90% increase in Decentralized Innovation over last 5 years

Innovation labs and sandboxes may stimulate ideas, though innovation itself unfolds as a broader continuum involving every party interacting within an ecosystem. In this view, it is useful to extend the norms of technical pilots, and reconsider how three elements of innovation capability can evolve: Invention, Investment and Implementation.

Invention: With increasing focus on discrete innovation, alongside low-code enablement for automation and AI, enhancements and disruptive invention increasingly come from anywhere in the ecosystem of the entity driving the invention. Harnessing this distributed expertise helps determine and curate the full scope and context for invention.

Investment: With new demands for speed and resilience measures, more rigorous, creative scenarios and stress-testing, demonstrate a full extent of impact and sustained advantage. Institutions and even nations have historically lagged, by only measuring economic impact through the limited scope of the digital interface. In reality, digital inventions carry far-reaching impact across institutions, infrastructure and society, making broader forms of value increasingly important to understand.

Implementation: The scope of transformation through digitization and data architecture now touches practically all systems and processes, and executing at scale across multiple parties requires clear transition design and coordinated implementation. This supports not only positive disruption, but instilling of the vision and confidence to embark on transformation.

The evolution of these three elements of Innovation, sets a stage for how legacy technology functions are working toward coordinated integration with innovation ecosystems, using new techniques, measurement and incentive.

80% of IT environments expect increased use of enterprise open-source software across emerging technology environments.

Building blocks for the innovation ecosystem

The Art of Composition

Today, competitive and resilience posture increasingly hinges on the ability to reimagine an enterprise as a portfolio of resources, delivered dynamically through cloud models and an extended ecosystem. The analogy of monolithic software rearchitected for micro-communication increasingly extends toward the re-architecture of the monolithic institution, toward discrete, orchestrated provision of resources and communications. In this view, The art of resource composition and extended resilience increasingly emerges through democratized data exchange and adaptive data mesh architectures. An open flow and close design partnership between technology centers, partners, owners and users, reducing bottlenecks and building trust into the process. Innovation through upgrade and migration is continuous within technology environments, though often invisible across the wider institution. By surfacing continuous technology innovation within policy and design, more targeted innovation is achieved, with increased re-use, scale and value.

Furthermore, the constraints of regulation have historically not merely hindered, but also driven innovation. The capital markets, by example, regularly seek out advantage in market constraints. Today, forward-thinking technology functions are converting regulatory constraints in areas of data protection and innovating hybrid cloud models which better enable international cross-border flows. Concepts of resilience and security have increasingly become foundational enablers, bringing technology functions closer to strategic governance. New concerns in sustainability have increasingly sought input from technology functions, linking sustainability and digitization objectives across physical and virtual assets.

Simulation Environments

Innovation architecture is evolving toward increasingly sophisticated models of operational and physical environments, mapping risk data to event-chains and architectural design. By modelling out a Digital Twin Ecosystem, it is possible to play out scenarios and futures across internal and external ecosystems. In this view, new initiatives can be risk mitigated, curated and optimized for sustained growth, particularly in decentralized environments, with high levels of uncertainty. In traditional analytics, the view is often externally focused on trends and patterns, while a Digital Twin Ecosystem can surface interacting internal and external views to simulate emerging environments and accelerate informed adaptation.

Major migrations and transformations such as those seen in cloud-first strategies, have far reaching impact. Dimensions of people, process, partners, technology and data increasingly interact across the same transformation environment. A 360° simulation model, captures these dimensions to quantify impact, navigate interdependency and enable more effective measurement of the value. More specifically, a 360° model navigates the timing of innovation as the total environment matures its capabilities and interacts with new systems. Digital Twin Ecosystems create powerful environments for modelling futures, interdependency and adaptive capacity.

More than two-thirds of organizations show increased investment in digital twin and simulation environments.

Fraunhofer Centre for Virtual Engineering (ZVE), Stuttgart
Focuses: Virtual engineering, digital design, simulation, collaboration