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.

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.

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, as we explore in this Advisor, there are multiple challenges in the traditional way of aligning R&D strategy and program activities.

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.

Enterprise solutions providers now offer cloud-based platforms and services to help organizations with their customer experience (CX) management efforts. These platforms can support various CX scenarios, including omni-channel customer engagement, customer behavioral analysis, personalization, social video and messaging (for engaging with customers on social media platforms), customer loyalty, customer satisfaction assessment and measurement, customer intent/journey analysis and visualization, and visual search, among others. In our ongoing survey covering the adoption of CX practices and technologies, we ask organizations about their plans for using cloud CX platforms and services.

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.

Many technologies exist today that have the potential to change the manner in which we get work done. Currently, the software developer job is heavily labor-intensive. Yes, we use software tools to perform many of the repetitive tasks; however, for the most part, the programming job is performed by highly talented individuals who specify, design, code, and test complex pieces of code and make them work. We have attempted to automate such tasks, but we can best characterize current efforts as assistance (helping workers by providing guidance and information) rather than automation (replacing humans with machines). In this Advisor, we identify 20 technologies that have the potential to alter this picture in both the near and long term. Some present opportunities, while others will disrupt our environments. And some will fall on both sides of that equation.

Based on 60 years of study and practice in the field of creativity, we know that creative outcomes must be deliberate. We also know that the creative problem-solving (CPS) process is a universal set of four steps designed to frame a problem, find a novel solution, and formulate a plan of action. As we explore in this Advisor, these CPS steps comprise the building blocks of innovation. First up: Clarify a problem, challenge, or opportunity.

As we are making improvements in human-computer interfaces, we are subtly nudged into realizing that these interfaces are there only because the two worlds — human and computer — exist separately. Computers do what computers do, and humans do what humans do. Yes, computing has bled into the interface between the two, making the line in between a bit easier to traverse. And yes, we will continue to see improvements in this area as we move forward. However, none of these “advances” has accomplished any fundamental change in the division of roles and responsibilities across man and machine; they have not shifted the line between them. Arguably, what we have done over the past couple of decades is merely spread computing’s ability to automate specifiable rules across larger swaths of people. It may not be helpful to think of computer-based systems as tools — as human augmentation — anymore. We may need to rethink how we think about the computing landscape, and consider rejigging our tools of thinking, notably architecture. This Advisor suggests stretching in that direction so that we are positioned more effectively to meet a qualitatively different future as it charges rapidly toward, and at us.

Robotic process automation (RPA) has emerged as a popular technique to automate routine and repetitive human-system interactions across functional domains such as finance, marketing, human resources (HR), and other transaction-processing areas. Adopting such intelligent automation techniques allows businesses to enable efficiencies without major system transformations. Business leaders may find it compelling to invest in RPA tools and resources but should be aware of the foundational work required before rolling out the initial robots. This Advisor surveys the RPA vendor landscape, highlighting intelligent automation adoption across industries with a few user stories.

An organization seeking to become Agile should look to have a preponderance of generalizing specialists on their teams. There is, of course, room for some pure specialists, but too often businesses seek to hire a Python or Linux or Docker guru, when what they really need is someone “good enough” at one of those specialties but who also has broader technological and business understandings. Certainly, hiring a generalizing specialist may have some short-term downside in terms of cranking out the next couple of specialized projects, but most often that cost will be repaid several times over because it will create more cohesive and Agile teams in the long run.

Drones are in the ascent stage of the technology lifecycle — climbing out of the bleeding edge firmly into the cutting edge — and today’s potential for enterprise use of drones is unprecedented. Comparison with the path to maturity of the automobile is an apt one, and UAS (unmanned aircraft systems) industry growth and technology adoption will likely be just as circuitous and full of surprises, frustrations, and rewards.

This situation might sound familiar: your company brainstorms or applies other methodologies to generate a vast quantity of ideas; however, this process doesn’t seem to translate into a healthy, balanced portfolio of R&D projects. Certainly, there is an inherent ”creativity” among your people and a strategy is in place, but somehow the “killer ideas” just don’t seem to emerge. This Advisor examines four main challenges within ideation and idea management.