What's changing now

Data automation: Basic tasks like manual data entry and reconciliations will disappear. Not in some distant future - this is happening right now with tools that pull data directly from sources, match transactions, and flag exceptions. The boring parts of month-end close will fade away.

Take bank reconciliations - software now connects directly to bank feeds, matches most transactions automatically, and only flags true exceptions. What used to take days now takes hours or minutes. This pattern will extend to every repetitive task in accounting.

Real-time financial information: The monthly close cycle is becoming outdated. Businesses want financial insights daily or even hourly. This means fewer big month-end sprints and more continuous monitoring.

Companies like Amazon track financial metrics by the minute. Small businesses use QuickBooks and Xero dashboards to check cash positions daily. This trend will continue until the idea of waiting until month-end for financial data seems absurd.

AI taking first passes: AI systems already draft journal entries, suggest account codes, and identify potential issues. Your value comes not from creating these items but from reviewing them critically and making judgment calls.

Look at how expense report software now reads receipts and automatically codes them. Similar capabilities will spread into more complex areas like revenue recognition and lease accounting, where AI will suggest treatments based on contract terms.

The data quality roadblock

How many times have you run a transactions report, layered on a bunch of information through lookups to other data sources and then manually created a report? This presents the biggest roadblock to adoption of AI and other technologies.

Many organizations still struggle with:

• Inconsistent data formats across systems

• Manual workarounds that create gaps in data trails

• Legacy systems that don't communicate effectively

• Missing or incomplete transaction details

These issues create a "garbage in, garbage out" problem that no amount of AI can fix. Companies will need to invest in data cleaning, standardization, and governance before they can trust automated systems to handle critical accounting tasks.

This clean-up phase might take 2-3 years for many large organizations before they can fully implement the more advanced technologies.

Company size matters: The adoption gap

There will be a stark difference between how established and entrenched a company is in its current technology stack and the speed at which it can adopt anything new.

Startup advantages: Companies born in the cloud era start with clean data structures, integrated systems, and no legacy technology debt. A five-person startup launched last year might use more advanced accounting technology than a Fortune 500 company.

Enterprise challenges: Larger organizations face complex integrations across dozens of systems, stricter governance requirements, and institutional resistance to change. Their size makes wholesale technology shifts much harder.

This creates an interesting dynamic where mid-market companies might learn best practices from small businesses rather than larger enterprises - a reverse of the traditional pattern.

The offshoring connection

The intersection between technology and offshoring trends deserves attention. We've seen waves of accounting work move overseas over the past 20 years. The next phase looks different:

First wave: Simple transaction processing moved to lower-cost locations.

Second wave: More complex accounting tasks shifted as skill levels in offshore locations improved.

Current transition: Technology is now replacing some roles that were previously offshored, while creating new hybrid models:

• Offshore teams now manage automated systems rather than doing manual processing

• Global centers of excellence form around specific accounting technologies

• 24-hour accounting operations become possible through a mix of automation and global teams

This means the pure labor arbitrage play is fading. The new model combines technology with global talent in ways that create follow-the-sun coverage and deeper specialization.

How business models are evolving

Subscription economics: More businesses run on subscription models with complex revenue recognition. The accounting for these arrangements requires different skills than traditional point-of-sale transactions.

Remote-first companies: Businesses with staff spread across countries face complex tax, payroll, and regulatory issues. Country-specific knowledge becomes both more valuable and harder to maintain.

Digital assets: Cryptocurrencies and NFTs present unique accounting challenges that don't fit neatly into GAAP frameworks. The rules here are still developing, creating opportunities for specialists.

Look for Part 2 in the next post about what this all means for you.