From prompt to production: How to get the most out of AI in finance

Finance teams have moved quickly from asking what AI can do to asking whether they can trust it to deliver. Learn how leaders can turn promising AI experiments into governed, repeatable processes that create measurable business value.

Key takeaways 

• Focused, use-case-driven deployments are creating stronger results than broad transformation efforts.

• AI initiatives gain momentum when they start with a clearly defined business problem and measurable outcome.

• Governance, accountability, and trust become more important as AI moves closer to production.

AI conversations within functions have evolved quickly. Not long ago, the focus was on experimentation. Finance, risk, and compliance teams were testing prompts, exploring new tools, and identifying opportunities to reduce manual effort. Many of those early efforts produced encouraging results.

Today, leaders are asking tougher questions about governance, scalability, accountability, and measurable outcomes. That shift is exposing a gap that doesn’t receive nearly as much attention as AI’s potential: getting a useful output from AI is very different from building a reliable business process around it.

A strong prompt can produce an impressive answer. But a governed process is what makes that output repeatable, validated, and trusted. Here’s what we’ve learned it takes to close that gap.

Why focused use cases create stronger outcomes

AI conversations often begin with broad ambitions. Transform a function. Automate a department. Modernize an entire process. Those goals may be valid, but they’re often too broad as a starting point.

As teams move beyond experimentation, stronger results are emerging from focused use cases with clear boundaries. Instead of trying to solve every reporting challenge, start with a specific disclosure workflow. Rather than reimagining an entire controls environment, target a defined testing activity. Instead of pursuing a broad AI strategy, identify one process where the outcome can be measured and the value can be demonstrated.

Workiva’s recent announcement of specialized AI agents reflects this approach. Rather than introducing a single broad AI solution, they launched purpose-built agents designed for specific reporting and compliance activities, including tie-out validation, benchmarking analysis, and sustainability disclosures.

A narrower scope doesn’t mean a smaller ambition. It creates a stronger foundation for adoption, ROI, and expansion.

Focus also makes value easier to prove. When a use case has clear boundaries, teams can determine whether AI is saving time, improving quality, reducing risk, or creating capacity. Once value can be demonstrated in one process, there is stronger evidence to guide where AI should go next.

What separates AI experiments from business outcomes

AI initiatives rarely stall because the technology isn’t capable enough. They struggle because the business hasn’t clearly defined what the technology needs to improve. Before AI can create meaningful value, leaders need to answer a few fundamental questions:

• Has the process improved?

• Has risk been reduced?

• Has capacity been created elsewhere?

• Can the approach be repeated consistently?

• Can the output be validated and trusted?

The strongest initiatives begin with a specific business challenge, a measurable outcome, and a clear understanding of how success will be measured. Starting with a tool and searching for applications can create activity without a clear destination. Starting with the business problem creates focus.

For finance, risk, compliance, and reporting leaders, the goal isn’t simply to automate a task. It’s to improve an outcome the business is already prioritizing.

Featured case study

Unifying finance operations onto a single, scalable platform

A global food service organization needed greater visibility and consistency across finance operations spanning multiple brands and subsidiaries. Highspring helped unify operations onto a single platform with API-driven integrations across financial systems, reducing billing cycles from three days to hours and supporting the generation of more than 16,000 invoices during the first billing run.

Read the full case study

The distance between a great prompt and a reliable process 

Nearly every leader evaluating AI has experienced one of these moments:

• A prompt produces an impressive output.

• A summary eliminates hours of manual work.

• A report comes together faster than expected.

• A workflow suddenly feels easier.

Those moments show what’s possible, but they can also create a false sense of readiness.

Early success can be misleading

A useful output doesn’t automatically translate into a dependable process. What works once in a controlled setting may not work consistently across users, data sets, security requirements, and business scenarios.

As AI moves toward production, the question changes from “Can AI do this?” to “Can AI do this consistently, securely, and in a way people trust?” This is where many initiatives begin to face challenges. The prompt may be strong and the output might be useful, but the business process often isn’t ready to support it at scale.

Production introduces new requirements

Moving AI into a business-critical workflow requires more than a working prompt. It requires:

• Ownership and accountability

• Security and access management

• Controls and governance

• Repeatability and consistency

• Confidence in the outcome

• Clear measures of success

As AI moves closer to production, questions around ownership, accountability, data access, validation, and oversight emerge quickly. Addressing them early doesn’t have to slow progress. It makes it possible to move a successful experiment forward with confidence.

A successful experiment proves AI can help. A governed process proves the business can rely on it.

Practical considerations for leaders evaluating AI

For finance, risk, compliance, and reporting leaders, a few questions can help determine whether an AI opportunity is ready to move forward:

• What business problem needs to be solved?

• What outcome would prove value?

• Who owns the process and its results?

• What governance needs to be in place?

• What data, access, and security requirements apply? 

• How will success be measured? 

• Where can the scope be narrowed to accelerate progress? 

These questions provide a useful test for both new opportunities and existing pilots. If the answers aren’t clear, the next step may not be adding more technology. It may be narrowing the use case, defining ownership, establishing controls, or determining what success actually looks like.

The most successful initiatives aren’t necessarily the most ambitious. They’re the ones with a clearly defined objective, measurable outcomes, and a practical path to implementation.

Move from AI experimentation to measurable value

The next stage of AI adoption won’t be defined by access to more tools alone. It will be defined by execution.

For finance, risk, compliance, and reporting teams, the opportunity isn’t to experiment with AI. It’s to identify where AI can create measurable value and build a practical, governed path to implementation.

Ready to put your AI opportunities into focus? Schedule an AI Alignment Check to evaluate the best path forward.

Connect with Highspring at Workiva Amplify 2026 

Many of these same questions will be front and center at Workiva Amplify 2026, where finance, risk, compliance, and reporting leaders will explore how AI fits into increasingly complex governance and reporting environments.

Highspring will be attending in Las Vegas to continue those conversations. Whether you’re evaluating AI-enabled reporting workflows, exploring control testing opportunities, strengthening governance, or looking to move beyond pilots, we’d love to connect.

Learn more about Highspring’s presence at Workiva Amplify 2026