Machine Learning

What’s In Store for Enterprises Leveraging Machine Learning in 2018: Major Predictions

Artificial Intelligence and Machine Learning are at the forefront when it comes to revolutionizing the modern day tech

Artificial Intelligence and Machine Learning are at the forefront when it comes to revolutionizing the modern day tech sector. However, outside of the tech sector, the adoption of these two technologies is still at its infancy, and is more or less at an experimental stage.

Setting the backdrop

Many organizations are still unsure of the benefits that AI can bring.

According to McKinsey, investment in AI is growing rapidly while AI adoption still remains low. Their survey last year found that only 20% of AI-aware organizations adopted AI.

Tech giants, on the other hand, are shelling out a lot of money when it comes to acquiring AI/ML talent. The collective effort to explore ML/AI technologies and the rapid growth of the technologies indicate that we would be integrating them effectively into our economy sooner rather than later. However, this won’t be easy. As a matter of fact, the imbalance in the adoption rate of AI/ML technologies between industries complicates it further.

In a survey by Economist Intelligence Unit, 44% of the survey respondents agreed that a delay in AI implementation would make business vulnerable to agile start-ups.

This pretty much covers the entire backdrop of the role of AI/ML technologies today among enterprises. With that out of the way, let’s see what enterprises leveraging machine learning in 2018 will have in store for them. These predictions consider a variety of factors, the present market conditions, and inputs from experts of various industries.

More ML maturity

Machine learning noobs will mature into true believers.”

Achieving machine learning maturity would be a main goal for thousands of organizations dabbling in the technology. The McKinsey survey also found that more than 50% of the enterprises that invested in AI/ML technologies haven’t yet achieved good returns. This and the uncertainty surrounding the technology has made companies look at the technologies objectively while digesting their pros and cons.

Still, the ones that tasted success with ML and AI reaped rewards big enough to get strategic-minded business leaders intrigued. Many businesses still have a wrong notion that ML is just plug-and-play. But that isn’t necessarily the case. ML needs data and a good amount of personalization. Not all efforts would reap rewards overnight. We can only guess whether business executives will remain patient enough to see returns from their ML projects.

Machine learning data scientists will have it tough

ML’s accelerated growth in recent years consequently resulted in a big demand for academic talent and data scientists. Many businesses with big pockets are still looking to hire qualified candidates, mostly experts in deep learning, capable of handling their disruptive ML projects. Deep learning is fascinating for sure, but it’s too sophisticated. Even the few experts of deep learning find it difficult to use, particularly in robotics applications.

CIOs and CDOs (Chief Data Officers) will instead opt to train their existing workforce to achieve machine learning literacy. This could mean that the average data scientist would find it difficult to justify complex machine learning models for enterprises, while enterprises make do with less complex core-ML platforms that deliver good quality results at a smaller investment.

Black box ML won’t deliver desired results

The increasing adoption of ML seem to be hinting at a much more recognized role of machines when it comes to decision-making. But, 2018 will prove that humans will still be central to decision-making. Companies will soon realize that subject matter expertise is still better than top academic talents and expensive consultants. At the end of the day, deep knowledge on the business context, the industry’s value chain dynamics, and human judgment gets the job done.

There are many offers in the internet promising fully automated end-to-end machine learning solutions that basically solves a bunch of problems provided they are fed enough data. These solutions limit the intervention of actual ML professionals who possess the subject matter expertise to determine optimal ML-based resolutions to the issues.

Real machine learning solutions that can make a positive impact for enterprises aren’t just a mix of algorithms based on commonplace performance metrics. But such a mix is typically what black box ML offers, and companies would eventually see such solutions underperforming and not delivering on their promises.

Machine Learning as a Service (MLaaS)

MLaaS platforms are already making waves. This year MLaaS will be adopted more, particularly in private clouds of large companies and then some in public cloud environments of SMBs and startups. Compared to expensive ML consultants, MLaaS platforms have a much more reasonable cost structure. This merit and custom applications combined with proper abstraction will enable ML engineers to craft and quickly deploy effective point applications at scale in an enterprise’s ecosystem.

The cloud also will contribute to the increased adoption of MLaaS by eliminating much of the complexity and subsequently reducing the costs involved. The popular cloud machine learning platforms today also offer preconfigured frameworks that comprise of powerful and effective algorithms. Sophisticated infrastructure setup is not a concern now as well, and in addition it also makes integration quite easy with automated workflows.


The cloud’s presence makes it considerably easy for enterprises to leverage AI/ML technologies while various other factors contribute to increasing awareness on their pros and cons to enterprises. The aforementioned predictions are theories at this point that take many factors into account in order to intuitively surmise the changes that machine learning will bring into this world very soon.

This blog is a simple attempt to give readers an idea on what could unfold in various industries this year, and why ML would be a vital factor in the growth of a business in the coming years. To conclude, it’s now safe to assume that there would be a global machine learning awakening soon. Not all enterprises would be successful in leveraging the technology effectively unless they get help from subject matter experts; also considering that the talent pool is low at this point.

If you require an expert in ML and cloud technologies to initiate you in channeling new-gen technologies, AoT technologies welcomes you. Rest assured we are fully capable of guiding you in the right directions with powerful cloud-based solutions backing you up. Give us a call now to understand what we can do for your business.

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