Once upon a time, you sold a widget. Fixed price, one-off sale. Finance helped generate and approve a quote — if that was even necessary — then ship the invoice. The customer paid. The end.
Not anymore. Finance teams are being asked to manage more sales channels and more revenue models than ever before — while operating with the same tired technology they’ve been using for a decade, and the same headcount.
The result is a function under strain: teams buried in manual work, navigating fragmented systems, and expected to maintain iron-clad accuracy and compliance while keeping pace with a rapidly evolving revenue landscape.
Something has to give. For the finance leaders pulling ahead, that something is a warming to new technology — specifically AI.
How is technology hindering growth for finance teams?
Ask most finance leaders about their tech stack and you'll hear a familiar story: too many tools, too little (or cobbled) integration, and too much team time spent holding it all together.
- Spreadsheets remain the connective tissue of many finance orgs. Half of all finance leaders report that more than 20–40% of their finance workflows are still managed manually.1
- Manual input introduces inaccuracy. Most finance leaders say that at least one in 10 transactions contains inaccuracies somewhere in the quote-to-cash process.1
- Channels are growing. This longstanding point of friction is compounded as channels scale: 68% of finance leaders say the number of channels their business sells through has increased in the last year, and 57% now sell across six or more channels at once.1
- New revenue models. Consumption-based and subscription pricing models add further complexity because they require finance teams to track revenue in ways that traditional systems weren't built for.
How can you scale finance ops with consolidated tech and agentic AI?
Step one is clear: Ditch point solutions, including disconnected AI tools. Next, find a platform that can manage quote-to-cash in one place with the capacity to handle a growing number of channels and revenue models.
The impact is markedly less tool juggling. But there’s an added benefit here: consolidated data. Deal and customer information flow seamlessly through the sales process to final payment. No security vulnerabilities, no manual data transfers.
Even better: That consolidated data is what makes powerful AI possible.
Here’s where things get sticky for some CFOs. For a long time, AI felt more like a financial liability than a solution — inconsistent outputs, hallucinations, and human review that consumed as much time as just doing it manually. CFOs, rightly focused on accuracy above everything else, kept their distance. That hesitancy made sense then. It doesn’t today. AI has matured dramatically, and the latest evolution — agentic AI — is purpose-built for the high-volume, high-stakes, multistep work that consumes finance teams.
When it’s grounded in clean, complete data, agentic AI can really shift how finance teams operate. Leaders are starting to take notice: 33% now list "scale use of agentic AI" as a top 2026 strategic priority.1
Here's what makes agentic AI different in a finance context — and why it addresses the core problems many CFOs currently face:
- Consolidated data, end-to-end deal management. When customer, deal, and transaction data all live on a single platform, deals flow from quote to invoice to payment without switching systems or reconciling across tools. Finance and sales work from the same data, making accuracy achievable at scale.
- AI agents handle the heavy lifting. AI agents operating within a secure, consolidated platform can take on high-volume finance tasks — approving quotes, generating and sending invoices, answering invoice questions, following up on overdue payments — autonomously and across channels and revenue models. Collections is already the #1 AI use case in finance: 61% of leaders use AI to automatically conduct outreach to customers with overdue payments.1 Compliance rules and governance guardrails can be layered in and customized to meet your specific needs.
- Natural language access to deal data. Finance teams interact with agents in plain language — no digging through spreadsheets, no toggling between a CRM and six other tools to get a status update. The information comes to you.
- Security and compliance are built in. When AI agents are grounded on a secure, governed platform, outputs are compliant by design. The model doesn't reach outside approved data sources, and human oversight can be calibrated to the level each team requires.
Agentforce Revenue Management (ARM) takes this approach — built natively on Salesforce, it unifies the full revenue lifecycle — from product configuration and quoting to contracts, orders, billing, and analytics — on a single platform.
Agents embedded in the workflow enforce pricing rules behind every deal, so what is quoted is exactly what is invoiced. Sales, finance, and legal teams share one source of truth; everybody gets real-time visibility into ARR, MRR, subscription health, and collections performance.
Case study: How property management leader EliseAI managed finances using agents during a 32x growth spurt
EliseAI, which provides AI-powered solutions for property management, tracks tens of thousands of buildings with over 100,000 product assets. It's a level of complexity that would break most manual finance processes — and it all happened fast.
CFO Andrew Korn describes five consecutive years of company doubling, representing 32x compounded growth. He managed it using Agentforce Revenue Management — a consolidated platform with AI agents, ready out of the box and fully customizable.
"I don't spend 32x more time on process than I did," Korn says. "I don't have 32x more technology. And I don't have 32x more people. I'm able to manage an increasingly complex business without adding all that bloat, and still have my fingers on the pulse."
ARM absorbed the operational complexity without requiring EliseAI to scale its finance team proportionally. Today, a contract moves through sales, into ARR recognition, and out to invoice on the same day — with the real-time visibility Korn's board expects.
What do finance teams need to get started with agentic AI?
The path to agentic AI in finance isn't complicated, but it does require getting a few fundamentals right:
- Clean data. Agents are only as reliable as the data they're grounded in. Bad inputs produce bad outputs. Audit your data for accuracy, duplicates, and corruption before deploying.
- A single platform. The power of agentic AI compounds when it can see the full picture — quotes, contracts, orders, invoices, payments — without leaving the system. Look for a consolidated platform that works for your business today, but can scale for the business in a year, two years, 10 years from now.
- Proper security and governance. AI must operate within defined guardrails on a governed platform. Look for solutions with high security protocols — data masking, encryption, and customizable security and governance settings.
- Willingness to trust the technology. The biggest barrier for many finance leaders isn't technical. It's cultural. The CFOs scaling effectively have made a deliberate choice to embrace AI as a force multiplier. To them, it’s not a threat to accuracy, but the best tool available to protect it. Before you onboard a solution, vet it with your team and give them the opportunity to weigh in on how to deploy so they’re actively engaged.
The future of finance ops has agentic AI front and center
Nearly seven in 10 finance leaders say their responsibilities grew last year, with revenue model complexity, governance, and AI strategy all expanding their remit.1
The ones navigating that expanded role most successfully aren't adding headcount or bolting on more tools. They're the ones who've simplified, consolidated, and let AI do what it was built to do.
1 According to Salesforce CFO research, conducted March-May 2026.
