Top Uses For Machine Learning In Life Sciences

Mandar Paralkar

Empowering business growth with disruptive technologies like the Internet of Things (IoT), predictive analytics, and artificial intelligence has become a norm in IT, and machine learning is leading the way, as software applications are becoming smarter to improve our business and personal lives. With massive improvements in hardware and Big Data, machines can sense, understand, interact, predict, and respond to solve industry business problems.

Bio-pharmaceutical brands are critical intellectual property for life sciences companies, and marketing intelligence and insights are powerful ways to improve brand recognition and marketing ROI. Similarly, service ticket intelligence can automate error and issue classification and customer support ticket responses, improving service levels for medical devices.

A few key questions can help determine whether a use case is fit for machine learning. For example, can you automate the high-volume task? Is there a pattern involved in the business process’ unstructured data sets? Enterprise data is transformed into business value, with the help of a model, by using input and output parameters. Predictive models may have some bias with respect to the degree to which a model fits the data, and the variance amount can change with a model’s parameters.

There are a number of potential use cases for machine learning in life sciences. Here are some that you may wish to incorporate into your business model.

  • Quality must be enforced in supply chain and manufacturing business process for regulatory compliance. Root-cause analysis is a key aspect of corrective and preventive action (CAPA), which aligns with industry initiatives like QbD (quality by design), PAT (process analytical technique), and CPV (continued process verification). There is a clear need to identify main causes for reported defects in material assets and understand the impact of identified causes to manage the overall defect count. Based on gathered data, machines can predict what production can be produced vs. planned for a specific duration (based on historical production), thereby preventing deviations and nonconformances. Analyzing the cause of deviation from standard cycle time for manufacturing equipment, and prescribing measures to achieve standard cycle time, affect yield and scrap.
  • Life science companies spend huge amounts on direct and indirect materials and services with contract organizations. Machine learning services help commodity managers optimize global spend. Common machine learning uses in strategic sourcing and procurement include: assessment of contract-negotiation behavior, optimization of contract awards to suitable candidates, detection of single-sourcing risks, and determination of components to outsource to contract manufacturers. Intelligent enterprise strategies can recommend replacements for poorly performing suppliers; replace a supplier that poses a compliance risk; select additional suppliers to comply with purchasing policies, expansion to a new territory, or adding a category of spend; or find cheaper options for materials or services.
  • Learning management is critical in regulated industries, and training is a big part of human resources’ duties in life sciences. In hiring, HR business partners can identify the best candidates by parsing resumes into structured information, then visualize candidate profiles by skills, education, and experience, to compare and generate best-fit scores of profiles to jobs and vice versa. Talent management can take a more personalized approach towards career mapping based on employees’ unique situations, skill trajectory, and training, thereby opening opportunities to employees for fast-track growth.
  • Consider use cases where matching algorithms are used extensively for shared services like cash. Matching incoming payments with invoices is now a simplified process for intelligent enterprises to clear volumes of backlog data. Machines can match accounts receivable invoices based on learned criteria and provide a confidence score to help finance to clear payments faster (e.g., if the matching rate is within a given threshold). For payments that cannot be cleared automatically due to lower confidence levels, a list of the best-fitting invoices can be generated in order to save time identifying relevant receivables.
  • Similarly, accounts payables must release payment blocks to pay supplier invoices and receive cash discounts for early payment. Based on historical data, current user interaction, and machine learning algorithms, the system can react automatically or suggest resolution proposals. Decisions may be based on supplier rating, deviation vs. cash discount available, or purchasing category. Matching invoice line items with purchase order line items, and providing remittance advice to reduce manual errors, are ways automation helps life science accounting.
  • Sales and marketing can leverage machine learning during sales negotiations with wholesalers, hospitals, clinics, and retail pharmacies by capturing keywords, sentiments, competitors, and new contacts to feed into deal scoring, ultimately improving the win rate. Bio-pharma sales reps can share marketing collateral of interest to physicians and key opinion leaders. Third-party prescription data can create target groups for behavior-based marketing campaigns to boost sales. Thus, machine learning can help build customer loyalty with proactive retention strategies in the life sciences industry.

Smart business process enabled by machine learning, automation, and artificial intelligence can help achieve intelligent enterprise goals for the life science industry, particularly as the IoT technology adoption rate improves.

SAP machine learning services in its SAP Leonardo IoT platform help life science companies automate and prioritize routine decision making processes in order to adapt to rapidly changing business environments.

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About Mandar Paralkar

Mandar Paralkar is the director of Global Life Sciences Industry Solution Management at SAP, where he has a leading role in creating the industry solution strategy and global business plans. He works with customers to define industry requirements to corporate development and shares global life sciences trends and solution innovations internally and externally. Further, he supports customer engagements with his deep industry expertise that includes a sound compliance and validation background.

We Were Kings (Or When Things Went To Zero)

Spiros Margaris

The title of this post was inspired by the 1996 documentary “When We Were Kings,” about the heavyweight fight of 1974 between two boxing legends, Muhammad Ali and George Foreman. In the not-so-distant future, it will also be a fitting phrase for many in the banking and insurance industries.

Readers may ask why I am talking about banking and insurance in such doomsday terms. My bleak forecast does not stem from the notion behind the common fintech (financial technology) and insurtech (insurance technology) industry pitch that they will change their respective industries with innovation and better customer experiences, although I firmly believe that some of the startups will cause significant pain to the incumbents and will indeed change their respective industries. One day, some of the existing and as-yet-unlaunched fintech and insurtech companies will also become incumbents that other startups aim to disrupt.

The real threat to the financial industry will come from a radical approach to penetrate the financial market—an approach that I believe has not yet been addressed or even conceived by the competition. The emphasis is clearly on “yet.”

What is this new concept? It is simply this: offering financial services at or below cost. I have mooted this idea at many think tank events, and I thought I should write it down to share it more broadly. It is, and should be, a terrifying thought for many, and I strongly believe this approach will be implemented in the near future. It will bring many of the incumbents to their knees, unless they prepare for what is to come by investing in technology and adapting radical business models.

People talk about the limited impact of fintech and insurtech on the incumbent business model. I must agree that at this point many startups have little influence, if you look only at the customers they have taken away from incumbents. What the startups are already doing, however, is forcing many incumbents to lower their fees to better match what the smaller players offer to their clients.

Moreover, startups have also changed customers’ expectations of the user experience. Startups will also use artificial intelligence and machine learning to compete against the established financial players that have more resources—such as money, data and clients—at hand to compete. There is no way around investing in AI and machine learning to compete successfully against tech-savvy competitors. Many startups and large companies already use machine learning algorithms to build better credit risk models, predict bad loans, detect fraud, anticipate financial market behavior, improve customer relationship management, and provide more customized services to their clients. Arguably, the biggest effect of startups is that they continuously put pressure on incumbent profit margins. Startups will continue to try to change the status quo because they smell blood in the incumbent water.

The real and biggest threat to incumbents will likely originate from tech giants, such as Amazon, Apple and Facebook, and other big non-tech companies that have large customer and employee bases. These organizations will use their customers and employees to sell banking and insurance solutions, and the big financial institutions will become at best dumb pipes. The technical approaches to doing business within the fintech and insurtech industries may provide some of the tools tech giants and other large companies need to execute this strategy.

I know some readers will say that regulators will stop any attempt by non-traditional players to provide many banking and insurance services. However, I do not think regulators can or will stop the new competitors, because these companies will either obtain the necessary licenses to operate or have a bank or insurer provide third-party financial services to them. This strategy is not unlike the way some fintech challenger banks use the licenses of an existing bank to operate.

Why should we expect this scenario of financial industry disruption to happen? In our case, we all seem to agree that the tech giants are the ones to fear because of the Big Data platform and technology knowledge they possess. In addition, tech giants have several advantages, such as the trust factor and the constant interaction with satisfied customers. Furthermore, studies have shown that millennials would prefer to bank with tech giants such as Amazon, Facebook, or Google than with the existing banking players. And last but not least are the tech giants and startups that keep setting the bar higher for exceptional customer experience (for instance Apple’s simplicity or Amazon’s instant gratification) and shape the client behavior and expectations, not the incumbents.

All that speaks to tech giants’ favorable circumstances as serious competitors that are not yet ready to come in at full speed and hit the financial industry broadly, but it does not point to the need to fear an extreme disruption as I projected. I do not believe we will see those tech giants providing whole-spectrum financial services anytime soon, but they have the potential to offer services in certain segments, such as providing payment, lending, or insurance options for their customers and employees.

What is terrifying to imagine is a situation in which tech giants or other big companies provide financial service solutions at or below production costs. No, that is not a typo; I mean providing financial services for nothing—for free.

If we take this scenario to its extreme—that is, selling banking or insurance services for nothing (yes, for zero pounds, euros, dollars, or renminbi)—then we have a situation in which financial institutions in their present forms will die or be reduced to shadows of their current selves.

That can and will happen, and here’s why: Large companies could do exactly that—sell at or below cost—to win or keep customers. The new competitors would not need to earn money and could even afford to lose money in offering financial solutions if these features entice customers and new potential clients to use the companies’ core offerings. Remember that Facebook, for instance, earns the biggest portion of their profits through advertising because they have created a great platform through which people love to interact. Financial solutions would be just another great offering (especially if they are offered for free) to entice many people to join the tech giants’ ecosystems.

Alternatively, car companies such as GM could provide their employees and customers with very cheap or no-cost (no cost to customers, at cost for the company) banking or insurance solutions. Don’t forget that banking and insurance solutions can be provided at very little cost as white-label services from third parties that already have all the necessary licenses, technology and infrastructure.

All is not lost for banks and insurers, but it will be very hard for them to compete against savvy tech giants on their technological home turf. The financial industry must think fast to find ways to compete before their business oxygen runs out.

One solution that banks and insurers should pursue aggressively is to embrace the fintech and insurtech industries for their innovative business spirit and fast, direct execution approach to new ideas. That means financial institutions should buy what they can or partner with startups to make up for all the shortcomings that legacy brings. Size and regulation will not be enough to protect incumbent financial institutions against new competitors, as we have seen in many other industries.

Another idea might be for financial institutions to place advertisements on their websites or apps to compensate for loss of profit margins. I do not think this is the only solution, but financial institutions must innovate beyond their core areas of expertise and standard industry practices. Why do you think Amazon, Uber, and Airbnb have been so successful at disrupting their industries? Because they thought and acted as if they had nothing to lose and everything to gain.

The “at or below cost” approach to financial service solutions is not a far-fetched scenario for tech giants and other companies that are trying to find new ways to attract and keep clients. The banking and insurance industries must at least get very comfortable with the idea that low-cost or free financial services are coming.

A tsunami is often unnoticed in the open sea, but once it approaches the shore, it causes the sea to rise in a massive, devastating wave. The financial industry needs to determine if the threat by tech giants and non-tech companies is a small wave or a tsunami and prepare accordingly. My recommendation to all financial institutions is this: You’d better prepare for a tsunami, even if all you see is a small wave on the horizon.

For more on digital transformation in the insurance industry, see Preparing For Digital Transformation: What The Insurance Industry Needs To Know.

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Spiros Margaris

About Spiros Margaris

Spiros Margaris is a Venture Capitalist & Thought Leader in the FinTech and Insurtech scene. He was ranked No. 1 FinTech and No. 2 Insurtech global influencer by Onalytica and regularly appears in the top three positions in several industry rankings. He is also ranked worldwide the No. 11 AI influencer by Jay Palter Social Advisory. Previously, he worked in banking and money management (hedge funds) and launched two start-ups in New York, one of which would nowadays be termed a fintech. He is a frequent speaker at international fintech and insurtech conferences and publishes articles on his innovation proposals and thought leadership. www.MargarisAdvisory.com Twitter @SpirosMargaris

How To Get The Best Out Of Automation

Dr. Markus Noga and Sebastian Schroetel

In 2016, the management consulting firm McKinsey predicted that up to 70% of all tasks are potentially automatable with so-called next-generation technologies. Companies worldwide jumped on that bandwagon and invested heavily in one of the hottest innovations when it comes to automation – robotics.

Today, only a few months later, some experts claim that robotic process automation (RPA) has only been a fast-paced trend based on the excitement of industry leaders, and it is not the predicted “panacea” for all the challenges enterprises face regarding automation. Recently, another blog post by McKinsey tackled this topic and qualified the initial enthusiasm for bots and their supposed potential to incur all sorts of back-office processes. In fact, the rapid adaptation of robotization waived the consideration of its potential downsides.

According to McKinsey, “installing thousands of bots has taken a lot longer and is more complex than most had hoped it would be” and “not unlike humans, thousands of bots need care and attention – in the form of maintenance, upgrades, and cybersecurity protocols, introducing additional costs and demanding ongoing focus for executives.” All in all, the authors state that the economic results of RPA underperformed the estimations, especially with regard to cost reduction. The impression has been strengthened that “people do many different things, and bots may only address some of them.”

The next level of automation

Considering the latest trends in robotics that came along with unexpected complexity, little flexibility, and additional maintenance, automation is still pushing forward. The aim is to help enterprises realize their potential and switch focus from just “keeping the lights on” through human manpower to growth generation triggered by automation technology. A dedicated automation approach to achieve the intelligent enterprise involves three interacting levels:

  • Software components, or “engines,” that provide automation relying on highly specific process knowledge
  • Machine learning that involves teaching a computer how to spot patterns and make connections by showing it a massive volume of data – algorithms that can learn from experience without having to be explicitly programmed
  • Robotic process automation software that operates another application without the support of a human user, helping to run repetitive, rule-based monotonous tasks and bridging temporary gaps

In contrast to the mere RPA approach many companies have pursued in the past, only the integration of all three layers lifts the enterprise to the next automation level. Engines are the basis of enterprise automation activities. They enable companies to shape their processes by making decisions on where to direct incoming inquiries at subsequent steps. But engines have a fixed logic and limited configuration possibility. Therefore, they cannot cover all facets of the business processes and have the potential to only facilitate automation in up to 60% of all cases.

In credit management, for example, credit-rules engines can help evaluate personal creditworthiness and process credit limit applications in a structured way. This is done by automatically categorizing them based on defined scoring rules and assigning a specific credit limit to the customer after the examination is completed.

Applications for machine learning

But what happens when a scenario occurs that wasn’t encountered by the operator? By adding intelligent automation technologies to the automation portfolio, processes become noticeably intelligent. Machine learning can upgrade the automation level of a process up to 98%. How? By setting up general guidelines without telling the system exactly what to do. The underlying algorithm learns from the operator’s previous actions and takes all available data into account to deliver the most relevant response to an occurrence.

Applying this to credit management, machine learning is useful in those cases where a customer lacks a dedicated credit history. Here, machine learning fills in with more accurate forecasting models based on people’s overall payment history, on information related to the borrower’s interaction behavior on the lender’s website, and other unstructured data sets.

Robotics, as the third automation layer, can help automate the remaining two percent of repetitive, monotonous tasks in a process. But due to its lower integration level, RPA is limited in its reach and adds the percentage on top on much higher costs. In financial risk management processes like bank lending, robotics can deal with requests for overdraft protection or credit card approvals.

A genuine alternative to mere bot systems

Related to the downsides of bot systems, a multi-automation-layer approach is the way to set up a stable and holistic automation concept as an alternative to pure RPA to flatten or avoid the disadvantages bot systems entail.

The McKinsey authors support the thesis that robotics should be used in exceptional cases, instead of being applied as the universal remedy to deal with repetitive tasks.

All in all, enterprises are actively searching for ways to shape their processes and automate parts of their work. Robots are perceived as being too inflexible, expensive, and complex in their maintenance to accomplish these goals in a satisfactory manner. By expanding the automation portfolio with engines and machine learning, a meshing system of automation technology can address these concerns and force a holistic implementation of automation throughout the enterprise.

Currently, companies and CIOs are resetting their bot programs. Figuring out the desired goal of automation might help to steer it into the right direction.

SAP’s automation strategy in general, and our cloud-based machine learning portfolio and related services in particular, are ready to step in and to fill the automation gaps that bots leave.

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Dr. Markus Noga

About Dr. Markus Noga

Dr. Markus Noga is vice president of Machine Learning at SAP. Machine learning (ML) applies deep learning, machine learning, and advanced data science to solve business challenges. The ML team aspires to building SAP’s next growth business in intelligent solutions, and works closely with existing product units and platform teams to deliver business value to their customers. Part of the SAP Innovation Center Network (ICN), the Machine Learning team operates as a lean startup within SAP with sites in Germany, Israel, Singapore, and Palo Alto.

Sebastian Schroetel

About Sebastian Schroetel

Sebastian Schroetel is a director at SAP for machine learning in the digital core. In this role, Sebastian and his global team shape and create machine learning solutions for SAP's core ERP products. Sebastian has 10 years of experience in innovation software development, with focus on automation, analytics, and data processing.

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.

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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

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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

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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.