AI in Finance Needs Talent and Workflow Redesign
AI in finance now requires two decisions at once: which tasks to automate, and which judgment-heavy roles still need experienced people. MAVI’s emergence from stealth adds a timely signal for enterprise finance leaders: software alone is not closing the accounting capacity gap.
According to TechCrunch’s report on MAVI’s launch, the company says it can connect U.S. employers with AI-proficient global accounting talent within days while also handling cross-border contracts, compliance, and payroll. That matters because the U.S. accounting pipeline is shrinking even as finance teams are being asked to absorb more AI tools, more exceptions, and more oversight.
Step 1: Separate task automation from judgment work
The first mistake in AI in finance programs is treating all finance work as equally automatable. It is not. Reconciliations, invoice routing, document extraction, and report preparation often fit structured automation patterns. Close management, policy interpretation, exception handling, and management reporting still depend on experienced review. Molly Liu’s core point, as reported by TechCrunch, is that removing entry-level work with AI can worsen the mid-level talent gap over time because fewer people are building the experience needed for higher-responsibility roles.
- List repeatable tasks by volume and error rate.
- Mark where a human must approve, interpret, or escalate.
- Identify which workflows already use AI finance automation tools.
- Flag bottlenecks owned by controllers, senior accountants, or FP&A managers.
A useful benchmark comes from McKinsey’s research on generative AI, which notes that activities with structured content and repeatable steps are more amenable to automation than work requiring nuanced judgment. In finance, that distinction is operational, not theoretical.
Step 2: Define what an AI-proficient finance role actually looks like
The market is moving from traditional role descriptions to hybrid ones. In this story, AI-proficient accountants are not simply users of chatbots. They combine accounting fundamentals with the ability to direct AI tools, review outputs, spot hallucinations or categorisation errors, and work inside AI-native finance systems. That is materially different from a job description that asks only for ERP experience and spreadsheet fluency.
- Add AI review responsibilities to role design.
- Specify tool oversight, not just tool usage.
- Include LLM prompt review, workflow testing, and exception analysis.
- Require familiarity with ERP AI integration where relevant.
This is consistent with the broader trend documented by Gartner’s finance function research, which has repeatedly argued that finance teams are shifting from transaction processing toward analysis, controls, and decision support. The non-obvious implication is that automate finance tasks does not mean shrink every role evenly. It often means fewer junior processing tasks and more demand for people who can supervise machine output.
Step 3: Map where AI tools increase managerial load
One of the more important details in the MAVI story is that more automation can add pressure to senior finance staff. That sounds counterintuitive, but it matches what many enterprises find after deploying multiple point solutions. Each new tool creates new output to validate, new exceptions to resolve, and new process handoffs to govern. AI in finance can reduce manual effort in one lane while increasing coordination costs across the function.
- Count how many tools touch close, AP, AR, reporting, and planning.
- Track where outputs must be reviewed before posting or reporting.
- Measure cycle time lost to exception handling.
- Note where AI workflow automation lacks a clear process owner.
The structural risk is tool sprawl. Deloitte’s finance transformation work has long shown that fragmented finance architectures can shift effort rather than eliminate it. In practical terms, adding another assistant into AP or reporting without redesigning approvals, controls, and escalation paths often creates hidden work for the people already under the most pressure.
Step 4: Decide whether the bottleneck is talent, workflow, or both
MAVI’s value proposition is not just faster hiring. It is the removal of hiring friction around cross-border contracting, legal coordination, compliance, and payroll. For enterprise buyers, that matters because sourcing speed and operating complexity are separate problems. A company may be able to find candidates quickly and still fail to onboard them into well-designed workflows.
This is where talent marketplaces and implementation services diverge. A marketplace can compress sourcing time. It cannot, by itself, standardise your chart-of-accounts logic, redesign approval chains, or connect AI outputs to the systems of record. For that reason, enterprises piloting AI in finance usually need a parallel operating-model track alongside hiring.
- If close is delayed by review load, prioritise workflow redesign.
- If the team lacks AI oversight skills, prioritise hiring or upskilling.
- If cross-border administration is the blocker, prioritise managed hiring infrastructure.
- If multiple tools are disconnected, prioritise AI integrations for business.
For companies moving from experimentation to execution, the most relevant internal path is Accounting and Reporting Automation with AI. It fits this use case because the problem in view is not abstract AI strategy; it is redesigning accounting and reporting workflows so automation, controls, and human review work together.
Step 5: Build around finance systems, not around isolated copilots
The next practical step is to anchor AI implementation in the systems where finance work is recorded and reconciled. That usually means ERP, accounting, reporting, procurement, and expense platforms before standalone assistants. ERP AI integration matters because finance teams are judged on consistency, auditability, and cycle time, not on how many pilots they launched.
A sensible sequence is to start with one workflow that has clear volumes, repetitive inputs, and visible review steps: for example, month-end reporting packs, revenue reconciliation support, or invoice-to-approval routing. From there, leaders can test whether AI workflow automation is reducing touches, reducing errors, or simply moving work to a different team.
- Start with a workflow that has baseline metrics.
- Connect AI outputs to systems of record, not spreadsheets alone.
- Define who signs off on exceptions.
- Review whether the process is faster after approval and audit checks.
This aligns with Microsoft’s guidance on AI in financial services and enterprise workflows, where successful adoption tends to come from process-level integration rather than standalone experimentation.
Step 6: Pilot hybrid teams before rewriting the whole finance org chart
The MAVI announcement matters most as a signal that hybrid finance staffing is becoming more viable. That does not mean every enterprise should rush into global hiring. The trade-off is clear: faster access to AI-fluent talent can help absorb workload, but distributed teams require strong onboarding, documented controls, and explicit accountability. Without those, companies can create new management overhead.
The better approach is a targeted pilot. Pick one finance process, one set of review metrics, and one role profile that combines accounting depth with AI literacy. Then test the interaction between tools, internal staff, and external talent over one or two reporting cycles. This gives finance leaders evidence on whether they need more software, more process redesign, or more specialised talent.
- Pilot one team or workflow for 30 to 90 days.
- Measure close speed, rework, and exception rates.
- Review whether senior reviewers spend less time on low-value checks.
- Expand only after role clarity and workflow ownership are proven.
The larger market lesson from MAVI is straightforward: AI in finance is becoming an operating-model question, not just a software-buying question. Enterprises that treat staffing, controls, and automation as one design problem will generally move faster than those that buy tools first and sort out accountability later.
You're done when... your finance team can point to one workflow where tasks are automated, exceptions are owned, human judgment is clearly assigned, and cycle-time improvement is visible in the numbers rather than assumed from the tool list.
Martin Kuvandzhiev
Co-Founder & CEO, encorp.ai
CEO and Founder of Encorp.io with expertise in AI and business transformation
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