Artificial Intelligence is being positioned as the next great leap forward for finance and operations. Vendors promise real-time insights, automated close processes, predictive forecasting, intelligent customer interactions, and frictionless workflows. For CFOs and finance leaders, the appeal is obvious: faster decisions, lower costs, and better visibility into what is happening across the organization.

Yet in practice, most large-scale AI initiatives in finance struggle or fail. Not because AI lacks power, but because organizations attempt to layer AI on top of fragile financial systems, fragmented ERPs, disconnected CRMs, and decades of accumulated technical debt.

The uncomfortable truth is that AI in finance is not something you “buy.” It is something you earn by first building modern, well-governed enterprise systems.

When ERP and CRM platforms are implemented properly, AI becomes a natural extension of those systems. When they are not, AI becomes a very expensive amplifier of chaos.

Finance Sits at the Center of the AI Conversation

Finance is uniquely positioned in the AI discussion because nearly every critical business process eventually flows through financial systems. Revenue, expenses, payroll, capital projects, procurement, billing, collections, forecasting, and reporting all converge in ERP and CRM platforms. If those systems are incomplete, inconsistent, or heavily propped up by spreadsheets, any AI layered on top will inherit those weaknesses.

Many organizations still operate with a patchwork of legacy ERPs, vertical-specific accounting systems, and custom-built tools created years or even decades ago. Over time, spreadsheets emerge as the glue holding everything together. They bridge gaps between systems, perform calculations that the ERP cannot handle, and create “shadow processes” that only a handful of people truly understand.

From a finance perspective, this environment already creates risk. From an AI perspective, it makes meaningful automation nearly impossible. AI models require structured, reliable, and accessible data. If finance teams cannot trust their own numbers without manual reconciliation, an AI system will not magically fix that problem.

This is why the path to AI in finance almost always begins with modernizing ERP and CRM platforms and reducing spreadsheet dependency.

ERP and CRM Are the True AI Platforms

There is a misconception that AI exists as a separate layer that can simply be bolted onto an organization. In reality, AI’s most powerful role will be inside enterprise systems, not outside them.

Modern ERP platforms are becoming the systems of record not just for transactions, but for business logic. CRM platforms are becoming the systems of engagement that capture customer interactions, pipeline activity, service requests, and contract data. AI embedded inside these platforms can:

• Generate real-time financial narratives from transactional data
• Identify anomalies in revenue, expenses, or cash flow
• Automate reconciliations and routine journal entries
• Predict customer churn or delayed payments
• Surface risks before they appear in reports

But these capabilities only work when ERP and CRM systems are implemented cleanly, configured thoughtfully, and governed consistently.

If finance data lives partly in the ERP, partly in spreadsheets, partly in disconnected billing systems, and partly in custom databases, AI has no single source of truth to learn from. In that scenario, AI does not become intelligent. It becomes confused.

Why CFOs Are Skeptical… and Right to Be

Many CFOs have grown cautious about AI hype, and for good reason. They have seen technology waves before. They remember large, expensive implementations that promised transformation and delivered marginal improvement. They recognize marketing language when they hear it.

From a finance leader’s perspective, the core question is not, “Does this vendor have AI?” The real question is, “Will this materially improve visibility, accuracy, and decision-making in my organization?”

That mindset naturally leads CFOs back to fundamentals:

• Can we close faster and with fewer manual steps?
• Can we trust our cash flow forecast?
• Can we see performance across entities, departments, and products?
• Can we explain variances quickly and confidently?

Until those questions are answered, AI remains a distraction rather than a solution.

Ironically, organizations that focus on fixing these basics often become “AI-ready” without explicitly trying to be.

The Hidden Prerequisite: Data Discipline

AI success in finance is largely a data discipline problem disguised as a technology problem.

Finance teams must define consistent chart of accounts structures, standardized dimensions, and clear data ownership. Customer records in CRM must align with customer records in ERP. Products, services, and projects must be defined the same way across systems. Access controls must be deliberate, not accidental.

This work is tedious. It does not make headlines. But it determines whether AI becomes useful or useless.

When data discipline exists, AI can start to answer meaningful questions such as: What is driving margin erosion? Which customers are most likely to pay late next month? How will a 5% drop in revenue impact cash six months from now?

Without data discipline, AI can only generate plausible-sounding noise.

AI Does Not Replace ERP or CRM. It Deepens Their Value

Some organizations hope AI will allow them to bypass costly ERP upgrades or avoid re-implementing CRM systems. This is a dangerous assumption as AI does not replace enterprise platforms - it intensifies their importance.

As ERP and CRM vendors embed more AI into their products, organizations with modern platforms will gain compounding benefits. Reporting becomes conversational. Dashboards become predictive. Workflows become increasingly automated.

Organizations on outdated platforms will be locked out of these advancements or forced to pursue expensive, brittle custom solutions.

In other words, ERP and CRM modernization is no longer just about operational efficiency. It is about future-proofing the finance function for an AI-driven world.

Start With Finance Use Cases That Matter

The most successful AI initiatives in finance are grounded in specific, high-impact use cases rather than grand visions.

Common starting points include:

• Cash flow forecasting and scenario modeling
• Revenue recognition and billing validation
• Expense classification and anomaly detection
• Collections prioritization
• Financial close automation

These areas are data-rich, repetitive, and central to decision-making. Improvements here produce immediate value that finance leaders can see and measure.

Once these foundations are in place, more advanced AI capabilities become both feasible and valuable.

Why Narrow Solutions Beat General-Purpose AI

General-purpose AI models are impressive, but they struggle with nuance, context, and accuracy in complex financial environments. Finance requires precision. A small error can have outsized consequences.

This is why narrowly focused AI, trained on well-defined datasets within ERP and CRM systems, consistently outperforms broad, generic models in finance applications.

Think less about a single super-intelligent system and more about a collection of specialized capabilities embedded throughout the finance stack. Each solves a specific problem extremely well.

This approach mirrors how finance teams already work: different tools for payables, receivables, planning, reporting, and consolidation. AI simply becomes another layer of intelligence within each area.

Governance Is Finance Responsibility

Because finance data is among the most sensitive in any organization, finance leaders must play a central role in AI governance.

This includes setting standards for:

• Data quality and ownership
• Model usage and monitoring
• Vendor access to financial data
• Auditability of AI-generated outputs

AI that cannot be explained, traced, or audited is unacceptable in finance. Establishing governance early prevents costly rework later.

The Real Path Forward

AI will absolutely transform finance. But not in the way marketing decks suggest.

The transformation will be quiet. It will happen inside ERP and CRM systems. It will show up as fewer manual steps, faster closes, more reliable forecasts, and better decisions.

Organizations that invest today in modern enterprise platforms, clean data, and disciplined processes are not falling behind. They are positioning themselves to capture the real value of AI as it matures.

Those chasing shortcuts will continue to experiment. Those building foundations will quietly outperform.

For CFOs, the message is simple: AI is not the starting line. ERP and CRM are the starting line. AI is what becomes possible once you cross it.

Learn More with FinFactor

In this episode of FinFactor, Blaine Bertsch sits down with Abhijit Verekar, founder of Avero Advisors, to unpack one of the biggest misconceptions in today’s tech landscape: that public agencies and large organizations can simply “buy AI” and transform overnight. AV breaks down why so many AI pilots fail, how legacy ERPs, siloed data, outdated infrastructure, and missing guardrails derail even the best intentions, and why the real work isn’t flashy—it’s foundational.

Together they explore how organizations can modernize responsibly, where spreadsheets actually outperform legacy green-screen systems, why vaporware is back, and how to design AI pilots that solve real problems without creating new ones.

If you’re a CFO or agency leader trying to make sense of AI amid tightening budgets, policy constraints, and messy data, this episode gives you the clarity and roadmap you need.

________

Dryrun delivers real-time, dynamic cash flow and financial forecasts with complete manual control and unlimited scenario modeling.
‍START YOUR TRIAL today!

‍