In today’s rapidly evolving landscape, business leaders are increasingly adopting AI technologies as they seek to enhance efficiency and drive improved business outcomes across various industries. The question has shifted from whether to adopt AI to how to demonstrate its value and justify further investments, especially when it comes to tangible return on investment (ROI).
According to Greg Holmes, EMEA Field CTO at Apptio, an IBM Company, organizations face a critical dilemma: balancing competitiveness in the AI era with the need to manage costs, resources, and intellectual property. While many AI-driven projects are initiated at the business unit level, collaboration with IT is essential for successful implementation. Unfortunately, many organizations still lack sufficient information to evaluate their technology spending decisions effectively.
Evaluating AI Investments
The dialogue in boardrooms has evolved from “What can AI do?” to “What value is it delivering, and at what cost?” This shift has heightened the need for clarity regarding the costs associated with AI, as organizations often find themselves evaluating ROI within silos. Different departments, such as finance and operations, communicate metrics in varied ways, making it difficult to track and realize the full value of AI investments once projects commence.
Achieving visibility across these metrics is crucial. A shared data source and a unified taxonomy help bridge gaps between departments to foster a more coherent understanding of AI’s value. As businesses assess AI’s performance, it is essential to translate technical metrics into business outcomes, especially given the compute and energy-intensive nature of generative AI workloads.
The Financial Implications of AI
Unlike previous technological rollouts, AI represents a continuous cost rather than a one-time capital investment. Data from Apptio indicates that over 90% of organizations expect their technology budgets to increase this year, with AI being a significant driver of new spending—a trend projected to continue through 2026. The ongoing costs associated with AI encompass IT infrastructure, energy consumption, human resources, and operational processes.
Organizations are tasked with training models and running inference, which requires substantial compute power, often hosted in energy-intensive data centers. Furthermore, the scarcity and expense of specialist AI talent compound the challenge, as boards scrutinize how these investments translate into measurable ROI. AI serves various functions, including data analysis, process automation, and fraud detection or cybersecurity, but clarifying the costs and benefits of scaling these applications is imperative.
Technology Business Management (TBM) frameworks can assist leaders in linking IT expenditure directly to business outcomes. This approach allows companies to identify wasteful spending, prioritize high-value projects, and avoid the overspending patterns that many have experienced with traditional cloud investments.
Strategic Data Management
Deciding where data should reside has emerged as a pressing concern for organizations looking to scale AI projects. Boards are increasingly anxious about potential intellectual property loss, regulatory compliance, and the risks associated with using sensitive datasets in third-party systems. While the cloud is essential for scalability, many organizations recognize that not every workload is suitable for cloud deployment. Some are opting to revert specific processes on-premises to regain control and ensure compliance.
This shift does not indicate a move away from the cloud but rather a more strategic application of cloud resources. A hybrid approach, which balances the agility of the cloud with the control of on-premises solutions, is becoming the default model for many organizations.
Strategic Recommendations for Business Leaders
As AI becomes an integral part of business operations, success hinges on treating it with the same discipline as any other strategic investment. Four guiding principles can help:
- Prioritizing visibility: Organizations need to monitor their AI investments and project performance closely to ensure that spending translates into tangible ROI.
- Taking a hybrid approach: Cloud strategies should be tailored to suit specific organizational needs, allowing teams to leverage scalability while maintaining data control.
- Staying on top of costs: The implementation of AI is not static, and continuous monitoring of costs is crucial for effective budget management.
- Considering the full picture: Teams must connect IT investments to measurable business outcomes, understanding that value extends beyond mere savings to encompass productivity and customer satisfaction.
Ultimately, technology leaders strive to make informed tech investment decisions that deliver real value and align with broader business objectives. As the landscape continues to evolve, collaboration among technology, business, and finance leaders will be key to demonstrating value and building internal expertise.
In this journey towards achieving AI ROI, the most successful companies will be those adept at managing trade-offs, making pragmatic investments, and strategically managing their data.
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