Will AI replace traditional Spend Analytics tools?

I started my professional career in Supply Chain and Global Procurement and ended up spending 12 years in various sourcing roles and later advising procurement leaders on their choices of technology at Sievo. With the development of AI tools lately, I've been closely following the market and debating if the traditional SaaS tools will disappear with the push of Agents and self-built analytics.

After coming across Stan's post on Zinit and the super easy "throw these files to Claude and see it yourself" self-eval (or demo as Stan called it) of the platform I grew very curious. Finally had the time this Sunday to run the eval and see it myself.

Few reflections here.

The visualization and findings are solid, clear McKinsey quality — similar to what I've seen numerous times before and something I've come to expect from working with McKinsey over the years.

Furthermore, the summary is crisp, well structured and to the point. This definitely drives top-down action. Back to this later.

My biggest concern: the data reality gap isn't addressed much at all. Quite honestly, this is what I was expecting AI tools to handle in the future. Yes, this is a demo only — but the file is pretty much dream data for anyone wanting to run analytics: clean item descriptions, fairly consistent supplier names, one row per transaction, pre-categorized. Real ERP exports never look like this, and this is where the value of spend analytics tools (should) be, per my experience.

Data extraction. Rarely does the data you need live in one consolidated ERP. It's actually scattered around multiple — sometimes hundreds — of ERP systems (for larger enterprises), Expense Management Systems, MRO systems, or Supplier Systems. APIs are getting better and data moves fast these days, but knowing what to extract and how to combine and cross-check these data points is where the real value lives.

Categorization. Validation of the classification is easily 50% of the work. I've done this hands-on for different industries, regions, SMBs and enterprises — and it always gets messy. The demo has a clean "Category" column. For indirect data in practice, you'd first rely on GL codes, then cross-check with invoice descriptions, enrich with supplier data (DHL won't provide catering services regardless of what the GL code says), and then make an educated guess on where this invoice should go. Apply once, let AI learn from here.

The "same item" problem (direct procurement). The demo has matching item descriptions and harmonized item numbers across sites, which is what makes price harmonization possible. In reality, Plant A calls it "Hydraulic Filter 10-Micron" and Plant B calls it "HF-10M Filter Hydr." — same part, different free-text description, so the algorithm sees two different items and misses the savings entirely. It's not unusual to find the same bolts and nuts re-created 100 times with a new item number for each product variant.

Unit of measure chaos. The demo uses consistent units — EA, PK, and so on. Real data has the same item bought as EA in one plant and BX in another, making price comparison meaningless without normalization. A 10-pack of O-rings and a single O-ring both showing "Unit Price: $0.42" is a common silent killer of harmonization analysis.

These are just a few highlights and examples. To come to a conclusion:

1. A crisp summary is just the top layer of the cake. How spend analytics systems handle the data classification problem is where the real value lies.
2. From high level to the details with a few clicks — in an ideal UI you can quickly validate your findings at the raw data level, confirm the findings, and then deploy actions.
3. Action is what makes the changes, and this is where most organizations still fail to execute. You analyze and summarize, and in the end procurement operations continue as before. I see big potential here with Agentic AI — and this is where Zinit also seems to have an offering, with compelling real-world use cases.

The Zinit demo files are available here for anyone interested: zinit.com/en/spend-analysis

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