How AI is Transforming Business Intelligence

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34% of European companies plan to adopt AI technologies in one form or the other this 2019, says research by IDC. What’s more surprising is, there are over 70% of those already implementing AI look forward to achieving business benefits within 24 months’ time. By 2022, around $11 billion is expected to be used on AI technologies and its applications across Europe. So, you can imagine what it’s going to be like worldwide.

Well, these do not require electric muppets, autonomous killer drones or white robots. To be realistic, let us draw our focus on how we can define and implement real-world AI strategies that can deliver valuable business.

The economics of computing has shown drastic shift and has moved towards a real-time analysis of rich multimedia data like facial recognition and the generation of multimedia like speech, artificially generating speech, written languages etc. This has also shifted the focus on video that can mimic and fake using techniques such as Generative Adversarial Networks (GANs). If you aren’t aware with the concepts such as deepfakes and deep voice research, then I recommend you to do a research on it and find out why debates around the ethical use of AI have been so prevalent these days.

AI is a lot more than this and there’s still a lot more to talk about.

The first and foremost thing you need to keep in mind is that AI is not one single thing. It is the first thought that triggers your head looking at the usability of “AIs” like Cortana, Alexa, and Siri. It is much more than that, they aren’t anything you can plug into business. AI is basically a set of tools and technologies that can be used for different applications. Artificial intelligence has brought substantial changes in the world of business, though the future remains blur, companies these days are embracing AI tools to help keep up the pace in this tech-driven job market. This is a good approach because businesses can add value out of it.

Artificial intelligence plays a major part in business intelligence, for an instance, some of the recently developed AI capabilities seem to come out of a fiction movie. AI is not just capable of doing science fiction things but also has the capability of making a huge difference in the analytics world. AI in business intelligence helps democratise data to improve the adoption of analytics. Before big data came into existence, business intelligence had very limited importance. It is because of big data that business intelligence is considered valuable.

Currently, there are many companies and enterprises looking to hire artificial intelligence professionals in business intelligence to make their company AI empowered.

Here’s how AI is transforming the business intelligence world for the better:

  • Transformation in different industries

It is rapidly keeping up the pace and transforming different industries. Industries such as financial services, healthcare, trading and life sciences industry have been impacted by AI. Let us take an example, in medicine, AI is now taking the role of a clinical assistant to help make quick and reliable diagnoses.

  • Powered decision-making

AI is powering modern decision making as well and bringing an impact on all aspects of modern businesses. Prior to the emergence of AI, businesses and leaders had to depend on data that was incomplete and not well structured. But today, AI feeds on big data, make extensive use of it and bring along actionable insight that helps leaders and executives power their decision-making processes. AI simulation and AI modeling techniques have helped provide reliable insights into buyer personas. In simple terms, these methods are ideal to predict the behavior of the consumer.

  • Provide actionable insights

Actionable insights are direct and meaningful predictions can be taken from any kind of data available, AI does that for you. In simple words, AI is the automation of different sequence of decisions that originates from prescriptive analytics. Its intelligence comes from its ability to provide real-time data feedback to enhance prescriptive models ensuring that the next described decision will be better than the previous

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