In these years of technological boom, I keep seeing the same mistake: many people treat Artificial Intelligence as a standalone, almost magical product instead of what it truly is, a tool meant to serve real, concrete solutions. AI is extraordinary, from Netflix recommendations to accelerated medical diagnostics, but its real value emerges only when it is integrated into existing products and processes, not when it is presented as an end in itself.
Recent research shows how widespread this misunderstanding is. A report from the Boston Consulting Group reveals that only about five percent of companies are obtaining measurable value from their AI investments, while the vast majority sees no significant impact precisely because of unclear objectives and lack of integration into business processes.
https://www.businessinsider.com/industries-seeing-value-from-ai-bcg-consulting-report-2025-10
An analysis from the Massachusetts Institute of Technology confirms this picture. The report The GenAI Divide: State of AI in Business 2025 shows that despite tens of billions of dollars invested, around 95 percent of organizations involved in generative AI projects obtain no measurable business return, while only the remaining five percent of integrated projects generate significant value, not because of limitations of the models themselves but due to integration challenges and a lack of contextual learning in actual implementations.
https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
Many failures come from an excessive focus on the model and too little on the real application. Several independent analyses highlight that the issue is not the technology but the way it is implemented and integrated into business operations.
https://medium.com/@truthbit.ai/why-88-of-ai-projects-fail-and-its-not-the-technology-9e88903f646b
The example of ChatGPT is telling. The initial attention peak came from the chatbot itself, but the lasting value appears in its integrated applications, such as Microsoft Copilot or GitHub Copilot, which improve productivity, workflows, and software development. It is the integration into daily operations that generates impact, not the standalone technology.
Major economic analyses confirm the same logic. McKinsey estimates that AI could add up to about thirteen trillion dollars to global GDP by 2030, but only if used as an operational engine that strengthens existing products and already functioning processes.
Looking ahead, value will not come from models showcased in viral demos but from adopting AI as the invisible infrastructure behind reliable tools, such as smarter e-commerce systems, optimized supply chains, and automations that meaningfully improve human work. The point is not to replace creativity and expertise, but to enhance them.
An excellent tool should not steal the spotlight; it should improve what already works and generate concrete business results.