CHALLENGE:
Implementing AI - Where to Begin?
Implementing any AI model requires two major checkpoints: choosing the right tools and ensuring organization‑wide adoption.
Despite a strong foundation for source-of-truth data and integrations with planning systems, planners remained heavily reliant on manual, time-consuming activities. They were downloading and consolidating data from ~3 systems, rebuilding the same reports in Google Sheets regularly, and spending hours reconciling mismatched datasets. Instead of shifting to strategic, value-adding work, teams continued to spend significant time gathering data, creating reports, and navigating fragmented processes.
Early estimates indicated that planners were spending 30-40% of their time on manual data handling, even with automated systems in place. The big question: Why was a fully integrated planning ecosystem still producing Google Sheets-driven workflows?
To uncover this adoption gap, the company partnered with Spaulding Ridge to better understand where technology and day‑to‑day planner workflows were misaligned and could drive outcomes that directly impacted the P&L.
SOLUTION:
Creating an FP&A AI discovery lab to uncover true pain points
Spaulding Ridge got to work by first establishing a structured FP&A AI Discovery Lab. The lab was an accelerated, 2-month deep dive into all expense forecasting processes to provide tangible, actionable ways to transform their AI landscape. The discovery lab produced:
- End-to-end workflow maps for each business unit
- Ranked backlog of AI use cases
- Quantified pain-point heatmap highlighting the highest-impact inefficiencies
- Roadmap to deliver AI use cases, backed by value each solution would deliver to the business
Examine workflows - Understanding the current state
To understand the current state, we partnered with the client on the approach and conducted 15+ stakeholder interviews across eight business units, meeting with their FP&A planning subject‑matter experts and planning groups. Through these interviews, we captured detailed insights into:
- Planner workflows
- Recurring pain points
- Operational challenges
- Report creation and data‑pulling methods
To ensure we understood their processes correctly, a validation workshop followed to confirm findings: Did we hear this correctly? Are these truly your shared issues? From these sessions, the team synthesized the desired capabilities planners needed to improve efficiency and accuracy.
Assess readiness - Quantifying the inefficiencies
Through our findings, we quantified:
- How much time planners spent on each pain point
- How many FTEs this equated to
- How inconsistent approaches affected reporting accuracy
Each pain point was scored across frequency, time consumption, and business criticality, enabling prioritization based on quantified impact. To establish a fact-based baseline, Spaulding Ridge used surveys and time-and-effort analysis to help planners quantify the hours spent per quarter and per year on their top 20 pain points. This provided a clear view of the true cost of inefficiencies, identified where AI could reduce manual work, and created measurable benchmarks for future ROI tracking.
As an independent third-party advisor, Spaulding Ridge also provided perspective from similar FP&A organizations facing comparable challenges. This outside-in view helped validate that many issues were systemic rather than isolated to individual teams and enabled objective, platform-agnostic recommendations focused on business outcomes, adoption, and long-term value.
For example, Legal FP&A planners were spending several days per planning cycle manually interpreting free-text ERP purchase order data to properly categorize journal entries. An expense labeled “November 2025” required investigation across Slack threads, historical records, and vendor context, introducing delays, inconsistency, and risk.
While this highlighted a broader upstream data issue, it also revealed an opportunity for near-term AI wins, including automated classification and context inference.
A cross-team roundtable enabled planners to openly compare challenges, dispelling the belief that issues were isolated to individual groups. Unlike traditional technology assessments, this approach ensured that we anchored AI opportunities directly in day-to-day planner behavior.
Align future AI initiatives with business reality - Readiness and alignment
The team then partnered with the company's technology leaders to prioritize initiatives based on business value. With multiple projects already underway, this assessment provided a clear picture of what truly mattered and ensured that dependencies, governance, and the enterprise AI strategy were taken into account. Once priorities were identified, we aligned the proposed solutions with the existing AI roadmap to avoid disrupting ongoing work. From there, we conducted a three‑pillar AI readiness assessment that evaluated technology, processes, and internal capability and adoption. Each criterion was critical to ensuring that the initiatives were both realistic and actionable.
Findings - Two Priority Focus Areas
Although the company had consistent systems, each planner pulled data differently, using unique processes for data gathering, report creation, forecast preparation, and final presentations. This created inefficiencies, inconsistency, and elevated risk.
We recommended both interim solutions and long-term improvements. The ultimate goal: Move planners away from transactional work and toward strategic forecasting and insight generation.
Based on impact potential, the initiatives were simplified into two core priorities:
- Faster Planning
- Introduce AI to check feasibility and reasonableness
- Improve headcount and OPEX forecasting by predicting baseline hire dates
- Begin testing and adopting AI Assist tools for quick queries (e.g., “Show me planned Q2 2026 hires”)
- Use AI to detect anomalies and direct planner attention to the areas of highest importance
- More Efficient Report Generation
- Automate recurring reporting tasks
- Summarize slides based on data living in reports and their data warehouse
- Use AI to check for data discrepancies across multiple presentations (e.g., when the same data is summarized for different audiences, a change to one value would be updated everywhere)
- Leverage AI to provide natural-language variance commentary based on data comparing forecasted and actual data
We developed baselines to measure efficiency and accuracy. This was the key step following the findings to ensure that AI initiatives had measurable ROI.
RESULTS:
Clear priorities, measurable impact, and a roadmap toward AI‑driven FP&A
The assessment provided the company with a quantified understanding of where time, effort, and cost were being lost across planning activities. This enabled the organization to:
- Understand the true cost of inefficiencies
- Equate current hours spent to hundreds of thousands of dollars in reclaimed productivity annually
- Identify where AI could reduce manual work
- Refocus planners on forecast accuracy and strategic insight
By implementing consistent processes, interim AI Assist tools, and a structured roadmap toward advanced AI, the organization established a clear path to shift planners from manual data assembly to insight generation.
Planners who previously spent hours compiling data can now access standardized datasets and generate insights using AI-assisted queries in minutes.
This initiative transformed AI from a theoretical investment into a practical, workflow-integrated capability, unlocking measurable efficiency gains and repositioning FP&A as a strategic driver of business performance.
