As the fervor surrounding generative artificial intelligence (AI) begins to wane, businesses are increasingly demanding concrete results from their investments in the technology. Arijit Sengupta, CEO and founder of AI app developer Aible and renowned for his contributions to Harvard Business School's "AI in Market Facing Functions" course, predicts that 2024 will be the year when companies unequivocally demand returns on their AI endeavors.
Sengupta, a former executive fellow at Harvard Business School, highlights the current discrepancy between the lofty promises of AI and its practical application in the business realm. He draws attention to a study by IBM indicating that the average return on investment for generative AI projects in the previous year was a mere 5.9%, significantly below the typical 10% cost of capital.
"The gap between user expectations driven by aggressive marketing and the actual capabilities of AI is widening," Sengupta explains, emphasizing the urgent need for tangible outcomes from AI initiatives.
One of the primary obstacles hindering the realization of satisfactory returns, according to Sengupta, is the prolonged lead time in implementing AI solutions. He argues that if the implementation process extends beyond a month, the underlying technology becomes obsolete before the project is completed, underscoring the necessity for swift deployment.
Furthermore, Sengupta critiques the tendency among companies to delay implementation until they believe they have attained "perfect" data, labeling this pursuit as a futile endeavor. He advocates for a more iterative approach, urging businesses to prioritize placing AI tools in the hands of end-users promptly and refining the technology based on their feedback to drive continuous improvement.
"While AI holds transformative potential, its efficacy is contingent upon proper configuration and context transfer, factors often overlooked in marketing narratives," Sengupta remarks, cautioning against inflated expectations that may lead to customer dissatisfaction.
In light of these challenges, businesses are compelled to recalibrate their strategies, focusing on expediting AI deployment and embracing iterative refinement to unlock its full potential. As the demand for tangible returns intensifies, the efficacy of AI investments will be scrutinized more closely in the year ahead.
