Breaking Barriers: How Enterprise CFOs Can Leverage AI to Optimize Costs and Drive Strategic Value

By Rich Nowalk, Chief Strategy Officer of Opex and Joe Mathews, CFO of RapidScale

AI is more than just a technological upgrade; it’s a strategic imperative for enterprise leaders, especially CFOs. The challenge isn’t whether to adopt AI, but how to do it in a way that delivers against strategic goals, with measurable cost optimization and long-term value. When effectively implemented and maintained, AI can streamline operations, enhance decision-making, and unlock new opportunities for financial control and growth.

CFOs don't need to be technologists, but when evaluating projects and business cases, they must understand both the project's data readiness to leverage AI and the financial tradeoffs of the different development and deployment models. As strategic resource allocators, CFOs can offer a critical eye on the technology decisions shaping long-term value.

Despite AI’s immense potential, the path to successful adoption is often filled with obstacles. These include scattered data, unclear data governance, and limited datasets. Here’s how CFOs can lead their organizations through a smarter, more cost-effective AI journey.

Get Data-Ready Before You Get AI-Ready

Success with any AI project starts with data, not infrastructure, and AI projects usually start as data architecture projects. Most enterprises struggle to make their data usable, let alone intelligent. Legacy systems, siloed platforms, and inconsistent source design (think data from a CRM like SFDC) make it difficult to extract meaningful insights. In fact, 81% of IT leaders cite data silos as a major roadblock to digital transformation. Additionally, not forecasting for these costs by pressure testing the team’s data readiness can cause projects to stall, or run out of budget quickly.  

To make AI work, CFOs and their C-suite colleagues must prioritize integration. Without unified data, automation becomes fragmented, analytics lose context, and financial forecasting suffers. Integration is the first step, centralizing data in modern warehouses or lakes, connecting systems through ETL pipelines and APIs, and fostering cross-department collaboration to break down silos. Metadata management also plays a key role in improving visibility and control.

But integration alone isn’t enough. Effective data governance is essential to ensure that AI initiatives are built on trustworthy, secure foundations. Governance acts like a security guard – setting clear rules, enforcing protections like encryption and access controls, and monitoring access through regular audits. Poor governance erodes trust in data security, creates ambiguity around ownership, and slows AI adoption.

By striking the right balance between accessibility and security, enterprises can empower teams to innovate with confidence. Unified data and strong governance not only unlock deeper AI insights but also simplify compliance, strengthen oversight, and build a more agile, data-driven culture.

Match Workload to Infrastructure – Not the Other Way Around

Once data is ready, the next decision is where to deploy and data-augment AI models. Public and private clouds both have merits, but cost-effectiveness can depend heavily on the workload and usage profile. For instance, long-running batch workloads using Large Language Models (LLMs) typically involve processing massive volumes of data offline, where the primary goal is high-throughput and cost-efficiency rather than immediate, interactive responses.

Public cloud environments like AWS, Azure, and Google traditionally deliver cost efficiency, but AI workloads can consume advanced hardware like GPUs in ways that don’t scale easily.

Here’s where private cloud shines. Unlike hyperscalers who must maintain excess GPU capacity for unpredictable demand, private cloud solutions can offer advanced hardware with consistent capacity, keeping access costs predictable, which is especially valuable  for workloads that require high-throughput processing (think large-scale classification against millions of invoices) or performing bulk content analysis (e.g. entity extraction across thousands of legal documents) where maximizing the number of documents or tokens per second is crucial.

For highly burstable production workloads that require extreme elasticity, public cloud may be worth the premium for guaranteed availability. But for more consistent workloads, even those with high compute requirements, private cloud can deliver the same performance at a fraction of the cost. It is critical that your AI workload characteristics drive infrastructure choices: batch LLM operations (such as information extraction or large-scale data processing) excel on a static, fixed infrastructure where predictable resource demands ensure cost-efficiency and control. Conversely, sporadic AI inference tasks with highly variable demand (conversational or Agentic AI) are a better fit for the elastic nature of public cloud infrastructure. By playing an active role in this technology decision making, CFOs can help ensure the chosen infrastructure matches the business importance and cost-effectiveness of each workload.a

Strategic Alignment

AI should never be a solution in search of a problem. Its value lies in how well it aligns with your business goals and how well your teams are prepared to use it. Too often, organizations pursue AI initiatives not tied to a clearly defined business or financial outcome.

The workforce is ready to embrace AI, and AI opportunities will only continue to increase in the workplace. According to the Cox Business Workplace Technology Survey, more than 60% of Gen Z and Millennial employees feel positive about AI’s growing role at work, and more than half believe AI increases their team’s productivity. For CFOs, this presents a powerful opportunity: align AI investments not only with financial objectives but also with the expectations and readiness of the workforce.  

With an engaged workforce at hand, CFOs can work with their teams to address financial challenges that AI can help solve, such as reducing operational overhead, improving cash flow forecasting, or enhancing procurement and supply chain efficiency. Start with proof-of-concept pilots to validate your approach and scale accordingly.

Bring These Strategies to Life in Your Organization

AI adoption is a fast-moving journey, and CFOs have the map – helping to steer decisions, manage budget, and avoid detours. With the right strategies, they help the organization reach its destination: smarter insights and data-driven growth.

We invite you to explore these strategies further at our upcoming event:

OPEX AI DAY

Hosted by RapidScale

October 23 | 1–6 PM EST

Register here: https://www.opexaiadvisor.com/ai-day

Don’t miss this opportunity to hear from industry leaders and discover how AI can drive smarter financial decisions.