Key takeaways for CFOs

• Cost volatility does not create margin erosion, weak decision discipline does.

• 65% of CFOs are actively adjusting forecasts due to volatility (PwC).

• Tariff pass-through averages 45%, meaning internal discipline determines margin outcomes (McKinsey).

• Weak governance creates decision latency when cost signals move faster than planning cycles.

• CFOs need driver ownership, scenario triggers, cross-functional decision forums, and pricing discipline.

Executive summary

Cost volatility is no longer an “input problem”. Today, it’s a management system stress test. When tariffs shift, supplier pricing moves, logistics costs spike, or energy swings, strong organizations don’t just “update the forecast” (like in the past). Instead, they execute a repeatable discipline: clear ownership of cost drivers, faster scenario cycles, integrated decision forums, and commercial rules for pricing and pass-through. Weak organizations revert to ad‑hoc escalations, spreadsheet battles, and slow decisions, exactly when the business needs speed.

Recent CFO and supply-chain leader surveys show how widespread the pressure has become: CFOs are actively adjusting forecasts and budgets in response to volatility, assessing tariff impacts, and implementing cost reductions beyond layoffs. Cost and supply-chain volatility is also landing at the top of executive agendas, while many organizations still lack formal senior-level (even board-level) governance routines to discuss supply-chain risk and responses.

The core message for CFOs: volatility doesn’t create the most damaging outcomes, but weak discipline does. It turns manageable cost movement into margin leakage, decision paralysis, and underperformance.

Problem

CFOs are operating in an environment where cost drivers change faster than traditional planning and governance can absorb.

• In PwC’s May 2025 Pulse Survey segment focused on CFOs and finance leaders, 65% report adjusting financial forecasts and budgets due to current volatility, and majorities are implementing cost reductions (excluding layoffs) and assessing tariff impacts.
• In Deloitte’s 3Q 2024 CFO Signals survey, CFOs cite tariffs and inflation as major watch items: trade policy and tariffs rank high among the areas CFOs monitor, while inflation is cited as one of the largest expected impacts on the business operating environment.
• On the supply-chain side, McKinsey’s 2025 supply chain risk survey reports that 82% of respondents say their supply chains are affected by new tariffs, and 39% are seeing increases in supplier/material costs. At the same time, companies often cannot cleanly pass costs through: the weighted average tariff pass-through rate is 45%, and fewer than one-fifth plan to pass through more than 80% of tariff costs.
• BCG’s global survey of 600+ executives (fielded Nov–Dec 2023) shows that while companies can hit initial cost savings, 35% struggle to sustain cost savings and 27% struggle to limit negative impacts on growth—classic symptoms of weak operating discipline around cost management.
• Grant Thornton’s Q4 2025 CFO survey highlights execution strain: only 53% of respondents report confidence in meeting cost control goals (and 57% in meeting supply chain needs).

Survey and market evidence at glance

How volatility reveals weaknesses

In stable periods, weak discipline can hide behind averages. Under volatility, it becomes measurable because the organization’s decision system gets exercised daily. Four failure modes show up repeatedly.

Governance gaps show up as “decision latency”

When costs move weekly but governance meets monthly, you get a predictable outcome: decisions drift downward (into functions) or upward (into executives). Either way, speed collapses.

McKinsey’s supply chain leader survey points to a senior-level governance gap: only a quarter report formal processes to discuss supply chain issues at board level, and regular reporting cadence for supply chain risk drops to one-quarter, with many reverting to ad hoc reporting. The unavoidable CFO implication: if formal forums don’t exist, the organization substitutes escalation and email. That is not governance; it’s friction.

Forecasting breaks when assumptions aren’t owned

Cost volatility punishes “single-number forecasting” and loosely governed assumptions. If procurement has one view of supplier pricing, operations has another view of yield and scrap, and finance has a third view of overhead absorption, the forecast becomes a negotiation not an instrument panel.

PwC’s May 2025 findings show CFOs are already responding by changing pace: 65% adjusting forecasts and budgets due to volatility. The question is whether those forecast updates are disciplined (driver-based, transparent assumptions, scenario triggers) or simply more frequent rework.

Decision rights fail under pressure

In volatile cost environments, the “right” decision is often uncomfortable: change sourcing, change product mix, adjust inventory posture, renegotiate contracts, alter pricing corridors. These moves sit across functions.

What happens in weak discipline environments is predictable: Procurement negotiates price; sales protects volume; ops protects service; finance protects margin. Without explicit decision rights and escalation thresholds, the business delays and the market decides for you.

Survey data reinforces that this is not theoretical. PwC reports that 57% of executives say they are missing opportunities because they can’t make decisions fast enough, and explicitly cites rigid decision-making and limited visibility as contributing factors.

Cross-functional coordination breaks first at the commercial edge

Tariff- and supplier-driven cost increases create an immediate commercial question: what gets passed through, how fast, and under which rules? McKinsey’s 2025 tariff survey suggests many companies will absorb or mitigate a meaningful share of costs, given the 45% average pass‑through rate and low share intending to pass through most of the cost.

That reality exposes a discipline question, not a pricing “best practice” question: Do you have a defined pricing governance and a repeatable playbook, or do you renegotiate deal-by-deal while margin leaks?

The discipline framework CFOs can operationalize

The goal is not “better planning.” The goal is decision-grade planning: disciplined visibility, fast scenarios, clear governance, and accountable execution.

Discipline framework with owners, cadence, and KPIs

The point is not to build a bigger planning machine. It’s to build a smaller set of disciplines that run predictably under stress, so the organization can act faster than its cost environment moves.

Pattern case studies from the field

These are anonymized patterns we see repeatedly when volatility hits.

The “spreadsheet consensus” manufacturer

A multi-plant manufacturer faces volatile input materials and logistics. Each plant updates a cost forecast independently; procurement maintains a separate supplier view; finance publishes a consolidated “official” view. Under tariff shifts and supplier price moves, forecast updates become reconciliation exercises, not decision tools. Leadership responds late, primarily through blanket cost freezes.

What changes performance is not a new model, it’s discipline: a single driver list, weekly driver refresh, and an integrated forum that can decide inventory posture, sourcing exceptions, and pricing actions within days (not weeks).

The “pricing by exception” distributor

A B2B distributor experiences supplier cost increases but lacks a segment-based pass-through rulebook. Account managers negotiate price on the fly; finance monitors margin after the fact. Under tariff pressure, margin erosion accelerates because the organization cannot pass through costs consistently, and approvals bottleneck at senior levels.

The fix is governance discipline: pricing corridors by segment, clear thresholds, and a weekly commercial-margin review tied to driver movements. This aligns with what tariff research suggests: companies often cannot pass through the majority of costs, so internal discipline determines margin outcomes.

The “invisible consumption” services business

A services or tech-enabled firm sees volatility in vendor and platform costs (cloud usage, software consumption, third-party delivery). The business treats these as overhead until growth slows and unit economics become visible. Then, leaders attempt blunt cuts without understanding real drivers.

Discipline here looks like cost-driver ownership (consumption metrics), a weekly review of unit economics, and decision rights to change product/feature behavior or customer contract structures, not just “reduce spend.”

Leadership call to action

Cost volatility will not politely wait for your next planning cycle. Surveys show leaders are already rewriting planning, forecasting, and cost response routines in real time. The CFO opportunity is to turn that reactive motion into a deliberate management system:

If you want one practical starting point, ask three questions:

1) Do we agree on our top cost drivers, and who owns each one?
2) How quickly can we translate a cost signal into a decision (not a report)?
3) Do we have a cross-functional forum with decision rights to act weekly when needed?

If any answer is unclear, volatility is already exposing weak discipline, it just hasn’t shown up cleanly in the numbers yet.

How Centida supports CFOs under volatility

Centida works with finance and operations leaders to embed decision-grade planning into everyday governance. That means clarifying cost-driver ownership, designing high-frequency scenario routines, aligning cross-functional decision forums, and strengthening pricing discipline.

The focus is not on adding tools, but on making the existing planning system run predictably under pressure. Volatility cannot be removed, but the way an organization responds to it can be structurally improved.

Why ERP transformations disrupt planning processes, and how mid-sized companies can design planning resilience

ERP transformations promise clarity. Companies want to have a single system, unified data, standardized processes, and faster insight. Leaders position an ERP change as the foundation for better planning and stronger control.

Yet in practice, the opposite often happens. During ERP transitions, planning slows down, assumptions break, and forecast cycles end up getting longer. As a result, leadership meetings shift from decisions back to reconciliation. The system may even go live, but confidence in the numbers start to quietly erode.

Let’s clarify this. This happens not because of ERP failures. But because ERP change exposes weaknesses in how real planning processes work.

ERP’s promise vs. planning reality

On paper, ERP implementations are designed to improve planning. They consolidate data, standardize master data, and connect transactions across functions. In stable environments, this often works.

However, planning is not a data problem. It is a decision system built on assumptions, ownership, cadence, and governance. When those elements are informal, undocumented, or person-dependent, an ERP change acts as stress test.

What previously “worked” through manual adjustments, local knowledge, and informal coordination no longer holds. The existing system stops compensating for hidden fragility.

How ERP change affects planning logic

Planning logic is often documented explicitly. In many organizations, it lives in spreadsheets, in individual experience, and in unwritten rules.

During ERP transitions, this logic must be translated into system configuration: drivers, hierarchies, workflows, validation rules. When that translation happens without first making planning logic explicit, inconsistencies emerge.

Forecasts that once reconciled manually no longer align automatically. Scenarios become harder to compare. Decision timing becomes unclear. And the truth is the ERP does exactly what it was configured to do, it’s just not what leadership expected planning to support.

Data migration and integration: the foundation problem

ERP projects often underestimate the impact of master data quality on planning reliability.

Legacy systems can tolerate inconsistencies. But modern enterprise ERP systems do not. During migration, issues that were previously manageable (or ignored), such as inconsistent product hierarchies, customer definitions, cost center mappings, suddenly affect every forecast and report.

Planning models depend on stable master data. When that foundation is unstable, forecast explainability collapses. Teams spend time questioning inputs instead of evaluating trade-offs.

This is why ERP-driven planning problems are often described as “data issues,” when in reality they are governance issues that surfaced through data.

Alignment and process redesign failures

ERP implementations frequently optimize functions in isolation. Finance focuses on closing and reporting, Operations focuses on execution, while Sales focuses on pipeline and demand.

And in reality, planning sits between them. And often, it breaks first. When Sales, Operations, and Finance enter an ERP transition with different assumptions, timelines, and definitions, the system amplifies misalignment instead of resolving it. Each function sees its own version of the truth, now with more precision and less flexibility.

Under pressure, this leads to slower decisions.

People and adoption: underestimating change management

ERP projects are still treated as IT initiatives far too often.

When planning teams are not deeply involved in design and testing, they fall back to familiar tools outside the system. Shadow spreadsheets reappear and are used, and parallel models persist. The ERP becomes a reporting layer, not a planning backbone.

Training delivered late, documentation delivered after go-live, and limited ownership all contribute to the same outcome: planning fragmentation disguised as system adoption.

Customization and complexity risks

To compensate for weak planning foundations, organizations often customize ERP systems heavily.

While customization may solve short-term gaps, it increases long-term fragility. Each exception adds complexity. Each workaround reduces transparency. Over time, planning becomes harder to maintain and harder to trust.

The system grows, but reliability shrinks.

Designing planning resilience into ERP change

Organizations that navigate ERP transitions successfully approach planning differently. They do not expect the system to fix planning. They design planning resilience deliberately before and during the ERP change.

Start with planning logic

Before touching ERP settings, resilient companies map how planning decisions are actually made today. They identify key decision loops, like pricing, capacity, cash, portfolio trade-offs, and document assumptions, inputs, ownership, and cadence. This logic becomes the reference point for ERP design.

Align ERP requirements to decisions, not features

Requirements are defined in terms of decision support. What decision does this process enable? What triggers action? Who owns the outcome? ERP functionality is selected and configured to support those answers, not the other way around.

Treat master data as planning infrastructure

Master data governance is established early. Definitions are agreed, ownership is clear, and changes are controlled. This stabilizes forecasting and restores confidence when volatility increases.

Build governance into the core planning cycle

Decision rights, thresholds, and escalation paths are defined before go-live. Governance is not added later as control, it is designed as an enabler of speed under pressure.

Make change management central, not peripheral

Planning users are involved early. Training focuses on decision-making, not system navigation. Adoption is measured by decision quality, not logins. This prevents workarounds and reinforces trust in the system.

Research confirms this pattern

ERP transitions are widely discussed in industry research, and several recent studies show that even technically successful ERP migrations often miss quality expectations and encounter operational challenges. This is especially true in complex environments and industrial companies.

For example, a 2025 survey of SAP S/4HANA transformations found that more than 60 % of companies experienced significant deviations from their original budget, schedule, and quality goals during migration projects. In many cases, implementations took 30 % longer than planned, and roughly two-thirds of companies reported moderate to serious quality deficiencies after go-live, indicating that project execution often underdelivers relative to expectations.

These findings are echoed in industry risk assessments which highlight that maintaining minimal disruption to ongoing operations is a primary concern for organizations migrating to new ERP systems, and that data harmonization, integration complexity, and governance gaps are frequently cited as barriers to seamless transition.

What these studies do not emphasize directly (but becomes evident in practice) is that the technical migration process interacts with the organization’s planning and decision systems. When planning logic, assumptions, and governance are not clarified before ERP configuration, the visible effects of budget overshoots and schedule delays are accompanied by a deterioration in planning reliability.

What leaders should ask before the next ERP phase

Which planning decisions must remain reliable during change?

Not all planning outputs matter equally during an ERP transition. Leaders need to identify the few decisions that must continue to work under stress. For example, these can be pricing adjustments, capacity trade-offs, cash visibility, or customer prioritization. If these decisions become unreliable during the transition, the organization will slow down precisely when pressure increases. This question forces clarity on what cannot break, even temporarily.

Where do assumptions differ across functions today?

ERP systems expose inconsistencies that were previously hidden. Sales may assume growth, Operations may assume constraints, Finance may assume cost stability. These differences often coexist quietly until the system forces reconciliation. Leaders should surface and align these assumptions early, before they are embedded into system logic where they become harder to correct and more politically charged.

Which planning steps depend on individuals rather than structure?

Many planning processes work because a few experienced people know how to “make the numbers work.” They remember exceptions, apply judgment manually, and resolve conflicts informally. ERP transitions remove these buffers. Leaders need to identify where planning depends on heroics rather than repeatable structure, and decide which of those dependencies must be designed into process, governance, or data before the transition progresses.

What governance enables faster decisions instead of slowing them down?

Governance is often seen as control that adds friction. In reality, the absence of governance creates delay. Without clear decision rights, thresholds, and escalation paths, every change requires alignment meetings and negotiation. Leaders should define governance that clarifies who decides what and when, so that decisions can be made quickly without reopening debates each time assumptions shift.

Centida’s perspective

Centida works with mid-sized companies on exactly this challenge: modernizing planning during ERP change without losing reliability.

We believe planning resilience comes from clear decision logic, shared assumptions, and governance that enables speed. Technology matters but only after the operating model is clear.

ERP change should strengthen planning under pressure, not weaken it.

Key takeaways

ERP transitions expose planning weaknesses; they do not create them.

If planning slows down during an ERP change, the root cause is rarely the system itself. The transition simply removes manual workarounds and informal coordination that previously masked weak assumptions, unclear ownership, or fragmented processes.

Planning breaks when assumptions, ownership, and governance are unclear.

Reliable planning depends less on tools and more on clarity. When assumptions are implicit, ownership is ambiguous, and governance is undefined, planning becomes fragile. ERP systems amplify this fragility by enforcing structure where none previously existed.

Resilience is designed before configuration, not after go-live.

Once system configuration is complete, correcting planning logic becomes expensive and slow. Organizations that succeed design decision logic, data governance, and ownership models upfront, then configure the ERP to support them, not the other way around.

Reliable planning supports leadership when pressure increases.

In volatile environments, leaders do not need perfect forecasts. They need planning processes that are explainable, trusted, and responsive. Reliability under pressure (not theoretical accuracy) is what allows leadership to act decisively during change.

Most planning projects don’t fail because the software is bad. They fail because the foundations are bad: unclear data and definitions, siloed process with weak change management, and scope that chases features instead of business outcomes. The cost of getting this wrong is real: PMI reports that organizations waste roughly 11–12% of project investment due to poor performance. That’s money you never get back.

This guide explains the three recurring failure patterns and gives you a practical playbook to avoid them.

The Three Failure Patterns You See Everywhere

1. Bad data and fuzzy definitions:

If your master data isn’t aligned (customers, SKUs, regions, calendars) and KPI rules aren’t signed off (gross margin, “active customer,” revenue recognition), you’ll spend every steering meeting reconciling numbers instead of making decisions. People fall back to spreadsheets because they don’t trust the output.

On top of that, poor data quality has a price tag. Gartner estimates it costs organizations $12.9M per year on average. Put bluntly: if inputs are a mess, no tool will save the project. Fix the definitions and data first.

2. Siloed process and weak change management:

Finance department plans one way, sales another, operations a third. Incentives differ, decision rights are unclear, training is an afterthought, and your “go-live” is just a well-polished pilot.

Research shows that effective change management correlates with a higher likelihood of meeting objectives, schedules, and budgets. Adoption is not automatic; it must be designed and led.

3. Tool-first scope and poor delivery discipline:

Too many projects start by shopping features (for example, “it’s driver-based”, “AI forecasting”) instead of scoping the decision you’ll improve in V1. Weak requirements are one of the biggest drivers of failure: PMI studies have long flagged inaccurate/insufficient requirements as a primary cause of missed goals. When requirements float, you rebuild models, rewrite calculations, and burn the schedule.

The Playbook: How to De-Risk Projects

Here is a simple, five-part method you can run on any stack.

1. Scope outcomes, not features.

Start with the decision you will improve, not the tool you will deploy.

–  Define v1 clearly: e.g., “Next-quarter rolling forecast by product and region, with three scenarios. Target: cut cycle time from 20 days to 10.” Name the decision owner and the KPI you’ll move, list the dimensions in scope, and time-box delivery to a single increment. If it doesn’t fit, shrink v1 until it does.

– Write acceptance tests up front: reconciliation rules to source, refresh SLA, performance thresholds, who signs off. Document the exact control report or query used for reconciliation and the tolerance allowed. Capture a baseline for performance so you can prove improvement.

– Name a real sponsor: a P&L owner who will make and defend decisions. Schedule them into key ceremonies and define a clear escalation path. Without an active sponsor, scope drifts and decisions stall.

2. Build a metric dictionary and a semantic spine.

Your model is only as good as your definitions.

–  Metric dictionary: write exact formulas (e.g., GM% = (Net Revenue – COGS) / Net Revenue; “Active Customer” = ≥1 order in last 90 days) and get finance, sales, and ops to sign it. Include edge cases (returns, credits, backorders) so calculations don’t change mid-UAT. Store it centrally and version-control updates.

–  Conform dimensions: customers, SKUs, regions, calendars must match across systems; publish a simple “data contract.” Declare a system of record for each dimension, map keys/hierarchies/grain, and fix duplicates. Freeze code lists for v1 and log exceptions instead of “quick fixes.”

–  Fund data cleanup: use the business case—bad data is expensive—to justify time and budget. Time-box remediation, track defect counts visibly, and tie fixes to the acceptance tests. If you can’t clean it now, scope it out of v1.

3. Integrate change management from day one.

Adoption is a workstream, not a post-go-live email.

–  Stakeholders and decision rights: make collaboration non-optional; design meetings that make decisions, not just share status. Write a simple RACI and define which decisions happen in which forum.

–  Role-based enablement: train finance, sales, and ops differently. Build job-aids and short videos for the tasks people actually do, and schedule floor support the first two cycles after go-live.

–  Communication cadence: match your planning rhythm. Publish a calendar for cut-offs, runs, approvals, and releases. Use a standard template for change notes so nothing is missed.

4. Delivery that survives reality (test, govern, promote).

Treat your planning build like a product, not a one-off report.

–  Phased delivery: v1 (must-have), v1.1 (quick wins), v2 (scale). Commit to a demo every 2–3 weeks and protect scope ruthlessly.

–  Testing discipline: unit tests for calculations, cross-context checks, and performance checks before promotion. For example, create a small test dataset with known answers and pair a business checker with a developer.

–  Promotion gates: use gated environments (dev/test/prod) and approvals. Require peer review for model and measure changes, and block promotion if tests or performance thresholds fail. No exceptions in crunch time.

5. Move from budget events to a rolling, cross-functional rhythm.

Static, annual budgets don’t survive contact with reality. Borrow from Integrated Business Planning (IBP): synchronize plans across the business, run rolling forecasts, and drive scenario-based decisions in one forum—not three. The goal is a single business-steering cadence, not function-by-function schedules.

Case Snapshot “Three Numbers, One Quarter”.

Imagine a situation in which you have the Finance department reporting $5.2M (ERP revenue), Sales reporting $5.5 M (these are signed deals in CRM that include unshipped), and Operations showing $4.9M (fullfilled from Warehouse Management System).

All three are “right” because they use different definitions and systems. The fix was not a new tool; it was a metric dictionary, conformed dimensions, and acceptance tests embedded in scope. After this reset, cycle time dropped and meetings turned into decisions, not debates.

What You Should Measure

–  Cycle time: days from kickoff to first usable plan/forecast; then per iteration. Track by stage (scope → build → test → approve) to see where delays actually happen.

–  Rework rate on calculations: % of measures changed after UAT. A rising rate means requirements are floating or definitions aren’t signed. Fix the dictionary, not just the DAX.

–  Variance reconciliation time: hours to reach “one number” per cycle. Your goal is hours, not days. If it’s longer, your dimensions or KPI rules are still misaligned.

–  Adoption: weekly active planners by function; % of decisions made in the new forum. Usage without decisions is noise—measure both.

–  Benefits realization: track outcomes quarterly. Tie results to business KPIs (time saved, accuracy, inventory, margin) and adjust the backlog to chase the wins that matter most.

Your Checklist: What to Do?

Scope

–  Decision to improve (owner named). Write a one-sentence decision statement and the person who signs it off. If there’s no owner, there’s no decision.

–  Outcome metric + target (e.g., cycle time cut in half). Make it numeric and time-bound so success is obvious. Avoid vanity goals.

–  Acceptance tests (recon rules, SLA, performance). Include a simple test procedure anyone on the team can run and record results against.

Data & definitions

–  KPI dictionary signed by finance, sales, ops. Store it in a shared location, lock it for v1, and route changes through a light review.

–  Conformed dimensions (customers/SKUs/regions/calendars). Document code lists and grain; resolve duplicates up front. Don’t “fix it in the model.”

–  Data contract + lineage sketch. Show sources, transforms, and consumers on one page. Call out any manual steps so you can automate later.

Change & governance

–  Sponsor active and visible. They open key meetings and explain why the change matters. Silence from the sponsor kills adoption.

–  Role-based training plan and comms cadence. Short, task-based training beats long classes. Send change notes on a fixed day so people look for them.

–  RACI for data changes; gated promotions (dev/test/prod). Clarify who approves new fields/measures and who runs tests. Enforce gates even when timelines are tight.

Phasing

V1 (must-have), v1.1 (quick win), v2 (scale). Publish what’s in each and don’t blur the lines. Use v1 to prove value, v1.1 to close gaps, and v2 to scale safely.

Table that Shows Failure Symptom > Diagnostic > Fix

FAQ

Q1: What really causes planning projects to fail?

Three things: bad data and fuzzy definitions; siloed process with weak change management; tool-first scope with weak requirements.

Q2:How do we align finance, sales, and operations on “one number”?

Publish a KPI dictionary, conform master data, and put reconciliation rules into the model and the scope. Don’t wait for UAT to argue definitions.

Q3: What should be in a planning scope?

The decisions you will improve, the exact KPIs and formulas, acceptance tests (reconciliation, refresh SLA, performance), and a phased plan with promotion gates.

Q4: Why move to rolling forecasts?

Because markets change faster than annual budgets. Integrated Business Planning (IBP) focuses on one cross-functional, rolling cadence, so you respond sooner and with one plan.

Q5: How do you measure if a planning project is successful?

Track both process and business outcomes: cycle time for forecasts, rework rate on calculations, reconciliation time to reach “one number,” adoption by function, and benefits realized (accuracy, time saved, margin improvements). Success isn’t delivering a dashboard, it’s proving faster, trusted decisions.

Q6: Why do teams fall back to Excel even after new tools are rolled out?

Because Excel is familiar, flexible, and always available. If definitions aren’t aligned, training is weak, or governance is too heavy, users retreat to what they know. Adoption comes from trust, usability, and integration into daily workflows, not from banning spreadsheets.

Q7: What’s the role of a project manager in planning projects?

The PM is not just a coordinator. They scope outcomes, enforce requirements discipline, keep sponsors active, and balance technical delivery with change management. In planning projects, a strong PM is the bridge between data teams, business users, and leadership, without that, even good tools won’t stick.

Bottom Line

Planning projects don’t fail because a tool can’t do driver-based planning or scenarios. They fail when data and definitions are loose, adoption is an afterthought, and scope chases features instead of outcomes. Get those three right, and the platform you choose will deliver. Ignore them, and you’ll be back in Excel with less trust than before.

At Centida, we’ve seen these challenges across industries and know they can only be solved together – with clients, not for them.

Our approach is to co-design the process, bring structure where it’s missing, and stay involved long enough to make sure solutions actually stick. If you’re looking for a long-term partner to make planning more resilient and practical, that’s the work we care most about.

MIT recently published a striking study: 95% of enterprise AI projects fail to deliver ROI.

At first glance, this makes AI look like a high-risk bet for finance leaders. But dig deeper and the issue is less about the technology itself, and more about how you define ROI.

Most organizations still measure AI the way they would an IT project. They use cost savings and headcount reduction. That’s a narrow lens.

Modern finance is about building resilience, speed, and better decision-making. If ROI is limited to “did we save FTE hours?”, the real value of AI in finance will always look invisible.

Why CFOs Need to Rethink ROI

This narrow view is creating two problems:

  1. Missed strategic impact. AI can improve forecast accuracy, shorten close cycles, and reduce working-capital drag. These are balance sheet and P&L levers, not IT metrics.

  2. Credibility gap. When AI pilots only track token savings, boards and executives lose trust in the technology’s ability to drive business outcomes.

The paradox, as MIT points out, is that while most AI projects fail, the small fraction that succeed generate outsized value. Because they treat ROI differently, less like a software pilot, more like a financial system upgrade.

Measuring ROI Through the CFO Lens

So, what should CFOs actually measure? The answer is to align ROI with the financial system’s performance. Think beyond tools and features, and look at the metrics that move enterprise value.

Operational impact:

–  Days to close: Has AI reduced the reporting cycle by 25–30%?

–  Forecast accuracy: Are MAPE/WAPE scores improving quarter by quarter?

–  Variance explanation: Can the system surface drivers in hours instead of days?

Strategic impact:

–  Scenario velocity: How quickly can the team build and test three alternative cases?

–  Capital allocation: Are IRR assumptions more accurate against realized outcomes?

–  Pricing and mix optimization: Has AI flagged margin opportunities earlier than traditional analysis?

Risk and control:

–  Exception detection: Is AI catching anomalies faster than manual controls?

–  Audit trail: Can outputs be explained and trusted by auditors?

–  Model drift monitoring: Is governance in place to ensure reliability over time?

Adoption:

–  User engagement: How many finance professionals actively rely on AI outputs?

–  Assisted actions: How many insights are acted upon, not just generated?

These measures speak the language of the boardroom. They also provide a balanced scorecard that captures both efficiency and strategic value.

Time Horizons Matter

ROI from AI won’t appear overnight. CFOs should set expectations in phases:

–  Near term (0–90 days): The first tangible benefits usually show up in process efficiency: faster close cycles, automated variance commentary, and better adoption of dashboards by business users. These early wins prove that AI can save time and reduce manual effort.

–  Mid term (3–9 months): Once the models stabilize, you’ll see sharper improvements in forecast accuracy and anomaly detection. This stage is where finance starts to trust AI outputs enough to use them in decision-making.

–  Long term (9–18 months): Over time, AI becomes embedded in core processes. The real financial impact shows up in better margins, improved ROIC, and stronger working-capital efficiency. These outcomes position AI not just as a tactical tool but as a strategic enabler.

Data Readiness Is Half the Battle

It’s worth repeating what both MIT and McKinsey stress: AI without data discipline is useless.

–  Standardize core definitions (ARR vs. NRR, SKU, customer, plant): Without alignment on how metrics are defined, AI models will produce inconsistent outputs that undermine trust across business units. A shared dictionary ensures comparability and accuracy.

–  Establish data quality SLAs: Clear standards for timeliness, completeness, and accuracy prevent downstream errors. SLAs keep both IT and finance accountable for the integrity of data feeding AI models.

–  Break down silos between FP&A, accounting, treasury, and operations: AI only adds value if it can connect the dots across the enterprise. Eliminating silos creates a single version of the truth and allows scenario planning that reflects real business conditions.

Treat AI Like an Engineering System

The lesson from the 5% who succeed is simple: don’t treat AI like a shiny demo. Treat it like software engineering. Build specifications and set guardrails. Run tests and establish feedback loops. And most importantly, measure ROI in terms of financial outcomes, not IT savings.

When CFOs apply this discipline, AI moves from experiment to trusted infrastructure.

Final Thoughts

AI in finance is about building systems that compound value over time. The right ROI lens transforms AI from a risky bet into a competitive advantage.

For finance leaders who want to accelerate this journey, external partners can help design ROI scorecards, align baselines, and implement governance frameworks.

At Centida, we support companies in structuring AI initiatives with measurable outcomes, ensuring investments drive both short-term wins and long-term enterprise value.

Self-service analytics has been sold for years as the holy grail: dashboards, filters, and drilldowns at everyone’s fingertips. In reality, most companies still struggle to deliver data tools that people actually use to make better decisions.

As Seattle Data Guy points out in his article “The Inconvenient Truths of Self-Service Analytics”, the problem is a combination of definition, design, and delivery. The good news? There’s a better way.

Leading analytics consultants take a strategic, start-to-finish approach: defining the problem, building governed data pipelines, designing role-specific outputs, ensuring adoption, and embedding industry context at every step.

Below, we break down the key pillars that separate failed “self-service” experiments from high-impact analytics initiatives.

Define the Business Questions Before You Build

If you don’t know exactly which decisions your analytics is supposed to support, you’ll end up with endless dashboards no one uses.

Direct answer: Start with the decision, not the data.

Why this matters: Without a clear purpose, metrics multiply without direction. Business users waste time digging for numbers instead of acting on them.

Example: A supply chain team asking “What’s our current on-time delivery rate?” might get a static report. But if they instead ask “How can we reduce delivery delays by 15% in the next quarter?”, the analytics team can build a model that directly supports that goal.

Best practice:

–  Run discovery workshops with stakeholders.

–  Translate goals into measurable KPIs before touching a BI tool.

Build Governed, High-Quality Data Pipelines

Even the most visually appealing dashboard fails if the underlying data is untrustworthy.

Direct answer: Governance is the foundation of reliable analytics.

Why this matters: Inconsistent definitions erode trust (“Why does this dashboard say revenue is $50M and that one says $53M?”). Without automated quality checks, errors slip through unnoticed.

Example: A finance team discovers a 5% variance in revenue numbers between two reports because one uses billed revenue and the other booked revenue with no clear documentation explaining the difference.

Best practice:

–  Assign data ownership.

–  Standardize KPI definitions.

–  Implement automated data quality monitoring.

Design Decision-Ready Outputs for Each Role

Different roles require different views of the data. Trying to serve them all with one dashboard is a recipe for frustration.

Direct answer: Tailor outputs to the person making the decision.

Why this matters: Executives need fast, high-level summaries. Operational teams need granular, real-time details.

Example: A CEO might get a weekly one-page snapshot highlighting key risks and trends, while the logistics manager gets a live map of delayed shipments with the ability to trigger follow-up actions.

Best practice:

–  Create user personas.

–  Map decision cycles.

–  Design visualizations with the end decision in mind.

Make Adoption and Training Part of the Delivery

Analytics isn’t valuable if it’s not used.

Direct answer: Treat adoption as a deliverable, not an afterthought.

Why this matters: Even the best dashboard will fail if users don’t know how to navigate it. Change management is often overlooked in technical rollouts.

Example: A retail company increased BI adoption by 40% after embedding “how to read this” tooltips in dashboards and running 20-minute role-specific training sessions.

Best practice:

–  Deliver bite-sized, role-based training.

–  Build in user feedback loops.

–  Keep documentation simple and accessible.

Prioritize Industry Context Over Generic Tooling

Great analytics teams know that domain expertise is worth more than flashy charts.

Direct answer: Context drives relevance and speed to insight.

Why this matters: Generic KPIs can hide operational realities. Domain-specific metrics and models improve accuracy and decision confidence.

Example: In manufacturing, metrics like machine uptime or maintenance backlog are more valuable than generic productivity stats.

Best practice:

–  Hire or involve domain experts early.

–  Choose tools or templates tailored to your industry.

–  Translate technical outputs into the operational language of the business.

Use External Expertise to Accelerate and Strengthen Delivery

When your team is already stretched, outside help can make the difference between a project that stalls and one that scales.

Direct answer: Consultants bring speed, perspective, and playbooks.

Why this matters: They’ve seen patterns across multiple companies and industries. They can set up governance, processes, and training that you can later own internally.

Example: A logistics company cut its BI project timeline in half by bringing in a consultant who had implemented similar shipment tracking models elsewhere.

Best practice:

–  Use external partners for complex phases.

–  Include knowledge transfer in the scope.

–  Transition ownership once systems are stable.

Turn “Self-Service” Into “Action-Service”

Dashboards are a means, not the end. The real value is in decisions and actions.

Direct answer: Design analytics to guide and trigger action.

Why this matters: Information without action is wasted potential. Closing the loop between insight and execution drives measurable impact.

Example: An inventory dashboard that detects low stock and automatically recommends reorder quantities creates an immediate link from data to business outcome.

Best practice:

–  Include next-step recommendations in dashboards.

–  Automate routine actions where possible.

–  Measure adoption and downstream results.

Final Word

Self-service analytics has been poorly defined, poorly implemented, and poorly supported in many organizations.

The companies that succeed are the ones that treat analytics as a business capability, not a technology feature – from defining the right questions to embedding the right actions.

And if you need help building that capability, firms like Centida provide end-to-end analytics services: strategy definition, architecture, governance, design, training, and adoption.