AI Maturity Gap Revealed as Companies Struggle with Implementation

A recent study by Futurum, commissioned by IFS, reveals a significant gap in the maturity of artificial intelligence (AI) implementation among companies, particularly in the manufacturing sector. While there is a strong desire for agentic AI to automate processes and enhance efficiency, many organizations are not prepared to deploy these technologies at scale. This discrepancy between ambition and execution poses challenges for firms seeking to leverage AI for operational improvements.

The research highlights that 66% of enterprises plan to invest in digital workers over the next year. However, only 10% currently operate predominantly with autonomous AI. This indicates a pronounced maturity gap, where organizations express a clear interest in adopting advanced AI solutions but lack the necessary infrastructure and capacity to do so effectively. More than 75% of leaders report delays in strategic initiatives due to limited capacity, and less than 6% trust AI to operate autonomously. This gap is particularly acute in the energy and utilities sector, where 52% of firms frequently encounter capacity constraints.

Key use cases for agentic AI in manufacturing include materials planning, customer order management, and inventory control. These areas are critical as companies strive to alleviate the burden of repetitive tasks that consume significant worker time—41% of which is reportedly lost to such activities. Early adopters of IFS's agentic AI platform, IFS Loops, have demonstrated tangible benefits. For instance, CDF Corp. has implemented an inventory replenishment agent that has freed up 20% of its purchasing staff's time, while AirBoss anticipates that 40% of customer orders will be managed without human intervention once its digital worker reaches full production.

The findings suggest that while there is a growing recognition of the potential for AI to enhance productivity, many organizations are struggling to bridge the gap between their aspirations and their current capabilities. This situation signals a critical need for companies to invest not only in AI technologies but also in the necessary training and infrastructure to support their deployment. The research indicates that without addressing these capacity constraints, firms may continue to face operational inefficiencies that hinder their competitiveness.

Looking ahead, the demand for agentic AI is likely to intensify as more companies recognize its potential to streamline operations and reduce costs. However, the success of these initiatives will depend on organizations' ability to build the requisite skills and frameworks for effective AI integration. As firms begin to adopt these technologies, the competitive landscape will shift, favoring those that can quickly adapt and implement AI solutions. Companies that proactively address the maturity gap and invest in tailored digital workers will likely gain a significant advantage in their respective markets, positioning themselves as leaders in the industrial AI space.