The generative artificial intelligence constitutes a strategic asset in the contemporary digital landscape, but deep divisions are emerging between leaders and practitioners. The visionary choices of the former often contrast with the pragmatic realities of the latter. This dichotomy raises major issues, particularly regarding technology adoption and risk perception associated with it.
The interpretation gaps surrounding the applications and added value of generative AI underscore the urgency for a common understanding. The necessity to converge the perspectives of different stakeholders appears to be a determining factor for maximizing investments in these emerging technologies. Questioning these disparities allows for a better grasp of the inherent challenges in effectively implementing generative AI within organizations.
Disparities in Perception of Generative AI
A report recently published by Publicis Sapient highlights notable disparities between leaders and practitioners of generative artificial intelligence. This document, deemed a reference, analyzes the diverse perceptions and adoption of this technology by these two groups, referred to as C-suite and V-suite, respectively. The findings illustrate a striking gap regarding expectations and concerns related to generative AI.
Different Focus on Use Cases
Members of the C-suite are primarily interested in visible use cases of AI, such as improving customer experience, optimizing services, and increasing sales. In contrast, V-suite professionals view AI applications in a broader range of areas like operations, human resources, and finance. This divergence in approach highlights a lack of shared understanding regarding the true capabilities of generative AI.
Perception of Associated Risks
The report also underscores a significant gap in the perception of risks. Fifty-one percent of leaders express concerns about the ethics and inherent risks of generative AI, while only twenty-three percent of practitioners share this sentiment. This difference in perception can be attributed to a more abstract view of potential dangers, where leaders envision scenarios of superintelligence à la Hollywood.
Maturity and Success Evaluation
The levels of maturity regarding generative AI vary considerably within organizations. Many companies struggle to define what success looks like with generative AI. More than two-thirds of participants in the study lack any effective means to evaluate the outcomes of their AI-related projects. This absence of evaluation criteria hinders the effective integration of this technology.
Transformations of Internal Functions
While the C-suite prioritizes high-visibility use cases, generative AI is quietly revolutionizing back-office functions. A majority of V-suite members view this technology as essential for the future of finance and operations in the coming years. This indicates a potential for optimization that remains largely unexplored by those in leadership positions.
Recommended Strategies for Success
To leverage the full potential of generative AI, the report recommends a portfolio approach to innovation projects. Leaders should focus on implementing projects, controlling shadow IT, avoiding duplications, and relying on expertise in specific domains. The connection between business units and the office of the Chief Information Officer must be strengthened, as well as the commitment to the risk office.
Future and Evolution of Practices
Five key steps have been identified to maximize innovation: adopt a portfolio approach, improve communication between the CIO office and the risk office, identify internal innovators, use generative AI for information management, and promote a corporate culture conducive to skill evolution.
Organizations must bridge the existing gap between the C-suite and the V-suite to unleash the undeniable potential of generative AI. The future of business transformations relies on a decentralized and collaborative approach to innovation.
Frequently Asked Questions about Generative Artificial Intelligence: Gaps between Leaders and Practitioners
What are the main differences in perception between leaders and practitioners regarding generative AI?
Leaders, often focused on visible use cases such as customer experience and sales, seem less aware of the opportunities that generative AI offers in various functional areas such as operations, human resources, or finance, which are more often highlighted by practitioners.
What is the importance of ethical concerns surrounding generative AI for leaders?
Leaders are generally more concerned about the risks and ethics associated with generative AI, with 51% of them expressing worries that only 23% of practitioners share. This shows a significant difference in approach to the potential consequences of this technology.
How can companies bridge the gap between management and practitioners regarding generative AI?
To reduce disparities, companies should adopt a decentralized approach to innovation, fostering communication between operational units and the CIO office, while integrating the concerns of the risk office from the outset of generative AI projects.
What steps can be taken to maximize innovation through generative AI?
The report outlines five steps: adopt a portfolio approach for projects, improve communication between the CIO office and the risk office, identify innovators within the organization, use generative AI to optimize information management, and enhance corporate culture through training and development programs.
What role does the heterogeneity of organizational maturity play in the adoption of generative AI?
Organizations often find themselves at different levels of maturity regarding the adoption of generative AI. This diversity leads to difficulties in defining success indicators and measuring project outcomes, which can result in the ineffective adoption of this technology.
Why might practitioners see more potential in generative AI than leaders?
Practitioners, who work directly with the tools and technologies, are often more aware of the various practical applications that generative AI offers, unlike leaders who may be influenced by strategic concerns or more abstract perceptions of innovation.
How can the success of generative AI projects be evaluated within a company?
Evaluating the success of generative AI projects requires defining specific criteria aligned with the desired outcomes of the business. However, more than two-thirds of companies admit to lacking means to measure these successes, which can hinder their AI initiatives.
