Why artificial intelligence #AI is not really artificial – it is very tangible

Why artificial intelligence is absolutely real — despite the hype

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

I’m inspired to write this post because while AI is a trending topic, it’s not just buzz. AI is already deeply ingrained into the strategy and design of our products – well beyond a shout-out in presentations. With my fellow product managers, we strive to optimize what we have to better serve our customers and partners, so 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 the solutions that run the business today.

Let’s first start with the basics: the difference between Artificial Intelligence, Machine Learning & 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 sub-field of AI that aims to teach computers how to accomplish tasks using data inputs, but without explicit rule-based programming. In Enterprise software, Machine Learning is currently the best method to approach the goals of AI.

Deep Learning (DL) is a sub-field of ML describing the application of typically multi-layer 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 such as, 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 on-site deployment. Users can take advantage of rapid iterations and optimize their processes around outcomes rather than upkeep.

Case in point: In intelligent ERP applications there needs to be a digital assistant included. Like a Co-Pilot which is a 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, Co-Pilot will allow every user to spend more time to concentrate on higher-value thinking instead of on repetitive tasks. Combined with the 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, best a cloud-based ERP can effectively 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 preprogrammed rules, ML updates regularly as strategies change — 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 their 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?

Already, Co-Pilot helps users keep better notes and take intelligent screenshots. Co-Pilot links notes to the apps users were working on when they were created. Intelligent screen shots allow users to navigate to the app from where the screen shot was taken and apply the same filter parameters. It recognizes business objects within the current application context and allows you to add them to your collection of notes and screen shots. And users can chat right from the business application without entering a separate collaboration room. The Co-Pilot is powered by ML so it helps you move faster as you use it more. This is just the start — we have a roadmap to roll out increasingly powerful ML-based capabilities with each quarterly update.

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

 1. Finance accruals. Finance teams go through a highly manual and speculative process to determine bonus accruals. Applying ML to these calculations could generate instead a set of unbiased accrual figures, so finance teams have more time during closing periods for activities that require review and judgment.

2. Project bidding. Companies rely heavily on personal experience when deciding to bid for commercial projects. ML would instead 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.

3. 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 to be consumed so that you can make sure to 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.

The challenge we have taken on is that SAP is moving beyond being just an ERP solution or vendor. We are now a strategic business partner to our customers and partners. We’re truly working alongside them to understand their needs, and deploying new capabilities based on AI, ML, and IoT to deliver the latest technology seamlessly within their systems. Our goal is to help everyone we engage focus on innovation, growth and rapidly change that the digital age demands. Reaching this goal requires a strong platform, an open approach to technology and strong partnerships across the industry.

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 with 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.

Looking forward to your feedback, and keep following me here or @SDenecken to stay connected.