Coined back in 1956, the word “Artificial Intelligence” (AI) has been the heart of any debate-national or global-about technology. Yet the full potential of AI is still under investigation. And rightly so.

Artificial Intelligence-is also the largest scientific advance in human civilization since Electricity 125 years ago. In the past five years, the technology landscape has grown more than in the last few decades. The growth of key trends, such as easy access to vast computational resources & storage, the surge in user-generated data, the connected environment that enables the processing of consumer data, and the innovation in machine learning algorithms & tools – both open source & business – empower us to make the three trends more successful. The fundamental promise of artificial intelligence has been to change the universe in which we work and transform it for good.

The substantial growth in AI in our everyday lives may be due to the explosion and abundance of data through consumers/businesses/etc. digital transition of modern businesses. They are generating innovative growth opportunities to bring value to their customers and to create alternate sales models for themselves. The secret to the legacy modernization is the large volume of data that is produced on a daily basis. Our growing contact with machines results in almost 2.5 million bytes of data being generated every day. And businesses use all of these data to develop more efficient and human-like AI solutions (Google Maps, Alexa, etc.).The days of the legacy system are minimal in the business world; they are either replaced or totally broken.

In its computational implementations, artificial intelligence has already begun to disrupt markets such as retail, hospitals, engineering, financial services and transport, by developing new and creative cost-effective technologies, optimizing productivity and saving human lives. AI, a concept that was limited to science fiction a little while ago, is now a vital part of our daily lives. We’re surrounded by AI systems from our phones to voice assistants like Siri, Google, Cortana, and Alexa, to Tesla’s self-driving vehicles.

Such notable examples of AI across various industry verticals include the partnership of Pittsburgh-based Argo AI and Ford Motor Corporation in NASCAR racing, where they are extending their deep learning to create safer vehicles in the field of auto racing. Using its deep-learning neural network, Argo AI has been able to recognize a single car that is on the cusp of failure during a race from a real-time dataset of thousands of car photos to prevent significant problems such as fires or malfunctions that could put the driver at risk.

In the world of entertainment, we all have knowledge of Netflix’s Machine Learning Workflow, where the company will determine what you want to see based on your viewing results. Although the healthcare industry uses AI to create applications that can help increase the functionality of the brain, such as implants and nanobots, and enhance the supply of oxygen in the body.

AI is making significant strides in the fashion and make-up industries, as well as in brands such as Proven, L’Oréal, Dior and Estée Lauder, adopting AI, VR (Virtual Reality) and AR (Augmented Reality) for product growth. Some examples of real-life use can be seen in the form of a smart hairbrush, using sensors to detect hair consistency, and then prescribing suitable hair products and AI-based skin assessment.

With the examples listed above, we can see that the industries have begun to accept AI for their companies. However, as per the business leaders, the rate of adoption is yet to be set. Despite the transformative opportunities and economic benefits that AI has to offer, there are still few obstacles that impede the adoption of AI.

The key obstacles facing companies include the scarcity of Data & Analytics Experts to enable their enterprises to embrace AI. We simply don’t have enough D&A leaders with a strong AI adoption strategy to drive adoption, add to that lack of AI unique expertise, and you’re looking at a major roadblock right at the start of the journey. Also market leaders with the experience in AI adoption are constrained by lack of evidence, inadequate technical resources, industry/business specific end-to-end constraints and, at times, lack of a good AI adoption strategy. In certain unusual cases, the implementation of AI suffers from bias over the data-based decision on AI, and is hesitant to switch from legacy systems to AI-based systems.

While one of these obstacles seems daunting than the other, each of them can be solved. And the positive thing is that much of it can be done at home. There is a plethora of training materials & case studies concerning AI and automation technology available to house AI leaders & talent. Organizations need to build an AI-first ecosystem to update and re-engineer their talent base. Top management wants to invest not only financially but also culturally in AI leaders.

We are on the brink of the 4th Technological Revolution, which appears to be influenced by emerging technology such as AI (artificial intelligence), ML (machine learning), deep learning, data & analytics, etc. Modern companies have begun to implement emerging technology such as robotic process automation (RPA), machine learning, artificial intelligence, data processing, and cognitive computing to create an integrated environment.

The influx of technology such as AI, Internet of Things (IoT), 5G, Block Chain, and the cloud leaves us to be prepared for them. According to a recently published study, by 2030, AI will generate about 38.2 million new jobs around the world to impact the global economy and enable us to be part of the change that humanity is experiencing. It’s our chance to be part of the transition now, or just be an onlooker.