement leaders, the immediate task is not to chase every new capability at once, but to establish a data foundation sturdy enough to support better decisions. Sievo’s assessment is intended to give that starting point, and the broader industry view suggests the same conclusion: procurement cannot become strategic if it is still spending much of its time reconciling its own numbers.

Source: Noah Wire Services