Your Emissions Data Is Already in SharePoint. It Just Hasn't Been Processed Yet.

The GHG Protocol found that 83% of companies reporting climate disclosures struggle to access their emissions data. Not calculate it. Not analyse it. Access it. And for most Australian businesses, the absurdity is that the data isn't hidden. It's sitting in a SharePoint folder, as a PDF, right now.

Accounts payable saved the electricity bill there last Tuesday. A site manager uploaded a fuel docket on Thursday. The waste manifest from the skip bin company arrived via email and got filed into the project folder on Friday. All the source documents your sustainability team needs for this quarter's emissions are already digital, already organised, already sitting in your shared drive.

Nobody's touched them. Nobody will - until someone panics in September because the NGER deadline is 31 October and half the year's data hasn't been entered.

This is the gap we keep seeing. Not a data problem. A processing problem. And it's costing Australian businesses weeks of labour every quarter that don't need to exist.

Documents arrive all month. Processing happens once a quarter.

Here's how it actually works at most mid-market companies with 20 to 100 sites.

Utility bills arrive from energy retailers as PDFs. Accounts payable downloads them from retailer portals and saves them to OneDrive or SharePoint - usually into a folder structure organised by site or cost centre. Fuel dockets come in from site managers as photos or scanned copies. Waste invoices get forwarded from the procurement team. Water bills arrive by post, get scanned, and land in the same shared drive.

This happens continuously. Bills arrive week by week throughout the month.

But emissions processing? That happens in a batch. Once a quarter, sometimes once a year. The sustainability team (often just one person) opens each folder, downloads each document, reads the consumption figure, looks up the right emission factor from the NGA Factors workbook, and types the calculation into a spreadsheet. For a company running 50 sites with four utility types each, that's 600 documents per quarter. At three to five minutes per document - reading, interpreting, entering - you're looking at 30 to 50 hours of manual data entry. Every quarter.

And that estimate is conservative. It assumes the bills are all there, all legible, and all in a consistent format. They aren't. Some retailers send bills as image-based PDFs that can't even be copied from. Some bills have multiple meters on one invoice. Some have estimated reads that need flagging. The real time is worse.

The ANAO audited 545 NGER reports and found 72% contained errors, with 17% including significant errors. The most frequent causes were gaps in electricity data and missing sources - not calculation mistakes. The maths was usually fine. The data entry was wrong, or the bill simply never got processed.

The overnight processing shift

The fix isn't hiring more people to type faster. It's removing the typing entirely.

Connect your OneDrive or SharePoint folder to Carbonly. Map each site's folder to the corresponding business unit. From that point, every new document that lands in those folders gets automatically detected, queued for processing, and run through the AI extraction pipeline.

The processing doesn't wait for quarter end. It doesn't wait for someone to log in. A gas bill saved to the Melbourne CBD site folder at 3pm on a Wednesday gets picked up, classified, read, extracted, matched to the right emission factor, and calculated - without anyone opening it. By the time your sustainability manager checks in Thursday morning, it's sitting in the Document Hub with a confidence score, an emission figure, and a full audit trail linking the number back to the source PDF.

This is what we mean by overnight processing. Not batch processing triggered by a human. Continuous processing triggered by the document itself arriving in the folder where it was always going to land anyway.

For companies with 50+ sites, the difference compounds fast. Instead of starting each quarter with a pile of hundreds of unread PDFs and a three-week manual sprint, you start with pre-processed data. The sustainability team's job becomes reviewing and approving - not typing and chasing. And because documents are processed as they arrive, errors surface early. A misread meter number in January gets caught in January - not discovered in September when someone finally gets around to entering Q1 data and notices the consumption figure doesn't make sense.

Where the documents actually come from

One thing we've learned building this: the data doesn't come from one place. It comes from everywhere, and if you only connect one channel, you're still missing documents.

SharePoint and OneDrive capture what accounts payable saves. That's usually utility bills - electricity, gas, water - downloaded from retailer portals. It also picks up scanned invoices, delivery dockets saved by site managers, and any other document that ends up in the shared drive as part of existing business processes.

But not everything lands in SharePoint. Site managers sometimes email fuel dockets directly to the sustainability coordinator. Supplier invoices arrive as email attachments that never get saved to the shared drive. Waste companies send monthly summaries via email that accounts payable never sees.

That's why folder sync works best when combined with email ingestion. Each project in Carbonly gets a dedicated email address. Set up auto-forward rules from your accounts inbox, or give site managers the address directly. Documents arrive via email, get processed through the same pipeline, and land in the same Document Hub alongside the SharePoint-synced files.

Between the two channels, you're catching documents from accounts payable (SharePoint), site managers (email or SharePoint), procurement (either channel), and suppliers who email directly. We won't pretend this captures 100% of everything - there's always a straggler document that falls through the cracks - but it catches 85-90% without any human intervention.

And here's a trick that works surprisingly well for companies using JotForms or Google Sheets to collect field data from sites. Export those form responses as CSVs or PDFs into the connected OneDrive folder. They flow straight into the processing pipeline alongside everything else. Same for any system that can export to a shared folder - accounting software, fleet management tools, waste management portals. If it can save a file to OneDrive, it can feed Carbonly.

The system handles PDFs, Excel spreadsheets, CSVs, Word documents, PowerPoint files, RTF, and images - including photos of handwritten dockets taken on a phone. So the format of the source document doesn't matter. What matters is that it ends up in a folder Carbonly is watching.

The verification agent catches problems before you do

Processing documents automatically is only useful if the output is trustworthy. And frankly, this is where most automation tools fall short. They extract numbers and present them as fact, regardless of whether those numbers make sense.

Every document that enters Carbonly - whether from SharePoint sync, email, or manual upload - gets run through a verification agent after extraction. This isn't a simple range check. The agent compares the extracted data against historical patterns for that site. If a building that normally consumes 40,000 kWh per quarter suddenly shows 400,000 kWh, it gets flagged. If a fuel docket shows 50,000 litres for a site that typically uses 5,000, it gets flagged. If the billing period overlaps with a previously processed bill (potential duplicate), it gets flagged.

The flags show up in the Document Hub with clear explanations. Not cryptic error codes. Actual descriptions: "Consumption 10x higher than site average for previous three quarters" or "Billing period overlaps with existing record from same supplier."

This matters more than people realise. Without verification, automated processing can introduce errors faster than manual entry. At least when a human reads a bill and types "400,000 kWh," they might pause and think "that doesn't look right." An AI extraction engine without a verification layer won't hesitate. It'll extract the number, multiply it by the emission factor, and give you an emissions figure that's off by an order of magnitude. Confidently.

The verification agent is the check on the extraction. It's what lets us say automation saves time without quietly making your data worse.

Bulk review: confirming a quarter's documents in one pass

So you've got 600 documents from Q2 sitting in the Document Hub. SharePoint sync and email ingestion brought them in over three months. The AI processed each one as it arrived. The verification agent flagged 15 of them for anomalies. The other 585 have high confidence scores and no flags.

This is where the bulk review agent earns its keep.

Instead of clicking through 585 documents one at a time to confirm each extraction, you run a bulk review. The agent scans every unreviewed document, checks confidence scores, confirms emission factor matches, and verifies that no duplicates slipped past the deduplication layer. Documents that pass all checks get marked as reviewed. The 15 flagged items stay in your review queue for human attention.

The quarterly review drops from a multi-day exercise to something you finish before lunch. Your sustainability manager spends their time on the 15 documents that actually need judgement - a billing anomaly, an unusual fuel type, a new supplier format the AI hasn't seen before - not on confirming the 585 that are fine.

For NGER reporters, this also creates the audit trail the Clean Energy Regulator requires. Every document retains a link to the source file, the extracted data, the emission factor applied, the calculation, and a timestamp showing when it was processed and reviewed. Records must be kept for five years from the end of the reporting year. The audit trail is generated automatically - not assembled retroactively in a panic before the auditor arrives.

Here's what that looks like in practice. Without automatic sync, you arrive Monday morning to an inbox full of bills forwarded from accounts payable. You save each one to the right folder. You open your spreadsheet. You start working through last week's documents one by one, looking up emission factors and entering calculations. By Friday, you've processed maybe 30 bills. There are 120 more waiting.

With automatic sync, you arrive Monday morning and the documents from last week are already processed. The dashboard shows 28 new records since Friday - 25 with high confidence, 3 flagged for review. You check the 3 flagged items (one was an estimated read, one had an unusual billing period, one was a format the AI hadn't seen from that retailer before). You run bulk review on the rest. It takes 20 minutes. The remaining four days of your week are available for actual analysis - identifying reduction opportunities, preparing for ASRS Group 2 disclosure, running scenario models for your transition plan. That's not a marginal improvement. It's a different job description.

The compliance timing argument

NGER reports are due 31 October every year. No extensions. The Clean Energy Regulator has demonstrated it will act on non-compliance - Beach Energy signed an enforceable undertaking in July 2025 after misstating NGER data across multiple years, resulting in three years of mandatory reasonable assurance audits at their own cost.

ASRS Group 2 reporting started from financial years beginning 1 July 2026. Those entities need Scope 1 and 2 emissions with an audit trail that a financial statement auditor will scrutinise under ASSA 5010 assurance standards. And NGER reporters are automatically pulled into ASRS Group 2 via the registration pathway - so if you're already doing NGER, you're about to do both.

If your emissions data processing only happens in a quarterly or annual batch, you're compressing months of work into weeks. Documents get missed. Errors compound. The review gets rushed because the deadline is next Tuesday and nobody has time to check whether that 400,000 kWh figure for the Parramatta site is real or a misread.

Continuous processing removes the compression. Data flows in throughout the year. By the time you reach reporting season, you're reconciling and reviewing - not starting from scratch.

We're honest that this doesn't solve every problem. Scope 3 supplier data still requires chasing people who don't want to be chased. Some documents - handwritten fuel dockets from remote sites, invoices in languages other than English, heavily damaged scans - still need manual handling. And we're still improving how the system handles multi-page consolidated invoices where one PDF contains bills for 15 different sites. But for Scope 1 and 2 source documents from Australian utility providers and fuel suppliers, the pipeline handles the vast majority without intervention.

The practical next step: pick one site. The one with the cleanest SharePoint folder structure. Connect it. Watch the documents get picked up and processed. Check the output against what you would have manually entered. Then connect the next site. Most companies are fully synced within a week, and the first unattended Monday morning - where you arrive to pre-processed data instead of a pile of PDFs - tends to be the moment people stop going back to spreadsheets.