3 Ways To Drive IT Agility In Service Of Innovation

Pat Saporito

At your company, how well does your IT organization serve the business? To answer this question, you first need to know what the business wants—and if yours is like most businesses in the digital economy, what it wants is innovation.

Why? Because since the year 2000, more than half of the Fortune 500 has disappeared. Many of these companies failed to innovate in a way that appeals to today’s customers.

Much of this churn has to do with digital disruption: Big Data, the Internet of Things, machine learning—the list goes on. But it’s important to understand that no technology silver bullet is by itself driving the digital economy. No single technology prevails.

Rather, businesses are finding creative new ways to combine growing data assets with newly available technologies in support of new business models and new forms of customer engagement. The implication here is that we are not so much determined by our technology as by our creativity. This is good news. But on the other hand, the directive is clearly: “Be more creative, right now, or else.” This can be quite debilitating.

Creativity, of course, is another word for innovation—which is what businesses want from IT. To rise to the occasion, IT needs to become more agile. It needs to facilitate change and unleash the creativity of the business for success in the digital economy. Here are a few suggestions for helping to move your IT group in the direction of greater agility.

Go hybrid and move to the cloud—somewhat

Few companies drop their on-premise investment entirely and move everything to the cloud. In most cases, it’s impractical—not to mention impossible. But most companies can benefit from cloud technology when it comes to “innovating at the edge.”

In other words, companies don’t need to rip and replace their on-premise core. Rather they can use cloud technology in parallel with their existing environment to expand operations and quickly deploy new solutions for new products and other targeted purposes.

This model is often referred to as bimodal IT, or two-speed IT, where the business maintains its existing IT investments for core system maintenance, stability, and efficiency, but innovates “on the edge” using the cloud. When an IT group is agile enough to offer a cloud option that builds on the business’s core without disrupting it—that’s innovation.

Get right with your data

In the digital economy, companies use data to engage their customers more effectively, to reimagine their business processes, and to deliver more products and services that lead to better customer outcomes, greater loyalty, and ongoing revenues.

Data, in fact, is at the heart of whatever companies do in the digital economy; it’s the fuel. So what, exactly, do companies need from their data? Good question.

What companies need is to transform data into insight, with abilities to analyze extremely large and diverse data sets in real time. They need the ability to act in the moment—using data to market to segments of one, engage customers on their terms, and ultimately deliver the goods. And they also need to use data to innovate in ways that are not yet foreseen.

When an IT group is agile enough to offer a data platform that can support these needs, that’s innovation too.

Create the environment for innovation

Many IT groups are consumed by the challenge of simply “keeping the lights on.” This is when most IT resources go to—servicing existing systems, with no leftover capacity for innovation. Innovation requires IT agility.

One solution to this challenge is to create a separate space—physically or organizationally—that is dedicated to innovation. For example, you can establish an innovation center of excellence as a separate group, as part of corporate strategy, and use techniques like design thinking to prototype new solutions. You can also partner with outside innovation-focused organizations such as incubators, universities, tech startups, or co-innovation labs that enable you to solve problems in collaboration with partners. IT groups don’t need to do it all in house – and realistically, they can’t be expected to. But what they can do is help vet various external innovation partners.

Culture plays a big role. Some organizations give their employees, say, 10% of their time to focus on innovation. Others establish a specific role of chief innovation officer whose job it is to get new projects off the ground and into fruition. IT groups provide further support with innovation sandboxes and agile development methodologies—such as DevOps—that emphasize communication, collaboration, and quick iterative cycles across product management, software development, and operations professionals.

An IT organization that is a partner with an innovation center of excellence, and supports various avenues for focused innovation, is a true partner with the business.

Know where you stand

Of course, before you get started on an IT agility initiative, it’s important to know where you stand. Are you agile already? Do you have a foundation to build on? Or maybe you need to start from scratch?

For complex IT groups, it’s not always that easy to know where you stand on the agility maturity curve. But this assessment tool just might help. Take 15-20 minutes to answer some core questions for IT; work with your business users to answer questions for them. What you’ll get in return is a report that spells out exactly where your IT group faces its most pressing challenges on the road to greater IT agility.

Good luck—and stay nimble out there.

For more insight on where the agile IT group is headed, see IT Roadmap For The Future.


Pat Saporito

About Pat Saporito

Pat Saporito is a senior director, Global Center of Excellence for Analytics and Analytics Strategy Program Leader at SAP.  She helps customers across industries develop a business and value-driven analytics strategy. Pat has 20+ years in data warehousing and analytics. She is an insurance industry analytics expert and author of the book, Applied Insurance Analytics (FT Press/Pearson). She is on advisory board for Stevens Institute of Technology’s Big Data and Analytics Masters Program and a mentor with the Global Insurance Accelerator an incubator for Insure Tech startups.

Digital Transformation: Opportunities In Manufacturing

Sarma Malladi

Part 1 in the 4-part series “Opportunities for Digital Manufacturing

I once explained to my then ten-year-old son that 30 years ago, passengers waited in queues for several hours  to buy train reservations from Mumbai to Hyderabad. It was a manual system long overdue for automation—even for its time. Having grown accustomed to spending his allowance online with a click of a button, he responded, “Dad, you must have been born in the Stone Age.”

Perhaps a slow-moving government bureaucracy was to blame for this inefficiency. But the perception that private companies always optimize their use of new technologies is far from the truth. Fourteen years have passed since I had that exchange with my son, and I still see large global enterprises sift through file cabinets to give customers hard copies of invoices and service reports that could be easily sent online.

We expect profit-motivated enterprises to be at the forefront of automation and innovation, and yet I am always surprised by the plethora of straightforward technological opportunities that go untapped. For example, polls show that manufacturing and field service industries are lagging in the pursuit of digital transformation—and from my experience, this has been my impression as well.

Companies in these industries have several low-hanging opportunities for technological innovation. This suggests that leadership is either unaware of such opportunities or does not fully appreciate the ROI from technological innovations. I suspect that leaders at some of these firms mistakenly see technological investments as a necessary cost induced by the competition. As a result, companies with large market shares—which are often best equipped to be at the forefront of technological innovation—will sometimes wait and react to quickly changing industry standards rather than strive to set them.

The pursuit of creating new disruptive technologies is often thought to be strictly in the domain of startups. I have heard some executives in manufacturing and service industries express that they could buy technologies from a startup or simply buy the startup when the need arises. However, these outside options are becoming increasingly more expensive. In addition to upward-trending direct costs, late adopters risk incurring a loss in market share, whether temporary or permanent, to firms that are successfully implementing technologies that enhance the customer experience. On the other hand, companies that proactively seek out opportunities to adopt new technologies can win significant edge over competitors. But ultimately, executive management needs to put in place the right leader and governance structure to capture that value.

There’s a lot to learn from the degree of success that enterprises that have crossed over from offering traditional products to wrapping in digital services around their existing portfolio. In the coming weeks, I will continue this series with a discussion on opportunities for digital innovation in manufacturing and services, and the role of leadership in the pursuit of digital transformation.

For more on this topic, read “The Digital Advantage: How Digital Leaders Outperform Their Peers in Every Industry,” MIT Sloan Management, sponsored by Capgemini, 2012.


Sarma Malladi

About Sarma Malladi

Sarma Malladi is an IT executive, working as a CIO for the past several years. He is passionate about leveraging technologies to build strategic business value. Much of his industry experience has been in manufacturing, field services, and consulting. All opinions expressed in this blog are his own and do not reflect the views of the current and or previous companies he has worked for.

GDPR: A Closer Look At A Company’s Stakeholders And Their Obligations

Evelyne Salie

Anyone involved in a company-wide European Union General Data Protection Regulation (GDPR) initiative will probably agree that it can’t be an ad hoc approach; it must be a high-grade, cross-functional program. Typically, GDPR programs involve several work streams running in parallel across multiple business lines and geographies. And no matter how many people and business processes are involved (or what their titles and roles may be), all will fall under one of four major stakeholder groups. In this blog, I’ll detail each group’s primary obligations.

CEO and board of directors

The CEO and board of directors will be interested in:

  • Impact of GDPR on business processes, top-to-bottom review of relevant privacy data being processed within the business processes; understand risks and challenges as well as new opportunities
  • Employee training about new requirements, creating awareness of how they should be taking notes and recording information about their customers, prospects, and employees
  • Protect against GDPR-related fines, impact on directors’ and officers’ liability insurance (also known as D&O insurance); the company’s current GDPR risk exposure
  • Cost-effectiveness of data. Is the company collecting and accessing more personal data than is needed? Check possibilities of reducing the amount of data gathered, since continued accumulation of silos of unused, and potentially toxic, data increases the need for encryption – which therefore will require more investments

CCOs, CROs, and related roles

In contrast to the data protection officer, the chief compliance officer (CCO) and chief risk officer (CRO) will focus on “Lawful Processing” Article 6 GDPR and “Accountability” Article 5 GDPR to demonstrate compliance by:

  • Introducing clear, company-wide data protection policies to ensure agility against potential breaches and the ability to quickly inform the relevant authorities
  • Establishing an accountability framework by adding documentation of current risks and controls for the GDPR regulation into the existing internal controls system
  • Incorporating a risk-based approach by assessing the “likelihood and severity of risk” of personal data processing operations
  • For example, “high-risk” processing operations will raise additional compliance obligations, such as data protection impact assessments (DPIAs) and so forth
  • Encouraging a culture of monitoring and assessing data-handling processes

Data protection officers

All businesses that market goods or services to customers within the EU and collect data must appoint a data protection officer. The DPO works on behalf of the customer’s privacy. Thus, many of a data protection officer’s recommendations will run contrary to the aims of other data roles within the company. The data protection officer (DPO) will:

  • Keep up on laws and practices around data protection
  • Conduct privacy assessments internally
  • Ensure that all other matters of compliance pertaining to data are up-to-date
  • Be responsible for advising the organization of its obligations and monitoring compliance
  • Report directly to the highest level of management and have “expert knowledge” of data protection – although the DPO can potentially be outsourced

CISOs, CIOs, and business process owners

These roles generally deal with keeping a company’s data safe and making sure that these troves of data are being exploited to improve business functions across the company. The chief information security officer (CISO) will:

  • Define GDPR requirements in the security strategy
  • Manage information risk management, security incidents, and crisis management
  • Be responsible for cybersecurity, including monitoring access to personal data and reporting of data breaches
  • Limit who has access to personal data and make sure that access is authorized and reflects personnel changes that happen within an organization

The chief information officer (CIO) can advise the DPO on technical solutions, and will typically focus on architecture and fulfillment of new rights of the data subject (Chapter 3 GDPR). These new rights include:

  • Data subject’s consent for processing of personal data, which might be revoked at any time
  • Data subjects – like customers, subscribers, users, employees, partner, external workforce, and so on – will get extended information rights: the right to correct information, the right to export and transfer, as well as the right to be forgotten
  • Information that is no longer required to be stored (for legal reasons, for example) is expected to be completely removed from all storage systems

As I stated earlier, actual titles and roles will vary from one organization to the next, but organizations subject to the EU GDPR will need to establish comprehensive programs addressing these key data-privacy areas. The more automated and integrated the program is (with existing business applications, audit, and compliance tools), the more effective, cost efficient, and preventive this program will become.

For more information on the new regulations, read our other GDPR blogs.


Evelyne Salie

About Evelyne Salie

Evelyne is a highly experienced IT-Solution Principal, Business Developer and Project Manager with over 10 years IT- industry experience within the Governance Risk and Compliance and Finance area of expertise. She currently works as a Senior Director in Business Development at SAP Finance and GRC solutions. In her business development role she is working on concepts and realization for new generation of Finance solutions, running in real time, integrating predictive, Big Data, and mobile, which will change how offices of the CFO work, how the business is run, and how information is consumed.

Diving Deep Into Digital Experiences

Kai Goerlich


Google Cardboard VR goggles cost US$8
By 2019, immersive solutions
will be adopted in 20% of enterprise businesses
By 2025, the market for immersive hardware and software technology could be $182 billion
In 2017, Lowe’s launched
Holoroom How To VR DIY clinics

From Dipping a Toe to Fully Immersed

The first wave of virtual reality (VR) and augmented reality (AR) is here,

using smartphones, glasses, and goggles to place us in the middle of 360-degree digital environments or overlay digital artifacts on the physical world. Prototypes, pilot projects, and first movers have already emerged:

  • Guiding warehouse pickers, cargo loaders, and truck drivers with AR
  • Overlaying constantly updated blueprints, measurements, and other construction data on building sites in real time with AR
  • Building 3D machine prototypes in VR for virtual testing and maintenance planning
  • Exhibiting new appliances and fixtures in a VR mockup of the customer’s home
  • Teaching medicine with AR tools that overlay diagnostics and instructions on patients’ bodies

A Vast Sea of Possibilities

Immersive technologies leapt forward in spring 2017 with the introduction of three new products:

  • Nvidia’s Project Holodeck, which generates shared photorealistic VR environments
  • A cloud-based platform for industrial AR from Lenovo New Vision AR and Wikitude
  • A workspace and headset from Meta that lets users use their hands to interact with AR artifacts

The Truly Digital Workplace

New immersive experiences won’t simply be new tools for existing tasks. They promise to create entirely new ways of working.

VR avatars that look and sound like their owners will soon be able to meet in realistic virtual meeting spaces without requiring users to leave their desks or even their homes. With enough computing power and a smart-enough AI, we could soon let VR avatars act as our proxies while we’re doing other things—and (theoretically) do it well enough that no one can tell the difference.

We’ll need a way to signal when an avatar is being human driven in real time, when it’s on autopilot, and when it’s owned by a bot.

What Is Immersion?

A completely immersive experience that’s indistinguishable from real life is impossible given the current constraints on power, throughput, and battery life.

To make current digital experiences more convincing, we’ll need interactive sensors in objects and materials, more powerful infrastructure to create realistic images, and smarter interfaces to interpret and interact with data.

When everything around us is intelligent and interactive, every environment could have an AR overlay or VR presence, with use cases ranging from gaming to firefighting.

We could see a backlash touting the superiority of the unmediated physical world—but multisensory immersive experiences that we can navigate in 360-degree space will change what we consider “real.”

Download the executive brief Diving Deep Into Digital Experiences.

Read the full article Swimming in the Immersive Digital Experience.


Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu


Why Artificial Intelligence Is Not Really Artificial – It Is Very Tangible

Sven Denecken

The topic of artificial intelligence (AI) is buzzing through academic conferences, dominating business strategy sessions, and making waves in the public discussion. Every presentation I see includes it, even if it’s only used as a buzzword – its frequency is rivaling the use of “Uber for X” that’s been so popular in recent years.

While AI is a trending topic, it’s not mere buzz. It is already deeply ingrained into the strategy and design of our products – well beyond a mere shout-out in presentations. As we strive to optimize our products to better serve our customers and partners, it is worth taking AI seriously because of its unique role in product innovation.

AI will be inherently disruptive. Now that it has left the realm of academic projects and theoretical discussion – now that it is directly driving speed and hyper-automation in the business world – it is important to start with a review that de-mystifies the serious decisions facing business leaders and clarifies the value for users, customers, and partners. I’ll also share some experiences on how AI is contributing to solutions that run business today.

Let’s first start with the basics: the difference between AI, machine learning, and deep learning.

  • Artificial intelligence (AI) is broadly defined to include any simulation of human intelligence exhibited by machines. This is a growth area that is branching into multiple areas of research, development, and investment. Examples of AI include autonomous robotics, rule-based reasoning, natural language processing (NLP), knowledge representation techniques (knowledge graphs), and more.
  • Machine learning (ML) is a subfield of AI that aims to teach computers how to accomplish tasks using data inputs, but without explicit rule-based programming. In enterprise software, ML is currently the best method to approach the goals of AI.
  • Deep learning (DL) is a subfield of ML describing the application of (typically multilayer) artificial neural networks. Neural networks take inspiration from the human brain, with processors consisting of small neuron-like computing units connected in ways that resemble biological structures. These networks can learn complex, non-linear problems from input data. The layering of the networks allows cascaded learning and abstraction levels. This can accomplish tasks like: starting with line recognition, progressing to identifications of shapes, then objects, then full scene. In recent years, DL has led to breakthroughs in a series of AI tasks including speech, vision, and language processing.

AI applications for cloud ERP solutions

Industry 4.0 describes the trend of automation and data exchange in manufacturing. This comprises cyber-physical systems, the Internet of Things (IoT), cloud computing, and cognitive computing – everything that adds up to create a “smart factory.” There is a parallel in the world beyond manufacturing, where data- and service-based sectors need to capture and analyze more data quickly and act on that information for competitive advantage.

By serving as the digital core of the organization, enterprise resource planning (ERP) solutions play a key role in business transformation for companies adapting to the emerging reality of Industry 4.0. AI solutions powered by ML will be a broad, high-impact class of technologies that serve as a key pillar of more responsive business capabilities – both in manufacturing and all the sectors beyond. As such, ERP must embrace AI to deliver the vision for the future: smarter, more efficient, more flexible, more automated operations.

Enterprise applications powered by AI and ML will drive massive productivity gains via automation. This is not automation in the sense of repetitive, preprogrammed processes, but rather capabilities for software to handle administrative tasks and learn from user behavior to anticipate what every individual in the company might need next.

Cloud-based ERP is ideal for companies looking to accelerate transformation with AI and ML because it delivers innovation faster and more reliably than any onsite deployment. Users can take advantage of rapid iterations and optimize their processes around outcomes rather than upkeep.

Case in point: intelligent ERP applications need to include a digital assistant. This should be context-aware, designed to make business processes more efficient and automated. By providing information or suggestions based on the business context of the user and the situation, the digital assistant will allow every user to spend more time to concentrate on higher-value thinking instead of on repetitive tasks. Combined with built-in collaboration tools, this upgrade will speed reaction to changing conditions and create more time for innovation.

Imagine a system that, like a highly capable assistant, can greet you in the morning with a helpful insight: “Hello Sven, I have assessed your situation and the most recent data – here are the areas you should focus on first.” This approach to contextualized analysis of real-time data is far more effective than a hard-programmed workflow or dump of information that leaves you to sort through outdated information.

Personal assistants have been around in the consumer space for some time now, but it takes an ML-based approach to bring that experience, and all its benefits, to the enterprise. Based on the pace of change in ML, a cloud-based ERP can best deliver the latest innovations to users in a form that has immediate business applications.

An early application of ML in the enterprise will be intelligence derived from past patterns. The system will capture much richer detail of customer- and use-case-specific behavior, without the costs of manually defining hard rule sets. ML can apply predictive detection methods, which are trained to support specific business use cases. And unlike pre-programmed rules, ML updates regularly as strategies – not monthly or weekly – but by the day, hour, and minute.

How ML and AI are making cloud ERP increasingly more intelligent

Digital has disrupted the world and changed the way businesses operate, creating a new level of complexity and speed. To stay competitive, businesses must transform to achieve a new level of agility. At the same time, advances in consumer technology (Siri, Alexa, and Google Now in the personal assistant space, and countless mobile apps beyond that) have created a desire and need for intuitive user interfaces that anticipate the user’s needs. Building powerful tools that are easy to interact with will rely on ML and predictive analytics solutions – all of which are uniquely suited to cloud deployment.

The next wave of innovation in enterprise solutions will integrate IoT, ML, and AI into daily operations. The tools will operate on every type of device and will apply native-device capabilities, especially around natural language processing and natural language interfaces. Augment this interface with machine learning, and you’ll see a system that deeply understands users and supports them with incredible speed.

What are some use cases for this intelligent ERP?

Digital assistants already help users keep better notes and take intelligent screenshots. They also link notes to the apps users were working on when they were created. Intelligent screenshots allow users to navigate to the app where the screenshot was taken and apply the same filter parameters. They recognize business objects within the application context and allow you to add them to your collection of notes and screenshots. Users can chat right from the business application without entering a separate collaboration room. Because the digital assistants are powered by ML, they help you move faster the more you use them.

In the future, intelligent cloud ERP with ML will deliver value in many ways. To name just a few examples (just scratching the surface):

  1. Finance accruals. Finance teams use a highly manual and speculative process to determine bonus accruals. Applying ML to these calculations could instead generate a set of unbiased accrual figures, so finance teams have more time during closing periods for activities that require review and judgment.
  1. Project bidding. Companies rely heavily on personal experience when deciding to bid for commercial projects. ML would give sales and project teams access to decades-worth of projects from around the world at the touch of a button. This capability would help firms decide whether to bid, how much to bid, and how to plan projects for greatest profitability.
  1. Procurement negotiation. Procurement involves a wide range of information and continuous supplier communication. Because costs go directly to the bottom line, anything that improves efficiencies and reduces inventory will make a real difference. ML can mine historical data to predict contract lifecycles and forecast when a purchasing contract is expected so that you can renegotiate to suit actual needs, rather than basing decisions on a hunch.

What does the near future hold?

An intelligent ERP puts the customer at the center of the solution. It delivers flexible automation using AI, ML, IoT, and predictive analytics to drive digital transformation of the business. It delivers a better experience for end users by providing live information in context and learning what the user needs in every scenario. It eliminates decisions made on incomplete or outdated reports.

Digitization continues to disrupt the world and change the way businesses operate, creating a new level of complexity and speed that companies must navigate to stay competitive. Powering business innovation in the digital age will be possible by building and deploying the latest in AI-powered capabilities. We intend to stay deeply engaged with our most innovative partners, our trusted customers, and end users to achieve the promises of the digital age – and we will judge our success by the extent to which everyone who uses our system can drive innovation.

Learn how SAP is helping customers deploy new capabilities based on AI, ML, and IoT to deliver the latest technology seamlessly within their systems


Sven Denecken

About Sven Denecken

Sven Denecken is Senior Vice President, Product Management and Co-Innovation of SAP S/4HANA, at SAP. His experience working with customers and partners for decades and networking with the SAP field organization and industry analysts allows him to bring client issues and challenges directly into the solution development process, ensuring that next-generation software solutions address customer requirements to focus on business outcome and help customers gain competitive advantage. Connect with Sven on Twitter @SDenecken or e-mail at sven.denecken@sap.com.