Artificial Intelligence Enables Humanity In Human Resources

Pekka Makkonen

New innovations, new technologies and new ways of thinking are driving business forward at an unprecedented pace. Digitalization changes everything. Robots will take your job. Artificial intelligence makes you an extension of a machine. Escape from your old job, because it won’t exist in 5 years anymore, and become an IT nerd while you still can!

Quite shocking phrases. Is everything above true?

In a way it is, so where did the humanity go from Human Resources?

Digitalization has already started changing everything that we do in Human Resources. It started long ago, before digitalization became a buzzword. It started when we wrote and printed the first employment contract using Word Perfect or Lotus computer program. And oh, the monotonous song of that matrix printer. Then we entered the new hire’s details into a tailor-made personnel database. Sometimes it was only a floppy disk that was passed around and copied—or, if we were not advanced enough, at least into a payroll accounting system.

Many of us weren’t even born back then. But even then, people were saying that the computers will change everything and will make a bunch of personnel administration employees lose their jobs. But we still do these things, right? Word Perfect is Word now, and the matrix printer has changed to a laser printer, and we sign “papers” electronically.

So what has changed in about 30-40 years or so? Why do we talk about digital transformation as if it is at least as big a thing as global warming?


Let’s start with—everything: Until the ’70s and ’80s, when computers started to change how we work and process data, the evolution of work had slowly changed from manual to processes and more efficient use of tools and machines. Appreciation of efficiency replaced the appreciation of professional craftsmanship and hard physical work. When factories became more efficient with assembly lines in the early 20th century, using computers to automate them expedited the evolution. The same thing is now happening in “knowledge work,” or work that is a bit harder to define, as much of it is done inside the human brain.

During the past 30-40 years, the processing of data using computers has become more and more effective, but when you look at the development now versus what’s coming, development has been quite moderate. Now we are on a threshold of what some thinkers say is a new Industrial Revolution (e.g., the Fourth Industrial Revolution, by Klaus Schwab). This revolution is not happening so much in how we produce physical goods, but with enormous data processing capacity, use of Big Data, and connecting not only humans via social media, but also all kinds of devices, and even the clothes we wear. We also have the capability to let machines think for us, a development that began with the invention of the pocket calculator. That was OK for some time, although today many of us are unable to make even the simplest calculation without using the mobile calculator app.

The Personnel function is now called Human Resources. That’s pretty much a huge paradigm shift on its own: Personnel has become “Human.” Employees have become the biggest asset to the company. And to support that idea, all the enabling HR systems, processes, and tools have been created. The role of HR has become more strategic and more business-oriented. And it does not stop there; there is already a post-HR in sight! From personnel to Human Resources, and then to what? The Industrial Revolution happening right now will change the way we think of HR.

Will robots take our jobs?

Some of the work in HR can be repetitive and, to be honest, a bit dull. Making sure the systems are reflecting the current situation, maintaining data, drawing out scheduled or ad hoc reports. Receiving a service request ticket, making the required transaction, closing the ticket, hoping the customer is happy and gives a good rating, then opening the next inquiry… Many people have said they work like a robot and they would like their job to have more meaning.

Well, guess what? Today, software robots can take the robot out of the human by taking on those repetitive, “robotic” tasks that are not value-adding (such as updating a database, transferring data from system A to system B, creating a same type of report each week from exactly the same sources of data, etc.). Using Robotic Process Automation (RPA), these repetitive, rule-based tasks can be given to a software robot, allowing us to focus on tasks that only a human can do.

Today, software robots are already working alongside humans in many knowledge work functions like finance, administration, and HR. They are giving us more time to do the value-add tasks: talking with our customers and employees, thinking of ways to improve work processes and add a more human touch in their work—and don’t forget innovating! The robot will be your new colleague to whom you outsource the part of your work that is not value-adding.

Artificial intelligence or augmented intelligence

Will artificial intelligence make you an extension of a machine? In a way, it will. But you should be happy about it! Here’s why.

First of all, robots are stupid by default. They do only what they’re told to do. They don’t bring you any surprises, positive or negative, they work 24/7 if needed—as long as the plug supplies electricity and the software is bug-free. But when it really starts adding value to you, as a human, is when you augment intelligence into it.

Augmented Intelligence may be a more descriptive term to use here than Artificial Intelligence. It better reflects the role of the machine and the human working together to get a better result than either could achieve by themselves. A powerful example is when you try to resolve a complex problem through analyzing data from dozens or even hundreds of sources. Or when you try to predict what could happen in the future using predictive analytics. A human being alone can use only a limited amount of factors to get any result within a reasonable timespan. But when you add enormous amounts of computing power, Big Data, machine-learning algorithms, etc., you can forecast quite accurately future actions, behaviors, or trends.

Here are some other application areas of AI in HR that already exist or will in the near future:

  • How to keep your best talent, and how to identify some trends that increase the risk of losing talent.
  • Finding specific ways to reduce attrition in your organization.
  • Quickly filtering top candidates from hundreds applicant CV’s by identifying not only the words used but also how applicants express what they can do.
  • Simulating the effects of a major organizational change to focus your change management activities

Escape or become an IT nerd

Now we are getting closer to understanding how Artificial Intelligence, Augmented Intelligence, and Machine Learning can help HR to become “post-HR:” A true “people function.” When you combine all the technologies already in use in a company’s business processes and add the data from a robust cloud-based HRIS system, HR can help companies to become people-centric: Decisions are made based of valid data and high predictability, minimizing assumptions and subjectivity. This increases fairness and transparency in decision making, employee engagement, and trust.

So should you escape or become an IT nerd? No to both.

However, we need to get used to our job requirements changing faster than ever. We must become data-driven and understand enough about how the new technologies work for our benefit. Augmented Intelligence and machines will allow us, in all levels of HR, to use our time on more value-adding and motivating tasks. Data will be our friend, because it’s enabling us to be innovative. The investment: We will be on a journey with continuous learning and change. The reward: We can bring more Humanity to Human Resources!

Where robots take the robot out of the human, artificial intelligence will not take out our intelligence. It will enable more humanity.

This article was originally published on LinkedIn Pulse.


Pekka Makkonen

About Pekka Makkonen

Pekka Makkonen is the HR Director for Nordic and Baltics at SAP. His responsibility is the overall leadership of the HR Agenda in the Nordics. Focusing on the word “we” instead of “me,” he is a true believer in collaboration and information sharing across all borders. Pekka is committed to ensuring a first-class employee experience and supporting SAP’s transformation into one of the most innovative cloud companies in the world. Throughout his 20 years of HR leadership experience in international high-tech companies, he has built and developed international HR teams and helped multiple organizations in their business transformations. Pekka holds a Master’s degree in Social Sciences from the University of Helsinki.

Four Ways To Fulfill Your Purpose Through Technology

Leanne Taylor

Because of their power and influence, every organization should operate with a purpose that transcends profitability. We know, modern consumers favor organizations that fulfill a higher purpose, so it’s not only smart, it’s the right thing to do.

We work in a world where we could offer significant impact if we strive to improve it through purpose-driven practice. Below are four ways your company can fulfill its purpose through technology.

1. Cut out everything that stands between your customers and what they want

You might not think you serve a higher purpose, but every organization does. Let’s say your company sells cars. Your purpose isn’t to provide customers with cars. It’s to give them freedom and convenience. Focusing on what your customers want delivers to their higher purpose.

Whatever you give customers, machine learning and AI assistants can cut the time it takes for them to reach it. This lets you fulfill your purpose faster every day.

A great example of this is Disneyland. The happiest place on Earth strives to bring people joy. The more its customers enjoy the rides and interact with employees, the more Disney fulfills their purpose. By using real-time analytics, Disney parks can deploy staff to shorten long lines in real time. This helps Disney bring more joy to people (especially parents who would otherwise be waiting in lines with impatient children).

2. Drive the global purpose of sustainability

Beyond giving your customers what they really want, preserving the world is a purpose we all serve. While technology has historically been the enemy of the environment, innovations in sustainability are moving the needle and driving much-needed change in this space.

An excellent example is healthy food maker Danone, which was able to make its product lifecycles almost completely sustainable.

By investing in sustainability innovations, organizations can align themselves with anyone interested in our planet’s survival. After all, they’re investing in the foundation of every organization: the Earth.

3. Sow seeds in emerging economies

As the economies of Africa, Asia, and India advance, organizations must invest in education of their future workforce. As well as fulfilling a noble purpose, advancing education in these areas gives organizations a head-start in securing top talent. It also offers a diverse workforce the skills they will need in a vastly changing world.

Technology is the best way to improve chances for children in emerging countries. This could involve education in critical skills like coding. It could also be as simple as holding virtual conferences with classrooms across the world. Whatever skills you can give to the world, technology enables you to give them to the places that most need them.

4. Connect with those who can help you fulfill your purpose

There is no doubt that technology has brought the world together. Today, you can use technology to integrate your operation with organizations who share your purpose. Whether it’s connecting nonprofits or overhauling your operations, you’ll fulfill your purpose faster by partnering with others.

At SAP we’re driven by our purpose to help the world run better and improve people’s lives. To help you fulfill that purpose, partner with SAP today.


Leanne Taylor

About Leanne Taylor

Leanne Taylor is Vice President Sales and Strategic Customer Program, SAP Australia and New Zealand.

Meet Machine Learning, Your New Favorite Colleague

Kirsi Tarvainen

What if you had a colleague who would take care of all the dull, routine tasks without complaining? A colleague who lets you do interesting and challenging tasks, helps you solve them, then happily lets you take all the credit. A colleague who stays after office hours doing prep work for you so you will have a good start the next morning?

Meet machine learning, your new favorite colleague, who will dramatically change customer service both for customers and for customer service personnel.

Machine learning boosts customer service

Think about insurance companies. It’s estimated that 70%-80% of insurance claims are pretty straightforward, so this is an area where machine learning algorithms can find the right solution. For humans, it is hard to stay motivated if you have to repeatedly work through tons of claims for stolen bikes or broken mobile phones. But if you have machine learning as a colleague, you can let it solve the simple cases so you can focus on the more challenging ones – and you will have more time to carefully address each one since you don’t need to worry about the bikes and phones.

Or think about contact centers. For customer service agents, it is difficult to answer similar, repeated questions over and over again. What if you let machine learning field the routine questions while you take the more inspiring cases where customers want to speak with a live agent? A great example of this is Finland Post, which created a Christmas bot to help handle pre-Christmas peaks in customer service demands. Customers could chat with the bot to get answers to the easy, but frequent questions like, “What is the last day to send my packet to France,” which freed a lot of human resources to help customers with more complex queries.

Add more time to your day with machine learning

Machine learning is a colleague who can make you look smarter and perform better in your work. About 25% of contact center agent’s time is spent searching for information from different systems. That’s one-fourth of the workday! It is a total waste of time and shifts attention away from the customer interaction.

What if you had a chatbot that digs the information you need from all the data sources and conveniently provides it in a matter of seconds? You could fully concentrate on listening and understanding the customer, thereby providing first-class customer service.

Machine learning is a colleague we will all know very soon. It will help us get quicker and smarter – and it will help us transform our business in ways we can’t even imagine right now. But the key is to start imaging and experimenting now.

Technology is evolving; in the future almost anything will be possible, but we need to start envisioning how our customer service will look in the era of intelligent machines. There are no ready answers yet, as we are all creating the future together.

For inspiration, here is a great, short video on vision, future, and machine learning.

This article originally appeared on The Future of Customer Engagement and Commerce.


Kirsi Tarvainen

About Kirsi Tarvainen

Believing strongly that we all deserve good customer service, Kirsi has been working in customer service field for more than fifteen years. In her current role in SAP Hybris she works for SAP Hybris Service Solutions, helping companies worldwide improve their customer experience.

Hack the CIO

By Thomas Saueressig, Timo Elliott, Sam Yen, and Bennett Voyles

For nerds, the weeks right before finals are a Cinderella moment. Suddenly they’re stars. Pocket protectors are fashionable; people find their jokes a whole lot funnier; Dungeons & Dragons sounds cool.

Many CIOs are enjoying this kind of moment now, as companies everywhere face the business equivalent of a final exam for a vital class they have managed to mostly avoid so far: digital transformation.

But as always, there is a limit to nerdy magic. No matter how helpful CIOs try to be, their classmates still won’t pass if they don’t learn the material. With IT increasingly central to every business—from the customer experience to the offering to the business model itself—we all need to start thinking like CIOs.

Pass the digital transformation exam, and you probably have a bright future ahead. A recent SAP-Oxford Economics study of 3,100 organizations in a variety of industries across 17 countries found that the companies that have taken the lead in digital transformation earn higher profits and revenues and have more competitive differentiation than their peers. They also expect 23% more revenue growth from their digital initiatives over the next two years—an estimate 2.5 to 4 times larger than the average company’s.

But the market is grading on a steep curve: this same SAP-Oxford study found that only 3% have completed some degree of digital transformation across their organization. Other surveys also suggest that most companies won’t be graduating anytime soon: in one recent survey of 450 heads of digital transformation for enterprises in the United States, United Kingdom, France, and Germany by technology company Couchbase, 90% agreed that most digital projects fail to meet expectations and deliver only incremental improvements. Worse: over half (54%) believe that organizations that don’t succeed with their transformation project will fail or be absorbed by a savvier competitor within four years.

Companies that are making the grade understand that unlike earlier technical advances, digital transformation doesn’t just support the business, it’s the future of the business. That’s why 60% of digital leading companies have entrusted the leadership of their transformation to their CIO, and that’s why experts say businesspeople must do more than have a vague understanding of the technology. They must also master a way of thinking and looking at business challenges that is unfamiliar to most people outside the IT department.

In other words, if you don’t think like a CIO yet, now is a very good time to learn.

However, given that you probably don’t have a spare 15 years to learn what your CIO knows, we asked the experts what makes CIO thinking distinctive. Here are the top eight mind hacks.

1. Think in Systems

A lot of businesspeople are used to seeing their organization as a series of loosely joined silos. But in the world of digital business, everything is part of a larger system.

CIOs have known for a long time that smart processes win. Whether they were installing enterprise resource planning systems or working with the business to imagine the customer’s journey, they always had to think in holistic ways that crossed traditional departmental, functional, and operational boundaries.

Unlike other business leaders, CIOs spend their careers looking across systems. Why did our supply chain go down? How can we support this new business initiative beyond a single department or function? Now supported by end-to-end process methodologies such as design thinking, good CIOs have developed a way of looking at the company that can lead to radical simplifications that can reduce cost and improve performance at the same time.

They are also used to thinking beyond temporal boundaries. “This idea that the power of technology doubles every two years means that as you’re planning ahead you can’t think in terms of a linear process, you have to think in terms of huge jumps,” says Jay Ferro, CIO of TransPerfect, a New York–based global translation firm.

No wonder the SAP-Oxford transformation study found that one of the values transformational leaders shared was a tendency to look beyond silos and view the digital transformation as a company-wide initiative.

This will come in handy because in digital transformation, not only do business processes evolve but the company’s entire value proposition changes, says Jeanne Ross, principal research scientist at the Center for Information Systems Research at the Massachusetts Institute of Technology (MIT). “It either already has or it’s going to, because digital technologies make things possible that weren’t possible before,” she explains.

2. Work in Diverse Teams

When it comes to large projects, CIOs have always needed input from a diverse collection of businesspeople to be successful. The best have developed ways to convince and cajole reluctant participants to come to the table. They seek out technology enthusiasts in the business and those who are respected by their peers to help build passion and commitment among the halfhearted.

Digital transformation amps up the urgency for building diverse teams even further. “A small, focused group simply won’t have the same breadth of perspective as a team that includes a salesperson and a service person and a development person, as well as an IT person,” says Ross.

At Lenovo, the global technology giant, many of these cross-functional teams become so used to working together that it’s hard to tell where each member originally belonged: “You can’t tell who is business or IT; you can’t tell who is product, IT, or design,” says the company’s CIO, Arthur Hu.

One interesting corollary of this trend toward broader teamwork is that talent is a priority among digital leaders: they spend more on training their employees and partners than ordinary companies, as well as on hiring the people they need, according to the SAP-Oxford Economics survey. They’re also already being rewarded for their faith in their teams: 71% of leaders say that their successful digital transformation has made it easier for them to attract and retain talent, and 64% say that their employees are now more engaged than they were before the transformation.

3. Become a Consultant

Good CIOs have long needed to be internal consultants to the business. Ever since technology moved out of the glasshouse and onto employees’ desks, CIOs have not only needed a deep understanding of the goals of a given project but also to make sure that the project didn’t stray from those goals, even after the businesspeople who had ordered the project went back to their day jobs. “Businesspeople didn’t really need to get into the details of what IT was really doing,” recalls Ferro. “They just had a set of demands and said, ‘Hey, IT, go do that.’”

Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants.

But that was then. Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants. “If you’re building a house, you don’t just disappear for six months and come back and go, ‘Oh, it looks pretty good,’” says Ferro. “You’re on that work site constantly and all of a sudden you’re looking at something, going, ‘Well, that looked really good on the blueprint, not sure it makes sense in reality. Let’s move that over six feet.’ Or, ‘I don’t know if I like that anymore.’ It’s really not much different in application development or for IT or technical projects, where on paper it looked really good and three weeks in, in that second sprint, you’re going, ‘Oh, now that I look at it, that’s really stupid.’”

4. Learn Horizontal Leadership

CIOs have always needed the ability to educate and influence other leaders that they don’t directly control. For major IT projects to be successful, they need other leaders to contribute budget, time, and resources from multiple areas of the business.

It’s a kind of horizontal leadership that will become critical for businesspeople to acquire in digital transformation. “The leadership role becomes one much more of coaching others across the organization—encouraging people to be creative, making sure everybody knows how to use data well,” Ross says.

In this team-based environment, having all the answers becomes less important. “It used to be that the best business executives and leaders had the best answers. Today that is no longer the case,” observes Gary Cokins, a technology consultant who focuses on analytics-based performance management. “Increasingly, it’s the executives and leaders who ask the best questions. There is too much volatility and uncertainty for them to rely on their intuition or past experiences.”

Many experts expect this trend to continue as the confluence of automation and data keeps chipping away at the organizational pyramid. “Hierarchical, command-and-control leadership will become obsolete,” says Edward Hess, professor of business administration and Batten executive-in-residence at the Darden School of Business at the University of Virginia. “Flatter, distributive leadership via teams will become the dominant structure.”

5. Understand Process Design

When business processes were simpler, IT could analyze the process and improve it without input from the business. But today many processes are triggered on the fly by the customer, making a seamless customer experience more difficult to build without the benefit of a larger, multifunctional team. In a highly digitalized organization like Amazon, which releases thousands of new software programs each year, IT can no longer do it all.

While businesspeople aren’t expected to start coding, their involvement in process design is crucial. One of the techniques that many organizations have adopted to help IT and businesspeople visualize business processes together is design thinking (for more on design thinking techniques, see “A Cult of Creation“).

Customers aren’t the only ones who benefit from better processes. Among the 100 companies the SAP-Oxford Economics researchers have identified as digital leaders, two-thirds say that they are making their employees’ lives easier by eliminating process roadblocks that interfere with their ability to do their jobs. Ninety percent of leaders surveyed expect to see value from these projects in the next two years alone.

6. Learn to Keep Learning

The ability to learn and keep learning has been a part of IT from the start. Since the first mainframes in the 1950s, technologists have understood that they need to keep reinventing themselves and their skills to adapt to the changes around them.

Now that’s starting to become part of other job descriptions too. Many companies are investing in teaching their employees new digital skills. One South American auto products company, for example, has created a custom-education institute that trained 20,000 employees and partner-employees in 2016. In addition to training current staff, many leading digital companies are also hiring new employees and creating new roles, such as a chief robotics officer, to support their digital transformation efforts.

Nicolas van Zeebroeck, professor of information systems and digital business innovation at the Solvay Brussels School of Economics and Management at the Free University of Brussels, says that he expects the ability to learn quickly will remain crucial. “If I had to think of one critical skill,” he explains, “I would have to say it’s the ability to learn and keep learning—the ability to challenge the status quo and question what you take for granted.”

7. Fail Smarter

Traditionally, CIOs tended to be good at thinking through tests that would allow the company to experiment with new technology without risking the entire network.

This is another unfamiliar skill that smart managers are trying to pick up. “There’s a lot of trial and error in the best companies right now,” notes MIT’s Ross. But there’s a catch, she adds. “Most companies aren’t designed for trial and error—they’re trying to avoid an error,” she says.

To learn how to do it better, take your lead from IT, where many people have already learned to work in small, innovative teams that use agile development principles, advises Ross.

For example, business managers must learn how to think in terms of a minimum viable product: build a simple version of what you have in mind, test it, and if it works start building. You don’t build the whole thing at once anymore.… It’s really important to build things incrementally,” Ross says.

Flexibility and the ability to capitalize on accidental discoveries during experimentation are more important than having a concrete project plan, says Ross. At Spotify, the music service, and CarMax, the used-car retailer, change is driven not from the center but from small teams that have developed something new. “The thing you have to get comfortable with is not having the formalized plan that we would have traditionally relied on, because as soon as you insist on that, you limit your ability to keep learning,” Ross warns.

8. Understand the True Cost—and Speed—of Data

Gut instincts have never had much to do with being a CIO; now they should have less to do with being an ordinary manager as well, as data becomes more important.

As part of that calculation, businesspeople must have the ability to analyze the value of the data that they seek. “You’ll need to apply a pinch of knowledge salt to your data,” advises Solvay’s van Zeebroeck. “What really matters is the ability not just to tap into data but to see what is behind the data. Is it a fair representation? Is it impartial?”

Increasingly, businesspeople will need to do their analysis in real time, just as CIOs have always had to manage live systems and processes. Moving toward real-time reports and away from paper-based decisions increases accuracy and effectiveness—and leaves less time for long meetings and PowerPoint presentations (let us all rejoice).

Not Every CIO Is Ready

Of course, not all CIOs are ready for these changes. Just as high school has a lot of false positives—genius nerds who turn out to be merely nearsighted—so there are many CIOs who aren’t good role models for transformation.

Success as a CIO these days requires more than delivering near-perfect uptime, says Lenovo’s Hu. You need to be able to understand the business as well. Some CIOs simply don’t have all the business skills that are needed to succeed in the transformation. Others lack the internal clout: a 2016 KPMG study found that only 34% of CIOs report directly to the CEO.

This lack of a strategic perspective is holding back digital transformation at many organizations. They approach digital transformation as a cool, one-off project: we’re going to put this new mobile app in place and we’re done. But that’s not a systematic approach; it’s an island of innovation that doesn’t join up with the other islands of innovation. In the longer term, this kind of development creates more problems than it fixes.

Such organizations are not building in the capacity for change; they’re trying to get away with just doing it once rather than thinking about how they’re going to use digitalization as a means to constantly experiment and become a better company over the long term.

As a result, in some companies, the most interesting tech developments are happening despite IT, not because of it. “There’s an alarming digital divide within many companies. Marketers are developing nimble software to give customers an engaging, personalized experience, while IT departments remain focused on the legacy infrastructure. The front and back ends aren’t working together, resulting in appealing web sites and apps that don’t quite deliver,” writes George Colony, founder, chairman, and CEO of Forrester Research, in the MIT Sloan Management Review.

Thanks to cloud computing and easier development tools, many departments are developing on their own, without IT’s support. These days, anybody with a credit card can do it.

Traditionally, IT departments looked askance at these kinds of do-it-yourself shadow IT programs, but that’s changing. Ferro, for one, says that it’s better to look at those teams not as rogue groups but as people who are trying to help. “It’s less about ‘Hey, something’s escaped,’ and more about ‘No, we just actually grew our capacity and grew our ability to innovate,’” he explains.

“I don’t like the term ‘shadow IT,’” agrees Lenovo’s Hu. “I think it’s an artifact of a very traditional CIO team. If you think of it as shadow IT, you’re out of step with reality,” he says.

The reality today is that a company needs both a strong IT department and strong digital capacities outside its IT department. If the relationship is good, the CIO and IT become valuable allies in helping businesspeople add digital capabilities without disrupting or duplicating existing IT infrastructure.

If a company already has strong digital capacities, it should be able to move forward quickly, according to Ross. But many companies are still playing catch-up and aren’t even ready to begin transforming, as the SAP-Oxford Economics survey shows.

For enterprises where business and IT are unable to get their collective act together, Ross predicts that the next few years will be rough. “I think these companies ought to panic,” she says. D!

About the Authors

Thomas Saueressig is Chief Information Officer at SAP.

Timo Elliott is an Innovation Evangelist at SAP.

Sam Yen is Chief Design Officer at SAP and Managing Director of SAP Labs.

Bennett Voyles is a Berlin-based business writer.

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


The Differences Between Machine Learning And Predictive Analytics

Shaily Kumar

Many people are confused about the specifics of machine learning and predictive analytics. Although they are both centered on efficient data processing, there are many differences.

Machine learning

Machine learning is a method of computational learning underlying most artificial intelligence (AI) applications. In ML, systems or algorithms improve themselves through data experience without relying on explicit programming. ML algorithms are wide-ranging tools capable of carrying out predictions while simultaneously learning from over trillions of observations.

Machine learning is considered a modern-day extension of predictive analytics. Efficient pattern recognition and self-learning are the backbones of ML models, which automatically evolve based on changing patterns in order to enable appropriate actions.

Many companies today depend on machine learning algorithms to better understand their clients and potential revenue opportunities. Hundreds of existing and newly developed machine learning algorithms are applied to derive high-end predictions that guide real-time decisions with less reliance on human intervention.

Business application of machine learning: employee satisfaction

One common, uncomplicated, yet successful business application of machine learning is measuring real-time employee satisfaction.

Machine learning applications can be highly complex, but one that’s both simple and very useful for business is a machine learning algorithm that compares employee satisfaction ratings to salaries. Instead of plotting a predictive satisfaction curve against salary figures for various employees, as predictive analytics would suggest, the algorithm assimilates huge amounts of random training data upon entry, and the prediction results are affected by any added training data to produce real-time accuracy and more helpful predictions.

This machine learning algorithm employs self-learning and automated recalibration in response to pattern changes in the training data, making machine learning more reliable for real-time predictions than other AI concepts. Repeatedly increasing or updating the bulk of training data guarantees better predictions.

Machine learning can also be implemented in image classification and facial recognition with deep learning and neural network techniques.

Predictive analytics

Predictive analytics can be defined as the procedure of condensing huge volumes of data into information that humans can understand and use. Basic descriptive analytic techniques include averages and counts. Descriptive analytics based on obtaining information from past events has evolved into predictive analytics, which attempts to predict the future based on historical data.

This concept applies complex techniques of classical statistics, like regression and decision trees, to provide credible answers to queries such as: ‘’How exactly will my sales be influenced by a 10% increase in advertising expenditure?’’ This leads to simulations and “what-if” analyses for users to learn more.

All predictive analytics applications involve three fundamental components:

  • Data: The effectiveness of every predictive model strongly depends on the quality of the historical data it processes.
  • Statistical modeling: Includes the various statistical techniques ranging from basic to complex functions used for the derivation of meaning, insight, and inference. Regression is the most commonly used statistical technique.
  • Assumptions: The conclusions drawn from collected and analyzed data usually assume the future will follow a pattern related to the past.

Data analysis is crucial for any business en route to success, and predictive analytics can be applied in numerous ways to enhance business productivity. These include things like marketing campaign optimization, risk assessment, market analysis, and fraud detection.

Business application of predictive analytics: marketing campaign optimization

In the past, valuable marketing campaign resources were wasted by businesses using instincts alone to try to capture market niches. Today, many predictive analytic strategies help businesses identify, engage, and secure suitable markets for their services and products, driving greater efficiency into marketing campaigns.

A clear application is using visitors’ search history and usage patterns on e-commerce websites to make product recommendations. Sites like Amazon increase their chance of sales by recommending products based on specific consumer interests. Predictive analytics now plays a vital role in the marketing operations of real estate, insurance, retail, and almost every other sector.

How machine learning and predictive analytics are related

While businesses must understand the differences between machine learning and predictive analytics, it’s just as important to know how they are related. Basically, machine learning is a predictive analytics branch. Despite having similar aims and processes, there are two main differences between them:

  • Machine learning works out predictions and recalibrates models in real-time automatically after design. Meanwhile, predictive analytics works strictly on “cause” data and must be refreshed with “change” data.
  • Unlike machine learning, predictive analytics still relies on human experts to work out and test the associations between cause and outcome.

Explore machine learning applications and AI software with SAP Leonardo.


Shaily Kumar

About Shaily Kumar

Shailendra has been on a quest to help organisations make money out of data and has generated an incremental value of over one billion dollars through analytics and cognitive processes. With a global experience of more than two decades, Shailendra has worked with a myriad of Corporations, Consulting Services and Software Companies in various industries like Retail, Telecommunications, Financial Services and Travel - to help them realise incremental value hidden in zettabytes of data. He has published multiple articles in international journals about Analytics and Cognitive Solutions; and recently published “Making Money out of Data” which showcases five business stories from various industries on how successful companies make millions of dollars in incremental value using analytics. Prior to joining SAP, Shailendra was Partner / Analytics & Cognitive Leader, Asia at IBM where he drove the cognitive business across Asia. Before joining IBM, he was the Managing Director and Analytics Lead at Accenture delivering value to its clients across Australia and New Zealand. Coming from the industry, Shailendra held key Executive positions driving analytics at Woolworths and Coles in the past. Please feel to connect on: Linkedin: Twitter: