Innovation & Insight



Endless Possibilities

Evolution from legacy systems to Business networks

Legacy Systems & Infrastructure

  • Backend infrastructure for internal use only
  • Siloed and inflexible set of closed systems and data, built for specific goal(s)
  • Cost center: infrastructure streamlines existing processes but does not attract new customers or create new revenue opportunities
  • The company creates, delivers, and maintains all value in the customer relationship via a single product or suite of products.

New Networked Platforms

  • Backend infrastructure also interacts with users and partners
  • Fully open and interoperable systems and data, built for third parties to 'plug and play'
  • Revenue generator: infrastructure supports a marketplace where third parties can create independent offerings that attract new customers
  • The company hosts a platform that enables customers, strategic partners, and other third parties to co-create and extract value as well
Business Networks

Legacy Information Technology

To rethink tech infrastructure, we have to understand where information technology (or IT) came from. IT originally was thought of as the 'pipes' of the business world: a necessary expense that ensured the functioning of tangible processes like products, manufacturing, service, or sales. In other words, IT was a utility that enabled analog businesses to function more smoothly. The pipes-based model of IT focused on streamlining sales and production but didn't provide capabilities for creating new value. In the old model, infrastructure is a cost center, and it requires its host organization to conceive of, design, and deliver all value to the customer.

Legacy  Technology Architecture

Legacy Technologies were usually focused on what happens inside the company's walls. Consequently, they were generally closed to other systems, proprietary, and difficult for end users to access. Because of the cost and the learning curve for new technologies, early IT projects focused on specific functions (such as account reconciliation within a finance department or inventory tracking in a grocery store chain). We can think of this era as "IT-streamlined" but not fully digital in the way the term is used now; most forms of IT work focused on 'faster, better or cheaper' versions of analog approaches to work. In other words, the benefits were still limited to business models and offerings which would be recognizable to someone who wasn't aware of computers.

Because of this, less attention was paid to interoperability and user interface than was paid to basic functioning. Value creation for companies with this mindset still tends to be limited to a linear, analog perspective, that of production 'pipelines.' Most corporate employees are familiar with what happened next: Systems needed to connect to each other as technology became more prevalent. Digital infrastructure became hard to manage, secure, and keep within budget. In response, many technology leaders attempted to create modular platforms and enterprise architecture to bring control and order to the rising chaos. Taking command-and-control management strategies of the industrial era to their logical conclusion meant consolidating systems into centralized IT departments.

This approach worked reasonably well for predictable, low-change functions like payroll preparation. But as more users, institutions, and datasets were added, technology interdependencies became ever more complex, causing challenges when one company needed to connect to (or had acquired) another. Centralized command and a one-size-fits-all approach to technology doesn't work for every situation. As infrastructure components advanced and new tech-savvy competitors arrived, the technology landscape became more heterogeneous and chaotic. Fierce competition in software and hardware industries often resulted in infrastructure becoming a tangled mess of interdependencies. Custom solutions were built for individual companies or even different departments within the same company. Many companies combined several strategies to address the need for technological innovation and efficiencies:

  • Internal, custom development of technology tools by IT departments or via contractors
  • Licensing general-purpose technology platforms like Microsoft Office (and its related tools)
  • "Hacks" of mainstream tools (like spreadsheets) by employees to get their needs met
  • Industry-specific tools (such as Bloomberg's stock trading software in the finance industry)

It was (and still is) common to see a combination of all of these approaches. When overwhelmed by the complexity, risk, and cost of those strategies, many companies postponed modernization altogether, creating what is sometimes referred to as 'technology debt,' or an ever-increasing backlog of necessary tech updates. The increasing importance of digitization gave rise to the concept of 'technology as a platform.' However, depending on who was asked, 'platform' could mean anything from a collection of software produced by the same developer (like Microsoft, Novell, or Oracle), a company-specific set of tools developed in-house, or a place where lots of different software mixed (where all the tech 'pipes' came together). 'Platform' connotes a shared infrastructure and, sometimes, rigidly hierarchical models of leadership and control. Many firms experience a constant tension between the alignment required for stability and the autonomy necessary for innovation especially for conglomerates or other complex firms which are growing in size to span many customer groups, geographies, and even moving into new industries.

It isn't sufficient to simply create an aligned set of technologies developed by a central power, because the speed and scope of innovation required for an ever-accelerating world make it hard to develop a central technology the use cases of technologies aren't as static as they have been in the past. Startups with less inertia and tech debt gladly mix and match more modern software, outpacing large companies' slower rate of innovation. Firms of all sizes need to have room to gracefully add or subtract new technologies in a stable and secure way and sometimes allow 'competing' approaches to co-exist, such as two different customer relationship management (CRM) tools for different parts of the business. Thinking of "platform" as only a technology strategy isn't enough to modernize in the face of the ever-changing needs of companies and users.

Shifting from IT as a utility to digital as a capability

One significant change that multi-sided platforms demand is that firms rethink the place of IT. Traditional IT skills and infrastructure such as networking, database management, and internet access are more important than ever. However, as organizations create or participate in multi-sided platforms, they have the opportunity to expand their focus on IT as a utility—basically confined to a single department in a business to digital as a capability in every part of the business. IT doesn't stop supporting essential functions of the company instead, it starts providing and connecting components needed for new and exciting digital value propositions. One of the most well-known examples o f this shift from utility to capability is Amazon's elevation of its internal web hosting technologies into their own product, Amazon Web Services, which then expanded into being a multi-sided platform in its own right.

As part of this shift, organizations and the individuals within them, must embrace data and APIs to ensure they can offer value in today's shifting digital landscape. This poses significant challenges for IT organizations as they sort out the cost centers of the business tangled webs of interdependent technologies while also trying to launch new revenue-generating functions.

Platform Technology: Rethinking Infrastructure

To benefit from 'digital,' companies need to rethink their infrastructure. From the early days of digital computers to the 1990s and early 2000s, companies integrated information technology into their existing processes to streamline production and sales. Most early gains with digital tech were created through improved efficiency.

But in the Digital Age, technology's role now shifts from optimizing production to powering multi-sided platforms. Given today's user expectations, new digital infrastructure needs to create entry points for participation, allowing users and strategic partners to co-create, consume, and extract value in surprising ways.

Platform Thinking: Reimagining Value

Tech platforms are just one part of the equation of a modern business. To truly realize exponential value, organizations of all sizes also need platform thinking. This means reconsidering where value comes from. Perhaps one of the most classic examples is the entrance of YouTube into the online media space. When YouTube was introduced, the prevailing focus of online video services was about the technology of streaming video, and assumed that content would continue to come from familiar media production sources like news organizations, musicians, and movie studios. YouTube, in contrast, concerned itself less with the technology of digital video and more with democratizing the creation of content (hence the "you" in the name). YouTube was focused on enabling co-creation on top of its technology, where users produced content and reviewed, promoted, and even collaborated on new content.

This mental shift from making and selling things to enabling value is fundamental to transitioning to a multi-sided platform (MSP) model and can profoundly transform a company's relationships with its customers from 'consumers' to 'co-creators.' You can read more about this shift in Platform Thinking: From Pipes to Platforms

Effective business model innovation involves unlearning as well.

One of the most useful things for a mindful and analytical entrepreneur is to reverse-engineer or extract beliefs and assumptions from existing ways of working and then test them to see if they are still the best strategies. Suppose these existing beliefs and assumptions prove not to be accurate. In that case, it's time to engage in unlearning: the practice of consciously letting go of limiting beliefs to create room for new ways of operating.

A common trait among entrepreneurs is, almost by necessity, an open mind and an understanding of the scientific method. Entrepreneurial organizations have learning and testing capabilities and, while they use their intuition to guide their ideas, they validate those ideas with well-constructed experiments. Conversely, learning and testing can be hard for established businesses because it's not often culturally accepted to challenge the norms and strategies of leaders.

Proactive leaders should assume that all business ideas and strategies, both new and those already in place, need to be tested and measured. As part of this, entrepreneurial organizations often have a more advanced and nuanced understanding of metrics and analytics. These organizations engage in 'instrumentation' connecting measurement capabilities, not just to productivity and sales, but also to customer-centric objectives and key results (or OKRs) to make sure they see the bigger picture.

The Learning Cycle.

Many companies think of their 'learning stage' as something that happened long ago (or at least before most of their current employees were hired). Such learning may even have occurred at another company that later merged with or was acquired by a conglomerate. When everything is going well and the environment around the business is mostly stable, this is not a big problem. However, when rapid change occurs, catastrophic failures (or significant missed opportunities) can happen quite quickly. It's as if some large organizations don't have a way to 'see' the big picture in time to change their ways of thinking and operating.

Entrepreneurs and intrapreneurs wishing to engage in business model innovation bring learning to the forefront through six key stages of making informed decisions about the business model and its various components.

  1. Define the opportunities or problems with the business model and practice computational thinking to define the scope of the decisions to be made or improved.
  2. Discover what existing resources and research could be used to accelerate your efforts, including finding others who have tried to solve the same problem before (inside or outside the team).
  3. Hypothesize which factors might lead to a positive outcome and reduce them into the smallest number of variables possible.
  4. Test hypotheses by designing and running experiments.
  5. Learn from experiments and use them to update the model (abstraction) of the problem and related systems.
  6. Act on the results, starting first by sharing results with others in the team and organization.

Business Modularization

A modular approach to business functions and components allows for more rapid change when necessary. These modules can be big, like an accounting department, or relatively small, like a technology microservice that converts scanned receipt images into database entries.

To ensure rapid change occurs without disrupting interdependencies within the business, abstraction layers between departments, such as application program interfaces (APIs) and internal billing structures, are used. For example, Amazon is infamous for requiring all data to be passed between different parts of the organization via API.

As business ideas are further tested and validated, they come out of alpha or beta mode and are often then adopted by other parts of the business. Modularization can allow firms to change quickly when new opportunities or risks arise. However, modularization of the company is a mindset about interoperability and preparation for constant change and continuous improvement, not a one-size-fits-all approach. It can allow parts of the business to use different tools for the same problem based on their specific needs. Strategies like Development Operations (DevOps), Continuous Improvement, and Enterprise Service Architecture are all examples of attempts to modularize technological parts of a business, but they need to be paired with comparable business mindsets in order to be effective. Striking a balance between innovation and standardization is perhaps one of the biggest challenges for business leaders in companies of large scale.

Rapid Prototyping Capabilities

Entrepreneurs and (entrepreneurial companies) are often noted for their focus on prototyping or creating early versions of products and services to test and de-risk potential shifts in the business model. While the topic goes far beyond the scope of this chapter, rapid prototyping marries a learning mindset with modular capabilities to quickly build new products, services, content, and technologies in a basic form. Such prototypes may not have complete functionality (sometimes referred to as alpha products), or if they are functional, they may have compromises to be produced more quickly (sometimes referred to as beta products). The ability to quickly construct 'minimum viable products' and other forms of prototypes inside an organization or with a close partner is an indicator of a company's entrepreneurial readiness.

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