by AI-driven technologies hold the promise of reducing costs, streamlining repetitive tasks, and boosting overall profitability, all while creating more meaningful roles for employees. However, the integration of AI into enterprise operations has been sluggish, with numerous organizations exhibiting restrained deployment of artificial intelligence. Experts point to executive-level factors such as nervousness, pride, and a lack of explainable AI as significant contributors to this gradual uptake.
The KPMG 2019 Enterprise AI Adoption Study sheds light on the current landscape, revealing that merely 17% of companies are leveraging AI and machine learning at scale. Among the 30 Global 500 companies surveyed, 30% reported utilizing AI for selective functions. Despite these relatively low adoption rates, around half of the surveyed companies express their intention to implement AI and machine learning at scale within the next three years.
One crucial factor impeding swifter adoption is the absence of explainable AI, a point emphasized by Traci Gusher, principal of data and analytics at KPMG. While AI-driven software may provide general explanations of its functioning, it often falls short of delving into the intricate algorithms that power it. This lack of detailed explanation contributes to a sense of nervousness among business users, particularly those at the executive level.
Gusher draws a compelling analogy, likening the experience of using AI to getting into an autonomous car. Even if the technology operates flawlessly, users may harbor unease due to the unfamiliarity and lack of understanding. This sentiment is mirrored in business executives who may be apprehensive about relying on automated systems for decision-making, considering it uncharted territory.
Michael Feindt, founder of Blue Yonder, adds another layer to the impediments to AI adoption, emphasizing the role of executive pride, especially when integrating automation into intricate processes like the supply chain. This transformation necessitates heightened connectivity among departments, improved communication among employees, and a willingness for executives to cede some decision-making authority to data-driven technology.
Resistance to change, fueled by the fear of losing power, is a prevalent obstacle within organizations, particularly at lower executive levels. Executives who ascended to their positions based on their decision-making prowess may find it challenging to entrust crucial decisions to automated systems, giving rise to a matter of pride.
Despite the growing recognition of AI’s indispensability, Feindt acknowledges the difficulty in initiating change. He underscores the consequences of delay, asserting that companies trailing in automation may face the peril of folding as competitors embracing automation gain a decisive edge. The reluctance to change, influenced by both nervousness and pride, underscores the intricate challenges hindering AI adoption in the enterprise.
As organizations grapple with these challenges, it becomes imperative to address the underlying concerns and facilitate a smoother transition toward AI integration. Exploring methods to enhance explainability in AI systems, providing comprehensive training programs, and fostering a culture that values innovation over resistance are essential steps. Moreover, showcasing successful case studies and tangible benefits of AI adoption can help assuage fears and build confidence among executives and employees alike.
In conclusion, while the potential benefits of AI in the enterprise are vast, the journey toward widespread adoption is hindered by a complex interplay of factors. Nervousness, pride, and the lack of explainable AI form a trifecta of challenges that organizations must navigate to unlock the transformative power of artificial intelligence fully. Recognizing and addressing these barriers is crucial for ushering in a new era where AI becomes an integral and seamless part of enterprise operations, driving efficiency, innovation, and sustained growth.
