High AI investment in operations is often misleading. Much spending optimizes legacy automation systems (e.g., predictive maintenance) that predate generative AI. The actual GenAI layer is frequently thin, limited to generating reports, masking a lack of true strategic adoption and readiness.
Traditional vendor benchmarks like Gartner's are now irrelevant for AI. Success hinges on internal systems and integration, not picking the 'right' platform. Focusing on such reports can misdirect valuable time and effort away from what truly matters for achieving AI readiness and ROI.
Despite people being the single largest barrier to converting AI adoption into value, organizations are drastically underinvesting in them. A Deloitte study found 93% of AI spend goes to infrastructure, with a mere 7% for people-related initiatives like training, creating a significant adoption bottleneck.
AI is increasing stress in customer service by automating routine cases and leaving humans with more difficult, emotional ones—often without proper training for this shift. This dynamic, causing anxiety and burnout, serves as a critical warning for how AI deployment can negatively impact employees if not managed holistically.
Despite lagging in AI deployment, finance departments lead in governance. Decades of experience with SOX compliance, audit trails, and fiduciary duty created pre-existing frameworks for managing risky tools, which they now apply to AI. This governance-first approach could become a long-term competitive advantage.
While 88% of sales teams claim to use AI, it's often shallow adoption like using ChatGPT for emails. Only 24% have integrated AI into core revenue workflows, indicating a significant gap between perceived adoption and deep, systemic implementation that drives real business value.