When finance leaders ask for more speed, they rarely mean faster reporting. They mean faster understanding, faster alignment, and faster decisions. Coming from a data and platform background and having spent years building the foundations that finance teams sit on top of, I have watched this gap widen from a particular vantage point. The problem is almost never the planning tool. It is what the planning tool is sitting on.
My background is in data: BI, data engineering, platform architecture. Over the years, working closely with finance and EPM teams, I kept seeing the same pattern repeat: Finance would invest in a new planning tool, run an implementation, and still struggle to answer a simple question quickly. The tool was rarely the problem. The data underneath it was. Preparing for Bangkok, I went back through the research and found that what I had been observing on the platform side is well-documented on the finance side too. Consider a scenario most finance leaders will recognize. A competitor moves on pricing. The CEO needs a response by Thursday. The FP&A team goes quiet, not because they lack the skill, but because the latest actuals are eleven days old, the data lives in three different systems, and building a credible model will take the better part of a week. By Thursday, the decision has been made without them.
This is the agility gap. And the evidence for it, increasingly, is not anecdotal.
Deloitte's 2026 Finance Trends research, drawn from more than 1,300 global finance leaders, ranks speed as the top strategic priority and points to advanced scenario planning and agile governance as the primary responses to uncertainty. In their Canada-specific lens of the same study, 75% of leaders said their organizations need at least a moderate increase in resources to maximize investment opportunities, with 30% citing stronger scenario-planning capabilities and 28% pointing to more agile governance as the most pressing improvements needed.
AFP's 2026 FP&A benchmarking is equally pointed. Finance professionals already spend 21% of their time on activities beyond traditional finance scope: strategy, technology, HR, operations. Yet the same research shows that coordination breaks down exactly where it matters most for speed.
That is the agility gap in operational terms. Strategy may be aligned at the top, but when assumptions, drivers, and operating responses diverge downstream, speed disappears. A finance team that cannot agree on what the numbers mean cannot move fast on what they imply.
Why dashboards and models have not solved it
For years, organizations tried to accelerate decision-making by adding more dashboards, more planning tools, more reporting layers. In most cases, the effort produced better presentation without better speed. The reason is that speed is constrained much earlier in the chain. AFP 2025 FP&A benchmarking is explicit: 61% of respondents identified lack of data reliability as a challenge, and 60% cited lack of data accessibility. AFP’s conclusion is direct: robust technology and data infrastructure is necessary if FP&A is to support fast decision-making and deeper insight.
EY makes the same structural diagnosis from its CFO organization work: finance teams are routinely pulled into data quality checks, reclassification work, and manual linkage between operational and financial data, precisely because the two are not sufficiently structured or connected at the source. The result is a function that is technically capable but architecturally constrained.
It is tempting to treat manual effort as a cost issue: hours wasted on reconciliation that could be automated. That framing underestimates the real impact. Manual work creates latency. And latency in finance is not just inefficiency; it is the gap between when a market signal arrives and when the business can respond to it. When a scenario model requires first extracting data from ERP, reconciling it against the CRM pipeline, aligning it with procurement cost updates, and rebuilding the planning model from scratch. The process could look like this:
APQC’s guidance on monthly close makes the point precisely: when fewer days are consumed by close and reconciliation, more days can be spent applying finance expertise to organizational decisions. Every day recovered from rework is a day reallocated to judgment. The competitive window for most market responses (a pricing move, a supply disruption, a customer escalation) is 48 to 72 hours. Three to five days does not close the gap; it confirms it. PwC’s 2025 Global Treasury Survey finds the same pattern in adjacent domains: only 57% of respondents use a treasury management system, while 36% still incorporate manual processes. PwC notes directly that manual approaches hinder the timeliness, accuracy, and auditability of risk insights. Finance’s manual exposure extends well beyond FP&A.
The problem has never been the willingness of FP&A teams to move fast. It has been that the architecture they inherited was designed for periodic reporting, not real-time decision support.
The organizations that are closing the agility gap are not necessarily the ones that have deployed the most sophisticated planning tools. They are the ones that resolved the foundation first. That shift has four practical dimensions.
When organizations make this shift in practice, a clear architectural pattern emerges. The governed data layer, holding immutable actuals, enforcing the data model, providing row-level security and full audit trails, sits at the center. Every planning tool, every BI dashboard, every scenario engine reads from it. None of them maintain their own copy of the actuals. Forecasts and budgets are written back to it. When that separation is clean, something important happens: Finance stops reconciling and starts analyzing.
The characteristics that matter most in that central layer are unglamorous but consequential. It needs to ingest continuously from ERP, CRM, procurement, and HR without manual intervention. It needs to hold every version of every number, not as a backup, but as a queryable history, so that restatements and audit requests become a query rather than a project. It needs to support scenario sandboxes that spin up instantly from current actuals without touching production data. And it needs to enforce consistent definitions of entity, cost center, currency, and time period so that Finance and Commercial are genuinely reading the same numbers.
This is precisely where modern cloud data platforms like Snowflake have become relevant to finance, not as planning tools, but as the governed foundation underneath them. Snowflake’s architecture is well-suited to the requirements above: continuous ingestion from any source system, Time Travel for point-in-time reconstruction of any financial state, zero-copy cloning for scenario sandboxes that don’t touch production data, and row- and column-level security that maps naturally to finance access hierarchies. Cortex AI, running within the same security boundary, means anomaly detection and variance narrative generation operate on governed actuals rather than on exported copies. None of that makes Snowflake a planning tool. It is not. But for organizations looking for a single, trustworthy foundation that any planning layer can sit on top of, it addresses the structural requirements directly. The planning tool choice becomes, in that context, largely a front-end decision rather than an architecture one.
AFP’s 2025 benchmarking found that only 23% of respondents were using AI in FP&A on a regular basis, though 40% were testing it and planning implementation within a year. That trajectory is real, and the capabilities are genuine: AI-assisted variance narrative, anomaly detection, accrual suggestion, natural language query – these reduce hours of manual work per analyst per month.
But AI does not remove the foundational requirement; it amplifies it. An AI model queried against governed, real-time actuals within a secure data environment is a reliable analytical tool. The same model queried against a manually assembled extract from eleven days ago produces confident-sounding outputs built on unreliable inputs. Poor definitions, disconnected data, and inconsistent drivers do not become strategic because they are processed by a model. They become faster ways to produce confusion.
EY’s 2026 FP&A perspective makes this point in implementation terms: AI can automate repetitive tasks and strengthen analysis, but it only becomes durable when finance has the right data foundation underneath it. That is not a caution about AI; it is a sequencing argument. Foundation first, then AI on top of it.
A governed data platform is not the planning tool. It is the layer that makes any planning tool trustworthy. It holds the actuals in immutable form. It enforces the data model. It provides row-level security, full audit trails, and the ability to reconstruct any number at any point in time. The planning tool reads from it and writes forecasts back to it. When this separation is clear, multiple tools can read from the same actuals without conflict, scenario sandboxes can be spun up instantly without touching production data, and every adjustment and forecast revision has a traceable history, not because someone documented it, but because the architecture enforces it.
I would summarize the current EPM moment in one sentence: speed is no longer mainly a tooling problem; it is a foundation problem. The organizations that close the agility gap are not necessarily the ones with the most sophisticated front end. They are the ones that make finance data trustworthy, connect financial and operational drivers, reduce manual interventions, standardize core logic, and redesign governance for faster response.
From a data architecture standpoint, none of this is surprising. It mirrors what happens in every domain where analytics outpaces infrastructure – until the infrastructure catches up, the analytics cannot be trusted, and the people who need to act on them know it. Finance is no different.
The foundational shift is not about slowing down to clean things up before value can be delivered. It is the opposite. It is the work that finally makes speed real, and sustainable, not dependent on a few people working late to hold the model together.
If you're ready to close the agility gap, talk to our team , we help finance and data leaders build the foundation that makes speed real.