Harnessing AI for Digital Transformation: Key Opportunities and Challenges.
The exploration of AI in the context of successful digital transformation unveils a promising and evolving discourse. The AI conversation has been around for some time but is now experiencing a resurgence, driven primarily by major corporations, reminiscent of the rise of big data in the early 2000s. This resurgence brings with it a wave of optimism about the potential of AI to enhance organisational performance and productivity.
In recent years, the business community has engaged in considerable discussion about the potential of AI to enhance their digital transformation initiatives. Miklošík & Evans (2020) note that AI is a rapidly evolving field with the potential to impact digital transformation in various sectors significantly. The generative AI boom is well underway, and a recent study by McKinsey revealed that ‘automation integrated with generative AI could accelerate 29.5 per cent of working hours in the US economy’ (CIO 2023). Companies like OpenAI, Microsoft, Google, Amazon, and Meta are investing billions in generative AI.
Much has been written about how Gen AI can significantly enhance business operations, from improving customer experiences through chatbots and virtual assistants to boosting employee productivity with streamlined workflows. An extensive range of use cases includes Content Creation, Media Production, Business Applications, Healthcare, Education, Finance, Legal and Compliance. The list goes on, and very few sectors will not be affected. Consequently, the rapid advancement of generative AI has sparked significant fears of job losses across various sectors as AI systems become increasingly capable of performing tasks traditionally handled by people. There is a growing concern that these technologies could lead to widespread unemployment. This apprehension is compounded by the speed at which AI is developing, potentially outpacing the ability of the workforce to adapt and reskill.
The integration of AI in digital transformation is inescapable and continues to reshape various industry sectors, bringing opportunities and challenges with it. AI is a moving target, and it is challenging for business leaders to stay focused in a constantly advancing area (Chui et al., 2018). Effectively managing data is a significant obstacle to harnessing the value of generative AI. According to a recent McKinsey survey, 70 per cent of top performers reported challenges in integrating data into AI models, citing issues such as data quality, governance processes, and adequate training data. The consequences of using poor-quality data in generative AI models are severe, leading to poor outcomes, expensive corrections, cyber breaches, and loss of user trust. Traditional methods for ensuring data quality are inadequate, necessitating improved and expanded data sources and advanced tools like knowledge graphs to enhance model accuracy and consistency (McKinsey & Company, 2024).
