The adoption of Generative AI, also known as GenAI, has reached a critical phase, with a majority of organizations increasing their investment in this technology due to its strong value, according to a recent survey conducted by Deloitte. The survey, titled “The State of Generative AI in the Enterprise: Now decides Next,” involved 2,770 director- to C-suite-level respondents across 14 countries. Despite varying levels of self-reported expertise in Generative AI, all participants have experience with AI and are currently piloting or implementing Generative AI within their organizations.
As promising experiments and use cases begin to pay off, it is evident that the adoption of GenAI is at a pivotal moment. However, leaders are faced with challenges such as data quality, investment costs, effective measurement, and an evolving regulatory landscape. Jim Rowan, Applied AI leader and principal at Deloitte Consulting LLP, emphasized the importance of change management and deep organizational integration to overcome these barriers and unlock value in GenAI for the future.
Costi Perricos, Generative AI leader at Deloitte Global, highlighted the enthusiasm among organizations for GenAI and the diverse benefits it offers beyond improved efficiency and cost reduction. These benefits include increased innovation, improved products and services, enhanced customer relationships, and other forms of value. This underscores the transformative potential and versatility of Generative AI technology.
One of the critical challenges in GenAI adoption is the presence of data gaps that hinder success. While there is still interest among senior executives and board members, enthusiasm for the new technology appears to be diminishing as time passes. Many GenAI projects are still in the pilot or proof-of-concept stage, with only a small percentage fully integrated into production. Data management is becoming a focal point for organizations as they scale their GenAI efforts, with investments in data-related technologies on the rise.
However, data-related issues remain a significant hurdle for many organizations, with 55% citing data-related challenges as a reason to avoid certain GenAI use cases. To address these deficiencies, organizations are focusing on enhancing data security, improving data quality practices, and updating data governance frameworks. These steps are crucial in modernizing data-related capabilities to meet the demands of Generative AI technology.
Despite recognizing the importance of managing GenAI risk, organizations still face several barriers to successful deployment. Key concerns include regulatory compliance, difficulty managing risks, and a lack of a governance model. Risks specific to GenAI, such as model bias and privacy concerns, are driving these worries. To build trust and ensure responsible use, organizations are establishing governance frameworks, monitoring regulatory requirements, and conducting internal audits on GenAI tools and applications.
As organizations move past the proof-of-concept stage and seek to define and measure the impacts of their GenAI efforts, measurement becomes crucial. Building trust and support from the C-suite and boardroom requires clear evaluation of GenAI performance using specific KPIs, frameworks for evaluating investments, and tracking changes in employee productivity. By demonstrating tangible value and impact, organizations can secure continued support and investment in Generative AI technology.