How to empower your employees to accelerate AI’s impact on your business and society? We have the answer!
Fujitsu / March 23, 2022
Few people doubt anymore that AI is going to transform business and society. But still, why are over seven out of ten companies struggling to get AI out of experimentation and into production? Oscar Jarabo, International Portfolio Manager, Data Intelligence at Fujitsu, and Albert Mercadal Playa, Global Head of Data Intelligence at Fujitsu, believe that creating MLOps factories is key to breaking through the AI logjam and empower employees to accelerate AI’s impact on business and progress towards a trusted society.
Companies are still struggling to embed AI into the business
Data-driven intelligent decisions depend on interpreting and manipulating data at a scale that is beyond human capacity. This is where AI plays a key role. The challenge is that when companies start experimenting with AI, they realize that it’s not easy. They can experiment fairly readily with two or three models, but how practical is it to scale to 20 or 30 use cases in different areas of the business? When companies try to apply AI to a companywide context then, the real logjam appears.
Difficulties range from the time and effort needed to find out what works experimentally, to the complexity of continuous integration of successful AI experiments into a development lifecycle that is sensitive to changing context.
Cultural and structural changes in the organization are also needed. Companies tend to think that AI is a plug-and-play technology without considering the potential for cultural friction within the organization. AI requires the integration of new roles, technologies and processes within an existing company structure and culture. Decision making is affected too, shifting from a top-down model to employee-empowered decisions based on data and model results.
Finally, there are questions like “does the data comply with privacy regulations?” “Can we trust the results to confidently make decisions and audit them?” And “are we using the models ethically?” Following traditional approaches is unlikely to be practical and may leave organizations exposed to reputational damage or litigation.
It’s not hard to see why many AI projects get stuck for too long in the backwards and forwards of the experimental phase and have difficulty scaling to production.
This is not what is happening in a few isolated situations. It is the norm. According to Microsoft , only 12% of companies are in a “formalization” phase, with AI models integrated into the business as an active component of corporate strategy. And only 11% are in an integration phase, on the pathway to formalization by building AI into processes, products, and services. While that sort of 23% overall adoption rate puts AI firmly in the “early majority” phase of the technology adoption bell curve, it also means that around 76% of companies either have not started at all or, more probably, are stuck in interminable PoC experimentations.
