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Beware The Feel-Good Work Culture

Meghan M. Biro

Screen-Shot-2016-02-08-at-8.31.02-PMHow critical is work culture to recruitment and employee retention? It’s important, I’ll give you that. But a feel-good work culture might do more harm than good. As the economy continues to grow, hiring managers and HR personnel realize how fiercely they must compete for top talent. As I recently wrote, if you’re looking to snag the best prospects, employee perks alone just won’t cut it.

Today’s modern employee is looking for much more than great benefits and a healthy 401K plan. Those recruiting for tech industries especially know that companies such as Google and Facebook have raised the bar when it comes to work culture and the lure of a fun, modern office space.

And yes, I regularly encourage businesses to think creatively, encourage a human-centered workplace, and build a collaborative and relationship-driven work culture. But there is a catch—a trap, if you will—to all these good feelings. Focusing too much on creating a happy workplace may mean you create one that’s less productive. Let’s take a look.

The bottom-line impact of an engaged workplace

It is easy to drift into Kumbaya-land when discussing how to build an engaged work culture. There is much talk of employee self-actualization and this new, seamless “work-life” way of living that impacts an individual from sunup to sundown.

But the real reason there is so much focus on employee engagement is because the numbers back up the fact that engaged employees are far more productive. Business leaders are beginning to understand that the bottom line is directly and greatly impacted by their work culture.

At the end of last year, CareerBliss released its 5th annual CareerBliss 50 Happiest Companies in America, and it is not surprising that it is populated with industry leaders. As CEO and Chief Happiness Officer Heidi Golledge says, “It is important to see how workplaces are constantly evolving and changing. Creating happiness at work is a very fluid process, building and adapting to a changing workforce, while accounting for the key factors that create happier environments.”

Too much focus on feeling good affects productivity

Employee happiness matters to business success, period. But let’s take a look at the flip side of the coin. As I mentioned, studies repeatedly show that positive work environments equal higher productivity, but I caution against going too far in the “feel good” direction. There is a big difference between creating a happy work culture and creating an environment where productivity comes second to individual happiness. Building a culture of engaged employees does not mean you create a stress-free zone where accountability and productivity take a back seat to personal needs and wants. In order to create an engaged, but successful workplace, a good portion of the “happy” culture must be connected to performance.

What good is a happy workplace if profitability is at risk? A happy employee is also an employed employee, and it is management’s job to keep the business alive and thriving. A culture of excellence, where employees and teams are recognized and rewarded for success, is a signature element of both a happy work culture and a profitable business.

Finding the balance between sound business and a positive work culture

Offering perks like free lunches, part-time remote work schedules, and flex time are benefits any employee appreciates. Paid leave and great healthcare also help to make employees feel valued. However, to build a truly positive work culture, go a step further and create a clear vision so that all employees understand your core principles. Create clear lines of open communication so both management and their reports know that they will be heard.

The danger in not having a strategy for how to achieve a positive, happy work culture is that management may make some very expensive decisions centered on perks and individual happiness without a clear connection to performance. If you’re not careful, you may create an environment where people feel that their right to be happy and not stressed out takes precedence over performance.

Getting the positive work culture right

For a good example of strategic planning for a positive work culture, we need look no further than Netflix. The slideshare presentation outlining the company’s corporate strategy lays the foundation with the title: Freedom with Responsibility. They clearly lay out that their “culture focuses on helping us achieve excellence.” There is no confusion about the goal—it’s clearly communicated right at the onset.

In the presentation, Netflix talks about values and directly connects them to performance, stating, “The actual company values, as opposed to the nice sounding values, are shown by who gets rewarded, promoted, or let go.”

The company nails exactly what I’m talking about. “Great values is not espresso, lush benefits, sushi lunches, grand parties or nice offices…We do some of these things, but only if they are efficient at attracting and retaining stunning colleagues.”

No one at Netflix can be confused about the company’s work culture and goals after watching its 124-slide presentation. They have obviously thought it through and clearly communicated their intentions.

What do you think? What is your experience with positive work cultures and strategies that create engaged employees? I’d love to hear your thoughts and experiences on this topic.

For more insight on employee engagement and creating a positive workplace culture, see 6 Surprising Insights On Successful Employee Engagement.

A version of this article was first published on Huffington Post on 12/30/15

The post Beware the Feel Good Work Culture appeared first on TalentCulture.

Photo credit: study world culture via photopin (license)

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How To Design Your Company’s Digital Transformation

Sam Yen

The September issue of the Harvard Business Review features a cover story on design thinking’s coming of age. We have been applying design thinking within SAP for the past 10 years, and I’ve witnessed the growth of this human-centered approach to innovation first hand.

Design thinking is, as the HBR piece points out, “the best tool we have for … developing a responsive, flexible organizational culture.”

This means businesses are doing more to learn about their customers by interacting directly with them. We’re seeing this change in our work on d.forum — a community of design thinking champions and “disruptors” from across industries.

Meanwhile, technology is making it possible to know exponentially more about a customer. Businesses can now make increasingly accurate predictions about customers’ needs well into the future. The businesses best able to access and pull insights from this growing volume of data will win. That requires a fundamental change for our own industry; it necessitates a digital transformation.

So, how do we design this digital transformation?

It starts with the customer and an application of design thinking throughout an organization – blending business, technology and human values to generate innovation. Business is already incorporating design thinking, as the HBR cover story shows. We in technology need to do the same.

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Design thinking plays an important role because it helps articulate what the end customer’s experience is going to be like. It helps focus all aspects of the business on understanding and articulating that future experience.

Once an organization is able to do that, the insights from that consumer experience need to be drawn down into the business, with the central question becoming: What does this future customer experience mean for us as an organization? What barriers do we need to remove? Do we need to organize ourselves differently? Does our process need to change – if it does, how? What kind of new technology do we need?

Then an organization must look carefully at roles within itself. What does this knowledge of the end customer’s future experience mean for an individual in human resources, for example, or finance? Those roles can then be viewed as end experiences unto themselves, with organizations applying design thinking to learn about the needs inherent to those roles. They can then change roles to better meet the end customer’s future needs. This end customer-centered approach is what drives change.

This also means design thinking is more important than ever for IT organizations.

We, in the IT industry, have been charged with being responsive to business, using technology to solve the problems business presents. Unfortunately, business sometimes views IT as the organization keeping the lights on. If we make the analogy of a store: business is responsible for the front office, focused on growing the business where consumers directly interact with products and marketing; while the perception is that IT focuses on the back office, keeping servers running and the distribution system humming. The key is to have business and IT align to meet the needs of the front office together.

Remember what I said about the growing availability of consumer data? The business best able to access and learn from that data will win. Those of us in IT organizations have the technology to make that win possible, but the way we are seen and our very nature needs to change if we want to remain relevant to business and participate in crafting the winning strategy.

We need to become more front office and less back office, proving to business that we are innovation partners in technology.

This means, in order to communicate with businesses today, we need to take a design thinking approach. We in IT need to show we have an understanding of the end consumer’s needs and experience, and we must align that knowledge and understanding with technological solutions. When this works — when the front office and back office come together in this way — it can lead to solutions that a company could otherwise never have realized.

There’s different qualities, of course, between front office and back office requirements. The back office is the foundation of a company and requires robustness, stability, and reliability. The front office, on the other hand, moves much more quickly. It is always changing with new product offerings and marketing campaigns. Technology must also show agility, flexibility, and speed. The business needs both functions to survive. This is a challenge for IT organizations, but it is not an impossible shift for us to make.

Here’s the breakdown of our challenge.

1. We need to better understand the real needs of the business.

This means learning more about the experience and needs of the end customer and then translating that information into technological solutions.

2. We need to be involved in more of the strategic discussions of the business.

Use the regular invitations to meetings with business as an opportunity to surface the deeper learning about the end consumer and the technology solutions that business may otherwise not know to ask for or how to implement.

The IT industry overall may not have a track record of operating in this way, but if we are not involved in the strategic direction of companies and shedding light on the future path, we risk not being considered innovation partners for the business.

We must collaborate with business, understand the strategic direction and highlight the technical challenges and opportunities. When we do, IT will become a hybrid organization – able to maintain the back office while capitalizing on the front office’s growing technical needs. We will highlight solutions that business could otherwise have missed, ushering in a digital transformation.

Digital transformation goes beyond just technology; it requires a mindset. See What It Really Means To Be A Digital Organization.

This story originally appeared on SAP Business Trends.

Top image via Shutterstock

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Sam Yen

About Sam Yen

Sam Yen is the Chief Design Officer for SAP and the Managing Director of SAP Labs Silicon Valley. He is focused on driving a renewed commitment to design and user experience at SAP. Under his leadership, SAP further strengthens its mission of listening to customers´ needs leading to tangible results, including SAP Fiori, SAP Screen Personas and SAP´s UX design services.

How Productive Could You Be With 45 Minutes More Per Day?

Michael Rander

Chances are that you are already feeling your fair share of organizational complexity when navigating your current company, but have you ever considered just how much time is spent across all companies on managing complexity? According to a recent study by the Economist Intelligence Unit (EIU), the global impact of complexity is mind-blowing – and not in a good way.

The study revealed that 38% of respondents spent 16%-25% of their time just dealing with organizational complexity, and 17% spent a staggering 26%-50% of their time doing so. To put that into more concrete numbers, in the US alone, if executives could cut their time spent managing complexity in half, an estimated 8.6 million hours could be saved a week. That corresponds to 45 minutes per executive per day.

The potential productivity impact of every executive having 45 minutes more to work every single day is clearly significant, and considering that 55% say that their organization is either very or extremely complex, why are we then not making the reduction of complexity one or our top of mind issues?

The problem is that identifying the sources of complexity is complex in of itself. Key sources of complexity include organizational size, executive priorities, pace of innovation, decision-making processes, vastly increasing amounts of data to manage, organizational structures, and the pure culture of the company. As a consequence, answers are not universal by any means.

That being said, the negative productivity impact of complexity, regardless of the specific source, is felt similarly across a very large segment of the respondents, with 55% stating that complexity has taken a direct toll on profitability over the past three years.  This is such a serious problem that 8% of respondents actually slowed down their company growth in order to deal with complexity.

So, if complexity oftentimes impacts productivity and subsequently profitability, what are some of the more successful initiatives that companies are taking to combat these effects? Among the answers from the EIU survey, the following were highlighted among the most likely initiatives to reduce complexity and ultimately increase productivity:

  • Making it a company-wide goal to reduce complexity means that the executive level has to live and breathe simplification in order for the rest of the organization to get behind it. Changing behaviors across the organization requires strong leadership, commitment, and change management, and these initiatives ultimately lead to improved decision-making processes, which was reported by respondents as the top benefit of reducing complexity. From a leadership perspective this also requires setting appropriate metrics for measuring outcomes, and for metrics, productivity and efficiency were by far the most popular choices amongst respondents though strangely collaboration related metrics where not ranking high in spite of collaboration being a high level priority.
  • Promoting a culture of collaboration means enabling employees and management alike to collaborate not only within their teams but also across the organization, with partners, and with customers. Creating cross-functional roles to facilitate collaboration was cited by 56% as the most helpful strategy in achieving this goal.
  • More than half (54%) of respondents found the implementation of new technology and tools to be a successful step towards reducing complexity and improving productivity. Enabling collaboration, reducing information overload, building scenarios and prognoses, and enabling real-time decision-making are all key issues that technology can help to reduce complexity at all levels of the organization.

While these initiatives won’t help everyone, it is interesting to see that more than half of companies believe that if they could cut complexity in half they could be at least 11%-25% more productive. That nearly one in five respondents indicated that they could be 26%-50% more productive is a massive improvement.

The question then becomes whether we can make complexity and its impact on productivity not only more visible as a key issue for companies to address, but (even more importantly) also something that every company and every employee should be actively working to reduce. The potential productivity gains listed by respondents certainly provide food for thought, and few other corporate activities are likely to gain that level of ROI.

Just imagine having 45 minutes each and every day for actively pursuing new projects, getting innovative, collaborating, mentoring, learning, reducing stress, etc. What would you do? The vision is certainly compelling, and the question is are we as companies, leaders, and employees going to do something about it?

To read more about the EIU study, please see:

Feel free to follow me on Twitter: @michaelrander

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About Michael Rander

Michael Rander is the Global Program Director for Future Of Work at SAP. He is an experienced project manager, strategic and competitive market researcher, operations manager as well as an avid photographer, athlete, traveler and entrepreneur.

Unlock Your Digital Super Powers: How Digitization Helps Companies Be Live Businesses

Erik Marcade and Fawn Fitter

The Port of Hamburg handles 9 million cargo containers a year, making it one of the world’s busiest container ports. According to the Hamburg Port Authority (HPA), that volume doubled in the last decade, and it’s expected to at least double again in the next decade—but there’s no room to build new roads in the center of Hamburg, one of Germany’s historic cities. The port needed a way to move more freight more efficiently with the physical infrastructure it already has.

sap_Q216_digital_double_feature1_images1The answer, according to an article on ZDNet, was to digitize the processes of managing traffic into, within, and back out of the port. By deploying a combination of sensors, telematics systems, smart algorithms, and cloud data processing, the Port of Hamburg now collects and analyzes a vast amount of data about ship arrivals and delays, parking availability, ground traffic, active roadwork, and more. It generates a continuously updated model of current port conditions, then pushes the results through mobile apps to truck drivers, letting them know exactly when ships are ready to drop off or receive containers and optimizing their routes. According to the HPA, they are now on track to handle 25 million cargo containers a year by 2025 without further congestion or construction, helping shipping companies bring more goods and raw materials in less time to businesses and consumers all across Europe.

In the past, the port could only have solved its problem with backhoes and building permits—which, given the physical constraints, means the problem would have been unsolvable. Today, though, software and sensors are allowing it to improve processes and operations to a previously impossible extent. Big Data analysis, data mining, machine learning, artificial intelligence (AI), and other technologies have finally become sophisticated enough to identify patterns not just in terabytes but in petabytes of data, make decisions accordingly, and learn from the results, all in seconds. These technologies make it possible to digitize all kinds of business processes, helping organizations become more responsive to changing market conditions and more able to customize interactions to individual customer needs. Digitization also streamlines and automates these processes, freeing employees to focus on tasks that require a human touch, like developing innovative strategies or navigating office politics.

In short, digitizing business processes is key to ensuring that the business can deliver relevant, personalized responses to the market in real time. And that, in turn, is the foundation of the Live Business—a business able to coordinate multiple functions in order to respond to and even anticipate customer demand at any moment.

Some industries and organizations are on the verge of discovering how business process digitization can help them go live. Others have already started putting it into action: fine-tuning operations to an unprecedented level across departments and at every point in the supply chain, cutting costs while turbocharging productivity, and spotting trends and making decisions at speeds that can only be called superhuman.

Balancing Insight and Action

sap_Q216_digital_double_feature1_images2Two kinds of algorithms drive process digitization, says Chandran Saravana, senior director of advanced analytics at SAP. Edge algorithms operate at the point where customers or other end users interact directly with a sensor, application, or Internet-enabled device. These algorithms, such as speech or image recognition, focus on simplicity and accuracy. They make decisions based primarily on their ability to interpret input with precision and then deliver a result in real time.

Edge algorithms work in tandem with, and sometimes mature into, server-level algorithms, which report on both the results of data analysis and the analytical process itself. For example, the complex systems that generate credit scores assess how creditworthy an individual is, but they also explain to both the lender and the credit applicant why a score is low or high, what factors went into calculating it, and what an applicant can do to raise the score in the future. These server-based algorithms gather data from edge algorithms, learn from their own results, and become more accurate through continuous feedback. The business can then track the results over time to understand how well the digitized process is performing and how to improve it.

sap_Q216_digital_double_feature1_images5From Data Scarcity to a Glut

To operate in real time, businesses need an accurate data model that compares what’s already known about a situation to what’s happened in similar situations in the past to reach a lightning-fast conclusion about what’s most likely to happen next. The greatest barrier to this level of responsiveness used to be a lack of data, but the exponential growth of data volumes in the last decade has flipped this problem on its head. Today, the big challenge for companies is having too much data and not enough time or power to process it, says Saravana.

Even the smartest human is incapable of gathering all the data about a given situation, never mind considering all the possible outcomes. Nor can a human mind reach conclusions at the speed necessary to drive Live Business. On the other hand, carefully crafted algorithms can process terabytes or even petabytes of data, analyze patterns and detect outliers, arrive at a decision in seconds or less—and even learn from their mistakes (see How to Train Your Algorithm).

How to Train Your Algorithm 

The data that feeds process digitization can’t just simmer.
It needs constant stirring.

Successfully digitizing a business process requires you to build a model of the business process based on existing data. For example, a bank creates a customer record that includes not just the customer’s name, address, and date of birth but also the amount and date of the first deposit, the type of account, and so forth. Over time, as the customer develops a history with the bank and the bank introduces new products and services, customer records expand to include more data. Predictive analytics can then extrapolate from these records to reach conclusions about new customers, such as calculating the likelihood that someone who just opened a money market account with a large balance will apply for a mortgage in the next year.

Germany --- Germany, Lower Bavaria, Man training English Springer Spaniel in grass field --- Image by © Roman M‰rzinger/Westend61/CorbisTo keep data models accurate, you have to have enough data to ensure that your models are complete—that is, that they account for every possible predictable outcome. The model also has to push outlying data and exceptions, which create unpredictable outcomes, to human beings who can address their special circumstances. For example, an algorithm may be able to determine that a delivery will fail to show up as scheduled and can point to the most likely reasons why, but it can only do that based on the data it can access. It may take a human to start the process of locating the misdirected shipment, expediting a replacement, and establishing what went wrong by using business knowledge not yet included in the data model.

Indeed, data models need to be monitored for relevance. Whenever the results of a predictive model start to drift significantly from expectations, it’s time to examine the model to determine whether you need to dump old data that no longer reflects your customer base, add a new product or subtract a defunct one, or include a new variable, such as marital status or length of customer relationship that further refines your results.

It’s also important to remember that data doesn’t need to be perfect—and, in fact, probably shouldn’t be, no matter what you might have heard about the difficulty of starting predictive analytics with lower-quality data. To train an optical character recognition system to recognize and read handwriting in real time, for example, your samples of block printing and cursive writing data stores also have to include a few sloppy scrawls so the system can learn to decode them.

On the other hand, in a fast-changing marketplace, all the products and services in your database need consistent and unchanging references, even though outside the database, names, SKUs, and other identifiers for a single item may vary from one month or one order to the next. Without consistency, your business process model won’t be accurate, nor will the results.

Finally, when you’re using algorithms to generate recommendations to drive your business process, the process needs to include opportunities to test new messages and products against existing successful ones as well as against random offerings, Saravana says. Otherwise, instead of responding to your customers’ needs, your automated system will actually control their choices by presenting them with only a limited group of options drawn from those that have already received the most
positive results.

Any process is only as good as it’s been designed to be. Digitizing business processes doesn’t eliminate the possibility of mistakes and problems; but it does ensure that the mistakes and problems that arise are easy to spot and fix.

From Waste to Gold

Organizations moving to digitize and streamline core processes are even discovering new business opportunities and building new digitized models around them. That’s what happened at Hopper, an airfare prediction app firm in Cambridge, Massachusetts, which discovered in 2013 that it could mine its archives of billions of itineraries to spot historical trends in airfare pricing—data that was previously considered “waste product,” according to Hopper’s chief data scientist, Patrick Surry.

Hopper developed AI algorithms to correlate those past trends with current fares and to predict whether and when the price of any given flight was likely to rise or fall. The results were so accurate that Hopper jettisoned its previous business model. “We check up to 3 billion itineraries live, in real time, each day, then compare them to the last three to four years of historical airfare data,” Surry says. “When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now,’ or ‘no, we think that fare is too expensive, we predict it will drop, and we’ll alert you when it does.’ And we can give them that answer in less than one second.”

When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now’.

— Patrick Surry, chief data scientist, Hopper

While trying to predict airfare trends is nothing new, Hopper has told TechCrunch that it can not only save users up to 40% on airfares but it can also find them the lowest possible price 95% of the time. Surry says that’s all due to Hopper’s algorithms and data models.

The Hopper app launched on iOS in January 2015 and on Android eight months later. The company also switched in September 2015 from directing customers to external travel agencies to taking bookings directly through the app for a small fee. The Hopper app has already been downloaded to more than 2 million phones worldwide.

Surry predicts that we’ll soon see sophisticated chatbots that can start with vague requests from customers like “I want to go somewhere warm in February for less than $500,” proceed to ask questions that help users narrow their options, and finally book a trip that meets all their desired parameters. Eventually, he says, these chatbots will be able to handle millions of interactions simultaneously, allowing a wide variety of companies to reassign human call center agents to the handling of high-value transactions and exceptions to the rules built into the digitized booking process.

Port of Hamburg Lets the Machines Untangle Complexity

In early 2015, AI experts told Wired magazine that at least another 10 years would pass before a computer could best the top human players at Go, an ancient game that’s exponentially harder than chess. Yet before the end of that same year, Wired also reported that machine learning techniques drove Google’s AlphaGo AI to win four games out of five against one of the world’s top Go players. This feat proves just how good algorithms have become at managing extremely complex situations with multiple interdependent choices, Saravana points out.

The Port of Hamburg, which has digitized traffic management for an estimated 40,000 trucks a day, is a good example. In the past, truck drivers had to show up at the port to check traffic and parking message boards. If they arrived before their ships docked, they had to drive around or park in the neighboring residential area, contributing to congestion and air pollution while they waited to load or unload. Today, the HPA’s smartPORT mobile app tracks individual trucks using telematics. It customizes the information that drivers receive based on location and optimizes truck routes and parking in real time so drivers can make more stops a day with less wasted time and fuel.

The platform that drives the smartPORT app also uses sensor data in other ways: it tracks wind speed and direction and transmits the data to ship pilots so they can navigate in and out of the port more safely. It monitors emissions and their impact on air quality in various locations in order to adjust operations in real time for better control over environmental impact. It automatically activates streetlights for vehicle and pedestrian traffic, then switches them off again to save energy when the road is empty. This ability to coordinate and optimize multiple business functions on the fly makes the Port of Hamburg a textbook example of a Live Business.

Digitization Is Not Bounded by Industry

Other retail and B2B businesses of all types will inevitably join the Port of Hamburg in further digitizing processes, both in predictable ways and in those we can only begin to imagine.

sap_Q216_digital_double_feature1_images4Customer service, for example, is likely to be in the vanguard. Automated systems already feed information about customers to online and phone-based service representatives in real time, generate cross-selling and upselling opportunities based on past transactions, and answer customers’ frequently asked questions. Saravana foresees these systems becoming even more sophisticated, powered by AI algorithms that are virtually indistinguishable from human customer service agents in their ability to handle complex live interactions in real time.

In manufacturing and IT, Sven Bauszus, global vice president and general manager for predictive analytics at SAP, forecasts that sensors and predictive analysis will further automate the process of scheduling and performing maintenance, such as monitoring equipment for signs of failure in real time, predicting when parts or entire machines will need replacement, and even ordering replacements preemptively. Similarly, combining AI, sensors, data mining, and other technologies will enable factories to optimize workforce assignments in real time based on past trends, current orders, and changing market conditions.

Public health will be able to go live with technology that spots outbreaks of infectious disease, determines where medical professionals and support personnel are needed most and how many to send, and helps ensure that they arrive quickly with the right medication and equipment to treat patients and eradicate the root cause. It will also make it easier to track communicable illnesses, find people who are symptomatic, and recommend approaches to controlling the spread of the illness, Bauszus says.

He also predicts that the insurance industry, which has already begun to digitize its claims-handling processes, will refine its ability to sort through more claims in less time with greater accuracy and higher customer satisfaction. Algorithms will be better and faster at flagging claims that have a high probability of being fraudulent and then pushing them to claims inspectors for investigation. Simultaneously, the same technology will be able to identify and resolve valid claims in real time, possibly even cutting a check or depositing money directly into the insured person’s bank account within minutes.

Financial services firms will be able to apply machine learning, data mining, and AI to accelerate the process of rating borrowers’ credit and detecting fraud. Instead of filling out a detailed application, consumers might be able to get on-the-spot approval for a credit card or loan after inputting only enough information to be identified. Similarly, banks will be able to alert customers to suspicious transactions by text message or phone call—not within a day or an hour, as is common now, but in a minute or less.

Pitfalls and Possibilities

As intelligent as business processes can be programmed to be, there will always be a point beyond which they have to be supervised. Indeed, Saravana forecasts increasing regulation around when business processes can and can’t be digitized. Especially in areas involving data security, physical security, and health and safety, it’s one thing to allow machines to parse data and arrive at decisions to drive a critical business process, but it’s another thing entirely to allow them to act on those decisions without human oversight.

Automated, impersonal decision making is fine for supply chain automation, demand forecasting, inventory management, and other processes that need faster-than-human response times. In human-facing interactions, though, Saravana insists that it’s still best to digitize the part of the process that generates decisions, but leave it to a human to finalize the decision and decide how to put it into action.

“Any time the interaction is machine-to-machine, you don’t need a human to slow the process down,” he says. “But when the interaction involves a person, it’s much more tricky, because people have preferences, tastes, the ability to try something different, the ability to get fatigued—people are only statistically predictable.”

For example, technology has made it entirely possible to build a corporate security system that can gather information from cameras, sensors, voice recognition technology, and other IP-enabled devices. The system can then feed that information in a steady stream to an algorithm designed to identify potentially suspicious activity and act in real time to prevent or stop it while alerting the authorities. But what happens when an executive stays in the office unusually late to work on a presentation and the security system misidentifies her as an unauthorized intruder? What if the algorithm decides to lock the emergency exits, shut down the executive’s network access, or disable her with a Taser instead of simply sending an alert to the head of security asking what to do while waiting for the police to come?

sap_Q216_digital_double_feature1_images6The Risk Is Doing Nothing

The greater, if less dramatic, risk associated with digitizing business processes is simply failing to pursue it. It’s true that taking advantage of new digital technologies can be costly in the short term. There’s no question that companies have to invest in hardware, software, and qualified staff in order to prepare enormous data volumes for storage and analysis. They also have to implement new data sources such as sensors or Internet-connected devices, develop data models, and create and test algorithms to drive business processes that are currently analog. But as with any new technology, Saravana advises, it’s better to start small with a key use case, rack up a quick win with high ROI, and expand gradually than to drag your heels out of a failure to grasp the long-term potential.

The economy is digitizing rapidly, but not evenly. According to the McKinsey Global Institute’s December 2015 Digital America report, “The race to keep up with technology and put it to the most effective business use is producing digital ‘haves’ and ‘have-mores’—and the large, persistent gap between them is becoming a decisive factor in competition across the economy.” Companies that want to be among the have-mores need to commit to Live Business today. Failing to explore it now will put them on the wrong side of the gap and, in the long run, rack up a high price tag in unrealized efficiencies and missed opportunities. D!

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Erik Marcade

About Erik Marcade

Erik Marcade is vice president of Advanced Analytics Products at SAP.

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Digital Transformation Needs More Than Technology (Part 2)

Andreas Hauser

In my last blog, I explained why design, design thinking, and experience matter in digital transformation, which goes beyond pure technology and business skills. The credo is to engage with your customers, and most importantly, with the users right from the beginning, in an iterative, user-centric design process.

Digital transformation is more than a one-time project; it is a journey. Ultimately, enterprises want to prepare their organization for a sustainable design-led digital transformation.

But how to achieve this? That is the focus of today’s blog.

Foster a design-led innovation culture

Changing a company culture is not easy. If you don’t want your company ending up like Nokia or Blackberry, you better start sooner than later experiencing new practices. More and more companies are training their people on design thinking and want to establish a culture of design-led innovation.Slide3.JPG

My formula for innovation culture is people + process + place.

You need to have the right skills in your organization (people): Business, technical and design skills to better understand the needs of customers and users. Business and technical skills are typically not the problem. But how many people with design background do you have in your organization? This is why more and more companies start hiring designers and train their employees on design thinking.

It is not just about having the people with the right skills. You also need to change the way (process) how you engage with customer and users. To be successful, you need to combine design thinking with agile methodologies. The process is pretty simple: You need to get people with the right skills together working as one team and iterate from the beginning to the end with the customers and users. Sounds simple, but it is sometimes difficult to execute in large global organizations.

Let me tell you a story.

We have trained about 300 people (business + IT) at one of our customers in design thinking and helped to establish five design thinking coaches in their organization. This was the most interesting outcome: After the exercise, seven out of 10 IT projects were initiated by the business. The customer told us that this was the first time where business proactively wanted to work with IT. This is a great start to improve a relationship which in recent years has gotten separated in many organizations.

In our experience, the place where people work together also has a huge impact on creativity. Therefore, we have established at SAP “customer-facing” co-innovation spaces – called SAP AppHaus – where customers and SAP collaborate and co-innovate as partners. Establishing creative spaces within your organization gives the cultural change a face. Skills and new ways of working are often not very visible. The space is physical and people see and feel that your organization is changing. Check out our virtual walk-through our AppHaus in Heidelberg.

In my last blog, I discussed our co-innovation journey with Mercedes-AMG, which paved Mercedes-AMG’s way to a sustainable digital transformation. Based on my experience, you first need to show with a lighthouse project that this methodology creates business value for the company. You can then build on this success and start the journey to establish a sustainable design-led culture in your organization.

Be prepared for a long journey. It takes time to change and influence the way how organizations work. There has never been a more exciting time for designers, because the industry is starting to see the huge value they can bring to organizations and the value they can bring for a successful digital transformation.

Have a look at a more detailed presentation and a video recording about the concept that “digital transformation is more than technology.” Learn more about customer success stories on the UX Design Services web page.

Stay tuned for more articles as part of this blog series, in which you can explore further perspectives on digital transformation and its various aspects, learn about organizational readiness for design thinking, and assess how ready your organization is to embark on this journey.

Read Part 1 of this discussion: Digital Transformation Needs More Than Technology.

This article originally appeared on SAP Business Trends.

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Andreas Hauser

About Andreas Hauser

Andreas is global head of the design and co-innovation center at SAP. His team drives customer & strategic design projects through Co-Innovation and Design Thinking. Before he was Vice President of User Experience at SAP SE for OnDemand Solutions.