A private equity-backed CFO’s guide to crafting a data-and AI-infused finance transformation

The first installment of our series explored the pivotal role that private equity-backed chief financial officers play in laying the groundwork for value creation across the organization. This value creation extends beyond finance and accounting to the entire business—creating the platform for growth throughout the transaction lifecycle. 

Once that groundwork is in place, the next step is aligning the organization on monitoring and driving value creation. At this stage, a key responsibility for the CFO is to capture a mix of quantitative and qualitative metrics. Increasingly, CFOs are turning to AI-enabled tools to collect, analyze, and visualize these metrics more effectively—transforming data into dynamic insights that power decision-making. 

The framework used to gather and synthesize internal and external KPIs becomes a critical enabler of improvements that lead to value creation. With AI and automation, that framework not only streamlines KPI collection but also uncovers trends, correlations, and anomalies that manual processes might miss. Organizations that embrace a value-based growth mindset constantly revisit their KPIs—integrating AI-driven analysis into daily, monthly, quarterly, and annual operations to accelerate performance. 

It’s equally important to recognize that quantitative KPIs alone don't tell the full story. Operational insights—often surfaced through AI-assisted qualitative analysis and natural language processing—add valuable context to financial performance and reveal where human expertise and automation together can drive the greatest impact. 

This framework serves as a tool for portfolio companies, empowering them as they enter their private equity sponsor hold period with a prioritized value-creation playbook. 

While it has the greatest impact when introduced during diligence, its flexibility allows companies to capture value at any point throughout the holding period. This article outlines that framework, illustrates how it’s applied in practice, and highlights how AI and other tech can further strengthen finance transformation.

Establish data alignment and readiness to fuel value creation 

As CFOs begin their data-gathering journey, the first step is aligning with the private equity sponsor to ensure complete agreement on the key performance indicators that will drive the value-creation strategy. Exceptional portfolio company CFOs not only offer regular updates to their sponsors but also actively involve them in the value-creation journey, setting clear expectations that invite collaboration and facilitate constructive feedback. 

To launch the quantitative data process, CFOs must understand both the location and quality of available data sources. Increasingly, they’re turning to AI-enabled data discovery and automation tools to map data, assess quality, and accelerate readiness. Here’s an example framework West Monroe teams have used in prior engagements: 

• Ready to Use: These KPIs already exist within source systems across the organization (e.g., CRM, ERP, WMS). Examples include inventory balances, gross margin percentage, SG&A expenses by department, and average order value. AI-based analytics tools can quickly consolidate and visualize these KPIs—providing immediate value for reporting and decision-making.
• Requires Improvement: The presence of a data point doesn’t always mean it’s reliable or insightful. For instance, a company might have a customer record in its CRM that isn’t linked to the customer master in its ERP—or the same term might mean different things across systems (e.g., an account in CRM equals a ship-to address in ERP). AI-powered data quality and matching algorithms help identify these inconsistencies, allowing CFOs to prioritize and resolve them efficiently.
• Not Available: Missing KPIs often highlight where an organization should focus its technology strategy. When critical metrics don’t exist, AI-enabled data engineering and process mining can determine whether existing systems can capture the necessary information—or if new tools are needed.
  

For example, a consumer products company that struggled to access production line utilization data used AI to collect and integrate information from legacy systems and sensor-based equipment—improving both accuracy and accessibility. This connection between AI-driven data refinement and value-creating KPIs strengthens the business case for capital investment and board approval. 

Finally, as CFOs begin collecting and managing “ready-to-use” KPIs, AI-driven automation can simplify maintenance and elevate forecasting. The infrastructure designed to collect and visualize data should anticipate future use cases—ensuring it scales as AI capabilities advance and the organization’s value-creation goals grow. 

Use qualitative insights to reveal the story behind the numbers

Quantitative data alone isn’t enough to build a value-focused finance transformation playbook. Equally important is understanding the why behind the numbers. To uncover organizational inefficiencies and identify new opportunities for value creation, CFOs must spend time within the business—observing day-to-day operations to gain a complete picture of how value is truly generated. 

Today, many CFOs are elevating this discovery process with AI-enabled tools that capture and analyze qualitative insights more effectively. For example, natural language processing can distill patterns from customer feedback, employee surveys, and operational notes—bringing structure to information that was once unstructured. Combined with human judgment, these tools accelerate the ability to spot emerging trends, sentiment shifts, and process bottlenecks that might otherwise go unnoticed. 

This framework should extend beyond finance and accounting to include broader cross-functional teams. AI-powered collaboration and workflow tools make it easier to collect qualitative feedback from across the organization and connect it back to financial outcomes. For instance, engaging with the sales team to understand pricing and discounting strategies—supported by AI analysis of historical sales data—can reveal meaningful links between revenue recognition challenges and operating model improvements. 

The following approach is commonly used by firms for qualitative data gathering to inform a value-based transformation roadmap. Just as with quantitative data—where integration often begins during diligence—this framework can be applied at any point during private equity ownership. When supported by AI, teams can spend less time compiling information and more time interpreting it, enabling faster, better-informed decisions that drive results. 

Make finance transformation a lasting advantage

Both qualitative and quantitative data remain essential for building a value-creation-based transformation playbook. The methods used to collect these benchmarks should be designed for continuous use throughout the transaction lifecycle. Increasingly, AI enables this continuity—automating updates, tracking progress, and identifying early indicators of value creation that might otherwise emerge later in the hold period.

When the transformation roadmap is aligned with KPIs supported by AI-enabled analytics, CFOs can more clearly demonstrate how every initiative ties directly to measurable value. Structuring data in accessible, AI-ready formats also enhances collaboration with third-party partners, investors, and advisors—creating opportunities to generate new insights and refine strategy in real time.

Ultimately, AI doesn’t replace the finance leader—it amplifies their ability to anticipate change, act decisively, and lead with confidence. When human expertise and AI-driven insight work in tandem, CFOs can transform finance from a back-office function into a true engine of enterprise value.

In the final installment of this series, we’ll outline a framework for synthesizing all collected data—quantitative, qualitative, and AI-enhanced—into an actionable, achievable finance transformation roadmap that private equity–backed CFOs can apply throughout the transaction lifecycle.