Artificial Intelligence Is the Technology of the Year.
According to the Harvard Business Review, Amazon has already sold 25 million Alexa speakers, and Google Assistant is now available on 400 million devices. Today, the market is filled with technologies that define every move you make as a consumer.
Right from the first click, you take on a website to convincing and upselling to you, AI and machine learning play a prominent role.
Here’s a list of top MNC’s who have already adapted to using Machine Learning and Artificial Intelligence in really cool ways.
Purpose – Artificial Intelligence for Industry, Power Grids, and Rail Systems
Siemens uses artificial intelligence not only in industrial settings, but also to improve the reliability of power grids by making them smarter and providing the devices that control and monitor electrical networks with artificial intelligence. This enables the devices to classify and localize disruptions in the grid. A special feature of this system is that the associated calculations are not performed centrally at a data centre, but decentrally between the interlinked protection devices.
In cooperation with Deutsche Bahn, Siemens is also currently running a pilot project for the predictive maintenance and repair of high-speed trains. Their Data analysts and software recognize patterns and trends from the vehicles’ operating data. Moreover, artificial intelligence helps us build optimized control centres for switch towers. From the billions of possible hardware configurations for a switch tower, the software selects those options that fulfil all of the requirements, including those regarding the reliable operation.
Purpose – Improves diversity hiring with the use of AI
Unilever wants to be a global leader when it comes to using artificial intelligence for hiring. Unilever has been using artificial intelligence to hire entry-level employees, and the company says that it has dramatically increased diversity and cost efficiency.
Instead of following the routine of sending representatives to universities, collecting résumés, and arranging follow-up phone interviews for the students that stuck out, Unilever decided to partner with digital HR service providers like Pymetrics and HireVue to completely digitize the first steps of the process.
If candidates pass the AI screening, they then go through an in-person screening to complete the hiring process. Candidates were asked to play neuroscience-based games to measure inherent traits, and have recorded interviews analyzed by AI. Unilever considers the experiment a big success and will continue it indefinitely in their hiring process.
HubSpot, Massachusetts, United States
Purpose – Smaller sales and marketing.
When Hubspot announced its acquisition of machine learning firm Kemvi, it was quite evident that they would use this emerging technology in the coolest way possible. They didn’t let us down!
HubSpot plans to use Kemvi’s technology in a range of applications – most notably, integrating Kemvi’s DeepGraph machine learning and natural language processing tech in its internal content management system that will allow HubSpot to better identify “trigger events” – changes to a company’s structure, management, or anything else that affects day-to-day operations – to allow HubSpot to more effectively pitch prospective clients and serve existing customers.
IBM, New York, United States
Purpose – Better Healthcare
IBM has managed to transition from older business models to newer revenue streams remarkably well. None of IBM’s products demonstrates this better than its renowned AI, Watson.
Watson boasts a considerably more impressive track record and has been deployed in several hospitals and medical centres in recent years, where it demonstrated its aptitude for making highly accurate recommendations in the treatment of certain types of cancers.
Watson also shows significant potential in the retail sector, where it could be used as an assistant to help shoppers, as well as the hospitality industry. As such, IBM is now offering its Watson machine learning technology on a license basis – one of the first examples of an AI application being packaged in such a manner.
Salesforce, San Francisco, United States
Purpose – Intelligent CRM via Lead Prediction and Lead Scoring
Salesforce is a titan of the tech world, with strong market share in the customer relationship management (CRM) space and the resources to match.
Lead prediction and scoring are among the greatest challenges for even the savviest digital marketer, which is why Salesforce is betting big on its proprietary Einstein machine learning technology.
Salesforce Einstein allows businesses that use Salesforce’s CRM software to analyze every aspect of a customer’s relationship – from initial contact to ongoing engagement touchpoints – to build much more detailed profiles of customers and identify crucial moments in the sales process.
This means much more comprehensive lead scoring, more effective customer service (and happier customers), and more opportunities.