European SMEs show a strong desire to integrate AI, yet many struggle due to operational challenges. A decision framework known as the AI Profit Toolkit has identified common barriers, such as inadequate data quality and lack of clear metrics. Notably, businesses often suffer from a vacuum of accountability and sponsor alignment, leading to ineffective AI project rollouts. Success stories reveal that companies can achieve significant efficiency gains—but only through structured approaches that address these underlying issues responsibly and strategically.
AI doesn't fail because of the tech. It fails because there's no business case, no clean data, or no one truly accountable for results.
What holds most SMEs back isn't ambition-it's operational friction.
Based on industry benchmarks and dozens of European case studies, the most frequent blockers include fragmented, poor-quality data that delays even basic experiments.
Lack of business-aligned KPIs-if no metric moves, no one notices; measurement gaps: no before-vs-after metrics, just vague goals like 'improve experience'.
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