Who Actually Controls Your Finance Function? (Hint: It Shouldn’t Be IT)
In 2025, what should be routine for finance leaders has become exceptional: the ability to change how their own teams operate without waiting for permission.
At small and mid-sized companies, a finance leader who wants to extend payment terms from Net 30 to Net 60 can still implement that change with a simple memo. Adjusting auto-approval thresholds or implementing early payment discounts remains straightforward. But as organizations scale, these same decisions become entangled in sprawling ERP systems , global shared service centers, and offshore BPOs — turning what should be routine finance operations into months-long technical projects requiring multiple handoffs and approvals.
Consider this paradox: A Fortune 500 Controller who oversees billions in cash flow must submit an IT ticket to modify a basic approval workflow that they’re already signed off on. A VP of Finance who built a multi-billion dollar forecasting model waits months for a simple ERP configuration change. A Global Accounting Director who can’t update a reconciliation process without a six-month implementation project.
Process improvement has become something finance teams request, rather than something they lead.
This isn’t just bureaucracy—it’s a fundamental inversion of organizational power. Core operational knowledge has been outsourced, and with it, control over how finance actually functions.
The Reality of Control Loss
The migration to BPOs and offshore delivery centers wasn’t a strategic choice to give up control — it was capitulation to complexity. As ERP system complexity made change prohibitively difficult, cheap labor became the path of least resistance. When a simple process update required months of planning, scoped development, technical testing, and cross-functional sign-off, delegating the whole thing to someone else felt like a reasonable trade.
Outsourcing was supposed to be a temporary solution to the IT bottleneck, but over time, this workaround calcified into strategy — and created a cascade of control failures:
- Knowledge exodus: Institutional expertise migrated offshore, encoded in BPO desktop procedures inaccessible to the companies paying for them
- Subject matter expert disempowerment: Finance specialists with deep business understanding became dislocated from the very processes they oversee
- Process rigidity: Workflows that should evolve with market conditions became ossified in ERP configurations and BPO contracts
The outcome is perverse: finance teams lost the autonomy to make even basic operational changes while ballooning headcounts that exist primarily to navigate complexity rather than deliver value.
But this loss of control isn’t permanent, or inevitable. AI is about to flip this power dynamic on its head.
AI Enables the Repatriation of Finance Expertise
The conventional narrative around AI focuses almost exclusively on efficiency gains — doing the same work with fewer people. This framing completely misses the transformative potential.
The true revolution isn’t just automating existing work. It’s fundamentally changing who controls the automation.
In today’s ERP-dominated landscape, process change follows a tortuous path:
- Finance identifies opportunity
- Business case development
- IT prioritization battle
- Scope definition
- Development by specialized ERP developers
- Testing across environments
- Change management
- Implementation
- Post-implementation fixes
This cycle takes months or years, assuming it ever completes at all. Each step creates friction, delays, and opportunities for the original intent to be diluted or lost entirely.
AI fundamentally disrupts this model by enabling subject matter experts to iterate significantly more quickly. Instead of submitting tickets and hoping for prioritization, finance experts can collaborate with AI on hypothesized better approaches, and the AI will support them in implementing them. Together, they can simulate, backtest, and refine process changes themselves, with AI handling the technical complexity that previously required specialized developers. They can even run process changes as A/B tests or in shadow mode to observe their impact, then propose changes through a streamlined approval and deployment pipeline much like developers shipping new features to production.
This isn’t a faster version of the current change management model, but a completely different operating paradigm. The IT function isn’t removed, but now able to focus on the infrastructure, connectivity, and interoperability of systems — while finance teams regain sovereignty over their own processes.
The Analytics Engineering Revolution: A Blueprint for Finance
What finance needs isn’t unprecedented—it’s already happened in adjacent domains. The rise of analytics engineering over the past decade provides a perfect blueprint for finance’s own transformation.
Before the analytics engineering revolution, business intelligence existed in a strikingly similar state to today’s finance function:
- Bottlenecked by technical complexity: Data analysts couldn’t implement changes without engineering support
- Siloed knowledge domains: Business context and technical implementation were separated
- Lengthy delivery cycles: Insights arrived too late to drive timely decisions
- Specialized skill requirements: Only specialized developers could make meaningful changes
The transformation began with a simple insight: what if the people who understood the business questions could directly shape the data pipelines that answered them?
This led to the development of tools like dbt (data build tool) that fundamentally changed who controlled data transformation. Analytics engineers—hybrid professionals with both business domain expertise and technical capabilities—emerged as a new class of professional. They weren’t just database developers or business analysts, but a fusion of both.
The results were transformative:
- Collapsed delivery cycles: Changes that took months now happened in days or hours
- Higher-quality outputs: Solutions built by those who understood the business context
- Scalable expertise: Subject matter experts with 10x productivity through code
- Empowered teams: Analytics engineers owned their full workflow end-to-end
Perhaps most importantly, ELT (Extract, Load, Transform) replaced ETL as the dominant paradigm. Instead of complex data pipelines managed by specialized engineers, analysts gained direct control over transformations in the data warehouse itself. They adopted software development best practices—version control, testing, CI/CD, documentation—that made their work more reliable and scalable.
The parallels to finance are striking. Both domains deal with critical business data. Both require specialized domain knowledge. Both traditionally relied on IT bottlenecks. And both can be revolutionized by giving subject matter experts direct control over their operational systems.
Finance is now poised for its own analytics engineering moment. AI will be the catalyst that makes this possible, serving as the critical bridge between finance expertise and technical implementation—just as tools like dbt bridged the gap for data analysts.
DevOps: When Ownership Drives Excellence
The analytics engineering revolution itself was built on foundations laid by the DevOps movement—another powerful example of what happens when you unify ownership and expertise.
Before DevOps, software development followed a similar dysfunction:
- Developers wrote code but handed it off to operations teams for deployment
- Operations teams managed production but lacked context on application design
- Handoffs created delays, miscommunications, and finger-pointing
- Neither group felt true ownership of the end result
The DevOps philosophy changed this by giving development teams direct responsibility for their production environments. “You build it, you run it” became the mantra that transformed the industry.
The results were dramatic:
- Deployment frequency increased from quarterly to daily or hourly
- Mean time to recovery from failures dropped from days to minutes
- Change failure rates plummeted
- Overall team productivity and satisfaction improved
This wasn’t just a technical shift—it was a fundamental realignment of incentives and responsibilities. When teams owned the full lifecycle of their work, they built more resilient systems and fixed problems more quickly.
Finance stands to benefit from this same transformation. When finance subject matter experts gain tools to directly implement, monitor, and iterate on their processes—backed by AI that handles technical complexity—we’ll see the same explosion in innovation and quality.
This isn’t just about making changes faster—it’s about making them better. Consider a major industrial manufacturer (one of the largest in its sector) that implemented a seemingly straightforward payment term change to capture early payment discounts from strategic raw materials suppliers. The change went through the standard multi-month implementation cycle, was thoroughly documented, and rolled out with formal sign-offs. Yet within weeks, the company discovered they’d lost millions when certain critical suppliers who should have been prioritized for early payment were incorrectly swept into the standard payment batch. The root cause? The team implementing the change lacked the contextual understanding of why these specific supplier relationships received preferential treatment, and the finance team that understood these relationships couldn’t properly validate the implementation details or catch the issue before it affected multiple payment cycles.
With an AI-enabled approach, this scenario transforms completely. The finance team could simulate the change across all historical transactions, identify affected suppliers, run parallel test deployments, and roll out changes incrementally with constant monitoring—the same observability and deployment practices that DevOps made standard in software development. The result isn’t just faster change—it’s safer, more reliable change with dramatically reduced risk.
Agility as Competitive Advantage
Beyond cost savings, the strategic implications are profound. In a business environment where adaptability determines survival, organizations with responsive finance operations possess a structural advantage.
When competitors still struggle with quarter-long change cycles while you implement process improvements in days, the cumulative effect creates widening performance gaps:
- Faster cash conversion cycles
- More effective capital allocation
- Enhanced risk management
- More rapid integration of acquired businesses
- Quicker response to market shifts
- Faster adaptation to regulatory changes
Each of these capabilities represents not just incremental improvement but potential competitive moats. The organizations that can rapidly evolve their financial operations will consistently outperform those locked into rigid systems and outsourced expertise.
What This Means for Finance Leaders
For finance and transformation leaders, this shift represents both tactical opportunity and strategic imperative.
Tactically, it reduces reliance on human labor and the supervisory overhead that comes with it. The “escalate and wait” cycle that dominates today’s finance operations gives way to direct action. More importantly, it accelerates iteration cycles — enabling SMEs to adapt processes quickly in response to internal decisions or external shifts, without months of planning and IT backlog navigation.
Strategically, it empowers your most capable people with true value-creation leverage. The finance experts who currently spend their days managing exceptions, navigating system limitations, and conducting endless alignment meetings will instead focus on improving business operations — spotting inefficiencies, implementing enhancements, and aligning operations with strategic priorities.
Finance teams will become more like product teams—guided by metrics, continuously improving, and directly responsible for outcomes. Subject matter experts will evolve into “Finance Engineers” who command both domain knowledge and the technical capability (amplified by AI) to implement their insights directly.
It’s a future where your most valuable resources create compounding value within the business — not just keep it running. Where expertise isn’t outsourced but amplified. Where finance becomes a driver of transformation rather than its bottleneck.
Scale Intelligence, Not Headcount
The back office has long faced a seemingly binary choice: scale people (through BPOs and offshore teams) or scale systems (through inflexible ERPs and point solutions).
AI introduces a third path entirely: scaling intelligence .
By arming your subject matter experts with AI-native tools, and removing the structural barriers that slow them down, you don’t just make processes more efficient. You make your entire organization more capable, more responsive, and more competitive.
The organizations that understand this shift will thrive. Those that continue to outsource control of their core operations will find themselves increasingly disadvantaged.
The future belongs to finance teams that take back control. Is yours ready?
CoPlane is building the intelligent finance operations platform that helps finance teams regain control of their processes. Want to learn more? Drop us a line.