10 AI Predictions That Will Define Winning Finance Teams [2026]

Finance leaders are entering a new operating era where speed, automation, and decision-quality matter as much as accuracy. Tim Neville (Managing Director and Head of Consero’s AI Fusion Lab) and Stephen Hedlund (Head of Finance, Rillet) share their predictions for how AI will define winning finance teams in 2026—covering AI adoption patterns, the rise of continuous close, and the skills finance teams will need to stay lean while scaling.

1. AI Moves from Experiment to Operating Model

Neville and Hedlund agree: 2026 is the year finance teams move from experimenting with AI to operationalizing it.

The finance function is shifting from periodic reporting to high-frequency visibility because the business moves too fast to wait for month-end answers.

• AI is accelerating finance’s transition from reporting to decision support.
• The advantage is compounding: faster closes and cleaner data create better inputs for better outputs.
• Many organizations are moving from “AI curiosity” to measurable adoption goals.

“We’re at a point right now within how finance and accounting supports companies that it’s going to be a lot more of what I would consider accurate weather prediction.” — Tim Neville, Consero

2. 2025 in Review: AI Adoption in Finance Outpaces Investment

The past year has seen a massive surge in AI testing, with nearly all finance leaders exploring how these tools can improve their workflows. 

Not limited to finance, AI adoption is spreading across every function, reinforcing the urgency for finance teams to keep pace.

As accuracy increases and automation becomes more reliable, the focus for the coming year is turning toward full-scale implementation.

The overarching goal is to automate workflows, increase accuracy, and ensure the underlying data is reliable enough to support AI-driven decision-making.

The Investment Gap: Why More Companies Aren’t Moving Faster

Consero’s 2025 survey of finance leaders revealed that, despite near-universal adoption, investment lagged behind –– highlighting that not every organization is moving at the same speed.

Some organizations stall due to uncertainty about where to start, how to measure value, or may still be waiting to see tangible gains before committing more capital.

Meanwhile, organizations at the forefront of AI-native tools are seeing outsized results.

• The rapid release of advanced models like Claude is already causing many companies to double down on their initial investments.
• The gap between leaders and laggards in AI investment is expected to widen quickly.

“We’re seeing our customers increase their AI investments significantly… we’re seeing folks double down there.” — Stephen Hedlund, Rillet

3. Automating Accounting Workflows and Reconciliations That Slows Down Cash and Close

Automation is moving beyond simple rules to intelligent systems that can handle nearly all reconciliation tasks. This shift allows revenue to be closed almost instantly and frees up accounting teams to focus on the “why” behind the numbers.

The fastest early wins come from eliminating manual accounting work through end-to-end automation of banking, sub-ledger activities, invoicing, and reconciliation workflows.

• AI-powered reconciliation is enabling some teams to achieve 99% automatic matching, reducing close timelines from 5–10 days to as little as Day 1 for revenue.
• Automated invoice workflows get cash moving faster, which directly impacts working capital and cash flow health.
• As reconciliations and journal entries happen in the background, finance teams are freed up to focus on strategic analysis rather than data entry.
• Automation reduces the need for large headcounts, allowing teams to stay lean while scaling.

“We legitimately have folks where 99% of their reconciliations happen automatically. Revenue is closed on Day 1 because of just the reconciliation happening.” — Stephen Hedlund

4. The AI-Native General Ledger Enables the Continuous Close

Traditional “retrofitted” systems struggle to keep pace with modern AI capabilities. Rather than bolting AI onto legacy systems, the next wave of finance transformation requires rethinking the general ledger from the ground up, purpose-built for continuous, AI-driven operations.

By using a general ledger built specifically for the AI era, companies are achieving continuous close cycles and significantly reducing the time spent on month-end tasks.

• Companies using AI-native ERPs like Rillet are reducing close timelines from 20 days down to 2 days, with the ultimate goal of a zero-day continuous close.
• One Rillet customer scaled ARR 10x in a single year while adding only one person to the finance team, demonstrating the scalability of AI-native infrastructure.
• Hundreds of millions of dollars in venture capital are flowing into finance and accounting technology, with companies like Ramp reaching a $32 billion valuation.
• Legacy systems cannot simply be patched with AI tools—the architecture must be rebuilt to fully unlock the potential of automation.

5. Real-Time Dashboards: Working Capital Monitoring as a Daily Habit

With integrated data flows and connected systems, finance teams can shift their focus from closing the books to monitoring working capital continuously.

• Integrated dashboards across tools like Brex, BILL, and Rillet allow teams to track working capital and cash burn on a daily basis.
• Real-time visibility means less time worrying about month-end accruals and more time focused on strategic cash management.
• The finance function evolves from backward-looking reporting to forward-looking business intelligence.

“You’re not constantly thinking about the close, you’re thinking about your cash, and you’re thinking about how to run the business in real time.”— Tim Neville

6. AI Adoption Evolves From Skepticism to Application

The journey of AI in finance has moved from skepticism in 2024 to hype in 2025, and finally to real-world application today. Finance leaders are now using AI for complex financial modeling and direct system actions.

• 2024 was characterized by skepticism from accountants and controllers who struggled to see clear applications of AI in finance.
• 2025 brought hype and ambitious promises, not all of which were fulfilled, but it set the stage for real-world testing.
• In 2026, practical application is overtaking speculation, with CFOs building financial models in Claude and teams booking journal entries directly from chatbots.
• Capabilities that were impossible just three to six months ago—such as editing contracts and creating customers from a chat interface—are now operational for early adopters.

7. Clean Data is a Competitive Moat

Data quality has always mattered in finance, but in the age of AI, it is the decisive factor separating high-performing organizations from those that struggle to keep up.

Companies that prioritize data cleanliness and integrity are finding they can move much faster and answer more complex questions than those with fragmented data.

• AI systems are only as reliable as the data they consume. “Garbage in, garbage out” is an operational risk.
• CRM, billing, and revenue data hygiene often becomes the hidden blocker.
• Companies like Vercel have invested heavily in data integrity by piping everything into Snowflake and building AI agents on top, allowing their CFO to query revenue and customer data directly in Slack.
• Leading companies are increasingly placing data teams under the CFO’s direct ownership, a shift that did not exist even a few years ago.
• Clean data accelerates audit readiness, transaction preparedness, and covenant reporting—reducing audit fees and enabling faster deal execution.
• Not every organization can afford in-house data teams, which is where partners like Consero play a critical role in establishing and maintaining data infrastructure.

“The data gap, data integrity, outfitting the pipes will have a massive differentiation for the CFOs that focus on it versus those that don’t.” — Stephen Hedlund

8. Narrative Intelligence and Anomaly Detection Replace Static Reporting

AI is changing reporting from “what happened” to “why it happened,” providing the narrative behind financial variances. 

By detecting anomalies in real-time, finance teams can act as an early warning system for the entire organization, catching issues like fraud or billing errors before they escalate.

• The finance team is evolving into a strategic forecaster for the organization, identifying red flags, timing issues, and variance drivers before they become problems.
• Real-time data flowing through Rillet’s ERP allows companies to catch fraud quickly.

• When close timelines stretch to 15 or 20 days, executives receive insights that are already stale. AI-driven reporting eliminates this lag.
• Department leaders can self-serve by querying AI systems directly, asking questions like why T&E expenses jumped month over month, without waiting for finance to produce a report.

“If I don’t know what happened in January by February 19th, we’ve already moved on. Business is moving too quickly to wait this long.” — Stephen Hedlund

9. The New Skillsets for AI-Native Finance Teams 

As AI automates routine work, the skills required of finance professionals are shifting dramatically from data processing to exception management, strategic analysis, and AI fluency.

“The role of the controller is shifting to almost being a systems engineer… understanding how the data flows through the different systems.” — Tim Neville

• Controllers will need end-to-end system knowledge, handling exceptions and edge cases rather than processing every transaction manually.
• FP&A professionals will gain earlier access to data, compressing the traditional waiting period for close and enabling analysis from Day 1 of the month.
• AI tools like Claude in Excel are allowing finance professionals to build complex analyses in minutes that previously took hours, fundamentally changing the analyst role.
• CFOs who do not model AI adoption for their teams risk falling behind, as leadership engagement is the strongest driver of organizational adoption.
• Some organizations are embedding AI usage into performance reviews, allocating dedicated AI budgets by department, and even tracking tool adoption across teams.

“The accountants that lean into this are going to see really high returns.” — Stephen Hedlund

10. Readiness Beats Tools, Scalable Processes Are the Foundation

AI success requires more than software: scalability depends on data process readiness—standardized charts of accounts, integrated systems, and clean data governance that allow organizations to grow, acquire, and transact without operational disruption.

• Modern ERPs allow for lean operations, even during 10x growth phases.
• Data readiness is the key to managing rapid ARR growth without massive headcount increases.
• Eliminating manual data transfers between systems—through integrations with tools like Brex, Ramp, and Rippling—removes critical failure points in the finance workflow.

Using modern Finance as a Service (FaaS) models, companies can handle explosive growth without bloated administrative costs.

Case Study: Scalable Growth at ForeFlight

ForeFlight, an aviation software company, needed enterprise-grade finance speed and reporting reliability after being acquired by Boeing. 

Consero helped redesign and run the finance close and reporting operating model:

• Implementation of the Finance as a Service (FaaS) model to replace traditional in-house accounting.
• Integration of Rillet’s AI-native ERP for automated reconciliations and real-time data flow.
• Provision of a full-stack finance team, including controllers and strategic advisors, to oversee the AI-driven system.
• Creation of real-time dashboards for continuous monitoring of business health.

“What we’re trying to do is leverage our decades-long experience on the finance side with an incredible AI-native ERP tool.” — Tim Neville

Results:

• A Day 1 close
• Clean, organized reporting into the parent-company environment 
• Transaction support and finance readiness when ForeFlight transitioned through a spin-off.

What CFOs Ask Most: QuickBooks, Billing, ROI, and Security

The webinar’s Q&A surfaced the real-world concerns finance leaders face as they evaluate AI adoption for their organizations.

Does AI Work for Small Companies Using QuickBooks?

Yes. Even on legacy platforms like QuickBooks, teams can export data into enterprise AI tools like Claude or ChatGPT for flux analysis and reconciliation.

AI-native tools like Ramp integrate directly with QuickBooks to apply automation within the AP workflow.

While an AI-native ledger provides the deepest benefits, surrounding tools can still accelerate teams on older platforms.

Best Way to Accelerate Billing with AI?

AI-native AR tools such as Tabs and Sequence can automate invoicing and billing workflows.

The best approach is to evaluate vendor-side AI integrations rather than building custom solutions from scratch.

Is AI Cost-Effective for Startups Under $5M Revenue?

Partners like Consero make AI-powered finance infrastructure accessible to companies that could not afford to build it independently, through economies of scale and established vendor partnerships.

Pre-revenue companies benefit from setting up scalable systems early, positioning themselves for growth without needing to overhaul their finance function later.

How to Balance AI Investment With Software Spend Cost Optimization?

Set specific budgets for AI tools and establish clear ROI timelines—if savings are not realized within a defined period, reevaluate.

Frame AI investment as a hiring delay strategy: if a tool can defer a hire by 3–6 months, the investment pays for itself.

Best Security Practices Connecting AI and Enterprise Data?

Working through established vendors like Rillet and Ramp provides built-in security protections, reducing the risk of exposing sensitive data.

Enterprise AI plans from providers like Anthropic offer strong data protection commitments, though teams should review terms and conditions with legal counsel.

For highly sensitive data, masking or anonymizing inputs before feeding them to AI tools is a practical interim measure.

How is AI Improving Working Capital Management and Cash Flow Forecasting?

AI-enabled cash matching gives teams near-instant visibility into burn rates and working capital positions.

Seasonality and anomaly detection—such as flagging large annual insurance payments—improve the accuracy and reliability of cash flow forecasts.

How Will AI Impact on Sales Ops and CRM Teams?

AI tools can auto-update CRMs by pulling data from emails and calls, eliminating a persistent bottleneck where sales reps are slow to update Salesforce or HubSpot.

AI agents in calls help capture and structure client needs, including mapping revenue process flows and pain points—improving how sales/assessment teams organize information and follow up.

Don’t Fall Behind: Turn AI Momentum into a Reality

The transition to an AI-native finance organization is a necessity for those who want to lead their industries.

If you want the speed and clarity that AI promises—faster close, real-time working capital visibility, cleaner data, and narrative-ready reporting—the fastest path is pairing the right tools with disciplined processes and a team that can operationalize change. 

Request a consultation with Consero to assess your AI readiness, identify the quickest workflow wins, and build a scalable finance operating model that stays lean as you grow.