Machine Learning is Happening Now: A Survey of Organizational Adoption, Implementation, and Investment
This is an excerpt from a survey which sought to evaluate the relevance of machine learning in operations today, assess the current state of machine learning adoption and to identify tools used for machine learning. A link to the full report is inside.
By Asir Disbudak, Entrepreneur & Marketing Consultant
Editor's note: This is an excerpt from the full report. You can read the full survey report here.
- Machine Learning (ML) is happening now. The majority of respondents (54 percent) have already implemented ML strategies, and nearly 28 percent considered themselves to be at a scale-up / transforming stage with their initiatives.
- Organizations are investing in Machine Learning. Among current ML implementers, we see a great increase in the number of data scientist when companies move up the AI maturity curve.
- Machine Learning provides faster actions and decisions. According to respondents, a key benefit of ML is the ability to make faster educated decisions, and 50 percent of current ML implementers felt they had already achieved that goal. Algorithms and Machine Learning technologies could provide valuable management guidance and support.
- The majority among Machine Learning implementers and planners hoped to benefit from the ability to extend data analysis efforts and increase data insights. Some 35 percent of both early-stage and mature-stage users say their ML efforts have resulted in better customer support, which generated demonstrable return on investment in terms of marketing and sales.
- Machine Learning implementers are pursuing a broad range of projects. The most common projects among current ML implementers are information processing (26 percent); NLP (19 percent), planning & exploring (17 percent), machine vision (16 percent) and handling & control (11 percent).
- Startups are realizing Machine Learning’s biggest potential benefits.
The survey was conducted on LinkedIn in April 2019 as a part of the University thesis prepared by A.Disbudak. The survey sought to evaluate the relevance of Machine Learning in operations today, assess the current state of Machine Learning adoption and to identify tools used for Machine Learning. The 140 qualified respondents represented a variety of company sizes from very small (one-person startups) to very large (multinationals with more than 10,000 employees).
Introduction: Machine Learning is happening now.
The next stage in the data revolution has arrived. A branch of computer science called Machine Learning, is the next big development powering the business world (Raconteur, 2015). Various businesses are developing strategies for technology adoption and already realizing genuine return on investment (ROI) (Insights, 2017).
The focus on Machine Learning (ML) among international businesses might seem like an overnight development, but the buzz around this technology has been steadily growing since the early days of big data in 2005. ML systems are used to help computers identify patterns from big data sets and enable them to perform tasks, such as predicting consumer behavior and forecasting how people will react to different marketing strategies (Raconteur, 2015). This technology holds the key to unlocking the value of big data. Innovation-minded business leaders are embracing ML as “the next big thing” and have already crafted ML strategies and initiatives that promise real benefits and ROI (Pettey & Meulen, 2018).
Relevance of Machine Learning in operations today.
USA tech and internet giants, so called GAFA companies (Google, Apple, Facebook, Amazon) have built their businesses by gaining access to huge amounts of data about customers and their online behavior (Raconteur, 2015). In addition NATU Companies (Netflix, Airbnb, Tesla, Uber) or BATX companies (Baidu, Alibaba, Tencent, Xiaomi) have all developed powerful Machine Learning capabilities with in-house platforms and models. The more data they gain access to, the better their Machine Learning performs. However, these Machine Learning capabilities are not easily accessible to most companies. They need to obtain different methods of accessing Machine Learning capabilities, so they can put their own unique and valuable data assets to work. The recent emergence of a new generation of Machine Learning platforms is set to make Machine Learning far more accessible to businesses of all types and sizes across all industries.
Advances in computational power and the big data phenomenon have propelled AI, Machine Learning and Deep Learning technologies into a new realm. Forrester forecasts that AI will attract three times more corporate investment during 2017 (Press, 2016). At the same time smaller companies claim to be more advanced in their AI developments due to the fact that they use AI as a core of their businesses from day one.
Machine Learning is enabling companies to optimize a wide range of business processes to meet their strategic digital transformation goals. If the organization remains hesitant, it will miss the valuable opportunities that Machine Learning technology offers businesses today. According to Accenture, managers are more willing to put their trust in an intelligent system if they understand how it works (Accenture, The promise of artificial intelligence report, 2016).
In 2019, companies are considered to be in the ‘growth’ stage of Machine Learning. This means that best practices are still being established. Machine Learning is mostly viewed as an R&D effort, and the infrastructure required for productionizing models is not yet accessible. Big companies have an advantage as opposed to smaller companies due to access to the data, budgets for R&D and variety of Machine Learning cases. Google was the first to provide market with countless data cleaning and model training solutions, called Hadoop.
We expect to see large leaps in productionized Machine Learning in 2020. Data Scientists are getting more tools for their AI experiments which allow them to manage their models and deploy them easily.
Business competition is increasingly defined not just by product quality or delivery logistics, but also by unique data assets and the ability to capitalize on them. It should be no surprise, then, that as companies invest heavily in AI strategy and execution, data scientists find themselves in heavy demand (AI Strategy, Forbes, 2019).
Data on business relevance is interpreted and translated into product and service innovations that have to be sold internally. Being able to connect business needs to Machine Learning tasks become the essential skill for data scientists and adds value to all participants.
Bio: Asir Disbudak is an entrepreneur and marketing consultant. He is an inspiring and enthusiastic international business student at the Amsterdam School of International Business. During this study, he has developed his vision as an entrepreneur.
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