The rise of agentic enterprise finance: Why automation maturity now requires orchestration

Business finance teams are adopting AI tools and moving steadily toward near-autonomous workflows to scale with company growth. McKinsey’s latest global survey found that more than three-quarters of organizations use AI in at least one business function, and 71% report regular use of generative AI in at least one function.

But adoption is uneven. Some finance teams already use AI inside everyday work – suggested AP coding, anomaly flags during close, drafted variance commentary – while others are still focused on standardizing processes, tightening controls and reducing manual effort before layering on anything new.

Even as your team thinks about adopting AI workflows, the next level is already here: agentic orchestration in finance. “For the foreseeable future, orchestration is the key,” says Billie Miric, Zone & Co’s head of product. It’s the command layer that coordinates how AI-driven actions run across your ERP, your subledgers and your end-to-end workflows.

Key highlights: 

• Finance teams are moving from AI that explains work to AI that executes work, which raises the stakes on governance and coordination.
• Automation scaled execution, but it did not solve cross-process coordination, so exceptions still spill back to humans once workflows span multiple platforms.
• Embedded AI is now common, but value depends on workflow redesign, and only a minority of organizations have fundamentally redesigned workflows even as adoption rises.
• Once multiple agents start initiating actions across those workflows, orchestration becomes the difference between controlled automation and fragmented autonomy.

How automation sped up finance teams

Automation in finance began as a practical response to scale. Organizations expanded across entities, regions and regulatory environments – so transaction volumes, reconciliations and reporting requirements grew quickly. Finance teams either standardized and automated processes or accepted a difficult-to-defend cost structure.

ERP centralization was the first major step toward efficient business finance processes. A system of record created consistent master data, standardized accounting rules and a shared audit trail across the organization. Once that foundation existed, rules-based workflow automation followed. Approvals, invoice matching, posting logic and close checklists could all be expressed as conditions and steps, allowing software to execute those processes reliably at scale.

The nature of finance work began to shift. Tasks didn’t disappear, but effort moved away from keystrokes and toward exception handling. That pattern reflects in Zone’s 2026 Controller Report, which showed that 90% of controllers overhauled at least one major finance workflow in the past year. And the workflows they touched most were the same ones that tend to be exception-heavy – accounts payable, payroll, close and reporting.

Automation became its own technology category. “Hyperautomation” platforms extended beyond point solutions to coordinate automation capabilities across enterprise workflows. Global Market Insights reported hyperautomation’s market value was around $46.4 billion in 2024.

Even with efficiency and operational gains, classic automation has limits. Exceptions accumulate and once workflows span multiple platforms, coordination often returns to humans. In practice, automation limits show up in three places:

• Rules-based workflows scale only what teams already understand.
• Automated tools can struggle with ambiguity, judgment and cross-process tradeoffs.
• They create brittle handoffs when automated workflows depend on other systems.

Automation accelerated execution. It didn’t create coordinated autonomy.

How embedded AI reshaped finance workflows

Embedded AI is changing what financial software can do. Instead of simply enforcing rules, systems can now detect patterns, interpret context, generate drafts and recommend actions inside workflows. That capability creates a different kind of leverage for finance teams, but it also introduces a different kind of risk.

McKinsey’s latest global survey shows just how fast “AI in production” has become normal: 78% of organizations report using AI in at least one business function and 71% report regular use of generative AI in at least one function.

The more important insight is what separates usage from value. McKinsey found workflow redesign had the biggest effect on whether organizations saw EBIT impact from gen AI. Yet only 21% of respondents whose organizations use gen AI say they’ve fundamentally redesigned at least some workflows.

That gap shows up clearly inside finance. In Zone & Co’s controller survey, leadership demand is expanding in the areas where AI tends to touch the workflow. Of the 100 controllers Zone surveyed, 24 cite new pressure for enhanced reporting, while 20 cite new leadership demands for AI initiatives, including requests for measurable AI KPIs and department roadmaps.

So the workflow itself changes shape. A traditional automated flow looks like:

capture → validate → post → report

With embedded AI, new decision layers appear inside the process:

capture → interpret → recommend → draft → escalate → post

“The more embedded you can make the technology you’re using — in this case AI — the better the user adoption,” Miric says.

Once software can recommend and initiate steps, the question “Can we automate this task?” becomes “How do we coordinate decisions across systems, owners and controls?” That’s where agentic workflows start, even if finance teams call them copilots, AI features or automation.

Defining agentic finance

Agentic finance is an operating model in which software can initiate and complete workflow steps on behalf of a finance team, within defined guardrails.

A lot of teams say “we’re using AI in finance” and mean three very different things. Those differences matter because they change risk, auditability and who owns the outcome. If you treat all three as the same, you end up applying the wrong controls – too little oversight where it’s needed, too much friction where it isn’t. Three things are often blurred together:

• AI insights generate forecasts, detect anomalies and produce narrative variance commentary.
• AI assistance drafts content, classifies transactions, summarizes information and supports users through copilots.
• AI agency initiates actions, sequences workflow steps, updates records and escalates exceptions when needed.

A single agent drafting a journal entry is manageable. A network of agents touching accounts payable and receivable, payroll, close and reporting creates a much larger surface area for misalignment, especially when those processes intersect across systems and teams. Then there’s the issue of the agents being able to communicate with each other. 

The governance implication isn’t theoretical. Controllers already operate in environments where decisions have to be justified, revisited and defended. Zone’s research shows only 7% of controllers use a structured, repeatable decision framework for cost control, while 93% rely on ad-hoc discussions with peers, other departments and past experience, often with CFO involvement.

That’s a useful reality check for agentic finance. If finance teams struggle to create repeatable decision structures for spend, what happens when AI systems begin initiating actions across workflows at machine speed?

This is where governance stops being a compliance checkbox and becomes a design requirement. NIST’s AI Risk Management Framework is explicit about governance practices, lifecycle risk management and operationalizing responsible AI.

Agentic finance works when agency is coordinated, traceable and governable. This is the setup for orchestration.

Why finance teams need agentic orchestration at scale

Most enterprise finance organizations won’t deploy one agent. They’ll deploy many, and they’ll come from multiple places.

Some agents will sit in procurement tools, billing platforms, payroll systems and data warehouses. Some will be built internally, others will come from vendors. Many will do useful work in isolation, especially early in adoption.

But issues occur when workflows and agents have to merge upstream. It’s more powerful to have AI agents sit within your system of record, turning it into a system of intelligence.

Fragmentation without coordination

Enterprise AI agents don’t communicate with one another – one agent may surface an account anomaly while another considers it an exception to pass off.

“The problem with multi-agent systems is they’re not coordinated or orchestrated.” Miric says. “Orchestration becomes critical, because then you can run one, and then second, and then third.”

Multiple AI workflows tend to fragment in these ways:

• Competing outputs: Two agents make conflicting recommendations based on different data snapshots or different definitions of materiality.
• Broken handoffs: One agent completes its task, but the next workflow step lives in a different system with a different owner, so the “last mile” becomes manual again.
• Invisible decision trails: Outputs appear in the general ledger, subledger or reporting layer without a clear chain of reasoning, approval context and evidence.
• Exception pileups: Agents don’t communicate with one another and escalate issues differently to humans, creating unruly queues full of exceptions to handle.

The more successful you are at deploying agents, the worse the fragmentation. And finance leaders are pressured to deliver faster, more precise and more frequent insights with stronger, defensible decisions. Adding more agents to support AI-enhanced workflows and efficiencies creates a coordination problem.

When outputs have to be reconciled, and decision trails must survive scrutiny, coordination is not optional.

Orchestration as financial infrastructure

Orchestration is the coordination layer that governs how agents operate together across end-to-end finance workflows. At scale, it answers the questions finance leaders care about during audit, close and board prep:

• Which agent acts first when two workflows touch the same transaction?
• What is the escalation path when confidence is low or policy is unclear?
• Where is the source of truth when agents pull from different datasets?
• How do we prove what happened, when it happened and why?

This is also where “AI adoption” turns into operational maturity. McKinsey’s latest research points to workflow redesign as the strongest driver of EBIT impact from gen AI, yet only a minority of organizations have fundamentally redesigned workflows. Orchestration is how finance redesigns without losing control.

Controllers are investing accordingly. In Zone’s controller survey, 71% plan to invest in ERP automation and optimization and 45% plan to invest in AI and machine learning over 12 months. The pattern is clear: enhance core workflows, then make the moving parts work together.

If your next wave of AI agents arrives this quarter, what coordinates them – a governed system, or spreadsheets, inboxes and tools outside your ERP?

Orchestration is no longer optional – it’s financial infrastructure

Enterprise finance has entered a phase where intelligence operates across domains, not just within them. When multiple AI agents draft entries, flag anomalies and trigger downstream actions simultaneously, coordination becomes a structural requirement, not a feature request. Manual supervision can’t scale across subsidiaries, currencies, compliance regimes and transaction volumes.

Orchestration is the layer that sequences actions, governs escalation, preserves ERP-native control and maintains unified audit traceability. In an agentic environment, it functions as infrastructure that keeps autonomy from becoming fragmentation.

Zone’s research signals why this is becoming urgent. Leadership is asking for sharper reporting, AI roadmaps and more frequent decision justification. Controllers are responding by modernizing workflows, investing in ERP automation and selectively adopting AI inside existing processes.

Once AI shifts from assisting to initiating steps across those workflows, finance needs orchestration to keep the architecture defensible and turn their ERP from a system of record into a system of intelligence.