Finding hidden costs in AI spending
As organizations rush to capitalize on AI’s potential, many find that high spending is yielding limited returns. Estimates suggest that around 90% of AI investments fail to deliver expected outcomes, a staggering figure considering the volume of resources allocated.
This often arises because companies lack a precise understanding of how to assess and optimize AI applications. Instead of clear visibility into which AI projects drive real value, enterprises frequently end up funding initiatives that absorb budgets without meaningful impact.
For most businesses, AI adoption involves a mix of machine learning, predictive analytics, and increasingly, large language models (LLMs).
Distinguishing valuable AI assets from those that drain resources, however, has become a key challenge. Without governance mechanisms that evaluate AI performance, ethical compliance, and operational impact, executives are left in the dark regarding their return on AI investments.
Organizations are starting to address this by investing in AI governance tools and processes that provide clear insights into AI performance and effectiveness. When properly managed, AI applications can greatly improve decision-making and competitive positioning.
Without strategic governance, however, AI investments risk becoming costly experiments with minimal business value.
Understanding the 90% waste problem in AI
Many companies deploy AI without a structured approach, resulting in fragmented efforts that lack alignment with core business objectives. Inefficiencies persists partly because identifying high-impact AI applications is inherently challenging.
AI models typically operate as complex, opaque systems, making it difficult to pinpoint direct outcomes from specific applications.
In addition to unclear ROI, companies face issues such as model bias and compliance risks, which add further complications. Since AI’s outcomes are not always explainable, companies may unknowingly deploy biased or ineffective models, contributing to this waste.
Ultimately, these inefficiencies point out the urgent need for systems that can monitor AI performance and provide transparency into how AI impacts business operations.
Billions in AI spending — Where’s the value?
The surge in AI spending is large, with companies like Accenture dedicating $2 billion annually to help businesses address the complexities of AI adoption. At the same time, companies such as Nvidia generate billions by providing the hardware and infrastructure that power these AI initiatives.
Despite substantial investments, many organizations still struggle to see value.
While large firms with deep pockets continue to funnel money into AI, they’re also searching for solutions that maximize their ROI and bring clarity to their investments.
For instance, without a clear view of which AI models deliver business benefits and which don’t, companies risk wasting funds on technology that does little more than increase their operational expenses.
Why AI demands a new approach to governance
While traditional governance focuses on managing assets, such as data and software, AI governance addresses unique issues like bias, model transparency, and effectiveness. These factors create new governance challenges that standard IT or cloud frameworks are ill-equipped to handle.
Bias is one of the most pressing issues in AI governance. For example, AI models may inadvertently favor or disfavor certain groups, leading to reputational damage or regulatory action.
Unlike IT governance, which primarily involves data security and compliance, AI governance requires continuous monitoring and evaluation of model behavior and outcomes to make sure they align with both regulatory standards and organizational values.
What sets AI governance apart
AI governance differs from data and cloud governance in that it involves oversight of model behavior, accuracy, and fairness.
Whereas cloud governance focuses on managing resources and infrastructure, AI governance dives into the algorithms and models that drive business outcomes. It’s a key distinction because AI can influence decision-making across finance, healthcare, and human resources—areas where biased or incorrect predictions can have severe implications.
Effective AI governance also emphasizes regulatory compliance, as industries face increasing scrutiny over algorithmic decision-making.
For example, financial services must make sure AI models don’t unfairly impact certain demographic groups, and healthcare organizations need to verify that AI tools support accurate diagnosis and treatment recommendations.
How holistic AI provides tailored oversight
Through integrating with existing data systems, Holistic AI offers an interconnected view of AI projects across the enterprise, letting organizations monitor and control their AI assets effectively.
Key features include AI project discovery, which identifies all AI initiatives within a company, and inventory management, which categorizes these projects to streamline oversight.
A unique benefit of Holistic AI’s platform is its automated risk alerts, which proactively notify companies of potential compliance or technical risks.
For example, if an AI model in the HR department exhibits biased decision-making patterns, Holistic AI’s platform can detect and flag this risk before it leads to regulatory or reputational fallout.
Making AI a high-return investment
Organizations view AI as an opportunity to gain competitive advantages, optimize operations, and create new revenue streams. The high-stakes nature of AI, however, means that without a solid governance strategy, investments could lead to more risks than rewards.
For Fortune 500 companies, the investment in AI can reach millions annually per application.
In high-stakes environments, AI applications that perform well can drive great value, while underperforming or misaligned models can create reputational or financial risks. In making AI governance a priority, enterprises can increase their chances of identifying and scaling productive AI applications.
How AI’s maturity translates into business value
Beyond generative AI, machine learning applications in areas like predictive analytics, customer segmentation, and operational optimization are already generating measurable returns for many businesses.
AI’s maturation as a technology now lets companies integrate it more deeply into their business models, provided they have adequate governance structures to manage risks. When done effectively, companies can capitalize on AI’s advanced capabilities while minimizing the risks associated with biased or inaccurate models.
Boosting visibility to drive accountability in AI
Companies often deploy complex AI models, yet lack insight into their inner workings, leading to challenges in accountability—affecting operational effectiveness and introducing risks—as models can make decisions that reflect unintended biases or compliance issues.
Transparent AI deployments let organizations track model decisions, assess their impact, and take corrective action if necessary.
For example, an opaque hiring model that unintentionally disadvantages certain demographics could lead to regulatory fines and reputational damage.
The fast pace of AI model innovation
With companies like Meta, OpenAI, and Google constantly releasing new versions of large language models, companies must decide which models best fit their needs.
Frequent upgrades lead to better performance and cost efficiency, but also require a strategic approach to model selection and deployment.
This competitive space benefits enterprises by lowering the cost of high-performance AI, but also creates complexity. Businesses need governance frameworks to manage this complexity, so that they can stay agile while mitigating risks associated with untested models.
Final thoughts
Are you steering your AI initiatives with the oversight needed to minimize waste, reduce risks, and truly amplify value? In a market where innovation and accountability go hand in hand, it’s time to ask: Is your AI strategy built to drive measurable success—or just riding the hype cycle?
