Design thinking is an elegant framing of problem-finding and -solving with a strong focus on delightful outcomes for the customer, while Agile practices focus on delivering the value envisioned in the design phase. This implies the two are equally essential to the team’s creative process. This Advisor describes the four steps in creative problem-solving that comprise the building blocks of innovation.

Many companies are unsatisfied with their innovation efforts and part of this is undoubtedly due to challenges around ideation and idea management. The required contribution of breakthrough innovation is ever-increasing, adding to this pressure. However, it is clear that some companies have developed strong processes and are reaping the rewards. The best practices outlined in this Advisor can help.

Recent research has shown that creating an Agile approach to designing for the con­sequences of technology is the best way to start building successful businesses that use responsible business practices to contribute to a better world. This Advisor explores a new Agile event called “consequence scanning.”

Smart automation is most effective when humans and machines work together to deliver desired outcomes. Effective design for automation is not only about how much can be automated but should also consider how automation works together with humans to deliver value. Several fallacies observed in smart automation initiatives across indus­tries can lead to failed initiatives that never see the light of day or that never deliver the promised outcomes. One fallacy that has led to such failures is the belief that all human activities can be automated.

An ongoing challenge with big data and other secondary analytics initiatives is getting access to data. This Advisor describes a risk-based approach to de-identification, in which the data is transformed and administrative and technical controls are put in place.

The road to automating software development is long and full of twists and turns. No doubt, there will be potholes and detours along the way. However, getting to the end goal now seems possible, if we travel a short distance at a time.

In this Advisor, we describe some of the more popular cognitive development platforms and services that are currently available. Although some cognitive development platforms are available for on-premise deployment, the ongoing trend is for providers to offer their cognitive products in the form of cloud-based environments that give companies the opportunity to license various API-based cognitive services for use in their own enterprise applications or commercial products.

This Advisor provides an overview of the importance of using visual techniques as part of a business architecture practice and highlights the use of visual design.

For most organizations that have deployed customer experience (CX) practices and technologies, it is still too early to tell if their efforts have actually allowed them to deliver a better customer experience. However, for approximately 31% of the organizations that have deployed CX practices and technologies, their initial efforts appear to be paying off. These findings come from the preliminary results of an ongoing CX management survey we are conducting.

The challenge for this issue of Cutter Business Technology Journal was to accurately represent the diversity of research in the decision support system (DSS) arena while also providing a glimpse of the cutting-edge DSSs of tomorrow.

Agile is never done. Without a conscious commitment to sustaining new ways of working, teams can fall back into old habits. Plus, staff turnover or growth brings in individuals who weren’t part of the original shared commitments. Naysayers find evidence that something’s not working — one more reason to subvert change.

Lean companies develop their capabilities and processes continuously as part of their culture. Continuous improvement allows them to align their activities flexibly, according to business strategy. Due to this holistic alignment, these companies achieve relatively high-performance levels compared to their competitors.

Companies often struggle to achieve balanced portfolios that align with company strategy. In this Advisor, we identify one common challenge in portfolio management — deciding the portfolio structure — presented as an anonymized case study.

There are a multitude of innovative applications across industries where RPA solutions are improving the quality of repeated tasks and releasing resources. In this Advisor, I share a sampling of a few interesting case studies and examples of RPA in action.

Businesses are implementing analytics and trying to use data to uncover new insights about their operations, customers, suppliers, employees, and so on. Even though the idea of using analytics is exciting, these types of projects are not for the faint-hearted — at least if you’re trying to implement analytics across the entire enterprise.

For decades, the commercial relationships between companies that provided software development services and their clients have been shaped by either fixed-price/fixed-scope or time-and-materials types of contracts. The drawbacks of both approaches have long been evident, but, nevertheless, both sides have learned to use them to protect their own interests. As we explore in this Advisor, an Agile ecosystem requires the creation of a systemic setup that works with the market, not just selected vendors.

The CTOs of global industrial and manufacturing groups with technology-intensive products and services will tell you that despite the undeniable importance of startups and other external innovation ecosystem partners, internal R&D still needs to be at the core of the innovation effort. External parties are usually unable to make the necessary resource investments for long-term, core R&D. Moreover, maintain­ing leading competencies in core technology areas is usually vital to sustaining competitive advantage. However, there are multiple challenges in the traditional way of aligning R&D strategy and program activities.

Organizational agility is a lifelong commitment. It begins with shared understanding, flourishes with mutual accountability, and perseveres with continual reaffirmation. And there is no finish line. This need for con­stant renewal calls into question the very idea of “Agile transformation,” a term that suggests a one-time organiza­tional metamorphosis rather than a process of ongoing evaluation, assessment, and change. The idea of continuous improvement is indeed continuous — we can always be better.

The existing body of knowledge on ambidextrous principles provides a compelling academic framework but falls short of making it actionable within a real-life corporate context. To overcome this, we have developed the Ambidextrous Organization Development Canvas. In this Advisor, we share how applying our model to a broad range of organizations across multiple contexts has enabled us to decode and understand the underlying DNA of ambidextrous organizations.

Identifying and developing new drugs and conducting clinical trials involve complex and lengthy (i.e., costly) processes that require researchers and drug manufacturers to integrate, manage, and analyze incredible amounts of data while at the same time collaborate with other medical research and pharma companies in their efforts. Pharmaceutical and biotechnology companies are using artificial intelligence (AI) to optimize the discovery and evaluation of new drug compounds, to explore patient and efficacy data, and to develop and bring new therapies to market.

Radical shifts in performance are the consequence of embedding proven technologies in the value stream to overcome factors that have traditionally limited performance. It is often unclear where to start the digital Lean journey and how to prioritize a company’s efforts and resources to drive tangible results. Indeed, choosing from among the plethora of new options provided by digital technologies is a real challenge.

Innovation ranges from new, radical business models (e.g., Uber) to low-technology marketing changes (e.g., Absolut honey-flavored vodka). This Advisor examines four main challenges within the ideation and idea management that is required before commercialization within technology-intensive industries.

The commercialization of cognitive computing holds the promise of transforming organizations through cognitive computing’s ability to ingest, analyze, and summarize massive data sets and to facilitate self-service analytics, intelligent decision support, and smart advisory systems via the application of natural language processing, natural language understanding, machine learning, and other artificial intelligence technologies. There is a tendency to think mainly of IBM Watson when considering commercial cognitive systems, and IBM is certainly the leader in commercial cognitive computing as well as advertising and marketing its cognitive products. However, several companies now offer products based on cognitive technology and the number is growing.

Information gathering and recording are plagued by fragmentation, context switching, and volatility. These problems seem to be inherent to working with data and constitute the data beast. The contradiction between, on the one hand, the search for consensus and, on the other, the fragmentation, context switching, and volatility of information that dilute this effort is a never-ending rodeo ride. The reasons behind fragmentation, context switching, and volatility are misunderstood. This triple plague is often seen as either an imperfection that requires fixing or a roadblock to digitization.

Decision automation means that software — not people — makes decisions. The concept of decision auto­mation is both deceptively simple and intriguingly complex. On the surface, the idea is to write a computer program that uses data, rules, and criteria to make decisions. Decision automation is programmed decision making. A decision automation system replaces and eliminates the need for a human decision maker in a specific decision situation. Through such a system, inputs and events trigger business rules and programmed instructions and then the program “makes” a choice and initiates action. The greatly expanded and evolving computing infrastructure makes it increasingly cost-effective to apply decision automation in situations that previously had been prohibitively costly. Increasingly, decision automation deploys as a distributed, cloud-based application that uses integrated networks and sensors to make decisions in a specific domain.