AI & Machine Learning

It should come as no surprise that large tech enterprises are readily embracing AI as a means of improving the way they operate and their ability to engage with clients. However, for smaller organisaitons and companies that are less tech savvy, the ability to identify and implement AI can be overwhelming and complicated in equal measure. What are the main barriers stopping organisations – particularly those outside of the tech sector – from taking advantage of this powerful technology?

In order to disrupt business, machine learning models must adopt a product-focused approach, which is a much more significant undertaking. For a product-driven approach to use machine learning, it is important to think about the problem you are trying to solve from the beginning and to have some initial idea of how the machine learning solution might be used. The first step is to understand what pain points you are trying to tackle, and what kind of service-level agreement in terms of quality, availability and responsibility you need.

In this short article, a simple example of the use of Azure ML studio is shown. It’s a very useful tool in the machine learning industry and, although it has some limits with limited number of records, limited choice of models. Even the most code-oriented data scientist will love this simple tool. It’s pretty worth mentioning that, paying the appropriate fee, ML studio can be used for real-time training and prediction thanks to its strong REST API interface. This enables many possible machine learning scenarios.

The future, in fact, will be driven by humans collaborating with other humans to design work for machines to create value for other humans. Any viable cognitive strategy is not to cut costs but to extend capabilities. So perhaps most importantly, what business leaders need to understand about artificial intelligence is that it is not inherently utopian or apocalyptic, but a business tool. Much like any other business tool its performance is largely dependent on context and it is a leader’s job to help create that context.

To take advantage of machine learning, businesses will need to do one of two things: Invest a lot of resources in data scientists or developers with a background in machine learning or utilize machine learning as a service (MLaaS) offerings. The latter option can be much more cost-effective for a business that may not have the luxury of hiring ultra-skilled employees.  In 2019, MLaaS will become mainstream. The technology behind MLaaS will become prominent in 2019 with the help of the cloud giants, media and consulting partnership opportunities.

While machine learning and other AI techniques will help improve the speed and quality of cybersecurity solutions, they will not be a replacement for many of the basic practices that companies often neglect. In cybersecurity today, we overestimate the capacities of machine learning. When talking about AI, many people have this illusion that they can just plug in software or hardware that is leveraging AI, and it will solve all their problems. It will not.

The emerging business process automation tool, RPA, is programmed such that it can deal with high-volume transactional tasks, invoice processing, email communication, and other back-office processes. The data that RPA deals with can or cannot be sensitive. The automation tool can even comprise of vital data like credentials, employee details, or customer information. What if hackers access the application platform, implant malicious code, and alter the rule-based processes? Well, if this happens, then businesses will face dire consequences. Let us now check the possible security risks in RPA.

Today, a new debate is likely to begin over artificial intelligence. Much like in the early 1970s, we have increasing investment in a new technology, diminished productivity growth and “experts” predicting massive worker displacement. Yet now we have history and experience to guide us and can avoid making the same mistakes. Investment in digital technology in the 70s and 80s was focused on supporting existing business models. It wasn’t until the late 90s that we began to see significant new business models being created.

Today we have Artificial Intelligence (AI) to help you search for a needle in the haystack. Using the power of AI to supplement (not replace) the human judgment can make the Talent Acquisition process more effective. Making an informed choice can be the first step in having happy employees. Using AI for sourcing, screening and setting up interviews can be great ways to improve the candidate experience. Here are some things that will improve the quality of your hires.

There is no doubt that automation led by AI and robotics will eliminate many jobs in the future. Routine work, be it white- or blue-collar, can be expected to be taken up by machines. But it is also time to dispel the myth around AI to see what it can actually do as a tool. Only in this way can we really hold honest conversations about how humans and machines can forge a new alliance.

Artificial intelligence (AI) is increasingly having a huge impact across the entire business landscape in industries as diverse as pharmaceuticals, law and education. Successful AI disruption comes from a willingness to embrace change. It is difficult to predict how AI will reshape different industries but what’s clear is that the most open, flexible and forward-facing companies have the best chance of adapting to the new landscape. Here are four rules for getting started.