Copilots Today, AI Agents Tomorrow — Autonomous AI is a Journey

The term autonomous finance is appearing in analyst notes and board agendas — and it’s starting to take on concrete meaning. Gartner describes an autonomous finance function as one where processes are largely operated by self-learning software agents that deliver real-time, predictive insight and compliance — an ambitious destination, but not science fiction anymore. Deloitte’s “Lights Out Finance” perspective points to the same direction: touchless, end-to-end workflows that you reach in steps, as data quality, guardrails, and operating habits mature.

Why should you lean in now? Moving toward autonomous finance raises capacity without adding headcount, shortens the distance from number to decision, and builds an audit-ready trail as a byproduct of the work. It’s also a hedge against complexity, such as multi-entity structures, new products, and nonstop change, because definitions, controls, and evidence travel together.

It helps to treat autonomous finance as a direction rather than a deadline. Early value shows up through Copilot in everyday tools, and the next phase, process agents, will build on the same foundation as Microsoft’s roadmap unfolds. The idea to carry forward is simple: the clearer the structure and guardrails around your data, the more these capabilities can lift the work when you’re ready.

Copilot Today: Assist the Work You Already Do

Before autonomous finance feels far off, it helps to see what’s already in reach. Copilot is live inside the tools finance uses every day, and the gains show up in familiar moments like close, variance, forecasting, and cash. Here’s what that looks like in real workflows now.

Close and reconciliations. Copilot compares defined data sets, applies your materiality thresholds, proposes matches, and drafts a short recap that rides with the packet. You review, adjust, and archive with the evidence attached. Microsoft’s finance agents documentation reflects this flow in Excel and Microsoft 365.

Variance analysis. Because the drivers in your model reflect how you explain performance, Copilot separates price from volume, calls out mix and FX, and distinguishes timing effects from true movement — then offers a concise narrative that you tune and ship. Microsoft’s variance guidance shows this pattern within the finance agents’ experience.

Forecast rhythm. Copilot assembles current drivers from certified tables and highlights deltas since the last cut. Analysts can explore scenarios in Power BI and Excel without rebuilding side files, making a rolling cadence realistic rather than heroic.

The effect is simple: fewer hunts, cleaner hand-offs, and more time for judgment.

Agents Tomorrow: Automate the Repeatable

Process agents are the next step. Think of them as small, dependable automations that run a defined slice of work under your rules and within your controls. Microsoft has been explicit about this trajectory for ERP — introducing agents to reduce manual effort and accelerate decisions while staying grounded in your data and permissions.

What that looks like in practice:

Think of a process agent as a reliable teammate that runs a well-defined slice of work on a schedule — using your data, your thresholds, and your approvals. It doesn’t invent rules. It follows the ones finance sets, leaves an activity trail, and hands people the moments that need judgment.

In reconciliations, the agent proposes matches on your cadence, carries forward unresolved items with age and notes, and assembles a tidy review packet with what cleared, what’s open, and who owns it next. You arrive at review with the story laid out, not a pile of tabs to untangle. The archive is complete because the agent saves the packet and the steps it took.

For variance analysis, the agent builds a ready-to-read pack per audience. It pulls the right views from the shared model, applies your drivers — price, volume, mix, FX, timing — and drafts a short explanation you can refine in minutes. It also tracks changes since the last cycle, so a leader sees what’s new without rereading the deck.

In rolling forecasts, the agent collects updates from owners, checks entries against simple rules (naming, ranges, required fields), posts the refresh on schedule, and flags anything that needs a human look. FP&A starts from a current baseline in Power BI and Excel and spends time on trade-offs instead of assembly.

The Data Work That Makes Both Possible

The throughline in this series is simple: when data mirrors how finance runs, AI can carry more of the load. Think of it as a small set of agreements — the pieces that keep numbers consistent, explanations repeatable, and access within your controls. Anchor on these essentials, and both Copilot and process agents will stay useful and trustworthy.

• One calendar, consistent currency policy. Time periods and FX rules line up so a number means the same thing everywhere.
• Shared measures and drivers. Plain-language definitions live where people can find them — and they show up the same way in Power BI, Excel, Copilot, and Dynamics.
• Conformed dimensions. Entity, region, channel, customer, product, and project use shared keys so joins are clean and trends are comparable.
• One dependable source. History lands once in Fabric’s OneLake, with refresh expectations and lineage you can see. That single history powers analytics, Copilot, and agents together.

When those pieces are steady, AI help shows up with context, respects permissions, and produces outcomes you can audit later.

How Your Microsoft Stack Supports the Journey

Once the structure is clear, your stack can function as a single system. Each layer plays a straightforward role—backbone, landing zone, analysis surface, and AI in the flow—so the path from number to decision gets shorter. Here’s how Dynamics 365, Fabric/OneLake, Power BI/Excel, and Copilot/agents line up to support that rhythm.

Dynamics 365 Finance anchors entities, ledgers, dimensions, and calendars. When those align here, journal detail and master data flow cleanly into analytics, and Copilot uses the same definitions your team relies on.

Microsoft Fabric with OneLake keeps finance history in one place on a predictable cadence. You see what refreshed when and trace a figure back to its source. That shared history becomes the foundation for both Copilot experiences and agents that run on schedule. Microsoft positions agents specifically to transform high-volume, rules-based activities in ERP — the kind finance runs every day.

Power BI puts the model in front of decision-makers. Executives review focused pages in Power BI and, when they want to explore, open the same definitions in Excel. Update a measure once, and the change flows everywhere, keeping conversations aligned.

Copilot and process agents add help where it matters. Copilot drafts reconciliation summaries, outlines variance drivers, assembles forecast inputs, and explains visuals in plain language. Agents take on steps that repeat the same way every time and keep an activity trail.

How Velosio Helps

We think autonomous finance is most successful when developed in stages. Shape the data so Copilot adds value now, then let small agents take on the repeatable steps as confidence grows. With a governed model and a Microsoft stack that works as one, you move toward decisions that keep pace with the business while your controls stay intact.

Velosio can help you pin down definitions, keep the data moving, and make Microsoft’s pieces work as one in the moments that drive decisions. The result is progress you feel this quarter and a clear path toward more autonomy as your structure and confidence grow. Reach out to our team to start the conversation.

This blog post is from our webinar, “Top Five Ways to Solve Data Management Issues in Finance Departments.” For more information on the subject, watch the webinar below: