It’s the current call heard ‘round the world: Company leaders are issuing directives to implement AI in finance back-office workflows. The problem: These directives state an AI adoption goal but lack the strategy, planning and execution for finance organizations to achieve meaningful, measurable value. CFOs and finance leaders are left to answer an important question: “How can we implement AI effectively and quickly in our day-to-day workflows and see a clear return on investment?”

This challenge, which comes with most AI implementations, is a top priority for finance functions. According to Protiviti’s latest Executive Perspectives on Top Risks and Opportunities Survey, 29% of CFOs rated among their top AI challenges the “inability to deploy AI at a competitive pace,” and 28% expressed concerns about “significant investments with uncertain returns.” 

These issues underscore the opportunities and challenges coming out of AI conversations in finance. AI can be a lever for cost and time savings – in fact, 72% of finance organizations are currently employing AI in some manner. However, this only happens when the return is measurable and sustained. 

Potential uses for AI in finance are extensive. From embedding a validation agent in procure-to-pay processes to understanding financial close results in real time, opportunities continue to emerge to streamline operations and automate repetitive processes. 

Yet with a wide range of AI tools and approaches at their disposal, CFOs must understand the limits, culture and appetite of their finance organization to proceed along the best road to AI adoption that will deliver long-term value and ROI. Rushing ahead with building and implementing quick AI use cases without a strategy and desired outcomes is not the answer. Instead, the CFO needs to establish an AI foundation that supports near-term value and long-term evolution.

In our view, there is a clear approach that bridges the disconnect between perceived AI readiness and actual capability and value delivery. We have defined six key actions for the CFO to take so that their finance organization will progress successfully on the road to value-delivering AI automation:

1. Align AI goals with business strategy.
2. Determine readiness based on maturity level.
3. Leverage AI embedded in ecosystem partners.
4. Determine value-add use cases for deployment.
5. Upskill and empower talent.
6. Prioritize resilience over capabilities.

By undertaking these actions, CFOs and finance leaders can:

• Define a long-term AI strategy that achieves quick wins while developing a strong foundation to enable continuous improvement. 
• Capitalize on their ecosystem partnerships to secure immediate AI-driven benefits and ROI.
• Identify the AI use cases that will garner expected results and ROI.
• Train and enable a workforce that not only understands AI but will evolve and grow as the technology changes.