Artificial Intelligence vs Business Intelligence

Artificial Intelligence vs Business Intelligence

Difference Between Artificial Intelligence vs Business Intelligence

Business Intelligence is a technology that is used to gather, store, access and analyzes data to help business users in making better decisions, on the other hand, Artificial Intelligence is a way to make a  computer, a computer-controlled robot, or a software that think intelligently like humans. Artificial Intelligence is based on the study that how human thinks, learn, decide and work in order to resolve an issue and then using the outcome of this study as a basis of developing intelligent software and systems.

Head to Head Comparison Between Artificial Intelligence and Business Intelligence (Infographics)

Below is the Top 6 Comparision Between Artificial Intelligence and Business Intelligence:

AI Vs BI Differences Infographics

Artificial Intelligence and Business Intelligence Comparison Table

Following is the comparison table between Artificial Intelligence and Business Intelligence.

Basis of Comparison Artificial Intelligence Business Intelligence
Philosophy AI is started with the intention of creating
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Artificial Intelligence in Banking 2020: How Banks Use AI

Discussions, articles, and reports about the AI opportunity across the financial services industry continue to proliferate amid considerable hype around the technology, and for good reason: The aggregate potential cost savings for banks from AI applications is estimated at $447 billion by 2023, with the front and middle office accounting for $416 billion of that total, per Autonomous Next research seen by Business Insider Intelligence.

Most banks (80%) are highly aware of the potential benefits presented by AI, per an OpenText survey of financial services professionals. In fact, many banks are planning to deploy solutions enabled by AI: 75% of respondents at banks with over $100 billion in assets say they’re currently implementing AI strategies, compared with 46% at banks with less than $100 billion in assets, per a UBS Evidence Lab report seen by Business Insider Intelligence. Certain AI use cases have already gained prominence across banks’ operations, with

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Geospatial Artificial Intelligence: Emerging New Business Intelligence

In a country smaller than the state of New Jersey, where sand reigns and water hides, an inconvenience of modern life intrudes. Traffic is a growing problem in Kuwait. With a high ratio of vehicles to residents and a low ratio of roads to vehicles, traffic in the capital Kuwait City is worse than that of Atlanta, Boston, and Madrid.

Congestion is unwelcome in any city looking to boost the productivity of its workforce, ensure the smooth functioning of commerce, and improve quality of life. In Kuwait, officials work tirelessly to ensure quality of life. As part of that effort, the country’s Public Authority for Civil Information (PACI) released a hyper-local search engine called Kuwait Finder, which connects citizens to the local goods and services they need. The application also provides turn-by-turn voice navigation to any business, with traffic information included.

However, in the absence of reliable traffic sensors in

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Is Your Business Ready for Artificial Intelligence?

AI requires more than data mastery. Companies also face many managerial challenges common to other technology-driven transformations. These include vision and leadership, openness and the ability to change, long-term thinking, close alignment between business and technology strategy, and effective collaboration. Companies also face AI-specific challenges.

Most basic—and most important—is developing an intuitive understanding of AI. J.D. Elliott, director of enterprise data management at TIAA, a Fortune 100 financial services organization with nearly $1 trillion in assets under management, said, “I don’t think that every frontline manager needs to understand the difference between deep and shallow learning within a neural network. But I think a basic understanding that—through the use of analytics and by leveraging data—we do have techniques that will produce better and more accurate results and decisions than gut instinct is important.”

A second challenge is organizing for AI. Adopting AI broadly will likely place a premium on soft

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Small Business Artificial Intelligence (AI)

Simply put, the new world of AI isn’t just for enterprises anymore. The combination of huge computing power advances and a world dominated by cloud-enabled, data-producing devices and apps changed the game. And companies like Salesforce, which have been committed to democratizing the technology of customer relationship management for years, are now doing the same for AI.

So AI is real. But how real? And how does all that matter to a business like yours? Probably the first thing to understand is that the AI people are already experiencing in movie recommendations, driving apps, and voice commands is changing their expectations. And the “they” here is none other than your customer. Quite simply, their experience of AI in consumer apps means they expect you to know more about them so that you can do more for them, even giving them the help and answers they’re looking for before they ask.

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| The World’s Number One Portal for Artificial Intelligence in Business

| The World’s Number One Portal for Artificial Intelligence in Business

Informing machine learning with time series analysis

by Jelani Harper 3 April 2020

Machine learning is bringing sophisticated pattern recognition capabilities mainstream. Advanced machine learning deployments can detect non-linear patterns, patterns with a staggering amount of variables, and patterns occurring over lengthy periods of time that are too difficult for humans…



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Artificial intelligence in business: Separating the real from the hype

Typically understood as being all about robots and whiz-bang machines, artificial intelligence (AI) can be tough for executives to wrap their business minds around. In this episode of the McKinsey Podcast, senior partner Peter Breuer and McKinsey Global Institute partner Michael Chui speak with McKinsey Publishing’s Simon London about burgeoning business applications of artificial intelligence, the line between hype and true use cases for AI, and how business leaders can separate one from the other.


Artificial intelligence in business: Separating the real from the hype

Podcast transcript

Simon London: Hello and welcome to the McKinsey Podcast. I’m Simon London with McKinsey Publishing. Today, we’re going to be talking about artificial intelligence. It’s certainly a hot topic in the business press and also starting to attract quite a lot of attention in the mainstream media.

You’ve probably read pieces about everything from killer robots to

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10 Steps to Adopting Artificial Intelligence in Your Business

Artificial intelligence (AI) is clearly a growing force in the technology industry. AI is taking center stage at conferences and showing potential across a wide variety of industries, including retail and manufacturing. New products are being embedded with virtual assistants, while chatbots are answering customer questions on everything from your online office supplier’s site to your web hosting service provider’s support page. Meanwhile, companies such as Google, Microsoft, and Salesforce are integrating AI as an intelligence layer across their entire tech stack. Yes, AI is definitely having its moment.

This isn’t the AI that pop culture has conditioned us to expect; it’s not sentient robots or Skynet, or even Tony Stark’s Jarvis assistant. This AI plateau is happening under the surface, making our existing tech smarter and unlocking the power of all the data that enterprises collect. What that means: Widespread advancement in machine learning (ML), computer vision, deep learning,

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