Guide
Top 10 accounting tech trends to watch in 2026
A revealing list of the changes coming to finance technology
Finance technology is evolving faster than most organizations can keep up with.
Tools that seemed experimental 18 months ago—generative AI, autonomous agents, predictive analytics—are now showing up in mainstream accounting software. There are endless examples of how AI is being used in accounting already. Meanwhile, the patchwork of disconnected systems that companies tolerated for years is becoming a liability as data quality and integration become prerequisites for everything else.
Studies show AI adoption among finance teams doubled from 23% in 2024 to 49% in 2025, according to a Leapfin report. Deloitte found that 63% of finance leaders have fully deployed and are actively using AI solutions in their finance function. And 73% of finance professionals say their business is growing faster than their team can handle manually—a pressure that's pushing automation adoption from a future consideration to an immediate priority.
Finance leaders now face decisions about which investments will deliver results and which will create more problems than they solve. Here's what accounting and finance teams should expect in the year ahead.
1. Companies will abandon multi-system setups for unified platforms
For years, companies have cobbled together their tech stacks: Salesforce for sales, QuickBooks or NetSuite for accounting, separate tools for billing, procurement, and reporting. The result: data that lives in silos, requires manual reconciliation, and never quite matches up.
That approach is losing favor. According to Deloitte, 49% of finance leaders say their inability to scale digital infrastructure is holding them back, while 45% cite inflexibility in legacy systems as a major barrier.
"The main issue is that you have multiple systems, and it's hard to grab data in one place," says Ben Robinson, Solutions Engineer at Accounting Seed. "You have to go to multiple places to find it. It also causes a lot of manual entry and time inefficiency on teams."
Robinson predicts companies will move toward unified platforms where sales, operations, and finance data share a single source of truth. That consolidation sets the stage for automation and AI to actually work rather than fighting data issues at every turn.
With Accounting Seed, you can house your sales, operations, and financial data in the same place because it's built natively on the Salesforce Platform. So you can be confident that you're always working with the most up-to-date and reliable information.
2. AI will make automation smarter
Automation in accounting isn't new. But the line between automation and AI in accounting is often blurry, and understanding the distinction matters.
"Automation isn't necessarily AI," explains Mary Balmer, VP of Product at Accounting Seed, CPA, and Salesforce Certified professional. "We have the ability to help customers automate everything without an ounce of AI. But AI makes automation smarter."
Basic automation can generate a payable invoice from an email. AI-enhanced automation can look at past invoices from the same vendor, identify the GL account you typically use, auto-select it, and route the invoice for approval.
Research from AvidXchange shows that finance teams using AI report increased efficiency (48%), faster insights (40%), and improved accuracy (38%). Those gains compound as AI handles more of the routine pattern-matching that used to require human attention.
3. Agentic AI will handle targeted finance tasks autonomously
AI in finance is starting to take the form of agentic AI, or task-specific agents built for day-to-day accounting work. These systems aren’t meant to be all-purpose tools. Each one is designed to handle a narrow set of responsibilities—such as collections, bill pay, or general ledger lookups—with limited manual input.
For receivables, an agent can track aging invoices, estimate when a customer is likely to pay, and send follow-ups based on that behavior. On the payables side, an agent can catch duplicate invoices, surface early payment discounts, and suggest which bills to pay first based on available cash. For reporting and analysis, finance teams can ask direct questions about transactions or balances and get immediate responses without pulling data into separate reports.
Gartner predicts that by 2028, roughly 33% of enterprise software applications will include agentic AI capabilities, up from less than 1% in 2024. Finance functions are well-suited for this technology because many accounting tasks are repetitive, rules-based, and data-intensive.
4. Data quality will determine AI success
Here's where many AI initiatives stall: the data isn't ready. AI models learn from existing information, and if that information and overall data structure is inconsistent, incomplete, or siloed across systems, the outputs will be unreliable at best.
"Get your data in order," Balmer says. "That is key. AI needs good, clean data. Period."
A Leapfin survey found that data quality issues are now the number one barrier to automation adoption, cited by 23% of finance professionals—overtaking concerns about cost or resources. And Precisely reports that while 60% of organizations consider AI a primary use case for their data, only 12% believe their data meets the quality and accessibility standards required.
Robinson echoes this concern: "Plugging AI into a broken system is a problem. There's a lot of internal work a company should do—a lot of data cleaning—before just throwing AI at their problem."
5. Smaller teams will shoulder greater strategic responsibility
The accounting talent pipeline has been shrinking for years. The number of CPA exam candidates has dropped 27% over the past decade, and 75% of current accounting professionals are within 15 years of retirement. At the same time, transaction volumes keep climbing. The math doesn't work without automation.
"Teams will be reduced," Balmer acknowledges. "We don't know by how much. But the people that remain will be spending less time on historical reporting and more time looking forward—focusing on the business, the strategy, and growing the organization."
This shift is already underway. Deloitte found that 64% of finance departments plan to add more technical data and analytics skills over the next two years. The accountant of 2026 will need to interpret AI outputs, run scenario models, and advise leadership on where to invest.
6. Predictive analytics will replace backward-looking reporting
Traditional accounting is backward-looking by design: you record what happened, reconcile the numbers, and report results weeks after the fact. But that timeline is compressing.
According to IMA and Deloitte research, 53% of finance professionals have either integrated or plan to integrate AI and advanced analytics into their cost and profitability models. The goal: move from historical reporting to predictive and real-time analysis.
"AI can file through millions of data records and recognize patterns that would take humans far too long to identify," Robinson notes. "Being able to predict business decisions—that's a process a lot of teams struggle with. AI will allow us to develop those patterns and give us options, and then we use our judgment to decide which is most likely."
Walmart's finance team, for example, has moved from monthly scenario planning to nearly daily updates using AI-powered models. That kind of responsiveness—adjusting forecasts as conditions change—is becoming the expectation rather than the exception.
7. Human judgment will remain essential
Despite all the automation hype, accounting still requires human oversight. Valuation, professional judgment, regulatory interpretation, and strategic decision-making can't be handed off to an algorithm.
"A lot of what AI is looking to replace is manual effort and data entry, tasks that many don’t like to do," Robinson says. "What can't be replaced is the judgment, the valuation, the human connection to accounting processes. There's a level of professional judgment every accountant has to use that isn't directly replaceable."
This is why the profession isn't disappearing; it's changing. Entry-level roles focused on data entry may decline, but roles requiring analysis, interpretation, and strategic thinking will grow. The accountants who thrive will be those who learn to work
8. Security and governance will move to the center of tech decisions
As AI handles more financial processes, the risk profile changes. Finance data is among the most sensitive in any organization, and giving AI systems access to it raises legitimate concerns. An IBM survey found that 93% of executives say they must factor "AI sovereignty"—control over AI systems, data, and infrastructure—into their 2026 strategy.
Governance goes beyond preventing breaches. It requires ensuring AI outputs are explainable, auditable, and compliant with regulations. Finance leaders will need to establish clear policies on which activities can be fully automated, which require human review, and how AI decisions get documented.
A lot of what AI is looking to replace is manual effort and data entry. What can't be replaced is the judgment, the valuation, the human connection to accounting processes."
9. Upskilling will become a baseline expectation
The technology is moving fast enough that standing still means falling behind. Finance professionals who want to remain relevant will need to understand how AI works, how to evaluate its outputs, and how to use data tools effectively.
"If I were mentoring someone in school right now, I would say really get into the technology," Balmer advises. "Learn about the different models, the different tech stacks. Companies may be laying off people who are resistant to change. Do not be resistant to change—change is inevitable in this industry. Embrace it."
Research supports this urgency: 61% of employees say AI makes their job less mundane and more strategic. But capturing that benefit requires developing new skills. Organizations that invest in training—and individuals who pursue it independently—will be positioned to take advantage of the shift.
10. AI-native accounting applications will begin to emerge
Most accounting software today was built before AI became viable at scale. AI features get bolted on—a chatbot here, an auto-categorization feature there. But what happens when you build accounting software from scratch with AI at the center?
"My bold prediction is that we'll see at least one, if not more, AI-native accounting applications," Balmer says. "Not just AI-first. AI-native. The whole front end changes. The user interface becomes AI-driven."
She describes a future where the transactional backend—billings, payables, payments, bank records—still exists, but the way users interact with that data is entirely different. Instead of navigating menus and clicking through screens, users might simply ask questions and receive answers drawn from across the system.
This is speculative for 2026, but development is already underway at multiple companies. Finance teams should keep an eye on emerging players building from this foundation.
Building a foundation for what's ahead
The trends converging on accounting and finance reinforce each other. Unified platforms enable automation. Automation frees time for analysis. Clean data makes AI viable. AI enables prediction. And skilled people are needed to interpret it all.
For finance leaders evaluating their technology stack, the decisions ahead center on timing and approach: how quickly to move and which foundations to build first. Starting with solid data, choosing platforms that integrate rather than fragment, and investing in team development will separate organizations that struggle from those that adapt.
Accounting Seed, built natively on Salesforce, provides a unified platform where sales and financial data share a single source of truth—eliminating the integration headaches that slow down so many finance teams. If your organization is evaluating its accounting technology stack, reach out to see how it works.
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