Sign up for our daily and weekly newsletters to get the latest updates and exclusive content on the leading advancements in AI. Discover More
The year 2023 will be remembered as the moment generative AI became mainstream, driven by companies inspired by ChatGPT’s success to adopt and launch their own AI solutions. Now, as 2024 begins, these organizations are aiming to integrate generative AI more deeply into their operations to fully harness its potential.
However, a survey by Forrester Consulting, involving 220 AI decision-makers from North American firms, shows that many are still worried about the risks of this technology and face challenges in implementation. Major roadblocks, such as issues like AI hallucinations, are keeping organizations in the exploratory phase rather than fully operationalizing their AI models.
This is a hurdle that organizations need to overcome if they intend to increase their investment in generative AI.
Organizations understand the transformative power of generative AI. Success stories abound online, and businesses across various industries recognize the potential. According to the Forrester survey, 83% of respondents are either exploring or experimenting with generative AI. Over 60% consider it critically or highly important for their business strategy and plan to increase their data and AI investments by up to 10% in the coming year.
Leaders in the field have already identified several potential applications for the technology. More than half of the respondents pointed to areas like enhancing customer experiences (64%), product development (59%), self-service data analytics (58%), and knowledge management (56%).
This shows a sentiment of exploration and curiosity as organizations anticipate numerous applications for generative AI in the next two years. These applications are expected to improve existing offerings, create new products and services, and optimize both internal and external operations.
Despite this optimistic outlook, there are still significant roadblocks to successful generative AI adoption. Some of the major concerns include the risk of violating data protection and privacy laws (31%), the challenge of developing the necessary skills and governance (31%), and the potential for biases and hallucinations affecting AI outputs.
These risks are further compounded when organizations lack the necessary infrastructure for generative AI. According to the survey, the most considerable barrier is inadequate data infrastructure, with 35% of respondents citing this as a key issue. Other challenges include difficulties in integrating with existing systems, computational limitations, governance mechanisms, interpretability and explainability of AI, talent gaps, and scalability of models.
To address these challenges, organizations can adopt AI platforms that provide collaborative capabilities, prepackaged solutions for development, easy integration methods, and robust frameworks for standardization, governance, and compliance.
McKinsey estimates that generative AI alone could add between $2.6 trillion and $4.4 trillion to global corporate profits annually, with significant impacts expected in the banking, high-tech, and life sciences sectors.
Stay updated with the latest news! Sign up for our daily updates and receive the latest stories straight to your inbox.
By subscribing, you agree to our Terms of Service.