Match the role to the decisions, authority and operating work the business needs.
Opening Thesis
Before approving the next finance hire, identify the decisions that person must help the business make.
Imagine hiring a CFO to improve cash flow while customer credit decisions remain with sales, inventory commitments with operations, and payment priorities with the owner. Each arrangement may be reasonable. But without a shared way to resolve tradeoffs, the new executive inherits responsibility for cash without a workable route to influence it.
A stronger appointment begins by defining what the role owns, what it can change, and what support it needs.
Business Situation
The September AICPA/CIMA economic outlook survey reported stronger hiring intentions alongside softer projected increases in technology, capital and training spending.1 For leadership teams weighing those commitments, an additional finance position needs a clear purpose.
“We need better financial leadership” leaves too much unresolved. The business may need reliable accounts, stronger commercial judgment, daily executive capacity, or continuity during a transition. Those needs can overlap, but they require different mandates.
Executive Perspective
An overdue close may require better processes and accounting capacity. Timely financial statements that leave management unable to evaluate a pricing decision may reveal a gap in analysis or judgment. A sound recommendation that repeatedly stalls may point to unclear authority or insufficient operating capacity.
The diagnosis matters. Hiring someone more senior will not, by itself, repair every broken handoff or give finance authority over decisions the CEO has left unresolved.
A workable finance mandate connects responsibility with the information, capacity and authority needed to act. Leadership should establish those conditions before holding the new hire accountable for results.
Decision Framework: The Finance Mandate Review
Start with three consequential decisions that were delayed, revisited or made with inadequate information during the last quarter. A customer credit exception, an inventory purchase or a hiring commitment can reveal more than a generic job description.
1. Locate the constraint.
For each decision, record its deadline and where progress stopped: information, analysis, recommendation, approval or execution. Ask the people involved what prevented the next step. This separates a shortage of staff from a process or leadership problem.
2. Match the capability to the work.
A reporting backlog may call for controller capacity and process repair. Recurring choices across cash, margin and growth may require CFO judgment. A vacant executive seat may require interim leadership with explicit authority. The frequency and continuity of the work should help determine whether support is temporary, fractional or permanent.
3. Make authority usable.
Specify who recommends, who approves and who executes. Establish access to operating information and a route for resolving disagreements. A CFO can coordinate the cash plan while sales and operations retain their responsibilities; the CEO must clarify how competing priorities get resolved.
4. Define a 90-day acceptance test.
Describe what management should be able to decide more reliably by then. Name the evidence, accountable people and review date. Adjust the scope to the starting condition; a reporting rebuild and a leadership transition will have different milestones.
For the hypothetical cash-flow appointment, the mandate might be to establish a dependable weekly cash outlook, connect material credit and inventory commitments to it, and bring unresolved tradeoffs to a scheduled CEO decision. Success would include evidence that commitments were reviewed before approval and resulting actions were completed.
Practical Implications
The CEO or owner should settle the mandate before approving the search. The board should test whether expectations are achievable with the authority and resources provided. Recruiters can then evaluate candidates against the work that actually needs doing.
Existing finance leaders should participate in that diagnosis. They may already have the required judgment but lack staff, information or access to the decisions they are expected to influence.
The review may justify a hire. It may also reveal that the immediate improvement belongs in the management process.
Executive Takeaway
Before opening the search, complete this sentence: “Within 90 days, this role should enable us to decide ___, using ___, with authority to ___.”
If leadership cannot agree on the answer, that disagreement is part of the work to resolve before making the appointment.
About CFO’s Office Executive Perspectives
Executive Perspectives is the thought leadership publication of CFO’s Office.
Each perspective examines an enduring executive leadership principle through the lens of current business conditions, helping CEOs, boards, investors, family offices, private equity firms, and executive leaders improve decision quality.
Rather than focusing on technical finance alone, Executive Perspectives explores how financial leadership strengthens enterprise value through Financial Clarity, Executive Confidence, and Disciplined Execution.
About the Author
Kyle Casey, CPA, writes CFO’s Office Executive Perspectives on financial leadership, governance and executive decision quality.
His leadership philosophy is grounded in a simple principle: Finance exists to improve decision quality.
Why leaders need ownership, monitoring, and escalation discipline
A risk register does not reduce risk.
It can identify risk. It can organize risk. It can help leadership have a better conversation.
But risk is not reduced because it appears in a table.
Risk is reduced when leadership changes what the organization monitors, who owns the response, when escalation occurs, and what decisions are made before the risk becomes loss.
That distinction matters.
Companies are not facing one clean risk at a time. Inflation, consumer demand, credit conditions, labor signals, supply-chain pressure, technology adoption, customer price sensitivity, and geopolitical uncertainty are moving together.
A static list of risks can create the appearance of control without improving the organization’s ability to respond.
The question is not whether leadership has documented the risk.
The question is whether the risk has changed the operating discipline of the company.
Business Situation
The current environment is sending mixed signals.
The Federal Reserve’s July 2026 meeting minutes noted that higher oil prices, Middle East conflict, and supply-chain uncertainty continue to affect the inflation outlook. Many participants also warned that continued inflation above 2% could influence inflation expectations and wage- and price-setting behavior. [1]
Inflation has moderated from prior peaks but remains relevant. The July 2026 CPI report showed prices up 3.4% over the prior 12 months, with energy prices up 14.7% over the same period. [2]
Demand is not entirely clear either. July retail and food services sales declined 0.6% from June, but remained 5.0% above July 2025. [3]
Labor data also became more complicated. July nonfarm payroll employment declined by 23,000, unemployment was 4.1%, and May and June employment were revised down by a combined 103,000 jobs. [4]
Credit conditions require nuance. The July 2026 Senior Loan Officer Opinion Survey reported basically unchanged standards for commercial and industrial loans and stronger demand from large and middle-market firms. At the same time, standards for loans to non-depository financial institutions remained at the tighter end of their historical ranges. [5]
The implication is straightforward.
Risk management cannot remain a documentation exercise when the signals affecting cost, demand, labor, credit, liquidity, and technology are moving in different directions.
Executive Perspective
Risk registers often fail because they stop at identification.
A company may list inflation risk, labor risk, credit risk, supply-chain risk, customer-concentration risk, technology risk, and liquidity risk. The document may be accurate. It may even be reviewed quarterly.
But if those risks do not connect to ownership, leading indicators, thresholds, mitigation options, escalation paths, and decision rights, the register has limited value.
The risk has been named.
It has not been managed.
A useful risk system should help leadership answer practical questions:
Who owns the risk? What signal tells us the risk is increasing? What information would confirm or challenge our view? What decision becomes necessary if the risk crosses a threshold? What options should we preserve before the risk becomes urgent? When should the Board, lender, owner, investor, or operating team be informed?
Without those answers, risk reporting can become ceremonial.
Leadership knows what might go wrong, but has not decided what it will do when conditions begin moving in that direction.
That is not resilience.
It is documentation.
Decision Framework: The Risk-to-Action Map
Leadership should convert material risks into an operating map.
Risk Definition What exactly could happen, and what business outcome would it affect?
Ownership Who owns monitoring, analysis, recommendation, and response?
Leading Indicator What signal would show the risk is increasing before the financial impact becomes obvious?
Decision Threshold What level of movement requires leadership action?
Financial Exposure What is the potential impact on cash, margin, working capital, covenants, capital needs, or enterprise value?
Mitigation Options What can be done now to reduce probability, impact, or response time?
Escalation Path When does this move from management issue to CEO, Board, lender, owner, or investor issue?
Review Cadence How often should the risk be reviewed, and what evidence should be updated?
This framework does not require bureaucracy.
It requires discipline.
Not every risk deserves the same level of monitoring. A low-probability, low-impact risk may only require periodic review. A risk that could affect liquidity, covenant compliance, customer trust, employee safety, regulatory exposure, or enterprise value deserves more structure.
The level of governance should match the consequence.
Practical Implications
For CEOs, risk management should clarify where the company still has options and where delay will narrow them.
For CFOs, the risk register should connect directly to forecasting, liquidity, working capital, debt capacity, margin analysis, reporting cadence, and Board communication.
For Boards, the question is not only whether management has identified risks. The better question is whether management has assigned owners, thresholds, mitigation plans, and escalation standards.
For lenders, early communication is easier when management can show how risk is being monitored before liquidity pressure becomes visible.
For private equity firms and family offices, operating-company oversight should include risk-to-action reporting. The issue is not simply what risks exist. It is whether management has the discipline to recognize and respond before value is impaired.
Executive Takeaway
Risk registers do not reduce risk.
Leadership does.
A risk register becomes useful only when it changes what the organization monitors, who owns the response, what threshold requires action, and what decisions leadership is prepared to make.
In stable conditions, a documented list may feel sufficient.
In uncertain conditions, it is not.
The stronger question is not: Do we have a risk register?
It is: Has each material risk been converted into ownership, evidence, escalation, and action?
That is where risk management becomes executive discipline.
Sources
[1] Federal Reserve, Minutes of the Federal Open Market Committee, July 28–29, 2026. [2] U.S. Bureau of Labor Statistics, Consumer Price Index, July 2026. [3] U.S. Census Bureau, Advance Monthly Retail and Food Services Sales, July 2026. [4] U.S. Bureau of Labor Statistics, Employment Situation, July 2026. [5] Federal Reserve, Senior Loan Officer Opinion Survey, July 2026.
Why leaders should define pricing, cost, and liquidity actions before margin compression becomes normalized
Margin pressure rarely becomes a crisis all at once.
It usually begins as a series of small compromises.
A supplier increase is absorbed. A freight surcharge is tolerated. A pricing decision is delayed. A customer exception is approved. A labor inefficiency is explained away. A product remains active because leadership has not agreed what threshold requires action.
Individually, each decision may seem manageable.
Together, they can turn margin pressure into a structural problem.
The issue is not whether costs are rising. Costs rise in many cycles. The issue is whether leadership has defined when margin movement requires a pricing decision, cost action, product-mix review, working-capital response, or liquidity escalation.
Margin pressure does not only need analysis.
It needs an escalation plan.
Business Situation
Cost pressure remains visible across the operating environment.
The Q2 2026 CFO Survey reported that financial decision-makers’ outlooks worsened amid concern over rising costs and prices. CFOs added 1.1 percentage points to their 2026 unit-cost and price-growth projections, and inflation returned to the top of CFOs’ concerns. The same survey found that two-thirds of firms affected by higher oil prices reported increased costs, while one-third reported increased prices. [1]
The Federal Reserve’s July 2026 Beige Book showed a similar pattern. Prices increased moderately overall, but non-labor input costs rose across services, construction, and manufacturing. The report also noted customer price sensitivity and margin compression where selling prices rose less than input costs. [2]
Credit conditions add another layer. The April 2026 Senior Loan Officer Opinion Survey reported tighter standards for commercial and industrial loans, higher premiums on riskier loans, tighter covenants, and tighter collateralization requirements. [3]
The implication is straightforward.
Leadership cannot treat margin pressure as a routine variance if credit, liquidity, customer behavior, and operating costs are moving together.
Executive Perspective
Many companies analyze margin pressure too late.
They see the variance. They discuss the cause. They explain the quarter. They wait for the next reporting cycle.
But margin pressure is not just a financial reporting issue. It is an operating signal.
It may reveal pricing weakness, purchasing exposure, customer concentration, product complexity, labor inefficiency, inventory problems, or a business model that has become less profitable than leadership assumes.
The danger is normalization.
When small margin erosion continues for several periods, organizations begin to explain it rather than confront it. The variance becomes familiar. The explanation becomes accepted. The underlying problem becomes part of the operating model.
That is how margin pressure becomes structural.
Finance should not merely report that gross margin declined. Finance should help leadership understand what changed, whether the change is temporary or structural, which decisions are available, what risks delay creates, and what action threshold has been crossed.
The leadership question is not simply: Why did margin decline?
The better question is: At what point does margin pressure require action?
Decision Framework: The Margin Escalation Plan
Leadership should define a margin escalation plan before the organization is under pressure to act.
What margin matters? Gross margin, contribution margin, EBITDA margin, product margin, customer margin, location margin, and service-line margin may tell different stories. Leadership should define which measure drives the decision.
What threshold requires attention? A one-period variance may require explanation. A repeated variance may require action. Leadership should define the level or duration of margin movement that triggers review.
Who owns the response? Margin pressure is rarely owned by Finance alone. Pricing, purchasing, operations, sales, product, supply chain, and executive leadership may all have roles.
What action is available? Potential responses may include pricing changes, surcharge adjustments, vendor renegotiation, product rationalization, customer profitability review, labor scheduling changes, inventory discipline, freight analysis, or cost redesign.
What is the customer impact? Not every cost increase can be passed through immediately. Leadership should understand customer sensitivity, contract terms, competitive position, service expectations, and the risk of delayed pricing action.
What is the liquidity impact? Margin compression can become a cash problem through slower collections, higher inventory, tighter vendor terms, or reduced operating cash flow. Margin review should connect to cash forecasting and working-capital visibility.
When does the issue escalate? Leadership should define when margin pressure becomes a CEO issue, Board issue, lender communication issue, or capital-allocation issue.
This framework is not intended to create bureaucracy.
It is intended to prevent drift.
Practical Implications
For CEOs, margin pressure should be treated as an early warning signal. The question is whether the business is still earning the economics required to support its strategy.
For CFOs, margin analysis should connect pricing, cost structure, cash flow, working capital, lender confidence, and operating discipline.
For Boards, oversight should focus on thresholds and management response. Directors should know when margin movement requires pricing action, cost reduction, capital restraint, or a revised forecast.
For lenders, margin pressure matters because it can precede liquidity pressure.
For private equity firms and family offices, margin pressure should be reviewed at the operating-company level, not only the consolidated level.
Executive Takeaway
Margin pressure needs an escalation plan.
A variance report can explain what happened. An escalation plan defines what leadership will do next.
When costs rise faster than prices, the organization has a decision to make. It can absorb the pressure, pass it through, offset it, redesign around it, or accept lower profitability. Each choice may be appropriate in the right circumstance.
But delay is also a choice.
Leadership should decide in advance what margin movement requires attention, who owns the response, what actions are available, and when the issue escalates.
The strongest companies do not wait until margin pressure becomes a liquidity problem.
They define the threshold before the pressure becomes normal.
Sources
[1] Federal Reserve Bank of Richmond, Duke University, and Federal Reserve Bank of Atlanta, The CFO Survey — Q2 2026. [2] Federal Reserve, Beige Book — July 2026. [3] Federal Reserve, Senior Loan Officer Opinion Survey — April 2026.
Why leaders need decision triggers before uncertainty becomes a liquidity problem
A base-case forecast helps leadership organize expectations.
It does not prepare the company to act when expectations change.
That distinction matters when costs, pricing, demand, labor, financing, supply chains, and technology investment are moving at the same time.
The risk is not that the forecast will be wrong. The risk is that leadership will wait too long to decide what the forecast being wrong requires them to do.
The base case is an assumption.
The plan is what leadership will do when the assumption changes.
Business Situation
Financial leaders are operating in a mixed environment.
The Federal Reserve’s July 2026 Beige Book reported slight to moderate economic growth across most districts, while also noting higher input costs, supply-chain issues, customer price sensitivity, and margin pressure where selling prices rose less than input costs. [1]
The Q2 2026 CFO Survey reported that financial decision-makers’ outlooks worsened amid concern over rising costs and prices. CFOs added 1.1 percentage points to their 2026 unit-cost and price-growth projections, and inflation returned as their top concern. The same survey found that roughly two-thirds of firms affected by higher oil prices reported higher unit costs, while only about one-third reported raising prices. [2]
Credit conditions also deserve attention. The Federal Reserve’s April 2026 Senior Loan Officer Opinion Survey reported tighter standards for commercial and industrial loans, higher premiums on riskier loans, tighter covenants, and tighter collateralization requirements. [3]
The implication is straightforward.
Leadership cannot rely on one forecast and one annual budget cycle to manage a multi-variable environment.
Executive Perspective
The base case is often treated as the plan because it feels disciplined.
It has numbers. It has assumptions. It has a revenue line, a margin expectation, a hiring plan, a capital budget, and a cash forecast.
But the base case is not the plan.
The plan is the set of decisions leadership is prepared to make when reality begins to separate from the base case.
That separation often begins quietly.
Gross margin compresses before cash weakens.
Receivables stretch before liquidity tightens.
Inventory builds before working capital becomes obvious.
A lender asks more questions before terms change.
A technology initiative exceeds its expected cost before the organization knows whether it is creating value.
In each case, the problem is not only the variance.
The problem is the absence of a pre-defined decision.
Scenario planning should therefore answer a different question.
Not: What do we think will happen?
But: What will we do if the conditions that matter begin to change?
That is the leadership value of scenario planning.
It converts uncertainty into a disciplined decision process.
Decision Framework: The Scenario Trigger Matrix
Leadership should convert major assumptions into decision triggers:
Liquidity At what cash level does leadership change spending, borrowing, vendor strategy, or owner communication?
Gross Margin At what margin movement does pricing, sourcing, product mix, or cost action become mandatory?
Working Capital What receivable, payable, or inventory movement signals that cash conversion is weakening?
Demand What order, backlog, traffic, or customer signal changes the revenue outlook?
Credit Availability What lender, covenant, collateral, or rate signal changes the financing plan?
Supply Chain What delay, shortage, cost increase, or supplier-risk signal requires action?
Labor Capacity What staffing, productivity, turnover, or overtime signal shows execution risk is rising?
Capital Spending What change in cost, timing, return, liquidity, or strategic priority changes the capital plan?
The matrix is not intended to make leadership mechanical.
It is intended to make leadership prepared.
When conditions change, the executive team should not need to debate from scratch what the signal means, who owns the response, or what information is required.
Those decisions should already be framed.
Practical Implications
For CEOs, the base case should be treated as a management assumption, not a commitment to ignore changing facts.
For CFOs, scenario planning should connect the forecast to liquidity, working capital, margin, covenant cushion, pricing, capital spending, and operating capacity.
For Boards, oversight should focus on triggers, not just forecasts. A Board should know which conditions require management to return with a revised plan, financing recommendation, pricing action, capital-spending decision, or risk escalation.
For lenders, early communication matters. Companies that understand their triggers can usually communicate risk more credibly than companies that wait until liquidity pressure is already visible.
For private equity firms and family offices, operating-company oversight should include trigger-based reporting. Resilience is easier to evaluate when management can explain not only what it expects, but what it will do if expectations change.
Executive Takeaway
The base case is not a plan.
It is the starting point for judgment.
Resilient organizations do not pretend that uncertainty can be forecast away. They identify the assumptions that matter, monitor the signals that could change those assumptions, and decide in advance what conditions require leadership action.
A forecast tells leadership what the company expects.
A scenario trigger tells leadership when the company must act.
By the time liquidity, margins, covenants, or execution capacity are visibly under pressure, the organization may already have fewer options.
The stronger question is not whether the forecast is right.
It is whether leadership has already decided what to do when the forecast is wrong.
Sources
[1] Federal Reserve, Beige Book, July 2026. [2] Duke University / Federal Reserve Banks of Richmond and Atlanta, The CFO Survey, Q2 2026. [3] Federal Reserve, Senior Loan Officer Opinion Survey, April 2026.
Why CFOs need consumption governance before AI scales
AI should not be governed like a traditional software subscription.
That does not mean companies should avoid AI. It means leadership should stop treating AI as a simple technology line item.
Traditional software budgets are usually built around licenses, seats, implementation costs, support fees, and renewal dates. AI is different. As usage expands, costs can move with prompts, tokens, agents, integrations, infrastructure, review time, compliance requirements, training, and the number of workflows where AI becomes embedded.
That changes the CFO’s role.
The question is no longer only: Should we invest in AI?
The better question is: Can we see, govern, and measure AI usage before it becomes a permanent operating cost?
Business Situation
AI adoption is no longer theoretical inside the finance function.
Deloitte’s Q4 2025 CFO Signals survey found that 87% of CFOs expect AI to be extremely or very important to their finance department’s operations in 2026. More than half said integrating AI agents into finance would be a transformation priority. [1]
KPMG’s 2026 Global AI in Finance research found that active AI use across finance more than doubled since 2024, increasing from 30% to 75%. The same research found that assurance-ready organizations reported materially stronger outcomes, including higher rates of error reduction and greater confidence in scaling AI. [2]
That distinction matters.
AI can improve speed, analysis, forecasting, decision support, and execution. But when usage expands faster than visibility, governance, and measurement, the organization may create a new cost structure before it understands the economics.
Recent reporting also shows that many companies still struggle to understand the full cost of AI, especially where usage-based pricing, token consumption, model selection, infrastructure, and human oversight make spending harder to forecast. [3]
That is not only a budgeting problem.
It is a governance problem.
Executive Perspective
CFOs should not become the department of “no” on AI.
But they also should not approve AI investment based on enthusiasm, vendor promises, or isolated pilot success.
The CFO’s responsibility is to convert AI ambition into decision-quality economics.
That requires more than asking how much the vendor charges. The invoice is only the visible cost. The real cost of AI may include usage growth, data preparation, integration work, human review, process redesign, controls, audit evidence, security monitoring, training, governance, and exception handling.
A company that budgets AI as a fixed software expense may underestimate the cost.
A company that budgets only the vendor invoice may underestimate the operating burden.
A company that approves AI without ownership may create a technology-enabled accountability gap.
AI becomes valuable when it improves a real decision, process, forecast, control, customer experience, or operating outcome. It becomes dangerous when it creates speed without clarity, output without ownership, or automation without evidence.
The issue is not whether AI spending should increase.
The issue is whether AI spending is connected to a governed operating model.
Decision Framework: AI Consumption Governance
Before scaling AI, leadership should be able to answer eight questions:
Use Case What decision, process, or workflow is AI intended to improve?
Ownership Who owns the business outcome, the model output, the exception process, and the budget?
Unit Economics What is the cost per task, transaction, report, forecast, customer interaction, or workflow?
Budget Behavior Does the cost behave like a fixed license, variable consumption, or hybrid operating cost?
Data Foundation Is the underlying data reliable, integrated, and current enough for the use case?
Control Environment What approvals, audit trails, human review, and escalation procedures are required?
Measurement What result will determine whether the use case is working?
Exit Criteria What result would cause leadership to stop, redesign, limit, or replace the use case?
This framework is not meant to slow innovation.
It is meant to prevent unmanaged acceleration.
A low-risk productivity tool does not need the same governance structure as an AI-enabled forecast, capital allocation model, customer-credit decision, procurement recommendation, or financial reporting workflow.
Governance should be proportionate to consequence.
But consequence must be defined before scale.
Practical Implications
For CEOs, the lesson is that AI ambition needs operating discipline. A good AI strategy should improve execution, not create another layer of hidden complexity.
For CFOs, the priority is visibility. AI should have a consumption model, a budget range, a business owner, a review cadence, and a measurement standard before it becomes embedded across the organization.
For Boards, the oversight question should move beyond whether management is “using AI.” The better question is whether management can explain where AI is used, what it costs, what risk it creates, what controls exist, and what value it is producing.
For private equity firms and family offices, AI should be evaluated as part of operating-company maturity. A portfolio company with fragmented systems, weak controls, unclear process ownership, and unreliable data may not be ready to scale AI into consequential workflows.
For finance teams, the practical challenge is capability. The future finance function will need people who can interpret outputs, evaluate data quality, understand process economics, and communicate what the business can responsibly rely upon.
Executive Takeaway
AI spending is not just a technology budget.
It is a test of operating discipline.
The companies that benefit most from AI will not necessarily be the ones that spend the most or move first. They will be the ones that know where AI is used, what it costs, who owns it, how it is controlled, and whether it improves decisions that matter.
Responsible AI investment does not require organizational perfection.
It does require visibility, ownership, measurement, and governance proportionate to the consequence of the use case.
Before AI scales, leadership should be able to answer a simple question:
Are we funding a governed operating capability — or are we allowing consumption to grow faster than accountability?
About CFO’s Office Executive Perspectives
CFO’s Office Executive Perspectives are written for CEOs, boards, owners, investors, family offices, private equity firms, and executive leaders facing consequential financial and operating decisions.
Each perspective is designed to improve decision quality, strengthen executive confidence, and support disciplined execution.
About the Author
Kyle A. Casey, CPA, is the Founder of CFO’s Office, LLC. He is an operating executive, former public company CFO, and former Interim CEO who helps organizations improve financial clarity, executive confidence, and disciplined execution during periods of complexity, transformation, and strategic change.
Sources
[1] Deloitte, “Technology Transformation Emerges as a Top Priority for CFOs in 2026: Deloitte Q4 2025 CFO Signals Survey,” January 13, 2026. https://www.deloitte.com/us/en/about/press-room/deloitte-q4-2025-cfo-signals-survey.html
[2] KPMG, “AI adoption in finance doubles, but assurance readiness determines who wins, KPMG finds,” May 11, 2026. https://kpmg.com/xx/en/media/press-releases/2026/05/ai-adoption-in-finance-doubles-but-assurance-readiness-determines-who-wins.html
[3] The Wall Street Journal, “Corporate America Has Suddenly Decided to Stop Blowing Money on AI,” July 2026. https://www.wsj.com/business/china-us-ai-model-costs-53a12e96
[4] National Institute of Standards and Technology, “Artificial Intelligence Risk Management Framework (AI RMF 1.0),” January 26, 2023. https://www.nist.gov/publications/artificial-intelligence-risk-management-framework-ai-rmf-10
How Leaders Should Sequence Systems Modernization and Automation
The executive decision is not whether the organization should pursue ERP or AI as competing technologies. It is how leadership should sequence process redesign, systems modernization, data governance, and automation.
Opening Thesis
Artificial intelligence will not repair unreliable transactions, fragmented systems, undefined processes, or unclear ownership.
Neither will ERP by itself.
The executive decision is not whether the organization should pursue ERP or AI as competing technologies. It is how leadership should sequence process redesign, systems modernization, data governance, and automation so that each investment strengthens the operating model rather than institutionalizing its weaknesses.
For some organizations, controlled AI experimentation can proceed now.
For others, the first responsible investment is repairing the system of record and the processes around it.
THE GOVERNING QUESTION The governing question is not simply: Where can the company use AI? It is: Is the operating foundation reliable enough for consequential automation to be trusted?
The Business Situation
Organizations are under pressure to adopt artificial intelligence.
Boards want to understand the opportunity. Employees are experimenting with new tools. Technology providers are embedding AI into enterprise applications and presenting automation as the next source of productivity.
Adoption is broad, but enterprise integration remains limited.
McKinsey’s 2025 global survey found that 88 percent of respondents’ organizations regularly used AI in at least one business function, while only approximately one-third had begun scaling AI across the enterprise.[1]
The distinction matters.
Using an AI tool is not the same as integrating AI into a reliable operating model.
For most organizations, AI remains primarily an automation and decision-support capability – not a substitute for executive judgment. It may prepare an analysis, recommend an action, classify information, route work, or automate part of a workflow.
Leadership remains responsible for setting objectives, evaluating tradeoffs, allocating capital, accepting risk, and deciding what should happen next.
The better executive question is therefore not where AI could be used.
It is which processes are reliable enough to automate – and which operating foundations should be repaired first.
Executive Perspective
AI scales the operating system to which it is connected.
If that system contains reliable transactions, standardized processes, clear ownership, disciplined controls, and governed data, automation can improve speed, consistency, and visibility.
If the organization depends on disconnected applications, uncontrolled spreadsheets, inconsistent master data, undocumented workarounds, outdated equipment, or employees manually reconciling conflicting reports, automation can scale those weaknesses as well.
In a manual process, an experienced employee may recognize that a transaction does not make sense. The employee may investigate an inconsistency, challenge an assumption, or correct information before it moves further through the organization.
An automated error can move through purchasing, inventory, billing, forecasting, customer service, and financial reporting before anyone recognizes that the underlying logic was wrong.
The organization receives an answer faster while becoming less certain that the answer is reliable.
The objective of systems modernization is not merely to prepare for AI.
It is to create an operating environment in which people and technology can perform reliably.
ERP Is Not the Cure
The phrase “ERP before AI” should not be interpreted as a universal requirement to replace the company’s enterprise platform.
ERP is not a cure for unclear ownership, poorly designed processes, weak controls, or inconsistent management decisions.
A new platform can institutionalize confusion as effectively as AI can accelerate it.
Before selecting or redesigning technology, leadership must understand:
What process the organization is trying to improve.
Who owns the process and its outcomes.
Which transactions and information are required.
What controls and approvals are necessary.
How exceptions should be identified and resolved.
How performance and reliability will be measured.
Whether the expected business value justifies the investment.
Some organizations need a new ERP.
Others need better configuration, integration, master-data governance, process discipline, training, equipment, or accountability.
The investment should follow the operating diagnosis – not precede it.
The Foundation People and AI Both Require
Reliable Information
Employees cannot make sound decisions when customer, product, inventory, pricing, supplier, or financial information is incomplete or contradictory.
Neither can AI.
Organizations need clear definitions, reliable systems of record, disciplined master data, and accountable ownership.
Without them, employees spend time locating information, reconciling reports, correcting transactions, and debating which version is accurate.
Automation may make the confusion move faster. It does not resolve the disagreement.
Standard Processes and Clear Accountability
Leadership should not automate a consequential process it has not adequately defined.
When departments perform the same activity differently, employees rely on memory, informal relationships, and individual judgment. Those workarounds make performance difficult to measure, improve, transfer, or scale.
Before automation, leadership should understand:
What initiates the process.
Who owns each decision.
What information is required.
Which approvals matter.
How exceptions are managed.
How completion is verified.
A technology team may configure an application.
It should not determine purchasing authority, pricing logic, accounting policy, inventory ownership, customer terms, or financial-reporting definitions.
Those decisions belong to operating and executive leadership.
Effective Infrastructure and Controls
Operating foundations are not purely digital.
Modern software cannot compensate for unreliable networks, outdated equipment, disconnected production systems, or physical workflows that prevent accurate and timely data collection.
Employees already absorb the cost through downtime, duplicate entry, weak interfaces, manual workarounds, and information that does not move with the work.
Automation also increases the importance of control.
As software recommends or initiates activity, leadership must understand:
What the system is permitted to do.
What requires human review.
How exceptions are identified.
How activity is monitored.
How incorrect actions can be interrupted or reversed.
Who remains accountable for the outcome.
NIST’s Artificial Intelligence Risk Management Framework emphasizes incorporating trustworthiness into the design, development, use, and evaluation of AI systems.[2]
Governance should be part of the operating design – not an addition after deployment.
The Modernization Decision Standard
Leadership should evaluate each proposed AI or automation use case against the condition of the underlying operating process.
Operating Condition
Leadership Decision
Reliable process, reliable data, and limited consequences
Proceed with controlled experimentation. Define the intended outcome, accountable owner, permitted use, review requirements, and success measures.
Reliable process but fragmented applications or interfaces
Improve integration and information flow before scaling automation. The process may be sound even when the architecture creates unnecessary friction.
Inconsistent process or unclear ownership
Redesign the process and assign accountability before making a material technology investment. Automation should not become a substitute for executive decisions leadership has avoided.
Unreliable transactions or master data
Stabilize the system of record before relying on AI for consequential analysis or action. Faster processing does not improve defective inputs.
ERP materially limits reporting, controls, scalability, or execution
Evaluate modernization or replacement based on business value, implementation risk, operating capacity, and total cost – not technology preference.
High-consequence use case without adequate controls or traceability
Do not automate consequential decisions until leadership can explain, monitor, challenge, and govern the resulting activity.
A Practical Modernization Sequence
Organizations do not need to delay every AI experiment until every system is perfect.
They do need to sequence modernization responsibly.
1
ASSESS
Understand how work actually moves through the organization. Identify fragmented systems, physical constraints, manual corrections, inconsistent data, unclear ownership, recurring failure points, and decisions that depend excessively on individual knowledge.
2
STABILIZE
Restore transaction discipline, reporting reliability, process ownership, infrastructure availability, control effectiveness, and management confidence. The organization should first be able to trust the basic activity upon which future automation will depend.
3
TRANSFORM
Redesign the process before automating it. Modernize ERP, equipment, integrations, workflows, and data structures where current conditions materially limit performance, visibility, control, or scalability.
4
OPTIMIZE
Apply AI and other automation to processes that are reliable, repeatable, controlled, and economically meaningful. Technology should improve a defined business outcome – not merely demonstrate technical capability.
5
SCALE
Expand only after the organization can monitor performance, cost, security, controls, employee impact, operating risk, and realized value. Scaling should be earned through evidence.
SEQUENCE BEFORE SCALE This sequence does not delay innovation. It prevents the organization from institutionalizing weak processes under the appearance of innovation.
Practical Implications
AI may provide the urgency required to fund systems improvements that leadership has deferred for years.
That may become one of its most important near-term contributions.
A company may justify an ERP upgrade, infrastructure investment, process redesign, equipment modernization, integration project, or data-governance initiative because leadership wants to become AI-ready.
The first return, however, may appear before consequential AI is deployed.
Employees may close the books faster.
Operating teams may locate information more easily.
The organization may reduce duplicate work.
Inventory accuracy may improve.
Customer issues may be resolved sooner.
Controls may become stronger.
Leadership may make decisions with greater confidence.
McKinsey’s research also suggests that the organizations reporting the greatest AI impact are distinguished by practices such as workflow redesign, senior-leadership ownership, defined human-validation processes, data and technology infrastructure, and embedding AI into business processes.[1]
The technology matters.
The operating model determines whether it creates value.
AI may be the catalyst. The operating improvement is the value.
Executive Takeaway
EXECUTIVE TAKEAWAY ERP before AI is not a rigid technology rule. It is a decision standard. Organizations should not automate consequential processes until leadership understands and trusts the transactions, data, infrastructure, controls, ownership, and operating logic beneath them. A new ERP cannot repair weak leadership or undefined processes. AI cannot make unreliable information trustworthy. Leadership must first create an operating foundation that employees can use, managers can govern, and executives can rely upon. Companies may modernize because they want to adopt AI. They should recognize that their employees needed the same operating foundation long before AI arrived.
About CFO’s Office Executive Perspectives
Executive Perspectives is the thought-leadership publication of CFO’s Office.
Each perspective examines an enduring executive leadership principle through the lens of current business conditions, helping CEOs, boards, investors, family offices, private equity firms, business owners, and executive leaders improve decision quality.
Rather than focusing on technical finance alone, Executive Perspectives examines how financial leadership strengthens enterprise value through Financial Clarity, Executive Confidence, and Disciplined Execution.
About the Author
Kyle Casey, CPA, is the Founder of CFO’s Office and an operating executive, former public-company Chief Financial Officer, and former Interim Chief Executive Officer. He advises CEOs, boards, business owners, family offices, and private equity stakeholders on executive financial leadership, enterprise transformation, governance, liquidity, finance modernization, and operational discipline.
His career spans public companies, consumer products, manufacturing, distribution, gaming, hospitality, entertainment, government, and regulated operating environments.
His leadership philosophy is grounded in a simple principle: Finance exists to improve decision quality. By building reliable operating foundations, disciplined processes, and decision-quality information, he helps organizations transform vision into sustainable results.
Sources and Notes
[1] McKinsey & Company, “The State of AI in 2025: Agents, Innovation, and Transformation,” November 5, 2025. The survey reports that 88 percent of respondents’ organizations regularly use AI in at least one business function, while approximately one-third have begun scaling AI across the enterprise. It also identifies workflow redesign, senior-leadership ownership, human-validation processes, data infrastructure, and technology infrastructure among the practices associated with stronger AI outcomes. Source
[2] National Institute of Standards and Technology, “Artificial Intelligence Risk Management Framework 1.0,” January 2023. NIST describes the voluntary framework as a means of incorporating trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems. NIST currently notes that AI RMF 1.0 is being revised. Source
A QUESTION WORTH ADVANCING If this perspective prompted a question about your organization, board, or portfolio company, I would welcome the conversation.
Why Leadership, Discipline, and Accountability Must Come Before Automation
Opening Thesis
Artificial intelligence will not repair a weak operating core.
It will expose and amplify it.
Organizations with reliable controls, disciplined processes, clear ownership, and decision-quality information can use AI to improve speed, analysis, and execution. Organizations without those foundations risk scaling inconsistency and creating greater confidence in unreliable outputs.
The constraint is not the technology.
It is leadership.
AI can accelerate execution, but it cannot establish accountability, assign ownership, or create trust. Those remain leadership responsibilities.
Business Situation
Organizations are investing aggressively in artificial intelligence.
The expected benefits are compelling: faster analysis, improved forecasting, automated workflows, reduced administrative effort, and greater operating leverage.
The same organizations may still operate with fragmented systems, unreliable master data, manual reconciliations, inconsistent reporting, weak documentation, unclear ownership, and processes that vary by person or department.
Introducing AI into that environment does not resolve the underlying weakness. It places greater speed and analytical power on top of an operating model leadership could not previously rely upon.
Established AI-risk frameworks reinforce the importance of governance, accountability, reliable information, measurement, and ongoing oversight. The National Institute of Standards and Technology treats governance as a cross-cutting responsibility throughout AI risk management—not as a technical step that can be delegated after implementation begins.[1]
This is often described as a technology challenge.
It is more accurately understood as a leadership challenge.
Technology reflects the quality of the organization that implements it.
Artificial intelligence is no different. It is a force multiplier that strengthens—or exposes—what already exists.
Reliable organizations become more efficient.
Unreliable organizations become inconsistently faster.
Strong data supports better analysis. Weak data produces more confident conclusions built on unreliable information.
Clear accountability scales. Confusion scales as well.
The operating core therefore determines whether AI creates responsible leverage or merely amplifies organizational weakness.
Leadership Builds the Operating Core
Organizations often describe AI readiness as a technology initiative.
It is not.
The operating core is built long before artificial intelligence is introduced.
Reliable controls support standardization.
Standardization supports automation.
Automation supports replicability.
Replicability creates confidence that information and processes will produce consistent results.
Leadership creates each of those conditions.
Without clear ownership, automation can move work faster without making anyone accountable for the outcome.
Without reliable data, AI can produce polished analysis that leadership should not trust.
Without standardized processes, technology may automate different versions of the same activity across the organization.
Without effective controls, speed can make errors, inconsistencies, and poor decisions more difficult to identify before consequences emerge.
Technology does not correct those conditions simply because it is more sophisticated.
Readiness Does Not Require Perfection
Leadership should not interpret this argument to mean that every process must be perfected before an organization experiments with AI.
That would replace one poor decision with another.
The appropriate level of governance should be proportionate to the consequence of the use case.
A low-risk productivity tool used to summarize internal notes does not require the same controls as an AI-enabled process influencing liquidity, pricing, financial reporting, hiring, credit, customer commitments, or capital allocation.
The governing questions are:
What decision or process will AI influence?
What would happen if the output were wrong?
Can the source information be trusted?
Who remains accountable for the result?
What oversight is appropriate to the risk?
Responsible experimentation is possible before the entire enterprise reaches operational maturity.
Consequential reliance is not.
Your Team Already Needs the Operating Core
The discipline required for responsible AI adoption is valuable for a more immediate reason.
The organization’s people already need it.
Employees perform better when expectations are clear.
Managers lead more confidently when information is reliable.
Departments collaborate more effectively when ownership is understood.
Finance performs better when controls can be trusted.
Operations performs better when processes are defined and repeatable.
Sales performs better when customer information is accurate.
Executives make better decisions when reporting is timely, consistent, and appropriately interpreted.
These are not AI benefits.
They are leadership benefits.
Organizations should not strengthen their operating core merely because AI is arriving. They should strengthen it because their people deserve an environment where disciplined execution is possible.
AI will benefit from that investment.
More importantly, so will every employee responsible for executing the organization’s mission.
Decision Framework
Leadership’s Operating Core Assessment
Leadership teams should apply this assessment to each consequential AI use case before approving deployment.
For every question, require evidence, identify the accountable executive, and classify the condition as:
Ready: The requirement is established and supported by evidence.
Controlled Exception: A known gap exists, but the risk can be contained through defined oversight.
Remediation Required: The weakness must be corrected before consequential reliance is appropriate.
Leadership Question
Why It Matters
What business decision or operating process is AI expected to improve?
Technology should be evaluated by the decision or outcome it strengthens—not by its sophistication.
Can leadership trust the source data for this use case?
Reliable information must precede reliable analysis.
Who remains accountable for the output and resulting decision?
Accountability cannot be transferred to a system or vendor.
Is the underlying process defined and consistently performed?
Automation scales the process that exists, including its weaknesses.
Are controls, approvals, overrides, and escalation procedures established?
Consequential outputs require governance before they are relied upon.
Can the output be tested, explained, challenged, and traced?
Leadership must be able to evaluate how an important conclusion was produced.
Are outcomes and risks measured after deployment?
Improvement requires objective evidence and continuing oversight.
Would leadership trust the underlying process without AI?
Technology should extend a reliable operating model—not conceal an unreliable one.
The governing question is straightforward:
Are we automating a reliable operating model—or accelerating an unreliable one?
Practical Implications
For CEOs
Evaluate AI by the quality of the business decisions and operating outcomes it improves.
Do not allow technology enthusiasm to substitute for a clear business case, accountable ownership, implementation capacity, and measurable results.
For Boards
Approval of consequential AI investments should include evidence that the organization can govern the use case responsibly.
The board should understand the intended outcome, material risks, accountable executive, oversight structure, success measures, and escalation process.
For CFOs
AI readiness begins with reliable controls, trustworthy reporting, disciplined processes, clear ownership, and decision-quality information—not software selection.
The CFO’s role is not to prevent experimentation. It is to ensure that investment assumptions, financial consequences, risks, and operating dependencies are understood before the organization relies upon the technology.
For CIOs and Technology Leaders
Technology strategy and operating governance should be developed together.
Implementation cannot be separated from process ownership, information quality, control design, user accountability, cybersecurity, monitoring, and executive oversight.
For Private Equity Firms, Family Offices, and Investors
Operating-core readiness should become part of AI diligence.
Productivity and value-creation assumptions should not be underwritten until leadership has evaluated data reliability, process ownership, controls, implementation capacity, and the organization’s ability to measure results.
An AI strategy without an accountable operating model is not yet an execution plan.
Executive Takeaway
AI does not create operating discipline.
It scales the discipline—or disorder—already present in the organization.
Before approving a consequential AI investment, leadership should require evidence of reliable data, clear ownership, standardized processes, effective controls, and measurable outcomes.
Those foundations should serve the organization and its people first.
When they do, AI can extend capability without weakening accountability.
Technology will continue to evolve.
Leadership remains the foundation upon which durable organizations are built.
About CFO’s Office Executive Perspectives
Executive Perspectives is the thought-leadership publication of CFO’s Office.
Each perspective examines an enduring executive leadership principle through the lens of current business conditions, helping CEOs, boards, investors, family offices, private equity firms, business owners, and executive leaders improve decision quality.
Rather than focusing on technical finance alone, Executive Perspectives examines how financial leadership strengthens enterprise value through Financial Clarity, Executive Confidence, and Disciplined Execution.
About the Author
Kyle Casey, CPA, is the Founder of CFO’s Office and an operating executive, former public-company Chief Financial Officer, and former Interim Chief Executive Officer. He advises CEOs, boards, business owners, family offices, and private equity stakeholders on executive financial leadership, enterprise transformation, governance, liquidity, finance modernization, and operational discipline.
His career spans public companies, consumer products, manufacturing, distribution, gaming, hospitality, entertainment, government, and regulated operating environments.
His leadership philosophy is grounded in a simple principle: Finance exists to improve decision quality. By building reliable operating foundations, disciplined processes, and decision-quality information, he helps organizations transform vision into sustainable results.
Sources
[1] National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework (AI RMF 1.0), NIST AI 100-1, January 2023. The framework organizes AI risk management around Govern, Map, Measure, and Manage, with governance designed as a cross-cutting function.
[2] National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile, NIST AI 600-1, July 2024. The profile provides actions organizations can use to identify and manage generative-AI risks in alignment with their goals and priorities.
If this perspective prompted a question about your organization, board, or portfolio company, I would welcome the conversation.
Enterprise Durability in an Era of Longer Private Equity Hold Periods
Why operational durability becomes more important to investment returns
Opening Thesis
The real story is not simply that private equity hold periods are longer.
The deeper implication is that longer ownership periods change what creates enterprise value.
Private equity firms invest with an eventual exit in mind. When exits are delayed, capital remains tied up and redeployment slows.
In that environment, enterprise durability becomes a larger contributor to returns.
Financial engineering can influence returns. Enterprise durability determines whether value endures.
Business Situation
The private equity exit environment remains constrained.
As of June 30, 2026, approximately 13,500 U.S. companies were held in private-equity portfolios; nearly 4,000 had been held for six years or longer, and approximately 1,500 for nine years or longer.[1]
Financial Times reporting on Bain & Company’s 2026 private equity analysis noted that unsold buyout investments reached approximately $3.8 trillion in 2025, while holding periods extended to about seven years, compared with roughly five to six years during 2010–2021.[2]
The exit window has not disappeared. It has become more selective.
Waiting is not a strategy unless the business is becoming more durable, cash-generative, and credible under scrutiny.
Executive Perspective
A longer hold period is not only an exit-timing issue.
It is an ownership economics issue.
Every investment thesis contains an assumption about time. When exits occur quickly, market conditions, leverage, valuation multiples, and strategic timing can contribute meaningfully to returns. As ownership periods lengthen, the thesis increasingly depends on the business itself. Operations, cash generation, governance, and execution have more time to compound and more time to be scrutinized.
Financial engineering has an important place in private equity. Leverage, capital structure, multiple expansion, portfolio construction, and exit timing remain valuable.
But financial engineering cannot permanently compensate for weak operating fundamentals. Its influence diminishes as time extends. Operational excellence compounds.
That is why enterprise durability matters. Over longer ownership periods, sponsors, lenders, buyers, boards, and management are evaluating whether the business can continue creating value while it remains owned.
Some growth strengthens the business while it grows. Other growth increases size while increasing fragility. Both can appear attractive in a favorable market. They do not age the same way.
Analysis
Longer ownership periods remove the ability to outrun operational weaknesses.
Time exposes what quarterly performance can temporarily conceal: cash generation, reporting quality, governance, forecast reliability, operating discipline, and management’s ability to execute.
That visibility is useful. It reveals whether value creation is durable or overly dependent on conditions outside the company’s control.
A favorable market can support valuation. Financial engineering can improve capital efficiency. But as ownership periods lengthen, the business must carry more of the return through operating performance.
Enterprise durability creates two forms of value.
First, it increases buyer confidence at exit. Reliable reporting, cash conversion, forecasts, KPIs, systems, and governance make a business easier to diligence and underwrite.
Second, it improves cash generation while the sponsor continues to own the business. Durable operations create value before exit.
The CFO’s role is to help management build that evidence.
Decision Framework
Enterprise Durability: Growth Quality Framework
Dimension
Growth that strengthens the business
Growth that increases fragility
Cash generation
Improves operating cash flow and flexibility.
Consumes cash faster than the business can support.
Margins
Protects or improves margin quality.
Increases revenue while weakening margin durability.
Customer quality
Improves retention, pricing discipline, or revenue reliability.
Depends on fragile demand, weak pricing, or low-quality revenue.
Working capital
Supports disciplined cash conversion.
Creates receivable pressure, inventory strain, or liquidity stress.
Forecast reliability
Is supported by credible drivers and accountability.
Depends on aggressive assumptions that are difficult to validate.
Systems and processes
Is supported by scalable systems and clean data.
Adds complexity faster than infrastructure can absorb.
Governance
Improves transparency and decision quality.
Obscures risk or weakens controls.
Exit readiness
Strengthens evidence buyers and lenders can diligence.
Creates performance that is difficult to explain, verify, or sustain.
ERP modernization, process standardization, data-quality improvement
Exit readiness
Would a buyer or lender trust the financial story?
Diligence preparation, working capital analysis, quality of earnings support
Practical Implications
Longer hold periods should prompt a sharper review of enterprise durability.
For private equity partners, the question is not only when the market will support an exit. It is whether the company is creating value while it remains owned.
For operating partners, CEOs, boards, and CFOs, growth should be evaluated by what it does to the business: resilience, cash generation, governance, accountability, liquidity, and exit readiness.
These are not accounting improvements. They are investments in enterprise durability.
Executive Takeaway
Markets influence valuation.
Financial engineering influences returns.
Enterprise durability determines whether value endures.
Great businesses are not defined by how quickly they exit, but by how consistently they create value while owned.
Time does not create enterprise value. It reveals whether the business was creating it all along.
About CFO’s Office Executive Perspectives
Executive Perspectives are published by CFO’s Office to help CEOs, boards, investors, family offices, and private equity firms improve executive decision-making. Each perspective explores an enduring leadership principle through the lens of current business conditions.
Footnotes
The Wall Street Journal, “Private-Equity Firms Are Sitting on a Nine-Year Backlog,” July 2026; citing PitchBook and PwC analysis.
Financial Times, “Private equity logjam hits record as firms struggle to sell,” March 2026; citing Bain & Company analysis.
Disclaimer: This article is intended for educational and informational purposes only. It reflects the author’s professional perspective on executive financial leadership and is not investment, legal, tax, or accounting advice.
