Introduction

This is when things start to get interesting. However, a few extreme situations, such as Netflix, Spotify, and Amazon, are insufficient. Not only is it difficult to learn from extreme situations, but when AI becomes more widespread, we will be able to find best practices by looking at a wider range of enterprises. What are some of the most common issues? What are the most important and effective ways of dealing with them? And, in the end, what do AI-driven businesses look like?

Here are some of the insights gathered to capture, learn from, and share from approximately 2,500 white-collar decision-makers in the United States, the United Kingdom, Germany, India, and China who had all used AI in their respective firms. They were asked questions, and the responses were compiled into a study titled "Adopting AI in Organizations."


Speaking with AI pioneers and newcomers

Surprisingly, by reaching out on a larger scale, a variety of businesses with varying levels of AI maturity were discovered. They were classified into three groups: AI-leaders, AI-followers, and AI-beginners, with the AI-leaders having completely incorporated AI and advanced analytics in their organizations, as opposed to the AI-beginners who are only starting on this road.

The road to becoming AI-powered is paved with potholes that might sabotage your development.

In sum, 99 per cent of the decision-makers in this survey had encountered difficulties with AI implementation. And it appears that the longer you work at it, the more difficult it becomes. For example, 75 per cent or more of individuals who launched their projects 4-5 years ago faced troubles. Even the AI leaders, who had more efforts than the other two groups and began 4-5 years ago, said that over 60% of their initiatives had encountered difficulties.

The key follow-up question is, "What types of challenges are you facing?" Do you believe it has something to do with technology? Perhaps you should brace yourself for a slight shock. The major issue was not one of technology. Rather, 91 per cent of respondents stated they had faced difficulties in each of the three categories examined: technology, organisation, and people and culture. Out of these categories, it becomes evident that people and culture were the most problematic. When it comes to AI and advanced analytics, it appears that many companies are having trouble getting their employees on board. Many respondents, for example, stated that staff was resistant to embracing new ways of working or that they were afraid of losing their employment.

As a result, it should come as no surprise that the most important strategies for overcoming challenges are all related to people and culture. Overall, it is clear that the transition to AI is a cultural one!

A long-term investment in change

But where does this adventure take us? We assume that most firms embarking on an organisational transformation foresee moving from one stable state to a new stable one after a period of controlled turbulence. When we look at how these AI-adopting companies envisage the future, however, this does not appear to be the case!

Conclusion:

To get a sense of what it'll be like to be entirely AI-driven, researchers looked to the AI leaders, who have gone the furthest and may have a better idea of where they're going. This group has already integrated AI in their business or plans to do so by the year 2021. You'd think that after properly implementing and delivering AI inside the organization, they'd be satisfied with their work. They're still not finished. Quite the contrary, they aim to invest much more in AI over the next 18 months, and on a far larger scale than previously. The other two groups had far smaller investment plans.

To know more about AI:https://blogs.nife.io/artificial-intelligence-critical-for-making-network-financial-decisions-ckwglah6363422vrul01y1syl/