Guide · April 16, 2025

How to Get FP&A Buy-In for AI Investment

Key takeaways

Even with full executive support, your FP&A team must embrace AI too — skipping their buy-in risks limiting the benefits and creating a host of headaches managing the team.
This guide lays out a 7-step playbook for earning FP&A trust: engage early, highlight AI’s potential, emphasize transparency, reinforce risk management, focus on data integrity, embrace ongoing training, and set performance benchmarks.
Loop your team in from the start — listen to concerns and involve members in designing, testing, and implementing the AI systems themselves.
Make AI explainable and auditable: show how models work, standardize transparency practices team-wide, and turn trained team members into informed ambassadors.
Publish clear metrics — accuracy, speed, ROI — and treat trust-building as a marathon of small, quick wins, not a single sprint.

Why FP&A Buy-In Matters

When you’re launching and sustaining a successful artificial intelligence (AI) integration, getting buy-in from the company leadership team is essential.

Yet even with their full-throttled support, your financial planning and analysis (FP&A) team must be all-in on embracing AI innovation. You otherwise run the risk of severely limiting the potential benefits and creating a host of headaches managing your team. In short, gaining buy-in within your FP&A team is crucial to success.

Of course, FP&A pros, by nature, can be a cautious crowd. Creating a solid foundation of trust is therefore vital to assuring a smooth AI integration. This playbook offers a clear 7-step path to building and maintaining trust.

Step 1: Engage Your Team Early — And Often

The time to loop in your FP&A team on an AI integration process is not hours before you present the plan to the executive team. After all, the last thing you want is FP&A pros to feel blindsided or overlooked as you prepare to launch an AI integration.

Here’s what to do instead:

Establish clear communications from the start of an AI implementation effort, providing opportunities for in-person meetings to provide updates and answer questions.
Be a good listener. The people on your FP&A team likely have varying experiences and comfort levels with AI. Accordingly, don’t dismiss concerns, particularly regarding how AI may impact an individual’s daily work.
Involve team members when designing, testing, and implementing AI systems to assure the technology reaps the most benefit by addressing real-world challenges. Collaborative efforts foster a sense of ownership and confidence in the results.

Step 2: Highlight AI’s Potential

Hearing real-life testimonials from finance peers about data-focused results provides powerful and tangible evidence about AI’s potential. Plus, those firsthand accounts offer a fast-track to building enthusiasm while addressing potential reservations.

Here’s how to best highlight AI’s potential:

Look for case studies relevant to your industry where AI has delivered measurable value, without over-hyping the potential.
Highlight case studies from peer companies in your industry — or, at the very least, in an industry like yours.
Capture the momentum by taking the time to discuss the case studies with your team. Especially, focus on how some of the benefits highlighted could be achieved at your company.

Step 3: Emphasize Transparency and Clarity

FP&A pros are accustomed to working with transparent, trusted methodologies. To gain and keep the FP&A team’s trust, AI solutions must be explainable, intuitive, and auditable.

Here’s how to ensure that’s the case:

Explain the model

Provide clear parameters, demonstrating how models work and explaining the logic that drives decisions and forecasts.

Standardize best practices

Identify and implement best practices that assure greater transparency, and then ensure the practices are applied across the FP&A team.

Empower ambassadors

Encourage your FP&A team — after you’ve built trust, of course — to be informed ambassadors who can effectively and confidently communicate about AI to colleagues.

Step 4: Reinforce Robust Risk Management

Building a robust risk management framework can help allay fears and build confidence within your FP&A team. Thus, be open about acknowledging potential risks and the need to create guardrails to minimize those risks.

Here’s how you can do just that:

Ensure AI’s integration complies with stringent regulatory and ethical standards. Then, establish a plan to track potential regulatory changes that might impact your organization.
Emphasize that AI systems are designed with robust risk management frameworks, which helps bolster buy-in.
Implement regular stress-testing, scenario analysis, and adherence to compliance requirements to highlight AI’s reliability and preparedness for wide-ranging situations.

Step 5: Focus on Data Integrity

AI systems are only as good as the data on which the systems are trained. Ensuring the accuracy, quality, and relevance of data used in AI models is thus a critical step to building trust.

Here’s how you can keep the focus on data integrity:

Govern the data

Ensure your team knows with absolute certainty that data governance practices — including regular audits and validations — are in place to prevent errors.

Surface bias

Encourage ongoing discussions about how to identify potential biases that could undermine decision-making or run afoul of ethical considerations.

Widen the lens

Consider engaging a diverse group of FP&A pros and co-workers from throughout the organization to help flag any blind spots that could introduce biases into data.

Step 6: Embrace Ongoing Training

Many FP&A pros have limited exposure to AI technology, which can breed skepticism. In other words, don’t just assume your team members understand and will embrace AI.

Here’s what to do instead:

Meet each team member where they are, tailoring trainings in ways that get each team member up to speed without overwhelming anyone.
Develop a robust continuous education program at the front end of an AI integration to ensure team members remain confident and comfortable as the technology evolves.
Hold regular workshops, webinars, and hands-on sessions to empower team members to use AI tools confidently.

Step 7: Establish Performance Benchmarks and Key Metrics

Finance pros are well-versed in how to keep work focused on performance and hitting key performance indicators (KPIs) and key metrics. That shouldn’t change when AI is introduced into the mix.

Here’s how to establish benchmarks and key metrics:

Provide clear metrics to evaluate AI performance, which helps finance professionals gauge its reliability on an ongoing basis.
Consistently publish performance benchmarks, error rates, and success metrics to build confidence in AI’s capabilities.
Tailor metrics to the specific needs of financial applications, such as accuracy in predictions, speed in forecast processes, or return on investment of AI solutions.

Building trust in AI among your FP&A team is ultimately a marathon, not a sprint. However, with purpose-built AI solutions, you can achieve AI trust and success with finance through small, quick wins by achieving KPIs in a short amount of time.

Conclusion

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FAQ

Frequently asked questions

Why is FP&A team buy-in important for AI investment?

Even with full leadership support, an AI integration needs the FP&A team fully on board. Without their buy-in, you risk severely limiting the potential benefits of the technology and creating a host of headaches managing the team — so gaining buy-in within FP&A is crucial to success.

What is the 7-step playbook for building FP&A trust in AI?

The seven steps are: engage your team early and often, highlight AI’s potential, emphasize transparency and clarity, reinforce robust risk management, focus on data integrity, embrace ongoing training, and establish performance benchmarks and key metrics.

When should finance leaders involve the FP&A team in an AI rollout?

As early as possible — not hours before presenting the plan to the executive team. Be a good listener about varying comfort levels with AI, involve team members in designing, testing, and implementing the systems, and establish clear communications with regular in-person updates from the start.

How can finance leaders show that AI systems are transparent and trustworthy?

Provide clear parameters that show how the models work and explain the logic driving decisions and forecasts, standardize transparency best practices across the FP&A team, and encourage trained team members to become informed ambassadors who can confidently explain AI to colleagues.

What metrics help build confidence in AI’s reliability for finance teams?

Provide clear metrics to evaluate AI performance, consistently publish performance benchmarks, error rates, and success metrics, and tailor metrics to financial needs such as accuracy in predictions, speed in forecast processes, and return on investment.