The ultimate goal of the business strategy is to ensure that the business can be scaled up to the level which matches the business objectives. This is usually a step-by-step process, involving exploration, acquiring early adopters, and then continuously growing the business. Which of the methods that have worked for successfully scaling purely digital businesses can be adopted by AIoT-enabled businesses? What are the pitfalls of scaling up a digital / physical business?
- 1 Understand strategy requirements
- 2 Clearly define your focus areas
- 3 Take a holistic view on product, marketing and commercialization
- 4 Ensure AIoT product / market fit (or solution / internal demand fit)
- 5 Ensure efficient exploration
- 6 Understand how best to cross the AIoT chasm
- 7 Understand implications of AIoT Short Tail vs. Long Tail
- 8 Ensure organizational scalability
- 9 Deal with repeatability, capacity and marginal costs
- 10 References
Understand strategy requirements
The first important step is to understand the key elements required for the commercialization and scalability strategy. These elements will be different for the Digital OEM vs the Digital Equipment Operator, as will be discussed in the following.
Digital OEM: strategy for smart, connected products
In order to successfully commercialize smart, connected products at scale, the Digital OEM will need to address three strategic elements: product strategy, go-to-market strategy, and revenue generation strategy. Product strategy ensures that the product has an excellent fit with the market needs. The go-to-market strategy ensures that those customers who are most likely to benefit are identfied and persuaded. The Revenue Generation Strategy ensures that money is coming in.
More specifically, the Product Strategy has to ensure the product / market fit, define the product launch strategy, and ensure continue continuous product improvemnts - especially utilizing the digital side of the digital / physical, AIoT-enabled product.
The Go-to-Market Strategy includes the marketing strategy, awareness & loyalty programs, lead generation, and retention management.
The Revenue Generation Strategy includes the monetization strategy (e.g. starting with a freemium model for digital services, which is then converted to premium-subscriptions), sales resource & effectiveness management (will existing sales people focused on physical products sales be able to cope with digital subscription sales?), and finally sales processes and tools.
Digital Equipment Operator: strategy for smart, connected solutions
The Digital Equipment Operator will often focus on creating smart, connected solutions to optimize internal processes. Consequently, commercialization is not so much of relevance here. Instead, continuous optimization is key - usually related to the different elements of OEE (overall equipment effectiveness): availability, performance rate, and quality rate. Consequently, the key elements of the best matching strategy include Solution Strategy, Rollout Strategy, and OEE Optimization Strategy.
The Solution Strategy includes a strategy to match the solution to internal demand, a solution launch strategy, and a strategy to continuously improve the solution itself.
The Rollout Strategy usually includes a strategy for site preparation, a retrofit program (how to retrofit 2,000 escalators at 200 train stations?), and an internal awareness and adoption program (how to convince the internal stakeholders to actually use the solution).
Clearly define your focus areas
The first step towards ensuring scalability of an AIoT-enabled product business or internal optimization effort is to clearly define the focus areas: Is this about optimizing core business processes by integrating them with intelligence from assets in the field? If so, which ones: marketing, sales, operations, manufacturing? Is the focus on revenue and profitability, or on OEE (Overall Equipment Effectiveness)? Is this about new or improved user experience, e.g. by adding a digital experience to a previously purely physical product? Or is this about disrupting channels, e.g. by opening up a new channel that could even be competitive with one of the existing channels. Or is it even about creating a completely new business, e.g. by creating a new digital / physical product category? Understanding and clearly articulating the focus area should be the first step of every AIoT-enabled digital transformation effort.
Take a holistic view on product, marketing and commercialization
Especially for Digital OEMs, it is important to establish a holistic strategy which includes product, marketing and commercialization. The product and its market will usually to through exploration, growth, and maturity phases. These need to be supported by marketing and commercialization.
During the exploration phase, marketing will need to support market need assessment and market validation. During the growth phase, it needs to support awareness & visbility, as well as lead generation. Finally, when moving to the maturity phase, support for customer loyalty and retention will become more important.
From the point of view of the commercialization strategy, in the exploration phase the analysis of economic feasibility plays a key role, including the analysis of realistic pricing models. Also, development of a strategic business plan is key. During the growth phase, lead conversion is important. Many digital companies are using freemium models to foster initial growth. For Digital OEMs, it will usually be important to ensure initial revenue generation, e.g. for the physical parts of the offering which can not be subsidized. Finally, when reaching maturity, converting freemium subscribers to a premium subscription will be key. Up and cross-selling can generat additional revenues.
Ensure AIoT product / market fit (or solution / internal demand fit)
A key prerequisite for successfully establishing a scalable high-tech business is to constantly focus on the product - market fit. This means that the product -- including the user experience (UX), the feature set and the value proposition -- must meet the undeserved needs of the target customers. Since the target customers are likely to also change over time, the organization must be able to react to the changing needs.
So who are the target customers? Digital OEMs operating in a B2C market will usually address needs such as convenience and offering cool, new features. For those in B2B markets, customers are more likely looking for efficiency improvements and cost reductions. The Digital Equipment Operator, on the other hand, will focus on operations effectiveness (OEE).
And what about the underserved needs? In terms of the target customers, the Digital OEM will usually address either a B2C or a B2B market, while the solutions for the Digital Equipment Operator will more often address the internal business units responsible for the physical assets.
How can this be met with a matching offering? The value proposition is defined by key capabilities. In "How Smart, Connected Products Are Transforming Competition", Michael Porter and Jim Heppelmann describe four key capabilities of smart, connected products: monitoring, control, optimization, and autonomy. In the context of our discussion, the new products and services provided by the Digital OEM will probably most benefit from control and autonomy, while the Digital Equipment Operator will utilize the monitoring and optimization capabilities for his solution.
The feature set for the smart, connected product will include both physical and digital features, the latter enabled by IoT-connectivity, software and AI. The Digital Equipment Operator, on the other hand, will usually not be able to change the features of the existing physical assets, so the focus here is on digital features.
From a UX point of view, we will again have to differentiate between the product and solution perspective -- smart, connected products will utilize the full breath of UX-related technologies -- including web technologies and mobile devices, but potentially also HMI embedded on the physical product. For smart, connected solutions developed with a focus on improving the operations of existing assets, the focus will often be more on utilizing basic web technologies for intranet-type applications. UX plays a role here as well, but will often not be as important compared to the product side. Take, for example, the escalator monitoring example from earlier. If the railway company is only making this available to a small set of technical operators, a simple UX will be sufficient. This would obviously be different for any operations support apps that are made available to a wider internal audience, such as train conductors. In this case an investment in a better UX (e.g., using a smartphone app) will be justified.
Ensure efficient exploration
Ensuring efficient exploration of new, AIoT-enabled opportunties is key for initiating scalable businesses. This is usually less of a problem for startups, but something which larger companies and incumbents can struggle with.
The first challenge here is the selection of suitable use cases. These should on clearly identified application areas and customer benefits. Properly evaluating the use cases with respect to their potential to scale is important as well.
The next point is "freedom to experiment": Especially in the early phases of exploration and technical feasibility assessment, the team should not be slowed down by corporate rules, standards and other dependencies. Instead, the focus should be on value creation and validation.
Infrastructure is important. Re-use of corporate infrastrucure can make sense, e.g. in areas like user management, billing, invoicing, and technical AIoT infrastructure. However, the exploration team should not be forced by corporate mandate to play guinea pig for new, immature infrastructure.
Cost are essentiall as well. One should not apply typical enterprise cost structures during the early exploration phase. However, the team must prepare early for the target cost model further downstrean.
Finally, "enterprisification" plays an important a role, especially in larger organizations. At some point in time, the team will have to adapt to corporate governance rules, standardization, and the integration into the enterprise application landscape with all its governance mechanisms. The point for full integration must be chosen carefully. Doing it too early can potentially kill the endeavour simply by adding too much overhead to a yet unproven and probably unprofitable early stage idea. Doing it too late can also prove to be difficult, because certain standards and compliance levels are necessary even before onboarding the first customers at scale. However, enforcement of corporate rules can be a real distraction from the business goals. Consequently, this should be treated like a consolidation project.
Understand how best to cross the AIoT chasm
The business book classic "Crossing the Chasm" by Geoffrey Moore describes the challenges of marketing high tech products, especially focusing on the chasm between the early adopters of a product and the mainstream early majority. This concept is especially important from the point of view of the Digital OEM.
Throughout the life-cycle of the product, he will face a number of different challenges, some of which will be very specific to AIoT. For example, in the early stage when addressing innovators, a challenge is to actually create a small series of physical products that appeal to innovators in combination with the digital features. Especially if AI is heavily used, this can be challenging in the early phase of the product life-cycle, because in this phase few reference data will be available for training the AI models. For asset intelligence enabled by AI, this will probably mean that simulation and other techniques will have to be applied. For any swarm intelligence required by the product, this will be even more challenging because the "swarm" of products in the field actually creating data will be relatively small.
When moving on to the next phase, serving the early adopters, an AIoT-enabled product will have to make difficult decisions about the MVP or baseline of the physical side of the product because this will be very difficult to change after the Start of Production. Another key point will be a strong UX to appeal to early adopters: something that start-ups tend to be better up than incumbents.
Finally, when crossing the chasm to the early majority and realizing significant growth, it will be vital to establish cost-efficient, high-quality product manufacturing. Scaling the physical side of the product at this point will most likely be more challenging than scaling the digital side of it. In addition, this market will not be a pull-market, so it will require excellence in sales and marketing.
Finally, manufacturing-centric companies often struggle with the fact that the product will have to be continuously improved to stay attractive to the users. This means not only the software side of things but also the continuous retraining of the AI models used.
Gabriel Wetzel, CEO of Robert Bosch Smart Home: "A key challenge are the often very high expectations, which don't anticipate the ‘trough of disillusionment’ which you usually have to cross before you will see new business at scale. You have to make sure to get through this, and not lose management support on the way."
Understand implications of AIoT Short Tail vs. Long Tail
A good way of looking at the scalability of the opportunities presented by AIoT is by categorizing them in the short tail vs. the long tail of AIoT: the AIoT short tail includes a relatively small number of opportunities with a high impact and thus high potential for scaling them. This usually means a high level of productization and a strong Go-to-Market focus, which requires a Digital OEM organization. The AIoT long tail, on the other hand, represents a large number of opportunities where each individual opportunity is relatively small. However, together these long tailed opportunities also represent a very significant business opportunity, provided an organization is able to harvest these smaller opportunities in an efficient way. This usually requires a "harvesting" type of organization (for internal opportunities), or a platform approach, as described earlier.
A good example for an organization that is focusing on harvesting a large number of small opportunities presented by AIoT is described in the "AIoT in High-Volume Manufacturing Network" case study in Part IV of the AIoT Playbook. This case study describes how Bosch Chassis Control Systems have built up a platform and global AIoT Center of Excellence to work closely with a global network of over twenty high-volume factories. This group is managing a portfolio of AIoT-enabled production optimization projects in different areas, but usually with a strong focus on OEE improvements for the factories. This is a great example of a "harvesting" type of organization that is required to realize the opportunities presented by the AIoT long tail in such an environment. Executing this at scale for over thirty factories requires a careful balancing between a centralized expert team and working with experts in the field who understand the individual opportunities.
When looking at scalability, it is important to understand which end -- the short tail or the long tail -- of AIoT one is working on, and what type of organization is required to be successful here.
Gabriel Wetzel, CEO of Robert Bosch Smart Home: "The short-tail opportunities will often be addressed by other market players as well. This means that investment size and time-to-market are absolutely critical. The long tail requires many custom solutions. You should not underestimate the required resources, the domain-specific skills and the market access. Not all of these can be easily scaled-up. Of course this can be addressed by a top-in-class partner management: but don't forget to budget for it!"
Ensure organizational scalability
Ensuring organizational scalability is another key success factor for smart, connected products. How can an organization successfully grow and evolve alongside the product as it matures from idea to large-scale business? DevOps mandates that an IT organization combine development and operations from the beginning, iterating together continuously through the build and improvement cycles. However, in an organization that must combine IT development and operations capabilities with physical product engineering and manufacturing capabilities, this will not be as straightforward.
Dattatri Salagame is the CEO of Bosch Engineering and Business Solutions. In the following, he will discuss the issues related to scaling up and evolving an organization for smart, connected products.
Dirk Slama: What is your take on the organization we need to build and sell smart, connected products?
Dattatri Salagame: Since AIoT is a relatively new space, organizations are finding their feet to unlock potential at scale. A typical AIoT product would need multiple players to come together conceive, develop and launch a connected product. As data twins are the backbone of the connected products, hence the diversity and complexity of the technology stack demands players with deep tech domain, cloud platforms, and connectivity to come together to orchestrate the end product or service. Most of AIoT projects go through different product phases without transition awareness of shifting to different phases in terms of Product Lifecycle Management (PLM) shift and capability needs. A connected product engineering organization needs to evolve with the product. This transition has to be managed while the business owner is able to do theirhis experimentation, validation in the market for scalability.
Dirk: And what are the required organizational capabilities during these different phases?
Dattatri: In the beginning you need a gang of hackers who can quickly hack a solution in a high-fidelity experimentation mode. I call them a gang of hackers because in the MVP (Minimal Viable Product) stage, you hack the solution, you are not really worried about the reliability of the product. You are worried about the feasibility of the product. Once you have confirmed the feasibility and you have received positive customer feedback, you need to transition into a lot more rigorous, disciplined product engineering process. Multi-disciplinary product engineering becomes a key competency. You will require competencies encompassing mechanical, electrical, and electronics engineering, power management, communication, coupled with data science, AI and security. It’s very important that there are team members who understand data, mathematical modeling of the data to mimic the physics and chemistry of the product, to create AI models, and to be able to mature the product in layers.
The product is going to mature in layers, this is important. The electronics layer, the communication layer, the network service provider, then the data, then the AI and then the reliability. So these layers mature at a different velocity, at different points of time, so you need a team which is a lot more multi-disciplined and engineering rigor in this phase. And finally, when we release the product, and we have crossed the initial validation of scale, then you need an organization to support the product introduction. This is a game of having good ecosystem to be able to manage the scale and to provide high-speed DevOps as the backbone of the digital services. So, you need these three flavors of the team during the course of the project:
- The Minimum Viable Concept/Product (MVC/MVP) – feasibility validation
- New Product Engineering (NPE) – Reliability validation
- New Product Introduction (NPI) – Scale and ecosystem
Dirk: Any recommendation on how to organize this?
Dattatri: Everybody is talking about digital transformation, and this is it: we need to transform existing, traditional, heavy engineering and manufacturing-oriented organizations, so that they play together with the more agile AI and software organizations to support smart, connected products. It is important to be transition aware through the phases of the product lifecycle in terms of PLM and capability shifts. Our experience has been to adopt a multi-speed model to navigate through these phases, with clear "Transition Awareness" to operate in right gears. To manage capability shifts, one needs to operate in a multi-threaded model covering classic product engineering to technology (AIoT) fusion. Otherwise, there is a risk falling through between the transitions, which we call the “valleys of death”. If you look at connected products, seven or eight out of 10 products don't actually pass in flying colors. Therefore, organizational agility and the ability to transform the organization along the way is an important part of the ability to "cross the chasm", as you have introduced it earlier.
Deal with repeatability, capacity and marginal costs
Digital businesses are seen as potentially highly scalable because their digital offerings are highly standardized and easily repeatable at very low extra cost. Physical products, on the other hand, can be much harder to scale, because scale effects in manufacturing often only apply when talking about extremely high production numbers. Even in this case, the marginal costs will not be reduced to a level as we are seeing this in the case of digital businesses.
For the Digital OEM, this means that their focus usually needs to be on creating highly standardized physical products, because any increase in variants and added complexity can potentially have a negative impact on scalability. Ideally, differentiation through product variations should be mainly focused on the software/AI side. An interesting example in this context is the Seat Heating-on-demand case, which is introduced in the product operations section: instead of having cars manufactured with individual seat heating configurations, all cars come with the same physical equipment and the configuration is done later on-demand by the customer. Of course this type of business case requires careful calculation of the marginal production costs vs. the downstream revenue opportunities over the life-cycle of the car.
For the Digital Equipment Operator, the topic of repeatability and capacity is also important. This links closely back to the long-tail discussion from earlier on: if the benefits of the individual AIoT-enabled solutions are only relatively small in comparison, then ensuring repeatability on some level is key. In the "Predictive maintenance for hydraulic systems" case study, Bosch Rexroth used AIoT to enable predictive maintenance for hydraulic components. However, since each customer installation uses the hydraulics components in a different way, AI algorithms have to be adapted individually for the customer. Bosch Rexroth has established a service offering that maximizes repeatability by standardizing the sensor packs, and establishing a standardized process for the customization of AI for individual customers. In this way, the predictive maintenance service offering is competitive, despite of its positioning on the AIoT long tail.
- How Smart, Connected Products Are Transforming Competition, Michael Porter and Jim Heppelmann, 2014, Harvard Business Review
- Crossing the Chasm, Geoffrey Moore, 1991, HarperBusiness