Article Getting AI Unstuck: How to Win Over Your CFO and Scale With Confidence
Why having your CFO on board is key to moving beyond pilot
By Insight Editor / 3 Jun 2025 / Topics: Data and AI
AI pilots can be exciting. They often show promise, deliver early results, and get teams thinking about what’s possible. But without the right structure — and without the right internal support — they tend to stall.
That’s where your CFO comes in.
When it’s time to move from experimentation to scale, the conversation shifts from technical feasibility to business value. You need more than a good demo. You need a clear, measurable case that speaks to cost, risk, and revenue.
You need to speak the CFO’s language.
This guide explores how to spot a working pilot, communicate value, scale responsibly, and turn early wins into lasting momentum.
How to tell if your pilot is working and what red flags to avoid
Green flag: Clear success metrics and a shared understanding of what the pilot is meant to accomplish
Green flag: Regular stakeholder feedback
Green flag: Every decision ties back to your goals and success criteria
Green flag: The wider organization is engaged and adapting to the change
Red flag: When one enthusiastic stakeholder is driving the entire effort solo.
A healthy pilot involves a range of voices and input from across the organization. If it’s too siloed, it’s likely not scalable.
Success starts with clear metrics and ends with alignment. Everyone — from the initial champions to the operational teams — should understand what the pilot is trying to prove and how to evaluate its outcomes.
What do CFOs actually care about when it comes to AI?
That means your case for AI needs to address both cost efficiency and business growth. What risks are you mitigating? What costs are you reducing? Will this solution help acquire new customers, unlock new revenue streams, or improve profitability?
It’s not enough to talk about what the tech can do. You must show how it directly supports the organization’s financial goals. That includes scale. Will it scale without runaway costs? Can it grow without adding headcount?
If the answer is yes, you’re on your way to a winning case.
Your CFO isn’t there to approve technology — they’re there to approve value.
How to scale AI without losing control
Scaling AI responsibly means not switching on every capability at once or trying to “boil the ocean.” Instead, start with lower-cost, foundational use cases. Things like lightweight inference or token-limited models that allow you to prove value and gather insights without triggering runaway costs.
To manage cost as you grow, integrate cloud financial management practices early. This helps teams track spending, forecast ROI, and prepare leadership — especially the CFO — for the financial implications of scaling.
What works during a pilot, however, often isn’t enough to scale. Many AI efforts stumble after early success because the team doesn’t evolve its approach. The strategy, stakeholders, and governance model all need to mature.
Scaling means treating the project as a new phase, not just a continuation of what came before. It demands different communication, deeper alignment, and more robust governance.
Get your CFO on your side
Learn how to turn your AI pilot into a CFO-approved success story.
How to build your AI business case for the CFO
Success hinges on building a relatable narrative. Sharing wins internally with leaders like the CFO means explaining what was solved and why it matters. This isn't about technical jargon. It’s about showing how a specific AI initiative led to a business outcome that aligns with organizational goals, such as reduced cost, faster time to insight, or improved efficiency.
Everyone, including finance leaders, connects with a story that reflects business impact over one with technical details alone. That narrative earns attention and trust. It sets the stage for what comes next.
Finance departments across industries are already using AI to streamline processes and improve outcomes.
Picture this: It’s quarter-end, but instead of scrambling to reconcile data, flag errors, and finalize reports, your team has already surfaced key discrepancies and automated the bulk of the manual work.
In successful deployments, AI-powered finance assistants have helped organizations reduce manual close activities by up to 40%, cut overtime costs, and — for the first time in recent memory — let teams head home on time for the long weekend.
While the story gets buy-in, data validates it. Dashboards, KPIs, and cost-saving metrics show that the results are real. This approach — story plus proof — helps your CFO see the value in concrete terms.
But more than that, you should build a culture of internal champions by giving teams a "sandbox" to experiment in. Give them the tools to start creating their own use cases. This grassroots momentum generates repeatable wins, which become a pipeline of future ROI.
That’s a powerful message to your CFO: not only did this project succeed, but it also laid the foundation for scalable innovation, and it's energizing the workforce to keep going.
Tell a compelling story of business impact, back it up with real data, and demonstrate how it’s empowering the rest of the organization to deliver repeatable value.
How Insight helps with implementation and development
Once your AI strategy is in place, implementation is where real progress happens. Insight brings a clear, proven methodology to guide every stage of development — from planning and execution to long-term growth.
Our approach connects strategy to outcomes through three core phases: planning, building, and scaling. These phases weren’t built in isolation. They come from years of working alongside clients across industries, distilling best practices into a repeatable model that aligns technology, business goals, ROI, and governance.
In the early phase, Insight helps you map out the journey ahead. We validate your use cases, prioritize them, and work with your stakeholders to define what success looks like. This includes assessing technical feasibility, designing architecture, building business cases, and aligning on cost expectations. It’s about creating a shared vision of where you're going and how to get there — before the real build begins.
The implementation phase is where we turn plans into action. We lead with an automation-first mindset to reduce complexity and accelerate progress. From infrastructure to fine-tuning models using services like AWS SageMaker, we build scalable solutions that align with your goals.
Alongside the technical rollout, we also focus on upskilling your teams. Whether it’s prompt engineering, data architecture, or embedding Retrieval-Augmented Generation (RAG) strategies, we work together with your developers and business users so they gain the skills to take the solution forward.
Insight doesn’t just build and leave. Our development support model helps you manage what’s been deployed, while continuing to evolve it. That means helping you refine models, integrate new use cases, and scale AI across your organization in a controlled, cost-effective way. We partner with your team to put governance structures in place, optimize performance, and keep pace with a rapidly changing technology landscape.
As AWS introduces new capabilities, we help you evaluate and adopt what matters — without starting from scratch. It’s this ongoing support that turns one successful use case into an enterprise-wide capability. With Insight as your implementation partner, you're not just deploying AI. You’re building an AI practice that can grow, adapt, and deliver value long after launch. Build the case, show the impact, and give your CFO the one thing they can’t ignore: results that scale.
