AI Customer Stories: Real-World Examples in Finance and Operations Planning 

Board customers across finance and operations are putting AI to work on specific planning and analysis workflows to cut manual work, speed up analysis, and make faster, better-informed decisions. This page collects their stories — with the specific tools, workflows, and outcomes involved.

AI proof, at a glance

AI in enterprise planning is moving from potential to practice

Finance and operations teams are past the “what could AI do” conversation. Below are real, named organizations already using or exploring Board’s agentic and analytical AI for variance analysis, reconciliation, forecasting, and demand planning. What these examples have in common is not simply the use of AI. They show where AI becomes useful in enterprise planning: when it is applied to a defined business workflow, grounded in trusted planning context and governed data.

Why organizations use Board AI for planning

General-purpose AI can make information easier to access and analyze. Enterprise planning adds another requirement: AI needs to understand the business context behind the numbers, including planning hierarchies, scenarios, versions, calculations, assumptions, and permissions.

Board AI operates within that governed planning context. This helps finance and operational teams apply AI to specific workflows such as variance investigation, reconciliation, forecasting, and exception management while retaining human oversight and traceability.

Three characteristics show up repeatedly across these customer stories:

• Grounded in planning context. Board Agents operate using governed planning data, business definitions, hierarchies, calculations, and user context, empowering users to work with the same business structures they already use for planning and reporting.
• Built for governed, auditable decisions. Transparency, explainability, and human oversight are part of the workflow, which matters in finance and accounting contexts where an ungoverned answer is a liability, not a convenience. Users can inspect the Board datasets behind an answer, challenge the analysis, and validate the output before acting. As Karndean’s Group Finance Director Chris Brown put it: “I can always ask the Board Agent where an answer came from. If it tells me my margins are strong, I can ask why, and it’ll show me its reasoning.”
• Different forms of AI for different planning problems. Board Agents support domain-specific analysis and use case driven workflows, while Board Foresight and Signals bring predictive and external economic intelligence into forecasting and operational planning. Together, they support Continuous Planning across finance and operations.

AI in the Office of Finance

Across the Office of Finance, Board customers are exploring how persona-based AI agents complement finance expertise.

The Board FP&A Agent supports planning, forecasting, and performance analysis. The Board Controller Agent brings accounting-aware intelligence to financial close, consolidation, reconciliation, and reporting.

These agents are designed to accelerate analysis and support decision-making inside governed planning workflows, while keeping finance professionals responsible for validation, judgment, and approval.

Together, they help finance teams move from gathering and validating information toward understanding performance and shaping decisions.

Chiesi: identifying variance drivers in seconds instead of hours

At Chiesi, the Board FP&A Agent helped to accelerate financial variance analysis and uncover the drivers behind performance changes. Instead of spending hours investigating discrepancies across the P&L, balance sheet, products, SKUs, and regions, the FP&A team gets root causes and answers in seconds — supporting a shift from reactive reporting toward proactive planning.

An important benefit identified is the efficiency of Board Agents. Tasks that previously required manual preparation in Excel can now be executed directly within Board, providing ready-to-use tables and charts in minutes without data export or pivot table creation.

Riccardo Filippi

Finance Application Manager, Global Finance Chiesi Farmaceutici

AI proof point: AI-powered variance analysis and root-cause identification are cutting financial performance analysis from hours to seconds.

Watch how Chiesi uses the Board FP&A agent:

EBSCO Industries: reducing manual analysis and eliminating Excel exports

EBSCO Industries explored the Board FP&A Agent for ad hoc financial analysis through natural-language interaction, removing the need to repeatedly export data into Excel.

The team gets contextual answers, understands the calculations behind them, explores financial information while retaining transparency and auditability, and the ability to validate the underlying data sources — freeing up time to evaluate what the numbers mean for the business rather than assembling them.

What impresses me most is the Board FP&A Agent’s ability to triangulate. It takes our detailed balance sheet and income statement and synthesizes everything into clear, actionable insights — incredibly valuable for us at EBSCO.

Adam Hancock

former Vice President, Financial Planning & Analysis EBSCO Industries

AI proof point: Natural-language financial analysis is reducing manual Excel exports and speeding up ad hoc insights, with transparent calculations throughout.

Watch how EBSCO Industries uses the Board FP&A Agent:

AMMEGA: accelerating analysis and reducing reconciliation work

AMMEGA is exploring multiple applications of Board AI across finance. The Board FP&A Agent automates manual sanity checks, accelerates financial analysis, and lets the team interact with trusted data through natural language — spending less time finding issues and more time resolving them.

In a separate finance workflow, AMMEGA reduced manual reconciliation work from almost a full day to one or two hours, alongside improved data quality, less dependence on IT support, and transparent, explainable answers.

AI proof points:

• Manual reconciliation reduced from almost one day to one or two hours
• Automated sanity checks and faster variance analysis
• Natural-language access to finance data
• Transparent, explainable responses
• less dependence on IT for routine analysis

Watch AMMEGA’s story:

AMMEGA: Accelerating finance transformation with Board AI

Ammega Reduces Manual Reconciliation from One Day to Two Hours with Board AI Agent

How Ammega Accelerates FP&A Reporting and Variance Analysis with Board FP&A Agent

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Karndean: turning consolidation investigation into a single question

Karndean is piloting Board Agents for the Office of Finance — including the Board Controller Agent — to move from reporting to investigation faster during financial consolidation. Rather than simply returning data, the agent highlights unusual balances, such as a receivables provision that looks low compared to other entities, identifies which entity is driving a variance, and explains why a balance may need attention. Karndean’s Group Finance Director can also challenge the analysis and ask where a conclusion came from, rather than treating the output as a black box.

Chasing down an intercompany variance used to mean report after report, drilling in a little further each time. Now I just ask one question, and the Board Controller Agent zeroes straight in.

Chris Brown

Group Finance Director Karndean

AI proof points:

• Exception flagging — for example, a receivables provision that looks low relative to peer entities
• Intercompany variance investigation reduced from report-after-report to a single question
• Transparent reasoning: Chris can ask the agent where an answer came from
• Finance leaders can use natural-language interaction to investigate governed planning data without repeatedly building additional reports
• Board Agent analysis is clear enough to hand directly to the CEO for self-service questions

Listen to Chris Brown from Karndean:

Diersch & Schröder Group: exploring AI-supported management analysis

Diersch & Schröder Group is exploring how natural-language interaction and AI-supported analysis can help controllers work with existing reports and financial data — identifying positive and negative variances, analyzing results across segments and periods, and investigating potential performance drivers at account level. This is an early example of AI making management information easier to explore, and helping controllers move faster from reported results to potential explanations.

We quickly saw the potential of Board Agent and learned how to tailor the prompts to our reporting priorities. It has become a genuinely valued member of the team, and we are excited about what comes next.

Katja Makiol

Head of Group Controlling Diersch & Schröder Group

AI proof point: Natural-language management analysis is helping controllers move faster across variances, periods, segments, and accounts.

Watch the video interview with Katja Makiol from DS Group:

Read the full case study of Diersch & Schröder.

AI in Forecasting & Operations Planning

Board AI in planning extends beyond agentic AI. Board Foresight applies analytical AI and econometric modeling to forecasting, while Board Signals brings relevant external economic and market intelligence into the planning process.

Rocky Brands: external signals and GenAI explainability in demand planning

Rocky Brands uses Board for Demand Planning together with Board Foresight and Signals to plan in a volatile footwear market. The organization reduced data preparation time by 80–90% and moved from static monthly forecasting to continuous, signal-driven planning. External indicators validate forecasts in near real time, supporting faster reforecasting and more granular visibility at SKU and customer level. GenAI explainability helps planners interpret the relationships behind forecasts and external indicators — supporting more confident inventory and supply commitments while keeping people responsible for the material decisions.

Planning shifted from data preparation to exception management.

Michael Harper

VP Supply Chain Planning Rocky Brands

AI proof points:

• Data preparation reduced by 80–90%
• External signals used to validate demand forecasts
• GenAI-supported interpretation and explainability
• Faster reforecasting and more granular inventory decisions
• Continuous, signal-driven planning

Watch how Rocky Brands uses Board Foresight:

Read the Rocky Brands AI-driven demand planning story.

Kraft Heinz: grounding go-to-market planning in macroeconomic intelligence

Kraft Heinz uses Board Foresight and Board Signals to bring macroeconomic intelligence into forecasting and go-to-market planning, helping the team see through pandemic-driven consumer behavior shifts and market volatility rather than relying on historical trend lines alone. The approach started as a pilot and has since expanded to Canada and more than 80 international territories, with Kraft Heinz noting that even a 1% improvement in forecast accuracy translates into millions of dollars in cost savings or revenue growth.

All retailers and CPGs should consider macroeconomic factors to improve their business planning. Not only will forecasting accuracy improve but go to-market planners will be fully educated as to what external factors are important to their markets.

Jac Connolly

Senior Economist The Kraft Heinz Company

AI proof point: Macroeconomic-intelligence-driven forecasting scaled from an initial pilot to Canada and 80+ international territories.

Read the Kraft Heinz Case Study or watch the webinar on demand for more details.

Whataburger: one foundation for sales, wage, and staffing forecasts

Whataburger uses Board Foresight as the base model for its sales, wage, and staffing forecasts — layering business-specific adjustments on top rather than relying on incomplete historical data alone. The team built 20 short-term (1–12 month) predictive models covering topline sales and regional performance across corporate and franchised restaurants, and uses the same forecasting foundation to inform HR decisions on staffing, cost, and retention.

We fully embrace Board Foresight as our base model, and then we layer on some adjustments on top of that. Now, the Board Foresight forecast is really how we start off our planning process.

Pete Valadez

Senior Director, FP&A Whataburger

AI proof point: Store-level sales forecasts accurate within a 1–2% error margin across 20 predictive models spanning sales, staffing, and wage planning — a rare example of one forecasting foundation supporting both commercial and workforce decisions.

Read the Whataburger case study

Kimberly-Clark: turning forecasting from a reporting exercise into a decision advantage

At Kimberly-Clark, the value of AI-driven forecasting extends beyond improving the forecast itself. The resulting forecast now informs decisions across finance, demand planning, supply chain, marketing, and revenue growth management.

Forecasting had been a backward-looking, Excel-based process built on linear trend lines and manual judgment overlays — hard to standardize across categories and difficult to explain to stakeholders, and fragile when the pandemic, inflation shocks, and private-label surges hit.

Since partnering with Board in 2023, the team has rebuilt category forecasting around an econometric, external-data-first methodology: one standardized framework applied consistently across categories while allowing each category’s own drivers reflect its specific market.

Machine learning continuously evaluating millions of external signals — economic, retail, consumer, and competitive data — to identify what statistically drives demand, removing human bias from driver selection. Every forecast now comes with an explainable “driver story” instead of just a number, and feeds directly into finance (budgeting, investor guidance), demand planning and supply chain (inventory, service levels), and marketing and revenue growth management (campaign timing, pricing).

The goal was never just a better number. The goal was a better decision.

Misty Alexander

Director Consumer Analytics Kimberly-Clark

AI proof points:

• Forecast accuracy within 2% of actuals overall (as tight as 0.2–2% in the facial tissue category)
• More than $10 million in realized savings, including $1.3 million in safety-stock reduction for every one-percentage-point improvement in forecast accuracy
• Quarterly forecasts now outperform annual forecasts in 7 of 11 categories, and are at parity in the other 2
• One forecast now feeds finance, demand planning/supply chain, and marketing/RGM decisions, instead of sitting in a single team’s spreadsheet

Learn more about Kimberly-Clark and Board Foresight

What these AI customer stories have in common

• AI is applied to defined business workflows, not simply used as a general-purpose add-on: variance analysis, reconciliation, account-level investigation, ad hoc analysis, forecasting, exception management.
• The main benefit isn’t just speed. Teams spend less time preparing and searching for information, and more time validating insights, testing scenarios, and deciding what to do next.
• Planning context matters. AI becomes more useful when it can operate with the business definitions, hierarchies, scenarios, calculations, assumptions, and permissions that shape how an organization plans..
• Trust is non-negotiable. Transparency, explainability, traceability, auditability, and human oversight underpin every story here — as Karndean’s Chris Brown put it, “I can always ask the Board Agent where an answer came from.”
• Different planning problems benefit from different forms of AI. Board Agents support domain-specific analysis and use case driven agentic workflows, while Foresight and Signals bring predictive and external intelligence into forecasting.
• AI supports Continuous Planning. Teams monitor changing conditions, understand their impact, and adjust plans and forecasts against current business reality.

From AI experimentation to confident decisions

AI in finance and planning is no longer limited to broad promises or isolated demos. These organizations are applying it to clearly defined planning and analysis workflows and evaluating its contribution to productivity, forecast quality, decision confidence, and business performance.

The common thread is context. AI becomes more useful when it operates with trusted business definitions, governed planning data, established calculations, and human oversight, rather than operating independently of the planning process.

Board combines domain-oriented AI agents, analytical AI, economic intelligence, and enterprise planning to help finance and operational teams continuously understand performance, evaluate options, and make confident, aligned decisions together.

Explore Board AI and its persona-based agents

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