Generative artificial intelligence (AI) can serve as a valuable tool for enhancing productivity, yet it also presents challenges for both IT and business clients. A recent study by IEEE authors cautions that the utilization of AI may not be as straightforward as perceived.
While the majority of organizations express a desire to harness the potential of AI, findings from a survey involving 350 global technology leaders in September underscore the necessity for education and organizational readiness.
Moreover, after immersing myself in Amazon’s AI classes over a weekend, I wholeheartedly recommend exploring them as well.
Despite the hurdles, there is a notable shift towards AI adoption. In the upcoming year, 70% of respondents revealed that they have implemented or planned to develop tools incorporating natural language processing, marking an increase from the previous year’s 67%. Key applications of AI in the near future include real-time security, supply chain optimization, expedited application development, automated customer service, and streamlined job applicant screening processes.
Transitioning to AI, however, is not an instantaneous process. The study’s authors stress that “integrating AI into existing workflows is not a simple task.” Drawing parallels to past technologies like cloud computing and IoT, which faced varying degrees of success, they highlight the challenges associated with digital transformation initiatives, often characterized by high failure rates due to unmet expectations, cost overruns, missed deadlines, or project abandonment.
The introduction of advanced technologies such as AI is speculated to pose even greater challenges, potentially making it a less appealing prospect for organizations. Concerns around “overreliance on AI leading to errors” were voiced by 59% of managers, underscoring a prevalent apprehension within businesses.
Furthermore, organizational leaders are leveraging small-scale AI solutions to drive substantial impacts within their operations.
Issues related to information sharing and employee training emerged as the next critical focus, cited by 50% of respondents. Managers expressed worries regarding their organizations’ ability to transfer institutional knowledge from seasoned experts to new personnel effectively.
As organizations gear up to embrace AI in the coming year, nearly half of the respondents (47%) identified challenges in integrating AI into existing workflows as a major concern.
The quality of training data emerges as a significant issue as AI adoption accelerates. Training AI systems heavily rely on data, which can often be tainted by biases or inaccuracies, perpetuating prejudices within AI algorithms. Paul Nikolich, an IEEE member, highlights the difficulty in validating training data due to its vast volume and undisclosed sources.
Additionally, familiarize yourself with using ChatGPT to craft your personalized AI experiences.
Given the relative novelty of generative AI, there is a scarcity of individuals equipped with the requisite expertise. Professionals were surveyed on the key attributes sought in AI-related job applicants, emphasizing technical proficiency alongside essential soft skills.
Yu Yuan, a senior IEEE member, underscores the importance of proactive leadership, innovative thinking, and the ability to assess AI outcomes for achieving meaningful results with generative AI.
Nevertheless, the development of AI necessitates further AI capabilities. Carmelo José Albanez Bastos Filho, a prominent IEEE member, highlights the scarcity of resources for coding as a significant impediment in the tech landscape. While intellectual work holds substantial value, certain software development tasks could benefit from immediate automation.
