By Margaret Laffan, Director of Business Development for Machine Learning, SAP
Recent advancements in machine learning are reaching a level of sophistication that’s exceeding the expectations of industry analysts and executives alike. We’re familiar with Google DeepMind’s AlphaGo that bested the greatest masters of the ancient Chinese game “Go” 10 years earlier than expected.
More recently a new exhibition at the New York Gallery Metro Pictures depicts machine made images to people using algorithms. Retailers are redefining customer experiences with real-time personalization and convenience. Even most stock trades are governed by automated analysis of market outcomes and determination of future trends faster and more accurately than humans alone. And this is only a small clip of a much longer list of achievements, that increases daily.
Each breakthrough achieved through machine learning inspires companies to take advantage of tools that support and improve data use. However, for some people, a sense of skepticism is bubbling up to the surface as they try to envision how a future with machine learning will impact their livelihoods, families, and communities. Will widespread adoption of machine learning make human-operated jobs obsolete? Or will it deliver unimaginable innovation that will help human talent step up their value?
No matter your stance in the debate, one thing is clear: Machine learning is quickly becoming a critical part of remaining competitive – and, yes, every business should care. Tech Giants are investing billions of dollars in AI, in machine learning, on acquisitions and R&D. The recent investment by John Deere in Blue River Technology (incorporates computer vision, robotics, and machine learning) to drive precision agriculture demonstrates how this technology domain is advancing product strategy and diversification. For those companies not considering investing and innovating they will soon be outperformed by the new economy that runs Machine Learning at its best with “lights out” operational processes and leading edge innovation in differentiating business processes and products.
The inner workings of machine learning
Machine learning uses complex algorithms – not programming code – to teach digital applications and computerized machines how to detect patterns and predict possible outcomes from large swatches of data. Over time, every form of technology in the IT ecosystem can “think” and solve problems with little to no human intervention.
Given the current capabilities of the technology today, businesses cannot ignore that there is a significant opportunity to gain better control of data that’s continuously growing in size and beyond human comprehension. In fact, 30% of CIOs believe that investing in smart applications is a top-five priority within the next three years, according to Gartner.
Algorithms are now mature enough to help decision makers learn things in real time that weren’t known nor existed a year ago. For the first time, organizations can leverage an infrastructure that captures, compresses, and processes structured, unstructured, and visual information in new ways to communicate what’s really happening behind that data. Plus, immediate access to on-demand insights gives businesses the edge they need to survive in a competitive landscape of 24×7 change.
Machine learning opportunities that build competitiveness – now
Based on my conversations with business owners and executives worldwide, machine learning is clearing pathways to businesses growth, process optimization, and daily employee empowerment. By automating redundant and low-value activities, organizations are addressing changes in real time and delivering the best-possible outcomes. Extending this further we are moving to a deeper emphasis on integrated intelligent systems leveraging collaborative workspace tools enabling greater efficiency.
Common areas where machine learning is transforming how these businesses run today include:
- Business processes: As the benefactor of perhaps the greatest potential, business processes can drive transformative change in how people work. For example, repetitive, time-consuming, and labor-intensive tasks such as invoice matching and sifting through a countless pool of resumes can be automated to serve up items that require exception handling or the best candidates to fulfill a business need with confidence. In this case, fast, accurate business intelligence means more time for more valuable, mission-critical activities.
- Intelligent Enterprise Systems – from core to edge: By uniting all data sources into a digital core, the IT infrastructure is prime for automating data analytics and maintenance. Machine learning can find more efficient, cost-effective scenarios to operate complex systems based on quality data without risking disruption and noncompliance. With this we are on the path to a self-learning Enterprise System, there is a clear demand for greater integration across the enterprise, to an Intelligent enterprise system that assists in the core activities of a company and takes decisions for repetitive end-to-end tasks.
- Collaborative Workspaces: Over time, machine learning can anticipate human behavior and detect missing capabilities and unfulfilled needs. With digital assistants and chatbots, users can get work done more easily and effectively. Natural Language Processing embedded in business functions such as customer support, sales, and human resources can automatically tag and group visual and text requests, advise appropriate action, and respond without the intervention of an intermediary – all within a matter of seconds.
Moving forward with machine learning
As machine learning continues to evolve, businesses will innovate cutting-edge applications and use cases that could drive increased efficiency, intelligence, agility, and customer-centricity. However, those that move their IT architecture to the cloud stand a better chance to get ahead of the competition and create a wave of disruption that sets the stage for market leadership.
By allowing company-wide access to the right data anytime and anywhere, employees can better follow processes, truly understand customer needs, and respond to market dynamics. More important, the entire workforce – regardless of role and organization – are encouraged to collaborate, to leverage and realize the full power of machine learning to build a stronger bottom line.
Upcoming events: If you are attending SAP TechEd Las Vegas 2017 there will be of number of Machine Learning sessions that you can sign up for. Alternatively follow @SAPLeonardo and @SAPTechEd @MargaretLaffan on Twitter and share your thoughts using the hashtags #machinelearning or #ml.
Read more, visit Machine Learning website.
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