AI investment is growing, but value remains elusive

AI has moved from isolated pilots to business infrastructure in just four years of widespread enterprise use. It now supports organizations ranging from financial services to healthcare and the arts, putting its servers, storage, networking equipment, devices, energy demands, and supply chains alongside other critical systems.

That shift changes the investment question. The challenge is no longer whether to fund AI, but how to create durable value while managing operational, security, compliance, and lifecycle risks.

AI spending is rising, but returns remain uncertain

The source article says 71% of CEOs rank AI as a top investment priority. Yet capital spending is often failing to translate into operational results. Gartner estimates that at least 50% of AI projects are abandoned after proof of concept, while 56% of CEOs say their projects have delivered neither revenue nor cost benefits.

The figures point to a gap between experimentation and execution. Deploying a model or application is only one part of the investment. Companies must also account for the infrastructure that supports it, including energy-intensive systems, refresh cycles, maintenance, governance, and eventual retirement.

Why upfront price can obscure the real cost

As AI systems become embedded in critical operations, traditional business cases can miss costs that accumulate after procurement. These include usage, energy consumption, compliance, security controls, infrastructure changes, model evolution, electronic waste, and lost residual value from underused assets.

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The source’s research found that 64% of organizations have rejected a superior technology solution because of its upfront price. That decision can ease immediate financial pressure, but may create operational friction, limit scalability, or reduce performance over time.

“There’s a very clear limit to the amount of value you can create by just focusing on productivity and cost reduction.”

Investment decisions are also frequently made in organizational silos, even though the costs, risks, and benefits of AI extend across finance, operations, security, compliance, and sustainability. The source says fewer than half of organizations rate data protection (49%) or compliance capabilities (46%) as a high priority when making technology investment decisions, despite security, privacy, and compliance ranking among their leading concerns.

Total Cost of Impact puts lifecycle effects first

The article proposes Total Cost of Impact (TCI) as a broader way to evaluate technology investments. Rather than measuring only purchase price, implementation cost, or near-term return on investment, TCI examines what a technology will require, enable, constrain, and expose the business to throughout its lifecycle.

The model covers four areas:

• Financial impact
• Operational impact
• Security and compliance impact
• Environmental and social impact

Used at the investment stage, TCI is intended to make downstream trade-offs and risks visible before procurement decisions are finalized. It also provides a common framework for different business functions, helping them assess whether an investment aligns with strategic priorities.

The model is described as technology-agnostic. It can be applied to AI-enabled products as well as the infrastructure behind them, from procurement and deployment through scaling, maintenance, reuse, and end-of-life management.

Circularity becomes part of infrastructure planning

The source argues that AI investment cannot be separated from the physical technology estate that makes it possible. Organizations need visibility into energy use, data requirements, asset governance, maintenance, refresh cycles, and end-of-life processes.

That makes circularity a practical investment concern rather than a separate sustainability program. Extending asset lifespans, improving utilization, recovering residual value, reducing waste, and managing end-of-life risk can help address hidden costs while strengthening infrastructure resilience.

The article does not provide a price for implementing TCI or a release date for a related product. Its central recommendation is a decision framework: evaluate the full financial, operational, security, compliance, environmental, and social impact of technology before AI systems and their supporting infrastructure become difficult to change.

This article was produced as part of TechRadar Pro Perspectives. The views expressed are those of the author and are not necessarily those of TechRadar Pro or Future plc.