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6 Current Machine Learning Trends that Predict the Future of Business Operations

The artificial intelligence leaders of today are the mainstream of tomorrow. The field is moving increasingly quickly, with new trends emerging on an almost monthly basis. That makes it hard to predict the future of machine learning.

At the same time, it becomes more straightforward if we assume a simple truth: current trends predict future status quo. Looking at today's leaders in the technology will help us understand what even small businesses in the Asia Pacific region can expect and leverage just a few years into the future.

To get to that point, a true understanding of current developments and initiatives is vital. In fact, these 6 current machine learning trends have the potential to predict the future of business operations. As they become increasingly accessible and affordable, they will dictate best practices and competitive balance for years to come.


1) Machine Learning is Entering Mainstream


Machine learning is no longer a novelty. That's becoming increasingly clear, as multiple countries and business sectors throughout Asia Pacific are beginning to adopt the concept to improve their business operations. According to one report,

The opportunity is tremendous. IDC estimates the global machine learning market to be US$47 billion by 2020. Gartner predicts that, by the same year, 30% of CIOs will cite smart applications as a top-five business priority. With Asia as the engine of global growth – the IMF reported in 2016 that the Asia Pacific region accounted for two-thirds of global growth– we can expect machine learning to be a huge opportunity for business and enterprise innovation in this part of the world.
The rising popularity is at least partially explained by the fact that the tools to build machine learning algorithm no longer require massive budgets or IT departments. Google's TensorFlow, for instance, is an "open source software library for Machine Intelligence." A relatively easy development process allows even smaller businesses to build algorithms that lead to smarter data analysis.

Combine the increasing availability of machine learning tools with its popularity, and you reach the potential for a major growth explosion. That, in turn, promises a near future where the concept becomes the norm for businesses of all sizes. It would not be a surprise to move into 2020 in a mindset where machine learning is a basic business ops expectation for all tech departments.


2) Data Science Integration Into the Organization


In addition to its rising availability, a second machine learning trend has to do with its integration into multiple facets of the organisation. The same algorithm type can now be applied to marketing, IT, analytics, and a number of other areas. That, in turn, is leading to a more comprehensive approach to the concept by many Asia Pacific businesses.

This increasing integration is already starting to lead to tangible benefits. For instance, software and platform developers are beginning to apply DevOps practices to machine learning, showcasing the potential intersection between two valuable concepts. Integrating a predictive data science approach into multiple facets of your organisation can lead to significant operations improvements.

Consider the importance of eliminating data silos. Put simply, machine learning is only as successful as the data you feed into the algorithm. If it's incomplete, even the most comprehensive scientific efforts will be worth little. Breaking down intra-organizational barriers is a core requirement for successful application of the concept in multiple areas of your business.

Again, the future results of this current integration trend are clear. A more integrated approach means more comprehensive applications for machine learning throughout the organisation. From daily operations to strategic analytics, it's further evidence that the concept will be a core part of doing business in the years to come.


3) AI Applications for Daily Operations


Closely connected to the previous trend is another worth monitoring: AI is no longer reserved for strategic initiatives. Instead, the concept - and especially its machine learning offshoot - has the potential to be just as applicable in the daily operations of your business.

Consider, for instance, the ability to improve customer service through the use of machine learning-powered chatbots. Within two years, a full quarter of the world's population is expected to be using chat-based apps to communicate with brands. Asia Pacific, of course, is driving much of that trend and growth.

Chatting individually with each customer, especially as your business grows, is almost impossible to scale. Through machine learning, that scaling is natural. Chatbots that learn about commonly asked questions and typical follow-ups can personalise their service and maximise customer satisfaction. It's no surprise, then, that chatbots are expected to cut global business operations costs by $8 billion before 2022.

Chatbots are only one example of the many benefits machine learning can provide for your daily operations. Over the next few years, it will allow you to automate routine processes while improving effectiveness and efficiency, leading to significant savings and the potential for resource reallocation.


4) Cyber Security Potential and Risks


One potential benefit of machine learning that has been in the news frequently is its ability to improve cybersecurity operations. Currently, this might be the single biggest threat your business faces. Inadvertently compromised data or purposeful attacks can sink your organization if you are not ready for them. Through machine learning, it's easier to be prepared.

In fact, the concept allows you to build automated systems that learn from attacks and dynamically fortify defences. These systems can react more quickly than any manual alternative, keeping your organisation and its data safe at all times.

For this recent trend to enter mainstream, however, Asia Pacific businesses need to undergo a significant shift in focus. Almost 60 percent of APAC organisations still believe that a 'detect and respond' approach to cybersecurity is most effective, which undercuts the potential of machine learning in this field. Only 27 percent of organisations in the region currently use the concept on at least a limited basis.

Cybersecurity will only rise in importance over the next 10 years, as attacks become more common and sophisticated. Whether machine learning is the future of strong protection should become clear over the next few years when organisational adoption rates either increase drastically or stagnate.


5) The Rise of Fake Data


In the trends above, we mentioned increased data availability as a core pillar for the future of machine learning. Naturally, actual data is crucial to build a comprehensive and accurate algorithm. But how can we get to that algorithm if not all data is initially available?

Fake or synthetic data is becoming increasingly popular to answer that question. The term refers to artificially produced data that mimics the properties of the real data that will eventually feed into and drive the algorithm. Building this data requires extensive modelling, but is no longer the challenge it was just a few years ago.

As a result, this trend connects closely to the increasing availability mentioned above. Especially smaller businesses who have challenges with finding large enough sample sizes appreciate the opportunities that synthetic data provides. As a result, they can build better algorithms, making their machine learning efforts more applicable to everyday situations.


6) Deep Personalization


What is the core benefit of machine learning? Clearly, its potential for smart automation leads to more efficient and fewer resources needed. But it doesn't end there. In addition, the concept also plays a core part in communicating with external audiences. Perhaps the best example of that is deep personalisation, which is just starting to become a popular concept.

As Evergage points out, machine learning has the potential to personalise messaging on a true one-to-one basis:

Machine learning helps you sift through all the different options you could display to her and select the best one. Whether it's selecting the best products, categories, brands, promotions, etc. to recommend to her; reordering or changing the navigation to help her find what she's looking for; sorting the search results to be relevant to her, etc., machine learning can make it possible.
In reality, we only see the start of the potential for this trend. Imagine a world in which, through comprehensive data availability and predictive analysis, machine learning allows you to personalise the messages for every single one of your customers. The potential marketing and revenue benefits for your organisation could be immense, and we're not far away from that potential being realised.


Preparing for the Future of Machine Learning in Business


Machine learning has the potential to impact almost every single variable in running a business. Regardless of your organisation's size, current trends show just how applicable the concept already is to your operations. And we're only getting started.

Naturally, the trends mentioned above are relevant to predict the future of machine learning. However, they don't account for potential new developments that we're not even aware of yet. How can you prepare for this development in a way that benefits your organisation?

Your best bet is to keep following the news surrounding this topic. Learn about startups using machine learning in new and innovative ways, as well as new trends beginning to emerge, download our eBook!





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Joe Liu

Director, Acquisition Sales

Hi, I am Joe Liu, head of a Global Acquisition team across AP. I responsible for building global and regional account teams to serve key Global customers. An energetic and engaging sales leader with a highly successful track record and recognized builder of high performing sales teams. I am passionate and believe how relationship business that can create mutual success, able to bring complex teams together to achieve growth and penetrating new markets and providing dynamic solutions to client’s needs.