In the vast majority of studies related to change management and business transformation, we have read that many change efforts end up failing. It is common to find citations such as "40 percent of automation efforts fail," "Seven from ten lean initiatives are not successful," or "70 percent of digital business change actions do not bring value." These statistics prove that the ratio of successful transformations, or change initiatives, (even supported by external consultants) fall miserably low.
With the above in mind, check out the top five reasons why change and transformation actions fail.
There is a big difference between the way humans communicate with one another, and the way we “talk” with computers. When writing programs, we have to use very careful syntax and structure, but when talking with other people, we take a lot of liberties. We make short sentences. We make longer sentences, we layer in extra meaning, we use puns and sarcasms. We find multiple ways to say the same thing.
Natural Language Processing (NLP) is an area of research and application that explores how computers can be used to understand and manipulate natural language text or speech. NLP is a sub-field of Artificial Intelligence (AI) that is focused on enabling computers to understand and process human languages, and to get computers closer to a human-level understanding of language.
To thrive in the near future means adopting automation as soon as possible. Those who invest, tinker, pilot, scale, and mature before the competition will have the advantage of greatly increased operational speed and agility, as well as a leg up above the coming waves of market disruption. To receive a copy in your inbox, please e-mail: firstname.lastname@example.org
The debate over Artificial Intelligence’s value in business is quickly moving into a new phase. Questions around why AI investments should be a strategic corporate priority are quickly fading, especially when firms such as PwC are estimating that AI could contribute $15.7 trillion to the global economy by 2030; no business wants to miss out on that kind of opportunity. As a result, discussions on when, how and in what form an AI implementation should take are accelerating in enterprises across industries.
With market hype around AI at an all-time high, it’s important to be clear about what AI encapsulates. In this report, learn more about:
- RPA, Machine Learning
- Logic-based/Statistical Data Science
- Cognitive AI, and much more.
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