A new report says 94% of mid-market companies now use generative AI in some form. However, many businesses still struggle to scale AI projects across operations. The findings show companies face issues around infrastructure, governance, data quality, and workforce readiness during wider AI adoption. The report highlights a growing gap between testing AI tools and deploying them successfully across departments. Leaders continue investing in AI despite concerns around long-term execution and operational complexity. The results reflect how businesses now move beyond experimentation toward broader AI integration and measurable business outcomes. For businesses, this shows that using AI successfully requires more than testing chat tools or pilot programs. Companies need clear workflows, reliable data systems, employee training, and strong oversight before scaling AI across operations. Long-term AI success depends on integration, governance, and operational readiness across teams.
A new report says 94% of mid-market companies now use generative AI in some form. However, many businesses still struggle to scale AI projects across operations. The findings show companies face issues around infrastructure, governance, data quality, and workforce readiness during wider AI adoption. The report highlights a growing gap between testing AI tools and deploying them successfully across departments. Leaders continue investing in AI despite concerns around long-term execution and operational complexity. The results reflect how businesses now move beyond experimentation toward broader AI integration and measurable business outcomes. For businesses, this shows that using AI successfully requires more than testing chat tools or pilot programs. Companies need clear workflows, reliable data systems, employee training, and strong oversight before scaling AI across operations. Long-term AI success depends on integration, governance, and operational readiness across teams.