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CFO agenda 2026: practical AI, cleaner data, faster forecasting, and a finance team built for volatility
In 2026, the CFO agenda centers on practical AI adoption, cleaner data foundations, faster scenario forecasting, and building finance teams resilient to volatility. The priority is no longer closing the books faster, but accelerating decision-making while maintaining trust, control, and adaptability in uncertain markets.
In a recent CFO Connect live session, we sat down with Tom Kelly (Director at Oracle NetSuite, 30+ years experience) to talk through what actually matters on the CFO agenda this year: where AI is useful right now, how to navigate uncertainty without drowning in scenarios, and how to build a finance function that is resilient and relevant.
Below is the distilled, highly actionable playbook based on that conversation.
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Watch the full CFO Agenda 2026 Webinar:
1) What Is the Real CFO Agenda for 2026?
Most finance teams are still judged on “closing fast” and “reporting accurately.” Those remain table stakes. The 2026 shift is that you will increasingly be judged on whether finance can help the business decide faster without compromising integrity.
That requires three capabilities:
First: trusted data. AI does not fix bad inputs automatically. If the business does not trust your numbers, they will not trust your AI outputs either.
Second: repeatable forecasting and scenario testing. Not “a forecast,” but a process where you can change assumptions quickly, understand second order impacts, and communicate consequences.
Third: communication that drives action. Finance is becoming more influential, not less. But only if you can translate analysis into a compelling narrative that non-finance leaders can operationalize.
Tom’s point was blunt: fear is the biggest risk. Not the tools. Not the models. Fear delays adoption, and delayed adoption turns AI into a firehose later.
What to do this quarter
Pick one decision that currently takes too long (pricing update, headcount plan, vendor terms, margin root cause, working capital initiative). Then build a finance “decision loop” around it: clean inputs, clear assumptions, fast iteration, a story the business can act on.
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2) How Should CFOs Use AI in Finance in 2026?
There is plenty of hype around AI replacing jobs or fully automating finance. The practical near-term win is more specific:
AI becomes a faster interface to your business data.
Tom described using an LLM connected to ERP data to reduce reliance on manual Excel steps. Instead of exporting multiple reports, stitching them, and iterating in spreadsheets, you query the system conversationally, validate results, refine the question, and arrive at an answer faster.
This is especially valuable for finance leaders because your day is filled with questions like:
What is driving margin erosion in Product X in Region Y?
Which vendors are dragging DPO and why?
What changed in cash conversion cycle since last month?
What is the break-even point for this new offer given current costs?
The “AI advantage” is not that it magically knows the answer. It is that it compresses the time from question to first draft, so your team can spend more time validating, interpreting, and influencing.
What to do this quarter
Run a two-week “analysis acceleration” pilot. Choose one domain (AP, revenue, margin, opex) and measure:
time to produce analysis
number of iterations required
how many stakeholders actually used the output to decide
If you cannot show time saved and improved stakeholder clarity, the use case is not ready.
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3) What Are AI Connectors (MCP) and Why Should CFOs Care?
A lot of finance leaders hear terms like “connectors,” “APIs,” and “MCP” and mentally file them under IT. The business impact is simpler:
connectors reduce integration complexity and make cross-system analysis easier.
In plain English, MCP-style connectors aim to make it easier for AI tools to interact with multiple systems using a consistent interaction pattern. For finance, this is less about novelty and more about speed:
less time normalizing data across systems
faster path to “one conversation” across ERP, CRM, and other sources
fewer manual steps to answer multi-department questions
Tom’s practical example was accounts payable analysis. A classic approach is to run aging reports, slice by vendor, then manually investigate why terms differ. With AI plus access to structured data (and even notes/history), you can jump to outliers and explanations faster, then validate.
What to do this quarter
Ask your team to document the top 10 recurring cross-system questions (ERP+CRM, ERP+payroll, ERP+billing). For each question, list:
where the data lives
what manual steps you take today
what “good enough” output looks like in 15 minutes
Then involve IT or your systems owner to see which are realistically connectable in the next 90 days.
4) How Do CFOs Ensure Data Quality When Using AI?
The webinar audience’s top concern about AI was quality and reliability of accounting information. That is the right concern.
Tom emphasized a principle finance already lives by, but must now apply to AI outputs explicitly:
trust but verify.
AI can return plausible answers that are wrong, incomplete, or based on the wrong definition. The fix is not “avoid AI.” The fix is process:
provide context in prompts and queries
require traceability (“prove your work”)
validate against authoritative reports and definitions
log the assumptions used to generate the output
A key nuance: context is everything. Tom shared a humorous example where a request for a “detailed five line journal entry” produced a literal five-line diary entry. Not because the model was broken, but because the instruction lacked accounting context.
What to do this quarter
Create a lightweight “AI output checklist” for finance teams:
What definition was used (revenue, ARR, margin, cash)?
What data source and time period?
Does it reconcile to a known report?
What exceptions or missing data were identified?
What is the recommendation and what would change your mind?
Use it in every AI-assisted analysis for one month. This single habit does more for risk reduction than almost any tool choice.
5) How Can CFOs Manage Volatility and Uncertainty in 2026?
A registrant asked about VUCA: volatility, uncertainty, complexity, ambiguity. The core finance problem in VUCA is not “we do not have a plan.” It is:
our plan cannot adapt fast enough.
Tom’s perspective was that opportunity comes from being able to test assumptions quickly, and to do it with enough rigor that leadership trusts the outputs.
In other words, when conditions change (pricing, demand, FX, tariffs, supply constraints), the winner is the team that can say:
here are the 3 scenarios that matter
here is what breaks in each
here is what we should do next week
Not next quarter.
A practical scenario framework (use this in your next forecast cycle)
Instead of building 12 scenarios nobody reads, build 3 that leadership can act on:
Base: current trend with known constraints
Downside: the one shock that would hurt most (demand drop, cost spike, churn)
Response: the actions you would take (pricing, hiring freeze, vendor renegotiation) and their modeled impact
Then review weekly, not monthly, during high volatility periods.
6) When to move beyond Excel for FP&A: the tipping point is decision speed, not company size
A common question: “When do we stop using Excel and adopt a planning tool?”
Tom’s stance was pragmatic. Excel is powerful, but it becomes a bottleneck when:
you have too many moving variables
changes require heavy manual rework
scenario turnaround is slow
version control becomes a risk
stakeholders don’t trust the source of truth
The tipping point is not a neat headcount number. It is whether the business needs faster iteration than spreadsheets can reliably deliver.
What to do this quarter
Audit your last two forecast cycles:
How many hours were spent gathering data vs analyzing it?
How many spreadsheet versions existed at once?
How long did it take to incorporate one leadership change request?
How many times did definitions differ across teams?
If “incorporate one change request” takes days, that is your signal.
7) AI and headcount: expect task reduction, but do not bet your strategy on layoffs
The fear question came up directly: is AI mainly about reducing headcount?
Tom’s answer was measured. Some manual tasks will go away, yes, especially routine data entry and low-judgment processing. But he also pointed to an important pattern seen in other fields:
when the cost of producing insights falls, organizations often increase usage, and the nature of roles shifts toward judgment and communication rather than pure production.
Finance is likely to see the same. The value shifts from “who can produce the report” to “who can interpret the signal, challenge it, and drive decisions.”
What to do this quarter
Start reskilling plans now. Identify 2–3 people who currently spend most time on repeatable tasks and give them a development track in:
analysis and business partnering
AI-assisted QA and controls
data governance and metric definitions
scenario planning facilitation
This is how you turn automation into talent leverage, not morale damage.
8) Building a finance function from scratch in 2026: fundamentals, attitude, and the right “fish tank”
Tom’s answer to “how would you build finance from scratch today?” was surprisingly simple and very CFO-relevant:
Fundamentals: strong accounting and finance foundations
Attitude: enthusiasm and willingness to learn
Environment: do not “shrink the fish tank” by keeping people stuck in small roles with no room to grow
One additional point was worth highlighting: don’t over-index on traditional backgrounds. People who can learn, communicate, and adapt can become excellent finance operators if you invest in training.
What to do this quarter
Run a “role energy” exercise with your team:
Which activities give you energy?
Which drain you?
What work do you want more exposure to (cross-functional, operational, strategic)?
What is one capability you want to build with AI this quarter?
Then adjust roles or project assignments to match motivation. Teams adopt change faster when they can see how it benefits their growth.
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9) Cross-functional influence: the fastest way to build credibility is to learn the business up close
A live Q&A asked how to train teams to interact with other departments. Tom’s advice was hands-on:
go spend time where the work happens.
Not a meeting. Not a Slack channel. Actual immersion: ride along, shadow operations, sit with sales, visit the production floor. When finance understands the operational reality, two things happen:
your analysis becomes more accurate
your recommendations become more practical and trusted
What to do this quarter
Require every finance business partner to complete one operational immersion per quarter and present back:
what surprised them
what metric matters most to that function
what finance can simplify, automate, or clarify
This is a low-cost move with high trust ROI.
10) Executive intelligence (EQ) in finance leadership: how to hire and develop it
Another Q&A hit on emotional and executive intelligence: empathy, resilience, risk tolerance. Tom’s practical approach: don’t rely on a stiff interview.
He suggested changing the setting (even a walk) and asking scenario questions that reveal how someone reacts when things go wrong. You’re looking for:
how they handle conflict
what they learned from adversity
whether they can adapt and bounce back
Adaptability matters more in 2026 than it did in 2016, because roles will keep changing.
What to do this quarter
Add two interview questions for every finance hire:
“Tell me about a time your work was challenged publicly. How did you respond and what changed after?”
“Tell me about a time you had incomplete data but still had to decide. What did you do to reduce risk?”
You will learn more than any technical test can reveal.
What most finance orgs are doing with AI right now (and what to do next)
In the session poll, many finance teams were either:
still exploring concepts with no defined use cases
running small pilots in limited areas
Tom’s view was clear: do not wait for perfection. Move forward in controlled ways. The longer you delay, the steeper the learning curve when AI becomes unavoidable.
A simple 90-day AI adoption plan for finance
Days 1–15: choose one use case with measurable impact (AP analysis, variance commentary drafting, forecast scenario iteration). Days 16–45: run the pilot with a verification checklist and clear definitions. Days 46–90: standardize the workflow, document prompts/assumptions, train two backups, and present results to leadership.
Keep it narrow, measurable, and repeatable.
FAQ: CFO Agenda 2026
What is the top priority for CFOs in 2026? The top priority is accelerating decision-making using practical AI, trusted data, and faster scenario modeling — without compromising financial integrity or control.
How should CFOs use AI in finance today? CFOs should focus on AI as an interactive analysis layer over ERP and financial systems, reducing manual data work and shortening the time from question to insight.
When should finance teams move beyond Excel for FP&A? Finance teams should move beyond Excel when scenario updates take days instead of hours, version control becomes risky, and leadership cannot get fast, reliable forecasts.
How can finance teams manage volatility (VUCA)? They should limit planning to three actionable scenarios, stress-test key assumptions weekly, and model response actions alongside downside risks.
Will AI reduce finance headcount? AI is more likely to reduce manual tasks than eliminate roles. The bigger shift is toward higher-value skills such as interpretation, business partnering, and scenario planning.
Closing thought: the CFO advantage in 2026 is interpretation, not computation
AI will change the mechanics of finance. But the core value of finance leadership does not go away. If anything, it increases:
you are still the people best positioned to interpret business performance, enforce definitions, stress-test assumptions, and explain what to do next.
The finance teams that win in 2026 will not be the ones who “use AI.” They will be the ones who use it to accelerate analysis, strengthen trust, and drive better decisions under uncertainty.
