Authors: Uli Eisert and Keywan Nadjmabadi
How to embed intelligent technologies into new products, services, and ultimately business models to achieve an intelligent enterprise
Abstract: Too often, innovative technological capabilities are just used to improve existing processes consisting of many repetitive, manual tasks with more automated and adaptive ones. This obviously drives down costs and leads to temporary competitive advantages, however, it neither changes the rules of the game nor brings about any 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 very busy to become fully digital in terms of the processes they use, the products and services they offer, and the customer experience they create, they are already confronted 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 good 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 for this change is the area of customer service where automation is increased by all sorts of bots. This ranges from simple mail bots that assign incoming mails to the right agents over chat bots that enable customers to help themselves concerning standard issues such as a password reset, to comprehensive voice bots that communicate with customers to resolve the vast majority of issues that come up in a typical call center. In addition, the remaining human workers in the contact center get support from artificial intelligence-based applications to find quickly the best solution for highly complex issues based on historical data. This is impressive, however, is this what is ultimately meant by the intelligent enterprise? Is the intelligent enterprise in the end just a highly automated and agile enterprise? And what kind of new, somehow creative and high-value tasks will evolve for the knowledge workers due to intelligent enterprise?
For us, it seems to be obvious that only newly defined services that complement existing products and that are embedded in new business models can fully utilize the potential of intelligent technologies. What makes up an intelligent enterprise is new experiences for customer, partners, and employees that are embedded in intelligent business models.
Think for example about complex machines and plants. In the past, the manufacturer designed, engineered and built them, then there was a hard cut after delivery and installation, and in many cases the manufacturer had no idea whatsoever what happened to their machinery during its long life in operation and maintenance. They had no insights concerning conditions and performance or which spare parts, extensions, or services could be offered. Vice versa, the operator and its service providers had no information about what the manufacturer could contribute in terms of tailored improvements and optimization. Now, a set of new technologies are available: the IoT to collect data from running equipment, advanced analytics to create insights about the performance, machine learning to predict failure and optimized maintenance cycles, and cloud platforms to share data and insights across all affected parties. This offers a new world of opportunities that go way beyond an improvement of 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 already true a long time ago, however, as you can imagine from the ongoing revolution described in the previous section, intelligent technologies nowadays 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’ [Doll and Eisert, 2014] approach as an example, we see that the underlying principles of such an approach are quite simple:
- Use suitable frameworks for the documentation of the business model, e.g. the business model canvas [Osterwalder et al., 2005] and a network view.
- 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 the iterations is key for the success of the approach. There are 4 distinct types of iterations:
- Analyze & Improve: During this iteration, you analyze parts of the business model in more detail to improve the business model based on the gained insights. One example could be to analyze competitors and improve the value proposition based on new insights to strengthen unique aspects.
- Challenge & 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 & 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 cheap as possible is crucial. Usually, these assumptions concern mainly customers’ needs, and wants, as well as their willingness to pay.
- Evaluate & Decide: To figure out which of the different business model options are most promising, one has to assess them. Furthermore, towards the end of the project the favored business model is 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 start-ups to huge multi-nationals. In the next paragraph, we will discuss 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 described iterations in business model design. The technologies themselves might be analyzed in detail to understand 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 is 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 away customers with better product-market fit
- reconfigure the value chain
- provide access to idle resources
- Enhance customer experience, employee experience, or partner experience
Based on these considerations and an understanding which technologies become relevant for the industry, opportunities and threads 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 brain storming, 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 before. 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 to 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, that were the foundation of many business model innovations in the past. Re-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 created a great collection of proven business model patterns [Gassmann et al., 2014].
- A third approach is to look out for existing, real-world examples of new business models. The most obvious variant for this approach is to look at peers and competitors. However, it might be more inspiring to look at other industries with similar challenges (in particular, if they were hit by these challenges earlier) and to analyze if there were successful ideas to tackle these strains. Last not least, in might be very inspiring to check what start-ups are doing to leverage new technologies or to tackle known challenges in completely new ways.
How do business models for the Intelligent Enterprise look like?
Firstly, let us come back to the machinery and plant industry. For the first time, new technological capabilities, like cloud platforms and IoT allow to let the vision of a full product lifecycle management come true: a seamless digital representation of an individual product through its entire lifecycle from cradle to grave – the digital twin. Even better, the twin can collect real-time data, allows for analysis and prediction, and last not least everything can be shared among all affected businesses. If we put ourselves for example in the shoes of the manufacturer, now it is obvious that the operation of the own machines and plants becomes much easier, less risky, and more efficient. And this forms the foundation for all types of equipment-as-a-service as well as output-based revenue models. One might argue that this is not completely new, however, now these business models can get out 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 parts and used parts as well as consumables. Manufactures are in a pole position to set-up and operate these platforms and to benefit via new revenue streams and increased customer satisfaction. In addition, the digital twin is also a step towards tailored offerings for software updates and value-adding services or even feature selling. Vice versa, operators could sell data back that is relevant for an improved design and engineering of new machines.
For those machines that are ultimately designed for consumers like household appliances, the advancements of the IoT offers opportunities to eliminate the layer between OEM and end consumer, i.e. the retailer or service providers. For the first time, the consumer gets a name and location for the OEM when the appliance is registered in the network. Afterwards, the OEM gets data how the machine is used and about its health status and now can start to offer services like predictive maintenance, new features like higher performance due to firmware updates or even physical products like complementing ware parts, 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 enables other data-driven business as well. While the insurance offerings based on mileage and driving behavior are already partly established, other industries tab into this opportunity as well. As an example, a spice company has started to leverage data from questionnaires and ratings of food or restaurants to create a taste profile and to offer a recommendation service for recipes and products. The insights are also shared with commercial partners to tailor their digital offerings to consumers.
The direct contact to the customer can also bring up new options in B2B industries. Think about a logistic provider, that offers everything from highly standardized services up to very complex and customer-specific offerings. For the 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 paper work. This is not just about lowering costs due to the fact that human operators are now only needed for more complex requests, but also about offering a new customer experience and real time business. Global track and trace can be an additional element in this scenario to facilitate real-time monitoring directly for the customer. In addition, the customer can see the historical data about past business transactions at any time.
Another superior way of customer experience is mass customization. Not a totally new concept if you think about the automotive industry. However, when it comes to low value products this was a no-go due to disproportionate effort and costs in the past. With the latest generation of intelligent and high-performance enterprise systems this is feasible for the cloths and apparel industry and even for the food industry. This is especially remarkable since these industries continue to do their regular mass production business in parallel. Same applies to the media industry that 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 offer the basis for new business models as well. For certain business contexts it becomes relatively simple and cheap to offer configured products and even individual pieces. Beside this aspect, 3D-printing allows for new ways of distribution or even totally new channels and partnerships, e.g. in the area of spare and ware parts supply.
From Business Model Design to Business Model Innovation
The fact that innovation consists of inventing and implementing something new is as well-known. Nonetheless, only the invention part is in the focus most of the times – simply because the implementation is the less fancy, but more time-consuming and tedious part. Nevertheless, it is extremely crucial for the success, in particular in larger enterprises where the existing (still successful) business models create a climate that tends to kill emerging business models, often called 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 the change in a series of smaller steps that are easier to digest and reduce the risk since they incorporate a commercial testing or validation step. In addition, if one wants 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 the current practices. The knowledge of all existing business models also enables the team in charge of implementing the new model to look for synergies and smart re-use of existing processes and capabilities.
The outlined practices can help a lot to become an ambidextrous organization that can run proven and emerging business models in parallel and handle disruptive change without critical fractions. Nevertheless, in the end no one will be successful in this endeavor without creating the required organizational and cultural prerequisites. The ‘ability to innovate’ of a larger firm consists of various dimensions from leadership, over strategy alignment, employee innovativeness, organizational setting, co-innovation and networks 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.
Cigaina, M. and Riss, U. (2016): Digital Business Modeling: A Structural Approach Toward Digital Transformation. SAP Whitepaper
Doll, J. and Eisert, U. (2014): Business Model Development & Innovation: A Strategic Approach to Business Transformation. 360° – The Business Transformation Journal, p.12
Gassmann, O., Frankenberger, K., Csik, M. (2014): The Business Model Navigator: 55 Models That Will Revolutionise Your Business, FT Publishing
Osterwalder, A., Pigneur, Y. and Tucci, C. (2005): Clarifying business models. Origins, present and future concepts. Communications of the association for Information Systems, 16(1)
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 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.