When Do Artificial Intelligence and Internet of Things Actually Create Economic Value?

Across energy, utilities, manufacturing, and infrastructure, organizations continue to invest heavily in Artificial Intelligence and Internet of Things systems. Yet empirical evidence shows that technology adoption alone does not guarantee productivity or financial gains.

This gap between technological capability and economic outcome is well documented in the academic literature.

Technology Is Not Productivity

Robert Solow famously observed in 1987 that “you can see the computer age everywhere but in the productivity statistics.”This insight, now known as the Solow Paradox, remains relevant in the era of Artificial Intelligence and Internet of Things.

Decades of research confirm that productivity gains emerge only when technology is embedded into organizational processes, decision rights, and incentive structures (Brynjolfsson & Hitt, 2000; Brynjolfsson, Rock & Syverson, 2019).

In asset-intensive systems, this distinction is critical.

Where Economic Value Actually Comes From

From an economic perspective, Artificial Intelligence and Internet of Things systems create value only when they alter one or more of the following fundamentals:

• Cost structures (not cost shifting, but cost reduction)

• Asset utilization and life-cycle performance

• Decision quality under operational constraints

• Capital efficiency and risk exposure

In neoclassical terms, technology contributes to growth through Total Factor Productivity, not just through capital deepening. Digital systems that increase data volume without improving decision effectiveness do not raise Total Factor Productivity—they often reduce it through added complexity.

Empirical studies support this view. Productivity gains from digital technologies tend to be delayed, uneven, and highly dependent on complementary investments such as process redesign, skills, and governance (Bloom, Sadun & Van Reenen, 2012; OECD, 2020).

Asset-Intensive Environments Are Different

Energy systems, utilities, industrial plants, and infrastructure networks differ fundamentally from consumer or software markets. They are characterized by:

• Long asset life cycles

• High sunk capital costs

• Tight operational constraints

• Regulatory and safety dependencies

In such environments, the economic question is not whether Artificial Intelligence and Internet of Things systems work technically, but whether they change operating decisions in ways that improve lifetime economic performance.

This includes measurable effects on:

• Operating expenditure versus capital expenditure trade-offs

• Downtime, degradation, and maintenance economics

• Risk-adjusted returns and capital allocation efficiency

From Visibility to Decision Authority

A recurring failure mode in digital transformation is the assumption that better visibility automatically leads to better outcomes.

Research and practice show that value is created only when data is coupled with:

• Clear decision ownership

• Embedded accountability

• Operational authority to act on insights

Without these elements, organizations accumulate dashboards rather than productivity gains.

An Economics-First Advisory Approach

At IoT Business Consultants, our work starts with economic discipline before technology deployment. We advise executives and investors on how to distinguish between:

• Automation that shifts costs versus automation that reduces them

• Pilot success versus scalable operational impact

• Technical feasibility versus economic relevance

The objective is not digital transformation as an end in itself, but sustained improvement in productivity, return on invested capital, and long-term asset performance.

Technology matters—but only when it is aligned with how organizations actually operate and how economic value is created over time.

Selected References

• Solow, R. (1987). We’d better watch out. New York Times Book Review

• Brynjolfsson, E., & Hitt, L. (2000). Beyond computation: Information technology, organizational transformation and business performance. Journal of Economic Perspectives

• Brynjolfsson, E., Rock, D., & Syverson, C. (2019). Artificial intelligence and the modern productivity paradox. NBER Working Paper

• Bloom, N., Sadun, R., & Van Reenen, J. (2012). Americans do IT better: US multinationals and the productivity miracle. American Economic Review

• OECD (2020). Digital Transformation and Productivity Growth