Why machine learning is the new proving ground for competitive advantage
- 50% of organisations are planning to use machine learning to better understand customers in 2017.
- 48% are planning to use machine learning to gain greater competitive advantage.
- Top future applications of machine learning include automated agents/bots (42%), predictive planning (41%), sales & marketing targeting (37%), and smart assistants (37%).
These and many other insights are from a recent survey completed by MIT Technology Review Custom and Google Cloud, Machine Learning: The New Proving Ground for Competitive Advantage (PDF, no opt-in, 10 pp.). 375 qualified respondents participated in the study, representing a variety of industries, with the majority being from technology-related organisations (43%). Business services (13%) and financial services (10%) respondents are also included in the study. Please see page 2 of the study for additional details on the methodology.
Key insights include the following:
- 50% of those adopting machine learning are seeking more extensive data analysis and insights into how they can improve their core businesses. 46% are seeking greater competitive advantage, and 45% are looking for faster data analysis and speed of insight. 44% are looking at how they can use machine learning to gain enhanced R&D capabilities leading to next-generation products.
If your organisation is currently using ML, what are you seeking to gain?
- In organisations now using machine learning, 45% have gained more extensive data analysis and insights. Just over a third (35%) have attained faster data analysis and increased the speed of insight, in addition to enhancing R&D capabilities for next-generation products. The following graphic compares the benefits organizations who have adopted machine learning have gained. One of the primary factors enabling machine learning’s full potential is service oriented frameworks that are synchronous by design, consuming data in real-time without having to move data. enosiX is quickly emerging as a leader in this area, specializing in synchronous real-time Salesforce and SAP integration that enables companies to gain greater insights, intelligence, and deliver measurable results.
If your organisation is currently using machine learning, what have you actually gained?
- 26% of organisations adopting machine learning are committing more than 15% of their budgets to initiatives in this area. 79% of all organisations interviewed are investing in machine learning initiatives today. The following graphic shows the distribution of IT budgets allocated to machine learning during the study’s timeframe of late 2016 and 2017 planning.
What part of your IT budget for 2017 is earmarked for machine learning?
- Half of the organisations (50%) planning to use machine learning to better understand customers in 2017. 48% are adopting machine learning to gain a greater competitive advantage, and 45% are looking to gain more extensive data analysis and data insights. The following graphic compares the benefits organisations adopting machine learning are seeking now.
If your organisation is planning to use machine learning, what benefits are you seeking?
- Natural language processing (NLP) (49%), text classification and mining(47%), emotion/behaviour analysis (47%) and image recognition, classification, and tagging (43%) are the top four projects where machine learning is in use today. Additional projects now underway include recommendations (42%), personalisation (41%), data security (40%), risk analysis (41%), online search (41%) and localisation and mapping (39%). Top future uses of machine learning include automated agents/bots (42%), predictive planning (41%), sales & marketing targeting (37%), and smart assistants (37%).
- 60% of respondents have already implemented a machine learning strategy and committed to ongoing investment in initiatives. 18% have planned to implement a machine learning strategy in the next 12 to 24 months. Of the 60% of respondent companies who have implemented machine learning initiatives, 33% are in the early stages of their strategies, testing use cases. 28% consider their machine learning strategies as mature with between one and five use cases or initiatives ongoing today.
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