By Dorcas Omotayo
Optimising marketing spend with machine learning-powered attribution for hyper-personalization, moving toward machine learning-powered predictive maintenance to decrease downtime, increase quality, and improve scheduling are areas that can be optimised by Artificial intelligence (AI) processes to improve Return on Investment (ROI) for businesses. This was revealed in a report by Dataiku, a leading Artificial Intelligence and machine learning platform.
According to the report, the ability to operationalise and quickly deploy machine learning models to production is always important, but even more so in times of change. Because, for new businesses, it’s the only way to realize ROI and actually start optimizing costs, increasing revenue, and seeing change. While for businesses that already have models in production, the underlying data has fundamentally shifted, and slow operationalisation capabilities mean a longer time to deploy new models that better fit today’s or tomorrow’s reality.
It was also contained in the report that, though spending on AI initiatives has increased exponentially in recent years, it has remained tangential to many companies’ central operations, viewed more as an experiment than an indispensable organisational asset. However, in the second half of 2020 and beyond, this paradigm is poised to shift as Enterprise AI becomes a critical component of companies’ strategy to recover from crisis and bring more preparedness for the future through AI systems that are persistent and resilient.
However, one of the challenges to ramping up AI efforts quickly across an enterprise is remote distribution. Whether teams are distributed during normal times or exceptional circumstances require everyone working from home, remote work is the ultimate litmus test of the data organization’s robustness. Many inefficiencies may go unnoticed when working in the office that ultimately lead to significant loss of time or of project relevance, but these issues can at least be partially mitigated by informal discussions and water cooler interactions.
Continuing, the report revealed that, the right data science, machine learning, and AI platforms enable remote work at their core by addressing all of these challenges. For instance, Dataiku allows people across the organization to access all data and work together on projects in a central location, facilitating good data governance practices combined with widespread vertical.
Ultimately, the fact remains that the goal of many AI systems is not to predict the future with 100% certainty, but to model and prepare for different scenarios. Nonetheless, for companies that survive through the economic downturn, AI will become more important than ever as an organizational asset for handling large-scale change with greater ease. No matter where organizations are on their AI journey, post-2020 should be a time to reassess AI use cases across the board, reduce costs surrounding AI initiatives and ensure robust model maintenance strategies.
