Business Transformation Requires Transformational Leaders

The success metrics for the knowledge economy are less about output and more about outcome and impact. To measure such intangible qualities and how they are produced requires distinguishing efficiency from effectiveness. A team’s climate reflects the organizational culture, and it is an indi­cator of that culture’s levels of efficiency and effectiveness. From an Agile perspective, the combination of efficiency and effectiveness speaks to a team’s confidence that it is free to experiment, take risks, and learn from failure. In this Advisor, we assert two fundamental value-creation metrics for Agile organizations.

Pursuing a digital transformation strategy to position an organization as a market leader can be challenging, as both internal and external factors can significantly complicate and hinder progress and delay achieving aspirations. These challenges may include resistance to change, lack of a clear vision, data overload and utility, inflexible development processes, and business models that are constantly being reevaluated. Design thinking has emerged as a key forward-thinking tool and mindset to help overcome these challenges and accelerate the timeline for transformational work. Design thinking is a human-centric, collaborative, action-oriented process with a set of techniques and tools that help an organization drive change. In this Advisor, we take a closer look at four organizations that have successfully applied the design process as a core business strategy.

Connecting a platform with an existing company to a platform organization is beneficial for both established companies and insurtechs. Without pursuing that ave­nue, the insurtechs face the risk that their competitiveness may decline if others can copy their digital skills at low cost. Thus, connecting their platforms with the incumbent organizations that possess hard-to-copy capabilities guarantees the uniqueness and sustainability of their own business model. The disadvantages of established companies, in comparison to insurtechs, are the reason why tradi­tional companies need platforms. Platforms require changing the culture and business logic in a company from product to service dominance, making proc­esses in relevant areas real-time capable, opening the company to the reuse and integration of solutions and services from other actors, and replacing a hierarchical culture with modern, agile, team-oriented approaches that make optimal use of the internal and external workforce.

Here in Part II of this Executive Report series on the rise of cognitive computing, we dive into the commercial cognitive products, including cognitive development platforms, domain- and industry-specific cognitive platforms, and cognitive-powered solutions

Here in Part II of this Executive Report series on the rise of cognitive computing, we dive into the commercial cognitive products, including cognitive development platforms, domain- and industry-specific cognitive platforms, and cognitive-powered solutions

Portfolio management plays a critical role in R&D management, as it structures a strategic process that allows management and R&D to make joint decisions that impact the range of R&D projects in the development funnel. The pooling of insights drives better decisions on the allocation of scarce technical resources based on the needs of the business and its capabilities. Portfolio management creates a dynamic capability to react purposefully to changes in the market, whether strategic, technological, or competitive. This requires clearly articulated projects that can link back to corporate strategy.

As companies evolve through the stages of increasing agility, the work of HR changes as well. At each stage, there are new priorities and new hurdles that should be both expected and managed.

Both the industrial and computer revolutions created a great deal of contemporary concern about massive unemployment, but these concerns never materialized beyond a few initial rough spots. Will the AI revolution prove equally problem-free? Probably not. Dangers lie ahead and will test the strength of our institutions as well as our technologists. The possibly massive job loss is the most tangible consequence; it would affect the greatest number of people. Smart robots keep replacing factory and warehouse workers and are showing up in service industries (e.g., self-checkout in supermarkets). AI’s machine learning and algorithms are industrializing much higher-skilled “artisanal” activity (e.g., interpreting x-rays, once thought invulnerable to automation). Nobody has good answers, but ideas like those explored in this Advisor are taking shape.