Defining a Financial Strategy for AI Investments

By Kayhan Iqbal and Poonam Bhargava

In today’s rapidly evolving technological landscape, one thing is clear: AI is not just a buzzword — it’s a game changer.

With each passing day, the AI arena witnesses the emergence of new players and services, contributing to an explosion of possibilities and a promise to add tremendous value to businesses. This promise ranges from quantitatively improving the bottom line and improving operational efficiencies, to qualitatively improving customer retention and providing an even greater competitive advantage.

However, amid these thrilling AI technology shifts and value promises, a familiar concern looms large, one that resonates with every forward-thinking company aiming to harness the power of artificial intelligence. This resounding concern being, “Will my AI investments be financially viable and actually generate value for my organization?” The ongoing AI costs can be astronomical if not planned, governed, and optimized properly.

At Slalom, we recognize that AI introduces a fresh set of challenges. Leaders are confronted with critical decisions as the AI landscape continues to expand with new startups and services, seemingly every day. They must determine the types of AI use cases worthy of their investments, choose the right partners or services, and decipher the intricacies of the new billing models and components associated with machine learning (ML) and AI services to establish appropriate AI cost visibility, cost predictability, and cost optimization.

Let’s take a deeper look at these burning questions:

• What are the different benefits you can expect from your AI investments?
• Why is having an up-front sound financial strategy and ROI analysis critical for your AI investments? And what makes AI investments complex to understand and manage?
• What are the critical steps to identify the right AI investments and ROI analysis to understand and realize AI’s value?

What benefits can AI provide?

Value in AI encompasses a broader spectrum of benefits, including but not limited to:

Increased sales

AI can intelligently and meaningfully map clients with direct, cross-sell and upsell products, increasing the probability of successful sales.

Enhanced customer experience and retention

AI’s power can be used to enhance customer experience all the way from providing AI-driven meaningful customer engagement and intelligently solutioning for their business problems as a partner, to even proactive prevention of production outages.

Reduced errors and increased efficiencies

AI can automate repetitive tasks, reducing costs related to human error and inefficiencies and freeing up employees to focus on more strategic initiatives.

Cost savings

Through intelligent insights and content generation, AI can lead to cost reductions in various business processes, such as marketing and sales. Thanks to levels of automation and process streamlining we’ve never seen before, AI can drastically reduce the time spent on cost consuming areas.

Innovation and competitive advantage

AI can generate new ideas, designs, and content, fueling innovation within an organization, positioning your organization as a leader in your industry.

Why is creating an AI financial management strategy early so critical for AI value realization?

Defining a financial strategy for AI investments is a key shield against potential investment pitfalls. It acts as a safeguard to ensure your resources are allocated with limited waste early on, but more importantly is key to ensuring that true value is being realized from your investments, helping foster innovation and achieve your business objectives.

The complexity of GenAI pricing models

Generative AI (GenAI) pricing models are more complex than previous IaaS/SaaS models due to:

• Nuanced granularity
  AI services often bill by the minute or even by resource consumption, making cost analysis more nuanced. AI models may require a burst of compute power for a brief period, leading to unexpected spikes in costs if not carefully managed.
• Wide resource diversity
  AI encompasses a wide range of services, from natural language processing to computer vision and beyond. Each service may have its own unique pricing structure and dependencies, making it challenging to compare costs and allocate resources effectively.
• Complex data dependencies
  AI models heavily rely on data, which can be stored, processed, and transferred within the cloud ecosystem. Understanding the data-related costs and optimizing data pipelines are critical aspects of managing AI expenses.
• Potentially expensive continuous learning
  Machine learning models are not static; they evolve over time through a process called model training. This ongoing learning incurs costs that must be factored into any company financial models and estimates.

Selecting the wrong AI use cases to invest in can be costly

Without understanding the total cost of ownership and the potential value of the use case, organizations might not be able to prioritize the right investments that align with their business strategy and objectives. Different executives might want to invest in different AI use cases, but which of those are financially viable and will yield most value?

Mitigating financial risks proactively

Amid continued recent financial uncertainty, as well as continuous innovation and developments required in other product areas and domains across companies, understanding and getting AI investments right without falling into costly black holes is critical. Many companies today cannot risk a multimillion-dollar bet on failed use cases, so it’s imperative to have a sound strategy early on in order to have guardrails to stick to.

What are the steps for realizing value with AI?

Business case alignment

Before embarking on an AI journey, it’s crucial to create a business case that aligns your investment with your business objectives and lets you analyze different investment scenarios for their potential ROI and payback periods. It is also important to clearly define how you envision AI to benefit your organization, and include these in your business case. Regularly assess whether your AI initiatives remain aligned with your evolving business priorities and keep the business case up to date.

Perform a total cost of ownership (TCO) analysis

Understanding the TCO of AI is vital for an accurate ROI calculation. TCO includes not only the initial investment in technology but also ongoing costs such as maintenance, training, and infrastructure. A comprehensive TCO analysis ensures you have a clear picture of the true financial implications of your AI projects. This analysis will iteratively feed into the business case alignment.

Assess data needs

Understand the data requirements for your AI projects. This includes data collection, storage, processing, and governance.

Choose the right services

Select AI services or build in-house capabilities based on your specific needs. Evaluate services based on not only how well their offering meets the needs of your AI use cases and business case but also how easy or complicated their pricing models are, their track record in the market, and the level of support they offer.

Implement FinOps up front

Historically, as organizations moved to the cloud, they did not consider how that would change their financial sustainment and value realization model. The significant operational paradigm shift brought forth by cloud computing has introduced a novel discipline — FinOps. One of the biggest lessons learned from migrating to the cloud has been to shift FinOps left by considering the following factors early in your AI journey:

• Implement cost visibility, allocation, and monitoring. Take advantage of cloud-native or third-party FinOps tools and services designed to view, monitor, and optimize AI costs. Since these tools do not yet provide a holistic picture of AI costs, there might be a need to do manual modeling to monitor all AI costs. Track these costs against your financial forecasts regularly and adjust as needed to stay aligned with your objectives.
• Determine and track key ROI metrics. By utilizing the FinOps disciplines within unit economics, you can determine and track key ROI metrics for monitoring the impact of AI on the business, which helps ensure that use cases with the greatest net positive to the business are being funded properly.
• Instate governing policies and processes. Implement cost-control measures, such as cost allocation policies, budget thresholds, and automated scaling, to prevent unexpected expenses. Instate governing gates requiring technologists to think through the cost implications of their design decisions, and have those approved before deploying changes.
• Invest in talent readiness. Ensure that your team is trained in AI and FinOps best practices. Training and awareness can help cross-functional teams to come together to do more accurate cost forecasting, make more cost-optimized decisions and proactively mitigate cost overruns.

Call to action

AI financial management is not a onetime effort but a continuous process of aligning your AI investments with your business value. It requires a collaborative and agile mindset, as well as the right tools and metrics to measure and optimize your AI performance. By adopting FinOps in AI, you can unlock the full potential of your AI transformation, delivering faster, better, and cheaper outcomes for your customers and stakeholders.

Slalom is a global consulting firm that helps people and organizations dream bigger, move faster, and build better tomorrows for all. Learn more about Slalom’s human-centered AI approach and reach out today.