Do Companies Have To Adjust To AI Or Vice Versa? | 7wData

• Read original article here

One out of ten. That's the number of businesses enjoying "significant benefits" from AI implementation globally. Although it may sound blunt, the truth is that several organizations have no idea about how to implement AI correctly. Unlike what many business executives may believe, AI is not just about the automation of business processes. Enterprise AI implementation must be made with long-term data-driven strategies in mind. To ingrain the technology within the fabric of your business, you'll need to clearly explore how your business and AI can align perfectly to maximize its potential for widening your ROI, revenue generation, growth and diversification. For that purpose, understanding the symbiotic relationship between AI and your enterprise is vital.

To say that AI is prevalent in the global corporate ecosystem would be an understatement in today's times. No less than 93% of businesses use AI in one form or another in 2021. As you can imagine, the implementation of enterprise AI transforms each facet of your organization. Therefore, enterprise AI adoption must be preceded by certain adjustments at different levels in your business. Such adjustments are generally made in terms of infrastructure, personnel and policies.

Massive, organization-wide transformation cannot float without businesses going about it holistically right from the onset. Failing to do so often leads to such initiatives falling through for most businesses. A lack of coherence is the main reason why up to 70% of major transformation projects fail in organizations.

One of the first adjustments to be made while attempting AI-driven transformation is moving business operations and applications from an offline environment to a digital one. Essentially, this involves making the necessary investments to acquire the tools, technological platforms, data scientists, developers, researchers, testers and others.

Data is the most important part of AI-driven transformation. As you may know, data is arguably the most powerful resource today and a major catalyst for the digital transformation of businesses. Possessing the machine learning tools that can carry out big data analysis will be integral to organization-wide enterprise AI implementation. Possessing the necessary tech infrastructure that can be used to harness the power of big data is useful for improved decision-making and business sustainability.

Now that the technology part is covered, arguably the most significant adjustment comes next—personnel training. Essentially, the employees in your organization must be trained to comfortably handle the new tools and technologies. However, apart from technological know-how, the entire workforce in organizations must possess data fluency.

Data literacy and fluency are the main competitive differentiators for data-driven businesses. Data fluency is the ease with which every individual in an organization can understand, interpret the data analysis and dynamics related to their role and communicate it with managers and employees from other areas of the business. For example, if AI-driven predictive analytics indicates that demand for a certain product in a specific zone will rise, the production manager can communicate it expertly with the procurement department to increase the material inflows to that zone in the next purchase cycle. Such expertise over data is increasingly becoming a necessity for employees in nearly all organizations. As per a study, 90% of business strategies will enlist information analytics and data fluency as an essential competency by 2022 if all solutions are backed by data—which is an inevitability in the future. Expertise in data analytics is one of the most pressing requirements in businesses today.

Unfortunately, several businesses around the world lack the kind of data fluency that is conducive for enterprise AI implementation. It is predicted that up to 75 million jobs will be lost to digitization in the future.