Why AI Success Hinges on Financial Discipline

Artificial intelligence (AI) is the biggest disrupter to business since the Industrial Revolution. Anyone who has managed a financial portfolio of technology initiatives knows it can feel like an inaccurate science. This is especially true when dealing with the volatile, consumption-based nature of AI innovation and its unpredictable costs and results. In addition, this new reality requires infrastructure, data, and security management teams to be a part of innovation planning from day one. This engagement is essential if teams are to mitigate risks and determine the impact of high dollar cloud transformation efforts. That shift throws traditional, and even agile, financial planning practices off balance.

Financial planning governance sits at the center of technology innovation and, in an AI-driven world, it must evolve. AI requires a new class of cost governance, including new approaches for how costs are estimated, budgeted, managed, and forecasted. Teams that treat AI as just another tool or project line item will fail to see the true value of their investments. AI must be viewed as a new type of resource cost, one that behaves differently than human labor or infrastructure.

These “AI assets” fundamentally change how work gets done; they are not just another server or software license. They must be trained, their capabilities evolve over time, and their value, and risk, scales with usage. Costs are driven by consumption, yet predicting spending is challenging due to token variability and the limitations of current FinOps and financial planning and analysis (FP&A) tools, which fail to address the real problem.

Applying proven strategic portfolio management to a new challenge

To succeed in the AI era, leaders must manage AI spending by looking both forward and backward, combining forecasting discipline with real-time accountability.

1. Forecasting AI costs (the forward look)

A new collaborative model requires a new approach to planning. You need a technical plan for AI initiatives that is approved by business leaders and includes infrastructure costs, agent maintenance, data management efforts, and security protocols.

This is achieved by creating two distinct, but connected financial views:

• A dynamic consumption forecast. First, you need a dynamic forecast of expected AI consumption and costs. This plan must be designed to quickly inherit the reality of change—as AI usage scales up or down, it must be versioned to reflect that new operational reality. While technology business management (TBM) models are popular in these scenarios, outside-in cost analysis is not granular enough in most cases. In addition, cloud costing modules are not inclusive enough of AI and labor resources for teams to see the full portfolio.
• An executive-approved investment line. Second, you need a portfolio manager's clear “line in the sand”—the budget that executives agreed upon for AI strategy and initiatives—to determine corporate and product AI objectives. This budget must be inclusive of the above costs and must be traceable so there can be accountability, both for predicting cost and ensuring investments deliver the value desired.

This means one system for infrastructure costs and initiative costs is needed to do effective portfolio planning and make the most accurate financial predictions. Without this multi-relational portfolio planning approach, your organization is exposed to fluctuating consumption patterns, while lacking visibility into whether expenditures are actually producing results.

A dual-plan model—dynamic consumption forecasts paired with an executive-approved budget—delivers a clear, versioned view of expected infrastructure and initiative spend. (This is in contrast to tools that use algorithms based on bills to provide inconclusive answers and curated explanations of unpredicted results.) With this structure, your newly formed, collaborative, and cross-functional teams gain the control, transparency, and confidence needed to scale AI responsibly and strategically.

2. AI variance analysis and total cost of ownership (the backward look)

A forecast is only useful if you can compare it to what actually happens. To govern AI spending, you must be able to track consumption costs in near-real time, at a level of detail that enables meaningful financial accountability.

This requires a system capable of financial transaction processing—performing debit and credit tracking of all actuals and interacting directly with your financial systems of record. This represents a move beyond AP processing and FinOps cost allocation to an understanding of AI cost at the technical resource level. Establish a bottom-up costing approach that is tied directly to the infrastructure and initiative plans outlined above.

This capability is the foundation for powerful variance management and, in this day and age of automation, it is simple. It allows you to track budget-to-actual information naturally, but more importantly, to see the variance between the dynamic operational forecast and the official initiative budget. This provides a complete picture of your AI initiatives' total cost of ownership (TCO) and delivers controlled, predictable AI spending that includes infrastructure enablement and supportability costs.

The benefits of Clarity by Broadcom: Automated financial governance for AI

Automating these processes within a single, centralized solution provides the guardrails required to adopt AI responsibly and to scale successfully.

Clarity is uniquely built for this challenge. Financial and resource management is the core of our architecture. These capabilities are supported by definable investment management, financial transaction processing, and a free, fully integrated data warehouse that is capable of reporting across millions of variables in real time.

Importantly, Clarity can work independently, managing the entire portfolio, or it can work in conjunction with other project, service, and or agile solutions. Governance without disruption is why our solution is used in the largest and most complex organizations in the world. We know you already have an ecosystem of information. Our solution is designed to give you financial traceability, accountability, and predictability—without forcing you to take on heavy change management.

Clarity delivers big data management for AI governance, offering these capabilities:

• Manages cost at a level that maximizes organizational collaboration and control. Clarity’s financial-grade rate matrix can manage the complex, consumption-based rate models and cost sources of AI and infrastructure services, eliminating the need for manual data mashing. This does not eliminate the need for data consumption analysis. Instead it gives your organization the middle ground it requires to raise the infrastructure cost context to application and product levels so stakeholders’ cloud commitments can be made with clear visibility into the impact of AI initiatives and regulations.
• Zero impact on, or replacement of, service or delivery management systems. This layer of financial governance elegantly controls financials without restricting operational delivery. Our solution is designed to integrate with and consume data from your other systems to provide a complete financial picture. With Clarity, you can track the realization of your benefits and OKR plans, without disrupting the way your teams work.
• Works seamlessly with your financial systems of record. This is a unique differentiator for our platform. We offer an operational system that provides financial transaction processing. The solution does financial attribution that is seamlessly maintained and governed against organizational delivery and team structures. This yields automated, real-time visibility that business, finance, and operations stakeholders can all understand. Clarity’s unique project accounting backbone provides the auditable, traceable financial control necessary to turn the disruptive potential of AI into tangible, well-managed assets.

Conclusion

Ultimately, the shift to AI requires a fundamental convergence of financial operating models. It demands a new partnership between finance, operations, and innovation, with financial governance providing the shared language that keeps these groups aligned.

Treating AI as just another line item on a spreadsheet is a fast path to uncontrolled spending and unrealized value. By establishing a centralized platform for financial governance—one that is proven, auditable, and built on a backbone of project accounting—you gain greater control.

This is how organizations move beyond pilots and proofs of concept. It’s how they turn AI from experimental cost into predictable value creation, with transparency, accountability, and confidence.

Ready to turn AI from a cost experiment into a governed, value-driven portfolio? Get Clarity.