Artificial Intelligence market is all set to grow at a compound annual growth rate (CAGR) of 57% from 2017 to 2025 to become a market worth of $36 Bn, according to Grand View Research. AI market had already seen a rise in investments in 2016 from $20 Bn to $30 Bn and these investments are growing rapidly.\r\n\r\nRegarding the investment pattern in AI services and technologies, companies are putting 90% of investment in Research and Development and deployment and 10 percent in acquiring cutting-edge startups working in technologies like Machine Learning, Deep Learning, and Neural Networks etc. that are the subsets of Artificial Intelligence.\r\n\r\nArtificial Intelligence has already started marking its presence in one or the other business processes for all major players in the different industries and is characterised as early movers in embracing this technological change.\r\nThe First Signs of AI transformation\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 \r\nSome of the businesses have already identified areas where Artificial Intelligence and Machine Learning can be implemented at first. For instance, some of the industries like Life sciences, Energy and Data Infrastructure are witnessing AI transformation early in comparison to others.\r\n\r\nThe characteristics of early Artificial Intelligence adopters are the ones that are Data Intensive or Digitally Mature, Reskilling or Acquiring AI workforce and intentions of C-suite to adopt AI in different business processes.\r\n\r\nLife sciences have been a data intensive industry because of the variety of combinations that arises from areas like clinical genomics, plant and animal research and they are correlated with each other. It is really time consuming and high error prone for scientists\/researchers to manually find the correlation and causation of different medicines and medical approaches for fatal diseases.\r\nWith the use of technologies like AI\/ML, these data intensive researches can be analyzed properly while humans can be free for more research work than applying different statistical models.\r\nAlike, industries such as Energy and Data Infrastructure where previous data can help in doing predictive modelling and maintenance for optimizing limited resources or identify potential cyber fraud and attacks can be one more significant area where AI is going to contribute.\r\n\r\nRegarding the adoption pattern for Artificial Intelligence and related technologies, across industries the world will witness gap between early adopters and laggards.\r\nWhy Digital Maturity, AI talent and agility of C-level decision makers are the early characteristics for AI adoption?\r\nAdvancements in Artificial Intelligence and Machine Learning are fueled by Big Data and data is the next natural resource available like water, air and oil. Easy available high computing resources, access to lots of unstructured data and better availability of high bandwidth are the main triggers for AI to come out of sci-fi movies and reach at its scale in the real world.\r\nThat\u2019s the reason data mature organizations are going to have the upper - hand in adopting and deploying AI for their businesses.\r\nArtificial Intelligence promises a lot of benefits in terms of reducing costs, channelizing human creativity in more productive tasks rather than mundane tasks and ensuring efficient data-driven operations. On the contrary, there lies a challenge of making the current workforce adaptable to leapfrog technological advancements coming in with Artificial Intelligence wave.\r\n\r\nBy 2020, enterprises are set to generate data is expected to exceed 240 exabytes daily, implying that there\u2019s more data and more workforce is required to extract insights out of this data. It means that organizations need to reskill their workforce to deploy, adopt and work in collaboration with Artificial Intelligence technologies.\r\n\r\nThere are organizations that have already started up-skilling their workforce that is capable of working in sync with AI and futuristic technologies. That\u2019s why trying to build a hybrid workforce to supplement the Artificial Intelligence technologies is also one of the characteristics of early AI adopters.\r\n\r\nThe third characteristic is very crucial as the whole initiative of driving Artificial Intelligence adoption is to be understood and phased by C-level executives of any organization. General AI is still not very prevalent for businesses and hence first movers in any industry need deep pockets to adopt and deploy it.\r\n\r\nCosts are involved and hence, C-level executives need to channelise investments accordingly, requiring aggressive AI-first approach.\r\nBecoming an AI-First business means embracing Artificial Intelligence to work in collaboration with human workforce and be the foremost in adopting data-driven culture in the industry to get ahead of the competition and becoming a leader in your industry.\r\nThe Overall Impact\r\nWith the rise of adoption in Artificial Intelligence technologies, the business world is going to end having two sets of \u2018Haves and Have nots\u2019 \u2013 One set, having characteristics to drive AI adoption aggressively while the others must be maturing to follow the league of AI-first companies.\r\n\r\nIn the years to come, AI-first enterprises will scale new heights by exploring this untapped data and extract incremental value. Big Data powered by Artificial Intelligence will affect almost all facets of our existence\u00a0ultimately influencing the way enterprises chase growth, to positive effect.