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Why Don't Your AI Projects Move Beyond the Innovation Stage?
An AI rocket that won't take off?
Despite the buzz surrounding artificial intelligence, most initiatives struggle to move beyond the proof-of-concept (PoC) stage. In fact, nearly 80% of data and AI projects fail during the implementation phase—not because of the technology itself, but due to a lack of business buy-in, a clear strategy, and effective leadership (Gartner).
As a result, 30% of CIOs do not know whether their PoCs are meeting the established KPIs (CIO.com), and 80% of organizations do not see any tangible impact of generative AI on EBITDA (McKinsey).
A Mixed Picture
Overall, only 26% of companies have the necessary capabilities to move beyond the PoC stage (BCG). Furthermore, by the end of 2025, 30% of generative AI projects will be abandoned due to a lack of data reliability, cost control, or proven business value (Gartner).
60% of executives view scaling as a strategic priority (CGI), and 75% of companies plan to integrate AI into their business processes by 2027 (Gartner).
The Causes of AI Project Failure: An Organizational Trilemma
- Insufficient engagement from business units, with a perception of AI that is disconnected from operational challenges
- A lack of training and confidence in technology, which fuels a climate of anxiety in companies (60% of professionals fear that AI threatens their jobs)
- Fragmented governance, without support from the Executive Committee, with vague or nonexistent KPIs
In addition, gaps in in-house AI expertise (fewer than half of companies believe they have the necessary expertise), as well as a technological infrastructure that is often inadequate (so-called “legacy” systems, data stored in silos, and limited human oversight).
Scaling Up AI Projects: Key Success Factors
- An AI strategy based on a “platform-centric” approach, involving the pooling of resources, the creation of centers of excellence, and the leveraging of knowledge
- An orchestrated infrastructure and high-quality data, supported by an end-to-end automation mechanism
- Prioritized use cases, previously evaluated based on the business value they deliver and their technical complexity, deployed within appropriate models
- Employees who have been trained in the use of this technology and are confident in its purpose, thereby facilitating its adoption and widespread use within the company
Training employees in AI is not just about keeping up with a current trend; it is about supporting the transformation of job roles. All job roles are—and will continue to be—impacted by AI. It is therefore essential to explain and demystify artificial intelligence before taking any steps in this direction.
Measuring the ROI of Your AI Strategy: Balancing Traditional Metrics and New Benchmarks
Today, defining a method for calculating the ROI of AI projects remains a challenge. The objectives and priorities regarding the value delivered by AI vary by company, industry, and use case.
In fact, when the use case in question has a strategic impact and is considered a “game changer” for the company implementing it, the ROI can be more difficult to define and measure than for use cases focused on productivity and cost reduction.
Furthermore, since this technology is new and evolving rapidly, it is difficult to accurately estimate the benefits it may offer compared to a technology that has been known and in use for several years.
What approaches do companies use to measure the ROI of AI projects?
Although methods may vary, there are best practices that everyone can follow:
- 48% of organizations use specific KPIs to evaluate the performance of their AI tools
- 38% are developing a measurement model that allows them to evaluate the investments they have made
- 38% track changes in their employees' performance
Best Practices to Keep in Mind
- Incorporate non-financial metrics focused on the benefits that technology provides to employees
- Collect sufficient data to enable a reliable and representative analysis of the project in question
- Tailor the metrics to the tools, the company, and its AI strategy
5 Concrete Steps to Take Right Now
- Sponsor AI projects at the highest level (Executive Committee)
- Discontinue low-value PoCs
- Organize “Learning Expeditions” to draw inspiration from success stories
- Identify the “Killer Use Cases” and invest heavily in them
- Appoint an “AI Champion” for each team to drive adoption
Looking Ahead to 2030: Strong AI, Quantum Computing, Robotics… and Human-Machine Collaboration
By 2030, AI will no longer be merely a tool for optimization, but a defining factor in our socioeconomic model: strong AI, quantum AI, companion robots, autonomous factories, brain-machine interfaces… The changes will be profound, and those who have successfully combined strategic vision, operational excellence, and human support will be best equipped to capitalize on them.
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