By Rachel Burger February 25, 2025
Finance pros, by nature, can be a cautious crowd.
Of course, there are plenty of good reasons for that caution. Succeeding in the financial planning and analysis (FP&A) function requires an obsession for accuracy, relentless vigilance for potential risks, and a measured approach to managing company finances. Through such an approach, FP&A professionals must balance investing in growth while assuring financial stability and consistent cash flow.
These traits can greatly benefit organizations through the charting of smart, strategic paths to profitability and progress. Yet the same traits can also lead finance pros to be skeptical of the significant change that comes with the introduction and integration of artificial intelligence (AI).
Still, the reality is that using AI technology is clearly becoming the standard operating procedure for organizations to effectively compete and grow. OneStream’s Finance 2035: Return to Investment shows proof of that, finding that 74 percent of chief financial officers (CFOs) believe that AI and automation will completely reshape organizations’ finance function by 2035. Plus, 70 percent of CFOs predicted that organizations who invest in AI tech infrastructure and skills won’t survive the next five years.
With all that in mind, CFOs and other proponents of AI innovation must particularly focus on building trust within their FP&A teams that ultimately will have the power to leverage AI in ways that can create cost-saving efficiencies and turbocharge growth.
Operating from a solid foundation of trust in AI can help assure smooth AI integration. That foundation can also create team buy-in and enthusiasm for AI that lays the groundwork for ongoing engagement and innovation.
The 9 Key Pillars for Building Trust in AI
Here are nine pillars that can build trust in AI and maintain that trust:
1. Engage Early – And Often
The time to loop in the finance team on an AI integration process isn’t hours before your presenting your plan to the executive team. By engaging finance pros early in the exploration development processes, you avoid the risk of team members feeling blindsided — and show that their opinions and insights matter. Looping team members in early leads to stronger alignment around AI solutions while surfacing specific needs and concerns at the front end of the process.
Further, involving team members and designing, testing, and implementing AI systems help assure the technology reaps the most benefit by addresses real-world challenges. Collaborative efforts foster a sense of ownership and confidence in the results.
2. Highlight the Potential
Seeing is believing. Accordingly, being able to see in detail how AI has benefitted other FP&A teams and their organizations can build enthusiasm while addressing reservations. Look for case studies relevant to your industry in which AI has delivered measurable value. Hearing real-life testimonials from finance peers about real-life results provides powerful and tangible evidence about AI’s potential.
3. Emphasize Transparency and Clarity
Finance professionals are accustomed to working with transparent and trusted methodologies. To gain these professionals’ trust, AI solutions must be explainable, intuitive, and auditable. Providing clear parameters, demonstrating how models work, and explaining the logic that drives decisions and forecasts is thus essential. Additionally, by emphasizing transparency within your finance team, you’ll create informed ambassadors who can effectively and confidently communicate about AI to colleagues throughout the organization.
4. Reinforce Robust Risk Management
Building a robust risk management framework can help calm fears and build confidence within your FP&A team. First, AI's integration into finance must comply with stringent regulatory and ethical standards. Second, demonstrating that AI systems are designed with robust risk management frameworks helps create buy-in in the finance team and across the organization. Regular stress testing, scenario analysis, and adherence to compliance requirements can highlight AI’s reliability and preparedness for wide-ranging situations.
5. Focus on Data Integrity
AI systems are only as good as the data on which they are trained. Ensuring the accuracy, quality, and relevance of data used in AI models is a critical step to building trust. In other words, your team needs to know with certainty that data governance practices, including regular audits and validations, are in place. These practices prevent errors or biases that could undermine decision-making or run afoul of ethical considerations.
6. Embrace Ongoing Education
Many finance pros have limited exposure to AI technology, which can breed skepticism. With training and education, step one is meeting each team member where they are with AI skills/knowledge. The second step requires tailoring trainings to get each team member up to speed without overwhelming them.
Further, developing a robust continuous education program at the front end of an AI integration is essential to ensuring team members remain confident and comfortable as the technology evolves. Regular workshops, webinars, and hands-on sessions can empower the team to use AI tools confidently.
7. Establish Performance Benchmarks and Key Metrics
Finance pros are well-versed in working in ways focused on performance and hitting key performance indicators (KPIs) and key metrics. That shouldn’t change when AI is introduced into the mix. In fact, providing clear metrics to evaluate AI performance can help finance professionals gauge AI’s reliability on an ongoing basis.
Regularly publishing performance benchmarks, error rates, and success metrics builds confidence in AI’s capabilities. However, make sure metrics are tailored to the specific needs of financial applications, such as accuracy in risk predictions or speed in transaction processing.
8. Plan for Potential AI Failures
No system, regardless of its potential capabilities and benefits, is perfect. AI is certainly no exception. Accordingly, clearly emphasize that the human element will also be a part of the AI equation and that consistent oversight by finance pros is essential. Establishing contingency plans and fail-safe mechanisms can also help finance professionals feel more secure in adopting the technology. By showing how potential failures can be identified, quickly addressed, and mitigated, you can reinforce a proactive approach to risk.
9. Keep Communication Lines Open
Regarding AI, your door should always be open for conversations about AI’s potential, risks, and challenges. Maintaining open lines of communication with and among your team allows for continuous feedback and improvement. Providing regular updates on AI advancements and creating opportunities for both scheduled and spontaneous AI conversations are critical to build long-term trust. Just as importantly, make sure that any raised concerns are quickly acknowledged and addressed.
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Ultimately, building trust in AI among your FP&A team is a marathon, not a sprint. Put in the time, commit to the training, and have a steady focus on your goals. When you do all three, you put yourself in a prime position to win the race to the future.
Curious whether your organization is AI-ready? Take our free AI-readiness assessment.
