Move from promised AI value to proven, sustainable net value
AI has crossed an important threshold. Leaders know that the technology can perform useful work, but many organizations are still determining how to convert its rapidly expanding capabilities into sustainable business value.
Productivity improvements, model performance and adoption rates can provide important signals. However, they do not show the complete economic impact of AI. Leaders also need to understand whether improvements are being captured, what AI costs throughout its lifecycle, which risks remain and what the organization is giving up by choosing one approach over another.
This practical guide introduces an executive framework for assessing AI Net Value:
AI Net Value = Realized Value + Future Value + Protected Value − Lifecycle Cost − Residual Risk − Economic Trade-offs
The framework is designed to support better questions and more complete AI investment decisions. It helps leaders look beyond the visible technology costs and immediate benefits to consider the broader economic system required to create, capture and sustain value from AI.
In this guide, you will discover how to:
- Distinguish between potential AI value and the value that is actually realized in business or mission performance.
- Identify where value can be lost through limited adoption, ineffective utilization, insufficient output quality, weak process integration or a failure to capture released capacity.
- Assess the future value created through reusable assets, organizational capabilities, learning and strategic options.
- Evaluate protected value by considering the losses, failures and disruptions AI may help prevent.
- Understand the complete lifecycle cost of developing, integrating, governing, operating and scaling AI.
- Examine the economic exposure that remains after controls and oversight have been implemented.
- Compare an AI investment with credible alternatives and make its dependencies, opportunity costs and strategic trade-offs visible.
The guide also explains why AI economics should be treated as an ongoing management discipline rather than a one-time calculation made before funding is approved. As evidence replaces assumptions and AI moves into production and scales across the organization, its value, costs and risks continue to evolve.
Download the guide to explore a more complete approach to assessing AI investments and turning technological possibilities into sustainable value.
Author
Pawel Andre, AI Director, NTT DATA Belgium
Pawel Andre leads AI transformation and strategy and advises client executives on data, AI and wider enterprise transformation. His work focuses on helping organizations convert AI and data into measurable business value and lasting transformation.
Contributors
- Midialis Gonzalez, Head of Strategic Partnerships, NTT DATA Belgium
- Elke Willems, Open Universiteit
- Mark Van Achter, Professor and expert in human capital, AI and economics, KU Leuven
Ebook - 20 min read