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on track, allowing human teams to review recommendations, approve changes, and focus their time on higher-value work.
And while Agentic AI adoption is still in its early stages, McKinsey reports that only 39 percent of companies see meaningful earnings impact at the enterprise level of existing AI innovation initiatives. At the same time, 76 percent of organizations( in the manufacturing industry) expect AI to deliver productivity gains of more than 20 percent over the next three years. This contrast highlights that current‘ generic’ AI implementations are not yet creating measurable value. To start to see value from AI investments, manufacturers must not treat AI as a technology experiment, but as a practical tool for solving real operational problems. This means being intentional in deploying AI around results-driven usecases, tailoring AI to be built around the way they work and embedding AI into dayto-day operations that are aligned to KPIs executives already track.
Reducing mundane tasks
What sets Enterprise and Agentic AI apart from more generic, rules-based AI implementations is its ability to fundamentally change how manufacturers handle repetitive, high-frequency tasks that have long consumed valuable human capacity. While AI promises to deliver numerous benefits, one of its pivotal strengths is enabling organizations to move away from manual, time-intensive activities by introducing end-to-end autonomy across business processes and decisions. With the right technology in place, routine quality checks, minor machine adjustments,
The gap between AI’ s promise and reality will not close on its own
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