Michael, the CFO of a consumer brand, was uneasy. Revenue looked strong, but cash flow was tightening and margins kept eroding. “Maybe our financial reports aren’t telling the real story. Maybe the problem is hidden in operations or pricing — but we can’t see it in time.” At basecamp, he needed more than hindsight; he needed a way to see the truth behind the numbers.

As the climb began, the hard questions surfaced. “What if cost-to-serve is eating up our margin? What if discounting is cannibalising profit? What if pricing tweaks could unlock EBIT growth?” With Fornax, those questions became simulations: profitability at SKU level, customer lifetime economics, and scenario models that connected finance with sales and operations.

Midway up, the fog lifted. Michael saw the real drivers of erosion — certain products looked profitable but collapsed once logistics and returns were factored in. Other segments quietly delivered outsized margins. The insight wasn’t just in the numbers, it was in linking financial data with operational and customer truths. For the first time, Michael had a forward-looking view instead of rearview reports.

At the summit, Michael acted decisively. He killed unprofitable SKUs, restructured contracts, and introduced guardrails on discounting. He automated reporting, freeing finance from manual churn. Margins stabilised, capital was redeployed, and finance shifted from being a scorekeeper to becoming a growth enabler. The board no longer asked “What happened?” — they asked “What’s next?” — and Michael had the answers.

Daniel, Co-Founder & CEO of a scaling omni-channel brand, felt the stack of trade-offs closing in: growth targets rising while capital tightened, AI pilots multiplying without clear P&L impact, and disclosure expectations (from ESG to AI governance) expanding. “Maybe we’re spreading capital thin across too many bets. Maybe our data and operating model aren’t set up for AI to pay off. Maybe talent and compliance risks are masking real margin.” She didn’t need more dashboards - she needed a single truth that connected markets, products, customers, supply chain, workforce and risk.

Before the QBR, Daniel and Fornax reframed the conversation from deliverables to decisions:

What if we model capital allocation by contribution and cash rather than headline revenue? What if we simulate exits from low-yield regions and double down where LTV/CAC and payback outperform? What if we stress-test SKU and channel economics including returns, logistics and discount leakage? What if we treat AI as embedded capacity - pricing guards, retention plays, demand sensing - governed by data quality and risk controls? What if workforce strategy anticipates role shifts and skills obsolescence rather than reacting late? With Fornax, each “what if” became a scenario with trade-offs and time-to-value, grounded in unified data and governance aligned with board expectations.

The fog lifted when fragmented views collapsed into one: which regions created cash vs. consumed it; which product lines truly carried margin after cost-to-serve; which cohorts responded to retention over discounting; where AI-assisted ops could cut delays without new headcount; where compliance risk clustered; and which capabilities the workforce actually needed in the next 12-18 months. Insight wasn’t a report; it was org-level clarity: how value is created, how AI and data governance turn from experiments into capacity, and which few moves would change the P&L slope.

At the summit, Daniel executed a portfolio of moves, not a single bet: exit/reshape two underperforming regions; consolidate long-tail SKUs; shift spend to retention and profitable segments; embed AI in pricing guardrails, demand forecasting and service workflows with explicit governance; launch a targeted reskilling program; and automate ESG/AI reporting to end last-minute fire drills. The result wasn’t just cost takeout - it was durable growth: cleaner unit economics, faster decisions, fewer firefights, and a board narrative that connected strategy to cash, risk and capability building.

Thomas, the CIO of a mid-sized retail-manufacturing group, walked into the steering committee with a familiar frustration. Every function had invested in tools - ERP for Finance, CRM for Sales, POS for Retail, WMS for Logistics - yet executives still complained they couldn’t get one version of the truth. MIS reports took weeks, powered by manual reconciliations between teams. Ad-hoc report requests consumed his data team’s time, while business users debated whose numbers were “right.” “Maybe the problem isn’t adoption. Maybe our data is scattered, duplicated, and mistrusted. Maybe the issue isn’t more tools - but smarter architecture.”

Before the QBR, Thomas partnered with Fornax to challenge assumptions. What if the enterprise could build a modern data foundation that stitches ERP, CRM, ecommerce, and supply-chain systems into one governed warehouse? What if definitions for metrics - revenue, margin, churn - were standardized so reports stopped conflicting? What if governance and access policies were designed first, so every ad-hoc request didn’t require a manual patch? What if AI pilots were prioritized only where clean data and ROI aligned? What if cloud migrations enabled faster decision-making instead of just shifting costs? These weren’t tool pitches - they were strategy models.

The fog lifted once silos were mapped. 40% of reporting delays stemmed from reconciling finance vs. sales numbers; duplication across ERP/CRM systems inflated inventory values; ad-hoc report requests were a symptom of users distrusting dashboards; and undefined metrics meant every function presented a different “truth” in the boardroom. Even AI pilots had failed quietly because they were built on inconsistent data. For the first time, Thomas could show that the root cause of inefficiency and mistrust wasn’t missing AI - it was fractured data foundations and absent governance.

At the summit, Thomas acted decisively. He greenlit a cloud-native data warehouse with governed pipelines, established a central business glossary of metrics, and set up self-service reporting with guardrails - cutting 70% of ad-hoc requests. Manual reconciliations were eliminated with a single source of truth for finance, sales, and ops. AI pilots in forecasting and churn prediction were relaunched on unified data, delivering early wins. Within two quarters, reports were trusted, decisions faster, and the CIO reframed not as an “IT cost manager” but as the architect of enterprise-wide clarity.

Carlos, the Head of Sales at a regional supermarket chain, felt the weight of targets slipping away. Stores were busy, but forecasts were consistently off, shelves saw frequent stock-outs, and end-of-quarter promotions drove volume but slashed profit. Sales reps reported activity, yet actual performance lagged. “Maybe we’re hitting traffic but not the right sales mix. Maybe our forecasts are too reactive. Maybe our teams are working hard but not effectively.” At basecamp, Carlos knew the gap wasn’t effort - it was visibility.

Ahead of the quarterly review, Carlos worked with Fornax to challenge assumptions. What if forecasts used live POS data and weather/holiday signals instead of last year’s averages? What if we scored store-level sales not just by volume, but by contribution margin? What if territory assignments reflected real neighborhood demand instead of static boundaries? What if we could see where reps spent time, and link it to actual lift? Each “what if” was modeled into clear, data-backed scenarios.

The fog began to clear. Carlos saw that 20% of stores consistently overshot forecasts while others underperformed, skewing overall accuracy. High-volume promotions were burning margin, with some SKUs driving traffic but not profit. Store visits by sales reps lacked focus, with 30% of time spent on low-impact accounts. Expansion opportunities weren’t in the city centers, but in fast-growing suburban corridors where competitors were thin. For the first time, the sales picture wasn’t just activity - it was performance with precision.

At the summit, Carlos made deliberate moves. Forecasting shifted to real-time signals, improving accuracy within two quarters. Promotions were redesigned around profitable baskets, not just traffic. Reps were redeployed to high-impact stores with clear productivity metrics. And expansion plans prioritized underserved suburban markets. The board no longer saw sales as unpredictable, it became a disciplined growth engine, balancing revenue with profitability and readiness for scale.