Small and medium-sized businesses (SMBs) increasingly adopt artificial intelligence (AI) and digital tools at unprecedented rates, yet the empirical record of value capture remains poor. Industry-wide data converge on a striking pattern: more than 80 percent of AI projects fail to deliver measurable value (Ryseff et al., 2024), 95 percent of generative-AI pilots produce no profit-and-loss impact (Challapally et al., 2025), and only 4 percent of companies generate substantial value from AI investments (Boston Consulting Group, 2024). Across three decades of IT-project research, the documented failure rate has remained near 70 percent (Standish Group, 2015; Project Management Institute, 2014). This paper examines the cognitive and managerial mechanism underlying this persistent gap and proposes that solution-first bias, the tendency of business owners to acquire pre-selected technological tools before diagnosing the operational constraint they purport to solve, is a primary, under-theorized cause. Drawing on Morozov's (2013) concept of technological solutionism, Wedell-Wedellsborg's (2017) executive survey on problem reframing, and the cognitive-bias literature (Kahneman Nickerson, 1998; Maslow, 1966), the analysis triangulates evidence from RAND, MIT, BCG, McKinsey, and OECD sources to demonstrate that diagnostic capability, not tool access, predicts implementation success. A bottleneck-first model, illustrated through the Agentes Para Tu Negocio framework, is proposed as a corrective protocol for owner-operated SMBs.
Humberto Inciarte (Sun,) studied this question.