Home Artificial Intelligence News Understanding artificial intelligence, machine learning and their business use case

Understanding artificial intelligence, machine learning and their business use case

artificial intelligence

Staying ahead in the accelerating artificial intelligence race requires executives to make nimble, informed decisions about where and how to employ AI in their business

Artificial intelligence (AI) and machine learning (ML) is an integral part of our daily lives today. From Siri, IBM Watson, Google services like search, maps, photos, etc all translate to artificial intelligent (AI)-powered products, and services that have made inroads into every aspect of our personal and professional lives.

Is Artificial Intelligence a threat or an opportunity?

Think and answer ‘Does your company see AI as a threat or an opportunity or both’? Do you think it’s a fair question to ask? It’s like asking a manufacturer at the end of the 19th century, the impact of electricity on business. Just like any foundation technology be it electricity, internet or Blockchain, AI can be applied in many different ways and it impacts every business differently.

In this digital era where advanced technologies are accelerating transforming businesses, dwelling in the past and being reluctant to embrace the present could cost business big time. Technologies like AI, ML, and data analytics are empowering almost all industries and organizations of all sizes. Every business has a different impact and business value extraction from AI. From startups to large enterprises – all are in some form consulting AI experts/professionals to be ahead of the game. It’s not surprising that Gartner has predicted that AI will be one of the top five investment priorities for more than 30% of CIOs globally by 2020.

Business use cases – AI reshaping IT Operations’ Day-to-day

AI has the ability to revolutionize all the industries, especially the ones mentioned below. Businesses have realized that excellent customer service is critical to running a successful business.

Retail improving customer experience

Automated bots are driving powerful and practical ways for retailers, E-commerce players in the retail industry. It helps them to engage the right customers with the right messaging at the right time — creating a lifelike, seamless customer service experience. It also addresses consumer queries based on their purchase history and known preferences.

BFSI addressing traditional customer service concerns

AI/ML tools in BFSI (financial institutes and banks) are continuing to transform the industry to overcome traditional customer service challenges, adhere to regulatory compliances, reduce risks and increase opportunities to provide greater levels of value to their customers.

AI revolutionizing Healthcare

To reduce the overall spending, streamline care and improve patient treatment, from small clinics to bigger hospital chains, all are adopting AI/ML and revolutionizing the way Healthcare sector works.

Real-time recommendations for Manufacturers

AI/ML solutions are making real-time recommendations about which materials to be injected at what time to ensure continuity of the manufacturing processes. Thus helping manufacturing companies to improve efficiency, continuity and customer satisfaction

Logistics boosting productivity

AI/ML technology has made inroads in logistics and supply chains with contextual intelligence that can be used to reduce the operating costs, manage inventory/warehouse and boost productivity.

With surging momentum of great progress in the field as well as promising business cases, the hype around AI may be warranted. So, for organizations that are getting started with AI and ML, this can be a bit overwhelming to keep pace with the fast innovation cycles, new hybrid IT landscapes, and deployment models. In order to implement and reap the actual benefits of these technologies, companies need to overcome the technological, organizational and cultural challenges.

Get started with AI-based solutions/ Time to engage with AI confidently

There are still enterprises out there that are rightfully reluctant to explore new technologies. However, it’s time to go beyond the traditional approach and the old paradigm. Earlier, the investment in new technologies used to depend on the turnover of the companies, which is not the case anymore. It is time for all the enterprises to start having investment and conversations revolving around the business value creation,

To get started with AI, businesses must keep these points in mind:

Focus: Collect your business data from relevant touchpoints

Identify: Look for the business challenges that are hindering your business efficiency

Define: What should the AI/ML solution you opt for achieve?

Educate/Train: Have the resources onboard to raise awareness on new technologies

Consult: Get in touch with a trusted IT advisory firm before investing

The march of AI and ML will continue and there is no force that will stop the advancement of these technologies. From the world’s largest enterprises to emerging start-ups, businesses are creating new insights, enabling new efficiencies, and making more accurate business predictions. With the help of broadest and deepest set of AI/ML techniques, business leaders will continue to recognize complex patterns and make smart decisions.

Source link

Must Read

Highlighting AI Bias

On Monday, IBM made a monumental announcement: the company is getting out of the facial recognition business, citing racial justice concerns and the need...

Artificial Brains Need Sleep Too

 States that resemble sleep-like cycles in simulated neural networks quell the instability that comes with uninterrupted self-learning in artificial analogs of brains.No one can...

Differenciating Bitcoin and Electronic Money

Bitcoin has the largest market share among virtual currencies, and is already being used on a daily basis overseas. Since it is a virtual...

Answering the Woes of Staking Centralization

What if better behavior on blockchains could be encouraged with fun rather than value?Josh Lee and Tony Yun of Chainapsis built a staking demo at the Cross-Chain...

The future of Machine Learning

Machine learning (ML) is the process which enables a computer to perform something that it has not been explicitly told to do. Hence, ML...

Is Automation the solution for rapid scaling in response to the Pandemic

Thanks to the pandemic, the nature of work for federal agencies changed almost overnight. Agencies are now attempting to meet the challenges of a...

Siemens and SparkCognition unveil AI-driven cybersecurity solutions

Today, Siemens and industrial AI-firm, SparkCognition, announced a new cybersecurity solution for industrial control system (ICS) endpoints.DeepArmor Industrial, fortified by Siemens, leverages artificial intelligence (AI) to...

Amazon and Microsoft follow IBM, no longer in Face Recognition business

At least its bandwagon-detection AI still worksMicrosoft said on Thursday it will not sell facial-recognition software to the police in the US until the...

Developing smart contracts with buffered data model

How specifying world state data model with protocol buffers can help in developing smart contracts

Reasons why your AI Project might fail

Here is a common story of how companies trying to adopt AI fail. They work closely with a promising technology vendor. They invest the...
banner image