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.

We believe existing organizational development approaches are not far-reaching or holistic enough when it comes to the scope of the issues they address. Most methods either focus on strengthening the scale/productivity dimension (often within the context of Lean models) or push the speed/creativity dimension (commonly referred to as the Agile model). However, choosing either the Lean or the Agile path does not provide the right mindset and tools to address the complexity and competitive challenges of most large organizations. Moreover, those frameworks that are ambidextrous are not operationally focused enough to enable day-to-day management and lack a link between strategy definition and organizational development. From our experience, these missing qualities are essential to making well-informed business decisions.

We are increasingly hearing about the rise of the “Chief Customer Officer” (CCO), who has the position and the authority to ensure that the organization provides a unified and seamless customer journey/experience (CX) across all customer channels. But just how standard is the role of the CCO among organizations? According to preliminary findings from our ongoing CX management survey, current use of CCOs — or someone with an equivalent title formally charged with overseeing the adoption of CX practices into the organization — is relatively popular, with approximately 27% of the organizations we have surveyed indicating that they have made such an appointment.

Nowadays, companies are struggling to deal with a more and more sophisticated customer. Online and offline touchpoints are generally unbound, failing to create the unique and continuous journey customers expect. Companies must embrace a new approach in order to give strategic relevance and a clear purpose to the digital marketing practice. As described in this Advisor, this approach is based on seven major activities grouped into three areas, which recur iteratively to achieve progressively more accuracy and commercial success.

Artificial Intelligence (AI) — is it hype, a new industrial dawn, or simply a means to increase leisure time? We are putting AI into nearly everything, including our refrigerators and other domestic appliances. So what about Agile teams — how should they use it? AI in project management tools is not new; indeed it has been a decade since global enterprise software company Planview introduced the optimization engine for capacity and demand. However, it is only now that this AI feature is becoming more widely used. This Advisor explores how organizations can use AI to increase the performance of Agile teams by supporting the product owner.

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 explores some of challenges facing organizations looking to adopt RPA.

There is no doubt that artificial intelligence (AI) can significantly enhance team performance regarding the collection, analysis, and presentation of data to teams for record keeping or trend analysis. This theme of indirect assistance continues with the use of AI by Agile coaches to assist with the continuous performance improvement of Agile teams. Most organizations use Agile coaches to support their Agile teams, along with AI tools to synthesize large volumes of benchmark data. The use of robotics is also a great way to automate repetitive and predictable tasks, such as the creation of status reports or the calculation of key performance indicators.

We can characterize the fourth stage of automation, smart automation, by intelligence embedded across customer channels (stores, call centers, websites, etc.), processes, systems, and platforms. Smart automation builds on the previous stages and uses intelligent means to bring automation to all aspects of a business value chain, from customer experience (CX), worker experience, internal processes, and operations to partner collaboration, covering all types of systems. As we explore in this Advisor, however, it is important that smart automation design carefully consider the subtle interplay between humans and machines to understand the nuances of those activities humans are good at and those for which machines are efficient and reliable.

Almost every organization has some room for grassroots Agile initiatives; projects for which no mandatory process has been defined or organizational units where managers are less interested in how their teams work than in whether they deliver expected results. Launching an experiment with an Agile delivery process is therefore relatively simple. It takes a project, a team motivated to try (or demonstrate) how an Agile approach works, and a project sponsor willing either to play the role of product owner or to appoint one. Such a project does not really challenge the status quo; its results are uncertain, so even naysayers tolerate it. In this Advisor, we share some of the challenges of taking such an initiative.

In light of the changing landscape of cyberattacks, it is critical that organizations change the way they address cybersecurity. Relying only on traditional methods of blocking attacks with firewalls, antivirus software, and passwords will be a mistake. Organizations instead need to implement cybersecurity measures that are capable of handling the new breed of cyberattacks. This is where artificial intelligence (AI) comes into play. AI-based cybersecurity solutions use machine learning (ML) techniques, which are a subset of AI. In this Advisor, we explore two broad categories of ML algorithms: supervised learning and unsupervised learning.

With so many variants of the core components of the blockchain definition — like validation, distribution, opportunities, and challenges — it becomes clear that exec­utives may need a matrix or rubric to decide if/how to adopt blockchain based on industry and objectives. Even if your company is not considering automating processes via blockchain, your competitors may be doing so, and government entities may even force your hand. Therefore, it is important to start the internal discussion now. In this Advisor, we’ve shared some questions to help your organization quickly engage in deep discussions.

We are now in the “post-Agile” age. In this Advisor, we introduce some of the cutting-edge ideas from the Agile community featured in this month's CBTJ, including Heart of Agile, Modern Agile, the GROWS Method™, BOSSA nova, and solutions-focused thinking. This issue also digs further into stories of politics in Zimbabwe, entrepreneurship in Pakistan, and running “agile classrooms” in several countries.

Industry proponents have been pushing the idea that artificial intelligence (AI) is set to have a major impact on customer experience (CX) practices. But how do end-user organizations feel about AI’s potential for facilitating CX? After all, they are the ones who will or will not utilize the technology. In this Advisor, we share some initial results from our ongoing CX management survey offer some insight into organizations’ attitudes toward AI’s potential impact on CX practices.