AI Buyer’s Guide for Finance: What To Look For, What To Ask and How To Get Buy-In

There’s growing pressure to adopt AI, especially as companies like Shopify and Fiverr roll out AI-first strategies across all their functions. And finance teams aren’t sitting on the sidelines either. 

In our 2025 State of Strategic Finance Report, 57% of finance leaders we surveyed said they’re already using AI in at least some areas of their operations. But while early adoption is underway, there’s still considerable room for improvement. 

Many FP&A teams still hit a wall when it comes to scaling AI use for more critical, everyday workflows. A separate 2025 report from The Hackett Group confirms this: the research revealed that only 4% of finance leaders are actively maturing and scaling their use of generative AI. One of the most commonly cited barriers to AI adoption is a lack of implementation skills and experience. 

The skill gap, along with concerns about data privacy, intellectual property leakage, and regulatory risks, makes it harder for finance teams to objectively evaluate AI tools, build consensus and prove ROI.

Another common blocker is resistance from IT teams. 

Many finance professionals have experienced this firsthand. Just bringing up a new AI tool can spark a wave of IT concerns about data security, integration challenges and compliance. And these concerns are valid—IT teams are only trying to manage risk. But when Finance and IT aren’t aligned early, AI initiatives stall in pilot mode, leaving teams stuck with manual processes and growing pressure from leadership to deliver more with less.

That’s why we created this guide to help finance leaders and AI committees confidently choose the right solution for their ideal use cases and secure buy-in from across the organization.

In the sections that follow, we’ll walk through how to:

• Identify where you can apply AI to your finance workflows

• Evaluate AI platforms for the most critical features, including data privacy 

• Ask the right questions when speaking to vendors to avoid buying the wrong tool

• Get buy-in from IT teams who are (rightly) cautious about the risks of AI in finance

The Top AI Use Cases for Finance Teams

While many finance teams are still figuring out how to scale AI, others are already automating critical parts of their workflows and gaining measurable traction.

In our 2025 State of Strategic Finance Report, we asked finance leaders which AI technologies they were already using in their workflows. The top use cases were data analytics and predictive analytics, followed by anomaly detection and generative AI.  

These applications are helping teams surface insights faster, strengthen forecasting, and reduce time spent on manual analysis. These signals of where AI is already delivering impact for finance teams can be instrumental when building a business case for your own company. 

Take the Association for Institutional Research (AIR) for example, a nonprofit that helps colleges use data to drive smarter operational decisions. AIR’s small finance team, like many others, faced resource constraints that made it difficult to dedicate time to important strategic work like scenario planning. 

To address this, Charles McCumber, AIR’s Director of Finance, began using Vena Copilot, an agentic AI assistant for FP&A, to automate the repetitive, data-intensive aspects of their budgeting and forecasting process, without adding headcount or straining resources.

Charles and his staff accountant can ask questions like “How does our budget compare to actuals?” or “How is Program A performing this quarter?” and get immediate, reliable answers they can act on right away. 

The impact has been clear: AIR’s finance team has cut recurring reporting time by 25 to 50 percent, completed audit season faster than ever, and now spends more time on strategic work. With Vena Copilot, they’re accessing insights faster and responding to internal requests without the 15 to 20 minute wait times that used to slow them down.

Still, stories like AIR’s are the exception. While many finance teams recognize the benefits of AI and automation, most are still in the early stages of their implementation journey. In many cases, getting buy-in from stakeholders, especially IT, slows progress or stops it entirely.  

That’s why you need to know how to evaluate the right tools, justify the investment, and preemptively anticipate objections before they become blockers.

So, what features should you focus on when researching and pitching an AI tool to stakeholders or your company’s AI buying committee?

What To Look for When Choosing an AI Tool for Your Finance Team

To build a strong case for AI adoption in a high-stakes function like finance, it’s essential to address the areas that raise the most hesitation and objections, especially from stakeholders like IT, Legal, and even your CFO.

That means looking beyond flashy features and focusing on capabilities that will move the needle for your team the most.

The table below maps common objections to key features to look for from AI solution vendors, along with suggested ways to frame their benefits when speaking to your buying committee or executive team.

Regardless of your workflow, the features outlined above are critical to prioritize when watching a demo, testing a tool, or speaking with a sales rep. 

When carrying out your research, you’ll also want to be sure to prioritize:

• Vendors who offer real-world use cases tailored for finance teams

• AI agents and tools with built-in learning support or onboarding help

• Simple, low-lift integrations that don’t require your IT team to set it up and maintain

Once you’ve checked those boxes, the next step is to ask the vendors you’re evaluating questions that go beyond surface-level promises to assess whether the platform can meet your team's specific needs, scale with your processes, and deliver real business value.

What To Ask Vendors When Evaluating AI Platforms

Most AI platforms sound impressive in a demo, but that doesn’t always translate to real value once the tool is in your hands.

And since finance teams operate in high-stakes, tightly regulated environments where speed, accuracy, and auditability are non-negotiable, it makes sense to go beyond surface-level evaluation. 

The questions below will help you validate capabilities, spot red flags early, and ensure you’re investing in a tool specifically designed for finance teams. 

Once you've validated the platform’s capabilities, reviewed pricing, and asked the right questions, the final step is getting internal buy-in. Your ability to translate features into business value will make or break the decision. 

How To Get Buy-in From Stakeholders

To get executive buy-in for your top choice of AI platform, you’ll need to tailor your pitch to different stakeholder priorities—from finance leadership to IT and procurement. 

Here’s how to build a business case that speaks to what matters most to each decision-maker.

• Start with the problem, not the product. Clearly define the pain points your team is facing today (such as slow reporting cycles, manual forecasting, missed opportunities), and the cost of leaving those concerns unaddressed.

• Tie AI features to specific outcomes. Show how the tool addresses those problems directly, such as faster reporting, fewer errors and more time for strategic planning.

• Reference internal goals. Align your proposal with broader company or department objectives like digital transformation, operational efficiency or cost reduction.

• Pre-empt stakeholder objections. Use the concerns table in the section above to proactively address potential pushback on security, complexity or ROI.

• Involve IT and data teams early. Bring technical and compliance stakeholders into the conversation to prevent delays later on.

• Show proof with case studies or pilots. Highlight examples of other companies using the tool successfully, or propose a short pilot to test value in your environment.

• Frame it as an enabler, not a replacement. Emphasize that the AI tool supports your FP&A team’s existing workflows rather than overhauling them.

• Quantify the potential impact. Estimate time savings, cost efficiencies, or accuracy improvements the tool can deliver using vendor-provided benchmarks or internal baselines.

• Ask for a small initial commitment. Propose a low-risk starting point like a sandbox trial, limited rollout, or monthly license to lower the barrier to approval.

Choose an AI Tool That Addresses Concerns Your Executive Team Has

AI adoption in finance is about trust, control, and measurable impact. That’s why it’s important to choose a platform that solves real problems in everyday finance workflows.

That’s where Vena Copilot stands out.

Unlike general-purpose AI tools adapted for finance, Vena Copilot was built from the ground up with input from real FP&A teams. It uses a proprietary rules engine trained on hundreds of everyday questions that finance professionals actually ask. The result is a tool that feels familiar, fits right into your existing workflows, and gives teams the visibility and control they need to move fast while maintaining data accuracy.

Here’s how Vena Copilot addresses the core concerns outlined in this guide:

• Data quality: Vena Copilot works with the same structured data your team already uses in Vena for planning and reporting. You can review how each answer was generated and give feedback to improve future responses.

• Data privacy and compliance: Vena follows enterprise-grade security protocols, including end-to-end encryption and strict data access controls. Your data is never used to train external models.

• Integration and workflow fit: Vena Copilot integrates with the systems finance already uses, including Microsoft Teams, helping enhance your existing processes without requiring major change.

• Explainability and oversight: Every output is tied back to its source. Finance teams can review, validate, and explain results clearly to auditors, executives, or regulators.

Several finance teams have already seen measurable gains after adopting Vena Copilot.

Kuali, for example, used Vena Copilot to help its lean finance team scale without adding headcount. By automating manual processes and enabling self-service analysis, Copilot freed up time for more strategic work and empowered business leaders with faster, more reliable insights.

Hear from industry-leading businesses as they share how they’re solving complex financial challenges with Vena Copilot and get practical advice for getting started with AI.