Enterprise AI

Enterprise AI is a category of software that applies AI to drive digital transformation and facilitate opportunities such as making assumptions on customer needs and also solve proactive threat detection. Embracing modern tools and technologies enables companies to build provisions and use AI applications at a high rate of scale. 

The study says that organizations still confront challenges to implement AI models and run them at a scale. Along with this, when an organization implements AI models in production, the task of managing and governing those models becomes difficult.

Enterprise 1.0

Today’s economy is filled with data operations. Companies are struggling with huge sets of data that they are accumulating and storing. However, this data collection and storage has become easy with the help of AI but to find out the insights that are hidden in that data burdens the data analysts. 

Many organizations think that with growing data it is necessary to employ more data analysts and data scientists or to provide other tools for data analysis to perform the complex task of data analysis. But the fact is that it is not just hiring more data scientists or giving training to the employees, it is something more than that. If the process of data accumulation and storage is automated then the process of extracting insights out of data should also be automated. Organizations need to implement automated intelligence to deliver insights and provide assumptions to drive real business value. 

Since businesses and organizations started implementing and applying AI solutions, there has been little progress in gaining significant insights in profitability and efficiency about the hype that generated initial expectations. With the emerging technology, automated intelligence often seems more about the hype than the product itself.

Evolution of Enterprise 2.0

Recent data shows that almost 90% of data science projects have never made it to production. It also shows that only 20% of enterprises are expected to achieve business outcomes by 2022. Even organizations that have created a broad enterprise AI strategy are seeing failure rates of up to 50%, as per the report. 

But this is just the first phase in the evolution of enterprise AI or rather enterprise 1.0. Next-generation enterprise AI has the potential to generate successful outcomes for businesses. Therefore organizations on the leading edge of AI innovation have progressed to the next generation which is the enterprise AI 2.0. This generation of enterprise AI will elucidate the evolution of big data, automation, and analytics. 

The difference between enterprise AI 1.0 and 2.0 is not something conceptual rather something practical. All the sectors from retail and healthcare to finance and media, the revolution of enterprise AI from 1.0 to 2.0 gives a chance to realize the mistakes done in the past, to accept the failures and adapt to new changes, to build strong belief for future uses and to support the increasing investments in AI across industries. 

It is expected that companies who are intending to achieve enterprise AI 2.0 first will be the big winners in the economy with transformed services, high market share. They will be able to position themselves as the leading market innovators. 

Facilitating digital transformations of the future with the revolution of Enterprise AI from 1.0 to 2.0 delivers a practical model for business leaders blooming new strategies to participate and compete in the age of automation and advanced analytics.