Data Centers, Dirty Data.
Amazon's data center networking breakthrough signals faster AI infrastructure — but your firm's bottleneck isn't compute speed, it's data quality.
Amazon's Data Center Breakthrough Means Faster AI — But Your Bottleneck Is Still the Chart of Accounts
Amazon just solved a core data-center networking problem that dramatically increases throughput across its cloud infrastructure. That speed improvement flows directly into the AI tools your firm is evaluating or already running — faster inference, lower latency on large document processing, more capable agentic workflows. But here's what that doesn't fix: if your client's GL is a mess of duplicate vendors, inconsistent coding, and manual journal entry overrides, faster AI just surfaces the chaos faster.
The infrastructure is no longer the constraint. Your data hygiene is.The Accountants Who Stay Billable Are the Ones Who Own Data Readiness
Every AI-assisted close, AP automation rollout, or audit prep tool has a prerequisite: clean, consistently structured data. Right now, most firms treat that cleanup as overhead — something to push through before the 'real' work starts. Flip that. Data readiness is the consulting engagement. The firms building practices around pre-implementation data assessment and ongoing data governance are creating retainer revenue that didn't exist three years ago.
Stop treating data cleanup as a cost. Start pricing it as a service.Agentic Close Workflows Are Now Running on Faster Cloud Rails — What That Means for Month-End
With AWS's networking improvements reducing latency across its cloud stack, agentic financial close tools — those that orchestrate multi-step tasks like matching, reconciliation, and variance flagging without human handoffs — are becoming meaningfully more reliable in real-time scenarios. If you've piloted these tools before and found them sluggish or prone to timing out on large transaction volumes, it's worth revisiting. The underlying infrastructure constraint that caused most of those failures has been addressed.
If an agentic close pilot failed on you in 2025, the infrastructure excuse is mostly gone — the data excuse probably isn't.AP Automation Vendors Are Overselling Readiness — Most Clients Aren't Actually Automatable Yet
The AP automation pitch assumes clean vendor master data, consistent invoice formats, and coded approval workflows. In practice, fewer than 30% of mid-market clients I've seen can onboard to a modern AP automation platform without six weeks of data remediation first. Vendors don't advertise that part, and implementation partners often don't scope it properly — which is exactly why so many automation projects stall at 60% match rates and never improve. The opportunity for accounting firms isn't reselling the software; it's being the firm that gets clients actually ready to use it.
AP automation doesn't fail because the AI is bad. It fails because nobody cleaned the vendor master before go-live.