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Machine Learning or AI: What is More Relevant for Digital Transformation?

By Lisa Carolan

38697529_s.jpgArtificial intelligence (AI) and Machine Learning (ML) have penetrated every sphere and industry. Both play an important role in turning data into assets as part of achieving digital transformation. However, organizations must gain a deeper understanding of their place in the process to effectively leverage them for business and process transformations.

While the terms AI and Machine Learning are widely used, they aren’t widely understood. The fact that Machine Learning is a subfield of AI doesn’t add clarity to the question of which is more relevant to digital transformation than the other. There are numerous definitions of AI and Machine Learning, but a good summary would be that AI focuses on developing intelligent systems without the need to explicitly define rules that determine behavior. Machine Learning learns from data in changing environmental conditions to make predictions and optimize outcomes. 

Despite the fact that AI and Machine Learning play different roles, they both play an important part in many sectors and varied use cases including: 

  • Healthcare

  • Life sciences

  • FinTech

  • Information management

  • Data analysis 

  • Digital transformation

  • Cybersecurity 

  • Consumer applications

  • Next-gen smart building technologies

  • Predictive maintenance and so much more

At this point, it becomes instructive to see how Machine Learning /AI are being used in the real world to impact digital transformations.

Artificial Intelligence and Machine Learning Real-World Uses

Besides the use of AI, Google and other social media platforms, supply chains, online retailers, and call centers also rely heavily on AI-powered robots for operations. Many enterprises back-end systems and customer-facing portals for companies like Uber and Amazon use AI. Marketing and sales in every sector are using AI to detect and direct advertisements towards very specific audience groups based on location and personality type. 

Teradata's recently released 2017 State of Artificial Intelligence for Enterprises report looks at companies that have implemented AI. The 260 company executives polled for the report all state that they expect:

  • A $1.23 ROI in the next three years for every dollar invested in AI today

  • An increase to $1.99 in the next five years

  • A further increase to $2.87 in ROI over the next 10 years

Artificial Intelligence is a broad concept that encompasses many technologies and approaches. While both AI and Machine Learning are powerful, Machine Learning uses go beyond those of AI due to the ability of Machine Learning algorithms to continually learn from more data. That’s why more companies and sectors are using Machine Learning in business processes and systems. This can include Machine Learning in network security to automate threat hunting while making faster decisions and then acting on those decisions to ensure network safety.
Machine Learning technologies can proactively assemble and deliver information through automated composition engines for machine-generated business content. Machine Learning is also a natural support tool for IoT and big data where algorithms rely on huge data sets derived from as many sources as possible to deliver actionable insights. 

As Machine Learning algorithms get access to more relevant and clean data, it becomes smarter and more accurate in its predictions and its ability to make decisions. Gaining accurate reliable forecasts quickly is the foundation for every business’s ability to plan, create budgets, and assign resource effectively. 

AI and Machine Learning Relevance to Digital Transformation

Machine Learning has nearly infinite uses across sectors such as manufacturing, healthcare utilities, retail and many others. Its ability to rapidly analyze and derive understanding from big data pouring in from IoT sensors enables identification of trends and anomalies via intuitive networks. While AI plays an important role in maturing as well as evolving technologies such as the use of digital twins, it is Machine Learning that makes it a practical part of digital transformation in different sectors. 

A recent Data Science Central article shows how Machine Learning/AI and digital twins intersect in the creation of digital replicas of physical assets, processes, or systems. It is Machine Learning that enables automated and continuous learning through monitoring, testing and ultimately providing possible actions to change or improve outcomes through data analysis. Moreover, the ability for Machine Learning to find identifying trends or anomalies in big data is transforming numerous industries from healthcare and clinical research to compliance and security.

Machine Learning is the engine driving the evolution customer service, user experience, and contextual marketing. Countless customer-facing industries are using Machine Learning algorithms within chatbots software to predict and respond to customer inquiries by learning and honing their ability to accurately respond to customers with each encounter. The result is reduced customer wait times and freeing call center agents to handle more complex queries. 

Conclusion

While AI is more focused on learning to deliver human-like intelligence, Machine Learning is focused on fulfilling and improving a task through data interpretation—with or without human intervention. What is most important for the business to focus on rather than the technology is the fact that digital transformation is more about embracing a holistic approach to change that results in business transformation. 

Digital transformation is about re-engineering and rebuilding your business in the era of cloud, mobile, IoT, analytics, and Machine Learning/AI to improve processes, customer experiences and decision outcomes. This definition shows why Machine Learning is more relevant to digital transformation because it can automate time-consuming tasks so that people and businesses can innovate. 

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