How AI Will Change The Way We Work In 2020

Several ways in which innovation in AI and machine learning (ML) will change our lives in the next five years.

If there is one technology that has become the buzzword of this decade, it would be artificial intelligence (AI).

In the beginning of 2010s, consumer natural-language processing (NLP) allowed us to talk to our phones and control smart home appliances reliably. At the time, a lot of people expected NLP to explode in other domains, but it never really materialized, either because of poor implementations or a focus on other types of development.

However, over the next decade, we can expect to see NLP put to use in complex software to lower the barrier to entry. For example, customer relationship management (CRM) software, which is crucial for any business, is finding higher adoption among salespeople thanks to conversational AI. The application of AI in different softwares also helps in identifying repetitive tasks and automating them, thereby improving employee productivity.

With AI, business applications will be able to answer questions, or help users navigate interfaces, and cloud vendors will need less support personnel to manage their load. An emphasis on diversity in development will create a landscape where NLP is better at understanding different accents and speaking styles.

When more businesses yield the benefits of NLP-powered analytics and conversational interfaces, the demand for single-vendor solutions will increase. Once C-level executives realize they can ask AI assistants to generate reports for them on the fly, they will want this functionality to work across their business, not just in one department where they’ve rolled out new technology.

The organizations that will be most successful using AI over the next decade are the ones implementing single-vendor technology platforms today. If data is scattered in applications using different data models, it’s going to be difficult to work with. But when all data is on a single platform, it’s much easier to feed it into a machine-learning algorithm. The more data that’s available, the more useful the predictions and machine-learning models are going to be.

Read more: https://www.entrepreneur.com/article/345535