In studying agile approaches at more than 50 companies, the authors found that the organizations that achieve the most success with digital projects use processes that allow for continuous learning and that support critical business goals. With everyone following the same processes, companies do better at juggling multiple projects and reaping the benefits of scale. One of the companies studied, Johnson & Johnson, shares its approach.

Few organizations have the resources necessary to transform operations, business models, and customer experience all at the same time. They need to prioritize. Focusing on the transformation of the customer experience should be first. This is not because it’s necessarily the easiest, but because doing so is far more likely to keep a company viable than changing other aspects of business.

Companies that aggressively pursue learning must accept the possibility of failure. But simply setting goals and being nonchalant if they fail is inadequate. Instead, companies should focus organizational energy on hypothesis generation and testing. Hypotheses force individuals to articulate in advance why they believe a given course of action will succeed. A failure then exposes an incorrect hypothesis — which can more reliably convert into organizational learning.

Leading marketers in the travel sector are using machine learning not only to measurably improve business outcomes but to fundamentally redefine what those outcomes should be. Travel marketers who take advantage of the large volumes of data their organizations collect will continue to pull ahead of their rivals.

Oftentimes contending with overwhelming quantities of data, the health care industry could lessen its reliance on intuitive decision-making by making better use of machine learning.

To thrive, retailers must match their belief and investment in machine learning with incentives that clearly connect to their broader strategic goals.

Executives in the automotive sector believe that machine learning can help them achieve their marketing goals, but that doesn’t necessarily mean they invest in that ambition.

Organizations need to focus on how blockchain can be used to support their unique strategies. Without this focus, they risk investing in initiatives that don’t provide meaningful value.

Not many companies are there yet, but there’s a developing framework for what it takes to lead an AI-driven company. Leaders at the forefront of AI have seven key attributes: They learn the technologies; establish clear business objectives; set an appropriate level of ambition; look beyond pilots and proofs of concept; prepare people for the journey; get the necessary data; and orchestrate collaborative organizations.

In the eyes of many leaders, artificial intelligence and cognitive technologies are the most disruptive forces on the horizon. But most organizations don’t have a strategy to address them.

IT alignment can produce inertia — unless it’s accompanied by the right culture. Sure, closely aligning IT with the rest of a company’s strategy can cut costs and improve the ability to collect data, facilitating the creation of early-warning systems and operational dashboards. But a less regimented approach has its place, too, allowing responses to changing business and economic conditions that are swift and creative.

In Part 1 of our eight-part video series, we explore the real power of the digital thread, which lies not just in a “cradle-to-grave” virtual rendering of the manufacturing process, but also in the possibility of taking the lessons learned from analyzing product performance and applying them to future generations of the manufacturing process and product design.

Many executives take the value of best practices as a given. We have an abiding faith in the idea that the most direct route to improved performance is to study what successful firms do and copy them. In reality, that is quite rarely the case.

Our eight-part video series explores how integrated digital technologies can transform business.

There are four different pathways that businesses can take to become top performers in the digital economy. Leadership’s role is to determine which pathway the company should pursue – and how aggressively to move.

Managers in manufacturing companies often keep process innovation activities tightly under wraps. Some companies have good reasons for keeping process innovations concealed. However, the authors’ research suggests that for most manufacturers, such defensiveness deprives companies of a valuable source of ideas for productivity improvement. Many manufacturers, they argue, can benefit from sharing process innovations rather than keeping them secret.

As sexy as it is to speculate about new technologies such as AI, robots, and the internet of things, the focus on technology can steer the conversation in a dangerous direction. Because when it comes to digital transformation, digital is not the answer. Transformation is. In various industries, including banking, paint, and shipbuilding, digital leaders are finding that technology’s value comes from doing business differently because technology makes it possible.

To reach its full potential, the popular innovation methodology must be more closely aligned with the realities and social dynamics of established businesses.

Most of us view our jobs as specialized or somehow differentiated, but the world of business and management increasingly feels otherwise. For many organizations today, the next big driver of job commoditization is automation driven by smart machines. Simply put, if a job is viewed as a commodity, it won’t be long before it’s automated. The key for workers whose jobs have traditionally seemed safe: Highlight the tasks that require a human touch.

Many managers are excited about smart machines but are struggling to apply machines’ limited intelligence. Indeed, computers can process data just fine, but to generate competitive advantage from machine learning applications, organizations must upgrade their employees’ skills. Companies will also need to redesign employee accountabilities to empower and motivate them to deploy smart machines when doing so will enhance outcomes.