AI

In a MuleSoft survey of 651 IT decision-makers from enterprises with more than 1,000 employees, respondents said integration was a major barrier to digital transformation.

And organizations have a lot to integrate; on average, companies with more than 1,000 employees use more than 1,000 individual applications in all of their businesses.

Integration between systems, the pitfall of digital transformation

A common way for businesses to connect applications to each other is to custom code their applications and APIs, often referred to as point-to-point integration.

However, for a company with more than 1,000 applications, this level of tight coupling can create problems when changes, evolutions, or updates occur.

IT decision-makers say that on average, only 29 percent of their business applications are currently integrated or connected.

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Digital disruptions continue to weigh heavily on businesses of all sizes and in all sectors, as consumers, customers, employees and partners now expect everything to be accessible on demand, at all times; in short, everything needs to be fully connected.

If your business is already in tune with the digital age, great!

Otherwise, “it’s time to wake up” because we are no longer in the era “the biggest eats the smallest” but rather “the fastest eats the slowest”.

In this second post, which relates the MuleSoft (Connectivity Report) survey of 650 IT decision-makers from companies, more than 1,000 employees, from the US, Australia, Germany, England, Netherlands, Singapore and China, we will focus on the challenges faced by IT decision-makers in their digital transformation project.

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According to Harvard Business Review, before thinking about AI and Big Data, companies should think Data Strategy because cross-sectoral studies show that, on average, less than half of an organization’s structured data is actively used to make decisions and less than 1% of its unstructured data is analyzed or used.

More than 70% of employees have access to data that they should not, 80% of the time of analysts is devoted to the discovery and preparation of data. Data breaches are common, unreliable datasets are spreading in silos, and enterprise data technology often does not live up to demand.

Having a Chief Data Officer (CDO) and a data management function is excellent but not necessarily possible in all organizations. On the other hand, each organization should, at its level, define a coherent strategy for the governance, analysis and deployment of information assets (data).

In short, a Data Strategy!

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This article is made out of excerpts from a joint Survey made by MIT & Google conducted in late 2016, and which you can download here.

Machine Learning is for many enterprises the new proving ground for Competitive Advantage. A recent survey conducted by MIT Technology Review Custom and Google Cloud reveals that while the majority of businesses are struggling to apply Machine Learning, others are hard at work developing strategies for the technology — and are already realizing genuine ROI.

The survey included 375 qualified respondents representing a variety of industries, with the preponderance coming from the technology industry (43%) but also business services (13%) and financial services (10%). Most qualified respondents were either C-level executives (39%) or enterprise developers (37%), rounded out by vice president-level executives (23%).

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The day is not far off when every company will integrate features of Artificial Intelligence and Deep Learning to its processes, there is no doubt about it.

The question is: where is your business today? Are you ready?

Technology now provides us with virtual assistance, it allows us to generate ideas and analyze unstructured data – allowing entrepreneurs to focus on growing their business. Bu that’s already yesterday!

Impact of Artificial Intelligence in Business

And now with AI’s ability to analyze a problem faster than a human mind could, you will be sure that you will have considered all possible scenarios for a given situation.

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Artificial Intelligence is only beginning to penetrate the workplace, and it’s causing leaders to rethink how their businesses run or so they should. But the thing is, far from killing the jobs of knowledge workers, Artificial Intelligence tends to free them up to do what they are paid for: innovate, model, refine, and improve on the expertise of their business.

“AI is not eliminating jobs, it is creating a shift in the type of jobs by eliminating tasks of jobs, and creating new jobs that are more human jobs,” says Josh Bersin, principal and founder of Bersin by Deloitte. Bersin defines “more human jobs” as those that require traits robots haven’t yet mastered, like empathy, communication, and interdisciplinary problem solving.

The integration of early artificial intelligence tools is causing organizations to become more collaborative and team-oriented, as opposed to traditional top-down hierarchy.

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Major disruptions in Banking and Financing on the horizon; thanks to new technologies from Big Data Analytics to Mobile Apps that are reshaping this sector at a fast pace, resulting in new ways for companies and customers & consumers to interact, to manage their money and to conduct transactions.

Global investment in Fintech ventures hit $5.7 billion in the first quarter of 2016, and about half the funding went to projects in Asia, primarily in China. “The reason funding is taking off now is that the four platforms – Mobility, Big Data (IoT) and Analytics, Blockchain Technology (Bitcoin) and Machine Learning (AI) – are in their infancy,” said Catherine Wood, CEO of ARK Investment Management (US) at the Annual Meeting of the New Champions in 2016.

The fact is that Banking and Financial Services is one of the largest industries that is still not fully digitalized. In this field of activities, the technological revolution has just begun.

So let’s see what we’re up to and what major disruptions we may foresee.

Overview of upcoming disruptions in Banking and Financing

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