The Quick Start Guide to Artificial Intelligence and Machine Learning

Learn about AI, common missteps, success criteria, and how to take advantage of these new capabilities.

Marc Teerlink
Oct 24, 2019 · 7 min read

Social psychologist and Harvard professor Shoshana Zuboff said, “Everything that can be automated should be automated” Zuboff’s viewpoint is particularly true for what we call “knowledge worker” tasks. Professionals and Knowledge workers typically make tactical decisions such as reconciliating invoices, extending a warranty period, determining replenishment levels when stock ratios suddenly drop, and focusing on the opportunities that are most likely to lead to winning a new customer.

As we think about automating such decisions, we can see an obvious fit of machine learning as a part of artificial intelligence, working in conjunction with human decision-making. Today, decisions and the different steps they incur are constantly adjusting based on real-time availability, data, and algorithms. The underlying information for these knowledge workers is more readily available, from data points that inform these processes.

This new data creates shortcuts that drive digital (process) transformation, which is creating new value. Artificial intelligence (AI) will help answer data questions. And when AI utilizes machine learning (ML), it means that software does not need to be continually updated or reprogrammed. ML works in tangent with AI to apply changes based upon the continual learning gathered from updated data points. In fact, a crucial understanding is that data changes everything, especially automation.

Before continuing, it is essential to share the definition of AI. For the record, AI is not “one” technology. It’s process knowledge, or a combination of different techniques, technologies, tools, and sets of training data. Think ML, blockchain, data intelligence, Big Data, IoT, predictive analytics, process automation, and conversational AI flows.

AI, powered by data science and ML techniques, can be used to have the machine make decisions or provide strong recommendations for actions. This is why we can safely say that a fair portion (say 50%) of business processes will be fully automated in the coming three years. Once existing processes are automated, new processes will be created as we move from a process-driven world to a data-driven world. Based on the insights from data, businesses will create new processes, reshuffle existing processes, and digitize the process experience.

For example, think about customers buying products. In the old days, this was a fairly linear process. I see an ad, I go into a store, I make a purchase. Today, research and shopping may be completely distinct from a purchase decision. The rise of social content has changed how we shop and leaves the purchase decision to a price point.

How can we address this? Within the stack of AI technologies, machine learning may be the strongest starting point, followed by conversational bots and intelligent Robotic Process Automation (iRPA) — especially if you want to conquer the millennial market, as studies show that the best way to engage millennials is by orchestrated chat, social, and messaging. (The worst way is to try to call them).

The question arises, like with every foundational change centered around a new technology: “Where does one start with artificial intelligence and machine learning, and does it pay off?

Fast Adopters Edge Ahead

Since Machine Learning (ML) is one of the underlying technologies which enable Artificial Intelligence, SAP and the Economist Intelligence Unit commenced, and released, a first-of-its-kind report on ML The good news is that the use of AI and ML are leading to revenue and profits within companies and giving them a competitive edge. In “Making the Most of Machine Learning: 5 Lessons from Fast Learners,” early adopters, whom the authors dub “fast learners,” are realizing better business outcomes:

  • 48% cite increased profitability (6% revenue growth) as the top benefit gained from ML
  • 36% are implementing ML into customer-facing and product development functions, such as contact center, marketing, data processing and analytics, and R&D
  • 41% say ML translates into higher levels of customer satisfaction

Clearly, based on these solid numbers, ML is having an impact on early adopters, and they see ML having a long tail. Cliff Justice, principal for innovation and enterprise solutions at KPMG and one of the participants, even went so far to say, “AI and machine learning impact the business model in a much more significant way than… any of the disruptions we’ve seen in our lifetimes.”

What Is Needed for Success: Strategic Clarity

  • Machine learning requires a great deal of high-quality data. In most organizations, this data is in existing business applications such as finance, logistics, and sales. The data in these systems has already been collected, cleansed, and stored over a long period of time, so there’s plenty of data available to create meaningful, useful predictive models.
  • Machine learning works best where there’s a tightly defined decision to be made, thousands of times a day, and using a small number of variables, and where errors are clear and can be quickly corrected to further improve the algorithm.
  • Machine learning is easiest to implement when the decision can be seamlessly automated as part of an existing business process, rather than a moonshot requiring new processes or cultural changes.

According to a 2011 article in the Journal of Change Management, the significant reason for the failures is a lack of alignment between the value system of the change intervention and of those members of an organization undergoing the change. Strategic clarity seems to be the differentiator. One of the most remarkable (and disheartening) aspects of organizational change efforts, however, is their low success rate. Substantial evidence shows that some 70% of all categories of change initiatives fail.

When trying to understand the fast learners, it is evident that they realized early that AI and ML work best where there’s a tightly defined decision to be made thousands of times a day using a small number of variables, and where errors are clear and can be quickly corrected to further improve the algorithm.

Looking into the research behind the ML study, it is interesting to see the gaps between the fast learners and everyone else. One striking difference is a 10-point variance linked around “lack of clarity on strategy.” Those that display a higher level of strategic clarity seem also to be better informed and have more realistic expectations on the possibilities and limitations of technology.

When we continue digging deeper into the belief systems of the fast learners, we see they realized early into their journey that machine learning works best where there’s a tightly defined decision to be made thousands of times a day using a small number of variables, and where errors are clear and can be quickly corrected to further improve the algorithm. For example, “Which of these bank payments correspond to this invoice?” is much easier to implement using ML than “How can we improve long-term lung cancer survival rates?” Solving a small recurring problem can lead to a big win.

Basic Ways Companies Are Implementing AI and ML Today

  • Extracting relevant payment or order data from unstructured invoices, forms, or emails (such as product names, amount, currency, payee, address, etc.)
  • Classifying transactions for tax compliance
  • Predicting when contracts, based on usage, will need to be renewed
  • Predicting and acting on stock-in-transit delays
  • Calculating the optimal length of time between physical inventories to ensure that it’s in line with automated systems
  • Routing customer service requests to the most appropriate teams
  • Comparing new regulatory documents with process or product descriptions, classifying and highlighting the nature, changes, and impact
  • Redlining, or comparing two or more contracts with each other, and identifying contrasting or conflicting terms and conditions

Fast Learners Vs Strategic Clarity?

A common misbelief about AI is that it will automate the economy and remove jobs. Instead, AI augmentation will free up capacity for employees to actually be more human within service processes and use their talents to create creative value. Most calamitous warnings of job losses confuse AI with automation, and this overshadows the greatest AI benefit — AI augmentation, a combination of human and AI where both complement each other.

Fast learners are retraining employees to focus more on higher-value tasks within their organization when their work tasks are displaced by machine learning. AI is at its best when a decision can be seamlessly automated or augmented to support an existing business process, rather than a moonshot requiring net new processes or radical cultural change.

As one hotel owner said ,when his front-desk team was relieved, through the deployment of conversational AI, from the daily barrage of repetitive mind numbing questions like “What is the WiFi password?”: “AI helps humans to be more human, AI returns humanity back to the business.

Concluding, AI doesn’t do a full human’s job, AI augments tasks — and at best automates a task or two. Humans have to decide when to tell AI to do the work. The human component, augmented by AI, and the importance of creativity when AI is in play is how the two seamlessly can work together and add value.

Where to start? Focus on cases that retrain employees who have tasks displaced by AI to learn higher-value tasks within their organization.
Let the robots process and the humans think!


Want to hear more on Myths, Facts and Digital Disruption around Artificial Intelligence or treating Data as an Asset? Watch Marc’s TNW Keynote

About Marc

Dr Marc Teerlink mba/mbi is SAP’s Global Vice President for Intelligent Enterprise Solutions & Artificial Intelligence. Marc is a serial corporate entrepreneur, responsible for vision and products that enable digital transformation of businesses around the world, through Innovation, Data Monetization, Artificial Intelligence and Machine Learning technologies.

Prior to SAP. Marc was IBM Watson’s Chief Business Strategist during IBM’s formative years of AI. Before IBM, Marc built expertise as a banker, business manager, consultant, and change leader within nine countries across three continents.

Marc is active in the startup community as an angel investor, mentor, and board member and has been an active participant of the World Government Summit’s Global Governance of AI Roundtable since its conception in 2018

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SAP Innovation Spotlight

Brand journalists cover tech and IT trends like Digital Transformation, Future of Work, Purpose, Customer Experience, and more. VISIT OUR ARCHIVES HERE: https://medium.com/sap-innovation-spotlight/archive.

Marc Teerlink

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TED Speaker, Corporate Serial Entrepreneur, AI monetization, Innovation Driver, ibmwatson alumni, Proud Father, Sailor, Global Citizen, Alpha Nerd, GlobalVP@SAP

SAP Innovation Spotlight

Brand journalists cover tech and IT trends like Digital Transformation, Future of Work, Purpose, Customer Experience, and more. VISIT OUR ARCHIVES HERE: https://medium.com/sap-innovation-spotlight/archive.

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