How do mid-market companies turn scattered experiments into something the whole company can build on?
A report from Kaufman Rossin reveals a substantial gap in mid-market AI strategy: while adoption is nearly universal, the infrastructure, governance, and organizational alignment needed to generate enterprise-wide results remain elusive for most companies.
Key takeaways:
· Generative AI adoption is near-universal among mid-market companies, but fragmented implementation creates new challenges.
· 94% of mid-market companies are already using generative AI. However, adoption is happening in silos; different departments and even individual employees are making independent decisions about which tools to deploy. This decentralized approach is overwhelming executives and complicating enterprise-wide strategy.
· The most common use cases today focus on accelerating knowledge work.
· Most organizations have moved beyond experimentation, but scaling remains elusive. The report found that 83% of mid-market companies have progressed from early dabbling to conducting deliberate trials or embedding AI into core processes. Yet only 2% have operationalized AI at scale, an indication that foundational elements for enterprise-wide AI transformation are still missing for most organizations.
· Three primary barriers are preventing companies from scaling AI programs: AI skills gap; cybersecurity concerns; and legacy systems integration.
· ROI measurement remains a universal challenge. Among companies using AI, time savings are the most frequently cited benefit. However, quantifying the financial return on AI investments continues to challenge nearly all organizations.
· Most mid-market companies plan to increase AI spending, viewing generative AI as essential to future competitiveness.
