Machine Learning for Organizations: Where Is It Now?

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Machine learning (ML), a branch of artificial intelligence (AI), was sometimes referred to as “cognitive computing” in the past, and certain academic circles still today. Machine learning applications have been used for decades to automate complex human tasks that require analytic thinking, but recently the technology has expanded to encompass more business functions.

Advanced ML algorithms have automated the game of chess, speech recognition, and some military activities. In the recent years, this forward-thinking science has invaded the mainstream industry sectors such as health care, banking, education, and marketing. Machine learning can help organizations identify which activities are most important for them to focus on, while allowing the machine to handle the rest.


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Industry experts confirm that ML enables businesses to perform tasks on a scale and scope previously impossible to achieve. This unique technology enhances speed, reduces errors, and improves accuracy. Business owners and operators using ML have not only have enhanced their business efficiency with ML tools, but have also discovered new opportunities to outpace their competitors.

Machine Learning Research

In 2016, Google’s AlphaGo, an AI system capable of playing against human players, demonstrated the superiority of machine thinking over human thinking. The machine-driven Chinese game of Go beat the legendary Chinese grandmaster Ke Jie. AlphaGo’s superhuman abilities displayed a strong resemblance to human cognitive intelligence.

Since then, machine learning has come far. Recently, ML research has centered around actual market applications, such as self-driving cars and search engine optimization.

Currently, scalable ML systems or solutions face a dual challenge, according to Scaling Machine Learning Applications. The development of a scalable system poses many challenges, but delaying a scalable ML system can mean bigger challenges like “lost customers or unrealized revenue.” Most scalable ML models either live or die during the production testing phase, where data size variation quickly reveals the execution problems.

Machine Learning for Organizations: Where Is It Now?

Machine learning is at the forefront of the AI race. It has been touted as the next breakthrough in AI, but what does this really mean?

As advanced ML algorithms are trained to make decisions based on what they have learned from previous experience, these smart applications have become very powerful in mainstream business practices. They are now being used in various industries to expand existing revenue channels while uncovering new opportunities.

Algorithmia’s survey findings indicate that six in 10 respondents (64%) say AI and ML priorities have increased relative to other IT priorities in the last twelve months. Twenty percent of enterprises increased their AI budget by over 50% between 2019 and 2020.

Machine Learning in Business

Machine learning algorithms are already aiding human-resources (HR) personnel in making better hiring decisions and controlling attrition; helping customer-service staff engage customers during first interactions while retaining existing customers; assisting C-suite executives in making sharp, informed decisions in seconds; and helping customers become active participants in the buying journey through virtual reality (VR) digital showrooms and customizable product designs.

Five Steps to Implement Machine Learning in Organizations stresses the phenomenal importance of predictive analytics or conversational systems. The author states that the best time to develop and deploy an ML strategy is probably now.

Currently, the main obstacle to deploying large-scale ML applications in businesses is regulatory concerns. As this IBM author points out, ML can help compliance teams accomplish their regular monitoring and recordkeeping tasks swiftly and accurately, and at a much lower cost. According to this author, ML has the potential to guide all applications through governance and compliance activities — drastically reducing the time for “key operational processes.”

Finally, here is a feature article that sums up how machine learning has been put to work across business sectors —  “from energy and utilities to manufacturing and logistics.” Two special sectors deserve special mention: education and health care.

Machine Learning in Education

Learning is something that requires continuous improvement. The phenomenal speed of technological advancements has kept the global educators challenged. The learning development field has struggled to keep pace with technology. All stakeholders engaged in developing or deploying technology-enabled learning systems have found themselves constantly facing new challenges while pursuing modern methods to educate the students.

Machine learning systems currently implemented in learning environments have provided for an integrated and engaging learning environment that is not only personalized as a system, but also personalized for individual students. That is, students are given access to custom learning opportunities that vary based on their specific learning goals.

AI is also being implemented in learning environments to help students with retention.

Machine Learning in Health Care

The health-care sector was one of early adopters of machine learning. The various ways that this technology is transforming health care today include faster and more accurate diagnosis, more efficient clinical trials, and enhanced patient-care systems.

 NHS in the UK has been a champion of ML technology from the very beginning and has implemented ML to automate a major part of its operational processes. An example is the NHS Health Hub, launched in 2015.

The Future of Machine Learning Is Now

Where is machine learning today? A couple of popular ML products available in the market today are Adaptive Insights and Economics Factory.

Adaptive Insights is a business plan-creation system with inbuilt ML capabilities to help users automate their business planning. This cloud-based software includes advanced features like predictive analytics, supply chain planning, and financial analysis, among others. One of the innovative features of the software is its ability to use predictive models to help businesses predict the future and prepare for the unexpected.

Economics Factory, a French based software vendor, has developed a tool for the creation and design of economic models to tackle business problems.

According to Forrester, the “future of machine learning is unstoppable.” This market speculator has indicated that most sectors are struggling to extract the true value of AI and ML technologies as they do not have adequate competencies to utilize these technologies. However, the future is not far off when ML will dominate across all industry sectors in the “near, short, and long term.”

The pandemic was an eye-opener to this growing ML trend. Natural language processing (NLP) and ML optimization came to the rescue during the pandemic. Digital transformation efforts suddenly became the top-most priority in 71% of global organizations.

Even CIOs took full advantage of these advanced technologies during the global crisis. According to CIO Dive, the sudden ML proliferation during the pandemic is a strong indicator that 75% of global enterprises will operationalize AI by 2024.

Given a sudden push during the pandemic, the “global ML market is projected to grow from $15.50 billion in 2021 to $152.24 billion in 2028 at a CAGR of 38.6%.”


Machine learning helps answer questions like when the manufacturing floor machines will need maintenance or will a particular restaurant draw the same, more, or fewer customers this season, or are marketing funnels in a specific sector performing as predicted.

ML algorithms provide answers to present problems while making reliable forecasts about the future of a business. To understand how far machine learning as a technology has progressed, review the article Advances in Machine Learning

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