This year signifies a pivotal moment for artificial intelligence (AI) as the EU AI Act, following three years of negotiations, has been ratified by the EU legislature. This advancement shifts the discourse on responsible AI from theory to reality, setting a new global benchmark for AI governance.
IBM has lauded this directive for its risk-based and pragmatic approach to overseeing AI. The rationale behind this support lies in the historical lesson that emphasizes the need to strike a balance between accountability and disruption when introducing novel disruptive technologies.
The pervasive influence of AI on various facets of our daily routines and professions has been well-documented for years. While considerable attention has been directed towards its potency in addressing critical global challenges, not all impacts of AI will be groundbreaking or headline-grabbing. The efficacy of these advancements will also hinge on their ability to enhance human efficiency.
Challenges in Production and Development
Presently, technological progress is outpacing performance improvements. A recent McKinsey report revealed a mere 1.4% increase in labor productivity in the United States, indicating a concerning trend. Restoring historical levels of productivity growth could inject $10 trillion into the U.S. GDP, addressing labor shortages, debt burdens, inflation, and power shifts. Despite the technological surge witnessed over the past 15 years, a parallel decline in performance is evident on a global scale.
Archaeologist Jason Hickel highlights that virtually every government worldwide, irrespective of economic status, is fixated on boosting GDP (Gross Domestic Product). This fixation is no longer a matter of choice.
Traditionally, GDP growth has relied on population expansion, productivity enhancement, and credit expansion. However, two-thirds of this growth formula—population and credit expansion—are unlikely to see significant increases in the near future. As the emphasis on skills-based education and recruitment intensifies, the rising population coupled with a shrinking workforce could lead to substantial skill gaps, particularly among highly skilled and educated workers. With the conclusion of a 15-year era of historically low interest rates, access to credit is constricting.
Consequently, boosting output is pivotal for driving GDP growth. Enhanced efficiency is imperative to foster economic progress and success for enterprises, highlighting the critical role of AI in addressing the productivity challenge, provided it is developed and utilized purposefully and ethically.
Harnessing the Benefits of Ethical AI
Gartner projects a surge in technology spending to reach \(5 trillion in 2024, escalating to \)6.5 trillion by 2026, serving as a primary driver for the impending global market upswing.
However, the 2023 IBM Global AI Adoption Index reveals that one in five surveyed companies do not have plans to integrate AI comprehensively into their operations. Their reservations stem from limited AI proficiency, data complexity, and ethical concerns. This status quo underscores the existing paradox. Nevertheless, the coexistence of responsibility and disruption is not only feasible but imperative.
Business leaders must prioritize expediting ethical AI adoption as governments pivot towards crafting intelligent AI regulations. Through my interactions with clients, I have observed four key considerations guiding the path to implementation: model selection, governance, knowledge, and explainable AI.
Facilitating model diversity is crucial for expediting AI integration. Different models excel in distinct areas, necessitating a tailored approach based on market dynamics, application scenarios, website requirements, and model scale. Consequently, leveraging multiple smaller models often outweighs relying on a single large model.
Moreover, with robust governance frameworks, organizations can ensure unbiased workflows that align with prevailing and forthcoming regulatory mandates.
In the contemporary job market, competencies hold more value than mere academic credentials. Given the rapid evolution of technology outpacing skill development, a skills gap has emerged, underscoring the urgency for leaders to prioritize skills acquisition, encompassing recruitment, training, and upskilling initiatives to thrive in the AI era.
Lastly, fostering the transparency essential for advancing responsible AI entails leveraging open-source models and proprietary models with well-documented data sources. Open-source models enhance diversity by exposing biases, promote sovereignty by facilitating source identification, and encourage collaborative learning within the community.
The unprecedented GDP growth potential associated with AI signifies a promising outlook for the emergence of new job opportunities. As upskilling gains momentum, new roles, markets, and industries are poised to emerge, akin to previous technological revolutions.
For both businesses and governments, 2024 should mark the transition from experimental phases to full-fledged deployment, ushering in widespread economic benefits from AI technology in the coming years. With a strategic vision and a commitment to responsible AI adoption, sustained growth and prosperity are on the horizon.
Rob Thomas serves as IBM’s Senior Vice President of Software and Chief Commercial Officer.
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