Business Model Innovation For The Intelligent Enterprise

Uli Eisert and Keywan Nadjmabadi

Too often, innovative technological capabilities are used only to improve existing processes consisting of many repetitive, manual tasks and replace them with more automated and adaptive processes. This obviously drives down costs and leads to temporary competitive advantages, however, it neither changes the rules of the game nor brings about innovation. The winners in the era of the intelligent enterprise will be those companies that utilize intelligent technologies to come up with new, intelligent business models.

From digital to intelligent enterprises

While companies are still too busy to become fully digital in terms of the processes they use, the products and services they offer, and the customer experiences they create, they are already faced with the next wave of disruptive change: the intelligent enterprise. The intelligent enterprise utilizes connectivity (between things, people, and enterprises), data, cloud applications, algorithms, and advanced analytics (instead of hard-coded rules and rigorous procedures). This enables them to come to the right decisions with minimal human involvement even in turbulent, fast-changing environments. To free up knowledge workers from repetitive tasks that a machine can do as well or even better, you need a combination of technologies to collect and process the data (e.g., IoT and cloud applications) and artificial intelligence.

The best example of this change is in customer service, where automation is increasing with the help of all sorts of bots. This ranges from simple mail bots that assign incoming mail to the right agents, to chatbots that enable customers to help themselves with standard issues such as password resets, and to comprehensive voice bots that communicate with customers to resolve the majority of issues that come up in a typical call center. In addition, the human workers remaining in the contact center get support from artificial intelligence-based applications to quickly find the best solution for highly complex issues based on historical data.

This is impressive; however, is it what is ultimately meant by the intelligent enterprise? Is the intelligent enterprise just a highly automated and agile enterprise? And what kind of new, somehow creative, and high-value tasks will evolve for knowledge workers due to the intelligent enterprise?

To us, it seems obvious that only newly defined services that complement existing products and are embedded in new business models can fully utilize the potential of intelligent technologies. What makes up an intelligent enterprise are new experiences for customers, partners, and employees that are embedded in intelligent business models.

Think, for example, about complex machines and plants. In the past, a manufacturer designed, engineered, and built them, then there was a hard cut after delivery and installation. In many cases, the manufacturer had no idea whatsoever what happened to its machinery during its long life in operation and maintenance. It had no insights about conditions and performance or which spare parts, extensions, or services could be offered. In addition, the operator and its service providers had no information about what the manufacturer could contribute in terms of tailored improvements and optimization.

Now, a new set technologies is available: the IoT to collect data from running equipment, advanced analytics to create insights about performance, machine learning to predict failure and optimized maintenance cycles, and cloud platforms to share data and insights across all affected parties. This opens a new world of opportunities that go far beyond improving existing processes. We will come back to these opportunities and their impact on innovative business models later.

In any case, ultimately successful business models mainly consist of two elements: lower entry barriers and smart lock-in mechanisms. This was true a long time ago, however, today’s intelligent technologies create a much bigger playground to invent even more intelligent business models. To benefit from these opportunities, all you need is a systematic approach.

Business model design – A craft rather than an art

If you are using a pragmatic approach for business model design, you can see that this is rather a craft than an art. If we take the business model development & innovation approach (Doll and Eisert, 2014) approach as an example, we see that the underlying principles of such an approach are quite simple:

  • Start with a baseline of the business model. This is either the business model that is currently used or (for a greenfield approach) the model that people would use, if they would have to go to the market immediately.
  • Develop the business model in a strictly iterative approach. The first iteration turns the baseline into an improved version, and then you continue with further iterations until you have found the appropriate business model to commercialize your new offering or business idea.

In the end, the right choice and sequence of iterations are key to the success of the approach. There are four distinct types of iterations:

  • Analyze and improve: During this iteration, you analyze parts of the business model in more detail to improve it based on the gained insights. One example could be to analyze competitors and improve the value proposition to strengthen unique aspects based on new insights.
  • Challenge and change: If there are valid triggers, typically opportunities or threats, it makes sense to challenge the current business model. These challenges will inspire ideas for the design of innovative extensions or completely new business models.
  • Test and validate: New business models are based on several assumptions that imply risks if they turn out to be wrong. Thus, testing and validating these assumptions as early and as cheaply as possible is crucial. Usually, these assumptions mainly concern customers’ needs, wants, and willingness to pay.
  • Evaluate and decide: To figure out which of the different business model options are most promising, you have to assess them. Furthermore, towards the end of the project, the favored business model must be evaluated in detail to provide the decision makers with a solid foundation for further planning or investment decisions.

This way to develop and innovate business models in a systematic manner has proven to work for all types of enterprises from small startups to huge multi-nationals. The next paragraph describes how this approach can be adapted to support business model innovation for the intelligent enterprise.

The role of intelligent technologies for business model design

Intelligent technologies can inspire all the iterations in business model design. The technologies might be analyzed in detail to examine how they could optimize certain elements of a business model, like channels or key activities. You can use these technologies to test an assumption in a smarter manner (think about running campaigns in social media to validate the assumed pain points of certain customer groups).

Nevertheless, the main role of intelligent technologies is to inspire the creation of new, more intelligent business models that replace those that are challenged by the market. So, the task at hand is about turning capabilities of intelligent technologies into new ways to offer value-adding products and services and to embed them into business models that facilitate intelligent value capture.

To challenge existing business models with new technologies, it makes sense to reflect which disruptive effects new technologies can have. They could

  • Make offerings fundamentally cheaper
  • Take customers with better product-market fit
  • Reconfigure the value chain
  • Provide access to idle resources
  • Enhance customer, employee, or partner experience

Based on these considerations and understanding which technologies are relevant for the industry, opportunities and threats can be collected and challenges for the existing business models can be formulated.

These challenges can trigger ideas for new business models. To inspire the ideation process beyond classical brainstorming, you can leverage the following methods:

  • The first approach to link new technologies with business model innovation is a very systematic way – like a morphological box that is used in product development (Cigaina and Riss, 2016). Think about the elements of a business model canvas, such as the value proposition or the channels that were mentioned above. Now, take a set of new technologies, such as IoT, Big Data analytics, or cloud platforms. For every combination of a specific business model element and a specific technology, all known or imaginable new options are collected to figure out how this element could be improved or even re-invented. In a subsequent step, new business model elements can be combined and implications for existing parts of the business model can be considered to come up with consistent future options for innovative business models.
  • The second approach is to use so-called business model patterns, which were the foundation of many business model innovations in the past. Combining existing concepts is a powerful approach to generate ideas for new business models that could be rather disruptive. To ease this process, the University of St. Gallen has a great collection of proven business model patterns [Gassmann, Frankenberger, Csik, 2014].
  • A third approach is to look for existing, real-world examples of business models. The most obvious approach is to look at peers and competitors. However, it might be more inspiring to look at other industries with similar challenges (especially if they were hit by these challenges earlier) and analyze if there were successful ideas to tackle these strains. Last not least, check what startups are doing to leverage new technologies or tackle known challenges in completely new ways.

What do business models for the intelligent enterprise look like?

Let’s come back to the machinery and plant industry. For the first time, new technological capabilities, like cloud platforms and IoT, allow the vision of full product lifecycle management to come true: a seamless digital representation of an individual product through its entire lifecycle from cradle to grave – the digital twin. Even better, the digital twin can collect real-time data, enable analysis and prediction, and share everything among affected businesses. This makes the operation of manufacturers’ machines and plants much easier, less risky, and more efficient. And it forms the foundation for all types of equipment-as-a-service as well as output-based revenue models.

It’s possible to argue that this is not completely new; however, now these business models can get out of the niche and become mainstream. The digital twin (utilizing the IoT as well as predictive maintenance algorithms) and the business network around it enable many other innovative business models as well. Think about platforms that bring together supply and demand for maintenance and repair services or for spare and used parts as well as consumables.

Manufacturers are in prime position to set up and operate these platforms and gain new revenue streams and increased customer satisfaction. In addition, the digital twin is a step towards tailored offerings for software updates and selling value-added services features. It also enables operators to sell back relevant data for improving the design and engineering of new machines.

For consumer machines like household appliances, the IoT’s advancements can eliminate the layer between the OEM and the end consumer (i.e., the retailer or service provider and consumer). The consumer gets a name and location for the OEM when the appliance is registered in the network, and the OEM gets data about how the machine is used and its health status, which allows it to offer services like predictive maintenance, new features like higher performance with firmware updates, and even physical products or complementary parts like vacuum bags or detergents. This is a classical reconfiguration of the value chain from B2B to B2C.

Getting data directly from consumers on a massive scale also enables other data-driven business. While insurance rates based on mileage and driving behavior are already partly established, other industries can tap into this opportunity, as well. For example, a spice company is leveraging data from questionnaires and food or restaurant ratings to offer a recommendation service for recipes and products. The insights are shared with commercial partners to tailor their digital offerings to consumers.

Direct contact with the customer can also bring new options in B2B industries. Think about a logistics provider that offers everything from highly standardized services to very complex and customer-specific offerings. For low-end applications, the customer can use a self-service portal to configure the service, and the system provides prices and dates immediately (based on intelligent algorithms and availability checks). The system can offer additional services and information (e.g., about customs duty and regulations in certain countries) depending on the data entered, and the contract signage and payment require no paperwork. This is not just about lowering costs because human operators aren’t needed for simple requests, but also about offering new customer experiences and real-time business. Global track and trace in this scenario can enable real-time monitoring directly for the customer. In addition, the customer can see their historical data about past business transactions at any time.

Mass customization is another way to produce superior customer experience. It’s not a totally new concept, if you think about the automotive industry. However, when it came to low-value products, this has been a no-go due to disproportionate effort and costs. With the latest generation of intelligent and high-performance enterprise systems, this is feasible for the clothing and apparel industry and even for the food industry. This is especially remarkable since these industries continue to do regular mass production. The same idea applies to the media industry, which can run various business models in parallel – from paper-based daily newspapers to tailored content management for online consumers.

In the context of mass customization, 3D-printing or additive manufacturing is also the basis for new business models. For certain business contexts, it’s relatively simple and cheap to offer configured products and even individual pieces. Besides this, 3D printing allows new ways of distribution or even totally new channels and partnerships, e.g., in spare and worn parts supply.

From business model design to business model innovation

It’s well known that innovation consists of inventing and implementing something new. Even so, most businesses focus mainly on the invention part – simply because the implementation is less fancy but more time-consuming and tedious. Nevertheless, it is extremely crucial for success, in particular in larger enterprises where the existing (and still successful) business models create a climate that tends to kill emerging business models (this is often called the corporate immune system).

In order to mitigate the risk where innovative business models are killed right away, it often makes sense to do a business model road-mapping exercise that slices change into a series of smaller steps that are easier to digest and reduce risk by incorporating a commercial testing or validation step. In addition, if you want to really understand the impact of a new business model in a corporation, it is imperative to understand the current business model portfolio and to carry out a detailed delta analysis to figure out which elements of the business model are really new or even in contradictory to current practices. Knowledge of all existing business models also enables the team in charge of implementing the new model to look for synergies and smart reuse of existing processes and capabilities.

These practices can help produce an ambidextrous organization that can run proven and emerging business models in parallel and handle disruptive change without critical fractures. Nevertheless, in the end, no one will be successful without the required organizational and cultural prerequisites. A larger firm’s ability to innovate consists of various dimensions from leadership, strategy alignment, employee innovativeness, organizational setting, co-innovation, and access to innovation assets, processes, and practices. Each dimension provides drivers that can be systematically influenced to create an environment that increases the likelihood of success with business model innovations for the intelligent enterprise dramatically.

With technological innovation at your fingertips, you can build an intelligent enterprise. Learn how in the Intelligent Enterprise Webcast series.


Uli Eisert

About Uli Eisert

Dr. Uli Eisert is Innovation Lead for Business Transformation Services at SAP (Schweiz) AG. He is a distinguished expert in business model development, design thinking, digital transformation and innovation management. He holds degrees in mechanical and industrial engineering as well as a PhD in business administration in the field of disruptive innovation.

Keywan Nadjmabadi

About Keywan Nadjmabadi

Keywan Nadjmabadi is Head of Business Transformation Services at SAP (Schweiz) AG. This includes responsibilities in enterprise architecture, transformation consulting, value management, business model innovation and design thinking. Keywan joined SAP in 2000 and holds a diploma in Business Administration and Business Informatics from the Georg-August-University of Göttingen (Germany).