Has your enterprise undergone a merger, acquisition, divestiture or carve-out in the past few years? How about a structural change linked to a successful growth initiative, cloud migration or AI deployment? On a related note: How much has your fundamental finance operating model (how finance delivers services, including informative data, makes decisions and supports the business and external customers) changed during the same timeframe?
In most companies, the gap between organizational transformation and finance model transformation is an ever-widening chasm. Rapid external changes, AI adoption and myriad internal overhauls inevitably expose cracks in the traditional finance operating model. These deficiencies, which include disconnected legacy systems, fragmented processes and disparate approaches to transformation, limit finance’s access to high-quality data and give rise to more than one “version of the truth.” As a result, finance operating model transformation is not just a CFO priority, but a strategic imperative to sustain the value finance contributes to the enterprise.
Flexible, synchronized delivery
Most traditional finance operating models consist of rigidly defined task-oriented functions: financial planning & analysis (FP&A), accounts receivable (AR), accounts payable (AP), record-to-report (RTR), treasury and so on. Historically, these capabilities have been arranged, delivered and streamlined with the support of automation, labor arbitrage and centralized structures.
As AI agents and tools take on more work in finance, service delivery is less constrained by time zones. And the value of finance’s emerging physical and digital workforce no longer derives principally from processing transactions. Instead, leading finance functions operate as business advisors by delivering timely, predictive insights (in addition to solid historical reporting). While this is a narrative that has unfolded for a long time, what’s different today is the availability of immensely powerful tools available to finance teams, enabling more advanced forecasting, profitability analysis and scenario planning that help the business spot and respond to trends before they fully materialize. Maximizing the potential value of these capabilities requires a new finance operating model, one that integrates data and technology tools throughout the finance ecosystem to keep the function synchronized with the business while supporting the flexible, timely delivery of finance services and intelligence.
Business changes and AI adoption are not the only drivers of finance operating model transformation. CEOs and boards are requesting more from the function, such that the CFO’s expanding role now encompasses data governance, cybersecurity disclosure, ESG reporting, AI adoption and expanded planning responsibilities. Delivering strategic insights in these areas requires end-to-end finance workflows and seamless data access, even when the data is non-financial (it doesn’t just come from the general ledger). Traditional finance transformation efforts often fell short of enabling this capability, either by focusing too heavily on new ERP systems or transforming individual, disparate finance processes and tasks in isolation, without touching the underlying enterprisewide operating model. These efforts are often suboptimal because an end-to-end approach most likely begins in operating processes outside finance and accounting.
Enablers of an emerging model
The essential purpose of the emerging finance operating model is to help finance leaders balance efficiency, effectiveness, intelligence, control and compliance. While designs will vary by organization, an effective model reduces fragmentation, clarifies accountability and improves service quality and timeliness. Further, the finance operating model should be an integral part of the enterprise’s overall governance, process, data, technology, control and performance infrastructure so finance helps shape decisions, not just report results.
When putting a new finance operating model in place or evolving it over time, it often helps to treat major structural and technology changes and trends as triggering events. After defining a transformation effort’s scope and establishing the appropriate governance mechanisms (oversight, objectives, accountability and project cadence), CFOs can work through the following actions:
1. Perform a current-state assessment. Rather than assessing their current operating model in isolation, CFOs should enlist COOs, CIOs, sales leaders, chief data officers, business unit leaders and other consumers of finance information in the effort to ensure an integrated approach. This 360-degree review should address an underlying question: Are you receiving the analyses you need to support the timely decision-making that matters? It also helps to ask finance process owners how their area is organized and then simply listen as they enumerate the workarounds, ad hoc practices and shadow systems that have accumulated over time. Other interviews, walkthroughs and document reviews (each of which can be enabled with AI tools) will give CFOs an understanding of the current state and may require an update of current-state process documentation or the development of new documentation. Technology architecture and data integrations should be mapped and workloads, metrics, backlogs and exception patterns assessed. The resulting current-state documentation offers a grounded fact base for the CFO’s team.
2. Diagnose root causes and prioritize improvements. Classify underlying areas of the finance operating model that require fixes by infrastructure categories: Is the issue related to policy, process, people, reporting, methodology or systems/data? Use root-cause analyses in high-risk areas while distinguishing quick fixes from structural redesign needs. Related improvement opportunities should be prioritized based on risk, effort and expected ROI.
3. Define the target operating model. The value of finance operating model transformation stems from redesigning discrete but related processes as end-to-end workflows that seamlessly integrate AI tools and other advanced automation solutions. The redesign starts by explicitly clarifying target processes that can be eliminated, centralized or automated, as well as highlighting changes in technology, roles, governance and resource mapping. The future-state service delivery model can then be defined and its benefits quantified for purposes of the business case, along with a game plan that covers:
-
- Governance and decision rights: Identify who owns decisions, how governance is structured and how accountability is allocated across the enterprise and regional and local levels in a manner that keeps the function aligned.
- Organization and roles: Use a RACI matrix to define how responsibilities are structured, the roles and capabilities that are needed and how adjacent enabling functions interface with finance.
- Processes and service delivery: Determine what services finance provides, who provides them and whether delivery is eliminated through automation, centralized, federated, hybrid, regional or local.
- Technology and platforms: Inventory the systems, applications and infrastructure used to support finance activities, including cloud-based applications and automation-enabled process improvement.
- Data and analytics: Identify how finance should access and leverage internal and external data to generate analysis, reporting and insights.
- Performance management and monitoring: Define the metrics, KPIs and incentives used to determine whether the model is working as intended and to reinforce desired behaviors.
4. Build technology and data enablement into the operating model. Accurate, accessible data is essential to AI optimization and equally important to an effective finance operating model. In addition to defining an end-to-end data model, finance leaders should align system and tool decisions with workflow redesigns by defining business requirements, then evaluating opportunities to optimize ERP systems, automate processes and deploy AI more effectively. The enabling tool may not be an ERP system; a reconciliation or reporting platform sometimes matters more. Data and integration issues that could hinder scalable reporting and controls should be identified and addressed early. Doing so strengthens data stewardship, analytics and decision support while establishing a scalable platform that can support growth, change and future-state execution. Systems integration is crucial; a fragmented environment frequently leads to data lakes and other costly workarounds.
5. Standardize processes, documentation and controls. As finance groups reimagine end-to-end processes, they need to document standard operating procedures while adjusting process maps, RACI matrices and risk and control matrices. The focus should be on building controls into the redesigned process rather than retrofitting the control environment afterward. While AI controls help enterprises avoid data security and privacy breakdowns, they also maximize the upside of AI investments to achieve expected returns.
Finally, finance leaders should address skill set, change management and training needs with an emphasis on preserving the desirable aspects of the existing culture and sustain the new operating model by establishing post-implementation KPIs and a reporting cadence that holds process owners accountable for adoption and performance. CFOs should periodically evaluate the model’s maturity as demands and expectations change over time.
Enterprises should not wait to redesign the finance operating model. Leaving the model untouched as everything around it changes, both externally and internally, inevitably leads to measurable costs and lost opportunities, along with inconsistent data and mismatched systems that obstruct AI adoption and value realization and produce multiple versions of the truth. Let there be no doubt: If the finance operating model fails to keep pace, business leaders across the enterprise will seek the data they need through alternative channels. In addition to signaling that finance is not meeting enterprise needs, the resulting dysfunction spawns reduced trust, slower decision-making, redundant efforts and fragmented visibility into performance, all of which give rise to operational risk. As they degrade the quality of financial intelligence the business relies on to respond quickly and effectively to new threats and market opportunities, these issues will ultimately find their way to the C-suite and boardroom.
