Artificial Intelligence (AI) and Machine Learning (ML) have over the years recorded significant milestones in historical evolution that have dynamically reshaped the mode of operations in various industries. These technologies (AI and ML) have advanced their capabilities and applicability, from early symbolic reasoning to the resurgence of neural networks and the advent of deep learning. AI and ML have emerged as powerful tools to accelerate human assessment, prediction, and optimise production performance.
Speaking on the impact, Certified Maintenance and Reliability Professional, Practicing Discipline Plant Engineer, Babayeju Olusile, believes that Artificial Intelligence and Machine Learning Models are shaping the data and technology landscape by refining several existing frameworks, and enhancing objectivity, through extensive algorithm evaluation.
“By harnessing the capabilities of AI and ML, organisations can implement more nuanced and personalised competence assessments. The synergy between Artificial Intelligence and Machine Learning, not only refines various assessment methodologies but also aligns workforce competencies with the demands of a rapidly changing professional landscape,” he said.
Babayeju who is also a Reliability Centered Management Expert noted that Artificial Intelligence has a massive role to play in the future developments of several industries, as it possesses the ability to produce optimal output by analysing already existing data, and regulate the required human input, thus reducing the margin of error.
“AI is considered the future of humanity and the supporter of the 4th industrial revolution. It brings new capabilities to talent management execution processes and the strategic ecosystem of organisations. The use of AI is on the rise across a variety of human resource management functions, example is learning and development. AI can augment and replace human tasks and activities in a wide range of industrial, intellectual, and social applications,” Babayeju stated.
He admitted that the full implementation of AI & ML models into the African continent came with several hurdles, including structural limitations, and cultural differences: “The infrastructural constraints, hinder the seamless adoption of AI and ML technologies in Africa. The scarcity of locally relevant datasets, poses a significant hurdle in training accurate models for competence assessment specific to the African energy sector. Cultural nuances and diversity in work practices further complicate algorithmic standardization, as explored in the research.”
He revealed that acknowledging these challenges is crucial for developing contextually relevant strategies that can leverage Artificial Intelligence and Machine Learning effectively, while addressing the unique considerations of African industries.
Babayeju further stated that the steady trajectory of Artificial Intelligence and Machine Learning models mean they will quickly become an integral part of virtually every field in the professional sector in the years to come: “With significant advances in algorithmic machine learning and autonomous decision-making, this new AI technological age is accelerating innovation in talent management, finance, healthcare, manufacturing, retail, supply chain, logistics, and utilities may be affected by AI.
“AI’s rapid emergence in business and management, government, public sector, and science and technology presents significant opportunities, realistic assessments of impact, challenges, and potential research agendas. The proliferation of edge computing solutions, is anticipated to mitigate infrastructural challenges, enabling more widespread adoption of AI technologies,” he added.
