Choosing the Right Tools: Build vs. Buy for AI in Finance

Artificial Intelligence is rapidly redefining what’s possible in the finance function—from predictive forecasting to automated reconciliations. But once finance leaders commit to pursuing AI capabilities, one critical question quickly follows:

Should we build AI tools in-house or buy off-the-shelf solutions?

There’s no one-size-fits-all answer. The right approach depends on your organization’s goals, capabilities, and risk appetite. What matters most is making this decision deliberately—with a clear-eyed view of cost, speed, customization, and control.

Artificial Intelligence is rapidly redefining what’s possible in the finance function—from predictive forecasting to automated reconciliations. But once finance leaders commit to pursuing AI capabilities, one critical question quickly follows:

Should we build AI tools in-house or buy off-the-shelf solutions?

There’s no one-size-fits-all answer. The right approach depends on your organization’s goals, capabilities, and risk appetite. What matters most is making this decision deliberately—with a clear-eyed view of cost, speed, customization, and control.

The Case for Buying: Speed, Simplicity, and Scalability

Most finance teams looking to achieve near-term impact will benefit from buying AI tools from established vendors. These tools are typically:

• Faster to deploy

• Easier to integrate with ERP and financial systems

• Supported with training, updates, and compliance safeguards

Example Use Case: Expense Management Automation

A mid-market retail company adopted an AI-powered expense management platform that automatically detects anomalies, policy violations, and duplicate claims. Rather than building a solution from scratch, they chose a SaaS vendor with pre-built models trained on millions of transactions.

Results:

• Reduced manual review time by 70%

• Improved policy compliance within the first quarter

• Achieved ROI within six months

For teams without deep AI expertise or data science resources, buying allows them to focus on business value rather than model training, infrastructure, or maintenance.

The Case for Building: Customization, Control, and Differentiation

In organizations with more mature data capabilities and a strategic vision for AI, building internal tools can offer long-term advantages:

• Tailored models based on proprietary data

• Greater control over logic, outputs, and compliance

• Opportunities to create competitive differentiation

Example Use Case: Forecasting Model for Cash Flow

A global logistics firm developed an internal AI model to forecast daily cash flow, incorporating real-time shipment data, seasonal variables, and payment behavior across regions. Existing tools didn’t meet their forecasting granularity or integrate well with their systems.

Results:

• Improved daily cash forecasting accuracy by 25%

• Enabled better liquidity planning

• Supported treasury decisions in volatile markets

Building also makes sense when data security, regulatory constraints, or internal knowledge require highly customized workflows.

Key Considerations: Build vs. Buy Framework

How to Decide

1. Start with the Problem, Not the Tool Define the business outcome you’re targeting—faster close, better forecasts, reduced errors. Then assess which model (build or buy) gets you there faster and more reliably.

2. Consider a Hybrid Approach Many organizations adopt a “buy for now, build for later” model. Off-the-shelf tools deliver quick wins while internal teams gradually develop custom models for more strategic use cases.

3. Don’t Underestimate Change Management Whether you build or buy, success depends on adoption. Invest in training, communication, and alignment across finance and IT.

Final Thought

Choosing between building or buying AI tools in finance isn’t just a technical decision—it’s a strategic one. The best approach aligns with your team’s capabilities, your timeline, and your long-term vision for how finance creates value with data.

The question isn't "Should we use AI?" It’s "How do we use AI to drive the right outcomes for our business?"

CA Dharmajan Patteri

🔜 Next in This Series: Setting Up a Cross-Functional AI Task Force