In 2025, finance leaders stopped debating whether AI “works” and started discussing how to deploy it at scale, with controls, and with measurable outcomes.
The conversation today has shifted from experimental pilots to end-to-end workflows, from month-end “events” to continuous closes, and from spreadsheet-driven processes to AI-native operating models.
To understand what lies ahead, David Abendschein (CDO, Consero Global) and Nicolas Kopp (CEO, Rillet) share their predictions on how AI will fundamentally change finance and accounting in 2026 and what leaders should be doing now to stay ahead.
How AI Will Transform Finance in 2026
- In 2025, the market moved beyond limited use cases faster than most organizations could operationalize.
- Manual month-end close will be obsolete in modern finance environments.
- The divide between truly “AI-native” and “retrofitted” finance teams is becoming a chasm.
- The biggest competitive gap in 2026 will come from speed-to-insight rather than cost-to-serve.
- Accounting teams will be leaner and more analytical, moving from preparation work to supervising automation, managing exceptions, and delivering near-instant answers.
- Month-end close will become a continuous close.
- The breakout AI capability is real-time, narrative financial intelligence.
- AI copilots will be the default for most mid-market and enterprise finance teams by year-end.
- Systems of intelligence will be more important than systems of record.
What was most surprising about the state of AI in finance and accounting in 2025?
In 2025, the industry’s mindset evolved faster than its infrastructure. The market moved beyond limited use cases faster than most organizations could operationalize.
Abendschein noted two primary surprises:
- Mindset over Deployment: Leaders pivoted from questioning AI’s reality to demanding safe, scalable operating models.
- End-to-End Maturation: AI quickly outgrew “point solutions” (like simple invoice coding) to encompass entire workflows, often maturing faster than the internal data structures of the organizations themselves
“Finance leaders moved very quickly from ‘Is AI real?’ to ‘We know we need this—how do we do it safely and at scale?’”
He also noted that while many companies are still catching up on fundamentals (process design, data quality), the leading edge has already progressed from point solutions into connected workflows:
“By the end of 2025, the more progressive teams were already thinking in terms of end-to-end workflows: data ingestion → classification → reconciliation → accruals → narrative insights.”
In Kopp’s view, 2026 will be the “cleansing” phase of the hype cycle.
“Last year, everyone was talking about the potential of AI. 2026 is the year CFOs prove and apply it.
At Rillet, we’ve long moved past the experimental phase into real execution, and the primary focus is tangible, measurable results.”
2026 Signals
- The bar is rising from pilots to production.
- Leaders will prioritize governance, accuracy, and auditability not just “cool demos.”
- The winners won’t be the teams that use the most tools; they’ll be the teams that redesign workflows around automation.
Which routine accounting tasks will be fully automated by the end of 2026?
By the end of this year, Kopp expects the “manual” month-end close will be obsolete in modern finance environments.
“The close becomes less of a monthly ‘event’ and more of a final review of numbers that have been updating throughout the month.”
For example, Rillet already automates the following:
- Bank and subledger reconciliations handled continuously by AI that matches transactions, flags anomalies, and proposes resolutions.
- Recurring and rules-based journal entries automatically generated and posted for depreciation, amortization, allocations, intercompany, and accruals.
- Revenue and invoicing workflows including invoice generation, billing schedules, automated posting of AR and revenue entries, and support for deferred revenue and revenue recognition events.
- Flux analysis and accruals explanations with automated period-over-period variance identification, root-cause drivers, and draft commentary to support month-end review and reporting.
As reconciliations and recurring journal entries become fully automated, Kopp foresees a material impact on month-end close windows.
“The impact on the traditional, manual month-end close will be dramatic. For well-designed processes on modern systems, I expect a shift from business day 10 closes to a business day 1-3 norm.”
When describing the automation trajectory, the numbers speak for themselves:
“For companies on Rillet, we’re seeing 99% of journal entries and 95% of reconciliations being handled by the system as transactions flow through the ledger in real-time.”
What will be the key differences between AI-native finance teams vs. those that retrofit AI?
The divide between truly “AI-native” and “retrofitted” finance teams is becoming a chasm.
| AI-Native Teams | Retrofitted Teams | |
|---|---|---|
| Default State | Automation (Data capture/coding); forward-looking (“what’s happening”) | Manual (Keying data/spreadsheets), backward-looking (“what happened”) |
| Finance Team Focus | Approvals, exceptions, insights, decision support | Status chasing, spreadsheet stitching, rework |
| Workflow | Automation-first, exception-driven | Manual-first, AI “side widget” assists occasionally |
| Primary Output | Strategic decision support | Monthly execution |
Abendschein envisions AI-native teams that are built on a single principle:
“Humans handle judgment; machines handle repetition.”
In AI-native finance organizations:
- Automation is the default (capture, matching, coding, posting).
- People step in at decision points: true exceptions, approvals, business communication.
- The team is designed around exception management and insight production, not transaction processing.
By contrast, David warned that retrofitting AI onto old workflows can produce a disappointing outcome: the same process, plus an “AI widget,” while spreadsheets and manual handoffs remain the backbone.
Kopp described the same shift through roles:
“In an AI-native environment, the human-in-the-loop framework becomes even more important. We let AI be the preparer and accountants and controllers are the reviewer and approver. Teams with automated workflows in their DNA will move out of mundane F&A work to perpetually stay a step ahead of the business.”
Where will AI create the biggest competitive gap in 2026?
The biggest competitive gap in 2026 will come from speed-to-insight built on clean, modern data—turning finance from retrospective reporting into real-time decision support.
Abendschein highlights that the winners aren’t those with the lowest cost-to-serve, but those who can answer complex questions, that historically required days of work, instantly:
- “What changed in gross margin this week, by product and channel?”
- “If we slow hiring in these three departments, what happens to runway and EBITDA over the next 12 months?”
The separating factor
Kopp aligned closely, noting that faster answers and faster decisions will be entirely dependent on the underlying data structure.
“Companies with clean, modern data structures are moving at a different velocity than those still wrestling with messy historical data. That’s why native integrations matter. At Rillet, our integrations are built in-house so we can ensure data syncs in a way that’s actually intuitive to accountants.”
Where the competitive gap shows up most
- Decision velocity (faster pivots, fewer surprises)
- Confidence in numbers (less debate, more action)
- Leadership credibility (finance becomes the “instant-answer” engine)
What will accounting teams look like, and which roles will change the most?
Accounting teams will be leaner and more analytical, with controllers and FP&A leaders shifting from preparation work to supervising automation, managing exceptions, and delivering near-instant answers.
With lean finance the expectation, Kopp boldly predicts that in 2026 we will see the first one-person finance team reaching $100M in ARR.
He also notes that businesses are changing how they evaluate talent and build their F&A department.
“Companies aren’t just looking for accounting degrees; they are prioritizing candidates who are comfortable using an AI-driven ERP. We’re seeing Boards and CFOs now expecting their controllers to be fluent with AI tools as a baseline requirement.”
Abendschein anticipates the largest shift in scope for:
- Controllers: owning exception queues, validating outputs, managing edge cases, and translating implications
- FP&A leaders: interpreting trends faster, partnering with the business, and working directly with AI tools (not just spreadsheets)
“Every role moves up the value chain.”
2026 Accounting role shifts
- From “preparing” → to reviewing and supervising automation
- From “reporting” → to explaining and advising
- From “spreadsheets as the system” → to AI + data model fluency
What will the month-end close look like?
Rather than fire drills, month-end close will resemble continuous readiness: most journals and recons handled in real time, with teams clearing a centralized exception queue instead of assembling the baseline at month-end.
Abendschein outlined a close that looks less like a finish line and more like an always-on system where:
- 80–90% of journal entries are auto-generated.
- 90%+ of reconciliations are handled continuously.
- Exception Dashboards replace manual checklists, surfacing only policy violations or unusual variances.
“Closing the books becomes less about building the numbers and more about validating and explaining them.”
What breakout AI capability is being underestimated right now?
The breakout shift is real-time, narrative financial intelligence.
Abendschein explains:
“The breakthrough isn’t just that the system finds an outlier. It’s that the system can now explain in plain language why something changed and suggest a specific action to take, all while the week is still in progress.”
When anomaly detection becomes narrative intelligence + recommended action, delivered in real time, CFO attention shifts from retrospective review to proactive steering.
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Narrative intelligence disruption
- Finance becomes a continuous early-warning system.
- Limit “surprises” to exception handling.
- Leadership decisions change from monthly to weekly (or faster).
What percentage of finance teams will run with a standardized AI copilot?
AI copilots will be the default for most mid-market and enterprise finance teams by 2026.
Abendschein expects AI copilots to be commonplace, embedded directly in daily systems whether teams call it a “copilot” or not.
But he’s clear that the biggest blocker won’t be model capability:
“The #1 adoption blocker will not be technology. It will be data and process readiness.”
He pointed to common failure points:
- Inconsistent charts of accounts
- Poor master data governance
- Bespoke, highly manual workflows
- Multiple, unintegrated systems
“If the underlying data is messy and the processes aren’t standardized, AI copilots can’t deliver reliable outputs. The teams that invest in data quality and process discipline now will see the biggest benefit from copilots later.”
Kopp added a pragmatic cultural layer:
“AI tool adoption is becoming table stakes for career competitiveness. Opting out isn’t an option, especially for roles expected to deliver answers quickly and operate AI-native systems confidently.”
Key takeaways for finance leaders
- “Copilot everywhere” is realistic but only if the foundation is clean.
- Data discipline and standardized workflows are the real accelerators.
How will the competitive landscape shift between legacy providers and AI-first startups?
By 2027, systems of record will matter less than systems of intelligence: startups will push automation and UX forward, incumbents will win only if they embed real AI, and tech giants will battle for the cross-system AI layer.
Abendschein expects three parallel dynamics:
- AI-Native Disruptors: Setting the pace for user experience (conversational interface) and “zero-touch” bookkeeping.
- Legacy Providers: Defend share by embedding AI into their core ecosystem and data models.
- Tech Giants: Competing for the “AI Layer” on top, providing the orchestration and conversational interfaces that sit across multiple platforms.
He anticipates continued demand for best-of-breed components (GL, AP automation, expense, CRM), connected by a unified data platform and an AI layer that delivers forecasting, anomaly detection, narrative insights, and decision support.
“The system of record is no longer the strategic moat. The new moat is the system of intelligence on top of clean data.”
Put AI-native finance into production with Consero + Rillet
2026 will reward finance organizations that move from experimentation to repeatable execution, modernizing not just tools, but the operating model, controls, and data foundation required for AI-native finance.
That’s exactly what Consero and Rillet are building together.
Consero’s Fusion Lab delivers Rillet’s AI-native ERP with Consero’s design, implementation, and processes mapped for your business, so you receive automation, intelligence, and real‑time insight from day one.
Ready to lead the AI transformation? Contact Consero today to build your intelligent finance infrastructure.
