This article focuses on the use of IT systems to meet rapidly changing customer needs. It places particular emphasis on the building of IT “systems strength.” This is defined at Accenture as a measure of a company’s ability to innovate at scale. It is calculated by assessing three factors: tech adoption, tech depth, and organizational culture. The article builds on multiple large sample surveys, as well as in-depth interviewing with large organizations. The second part of the paper considers what to do if your company is not a market leader. Here, the authors explain how “middle-of-the-pack” organizations can leapfrog other firms to accelerate their innovation-led growth. The key here is to change the IT budget allocation from an emphasis on operations to an increased emphasis on innovation.

Research shows that companies that are investing heavily in digital technologies to harness the power of human-machine collaboration are dramatically improving their bottom lines. But it takes people to conceive of and manage the innovations, and the authors are convinced that success in the future depends on a human-centered approach to artificial intelligence (AI). In this article they present their IDEAS framework, which calls for attention to five elements of the emerging technology landscape: intelligence, data, expertise, architecture, and strategy. The authors discuss each of these in turn, examining how companies such as McDonald's, Etsy, and the online grocer Ocado have implemented human-driven AI processes and applications to become leading players in their industries. If you're eager to transform your own business, the IDEAS framework can help you develop a road map for AI-enabled innovation.

Buying a high-tech solution that appears to quickly address an identified need or problem is tempting, especially during the COVID-19 pandemic. So why resist? In short, the attractive easy out rarely pays off in the long run. According to the authors’ research, the ability to resist tempting but low-value IT investments is a hallmark of higher-performing companies. This article examines five temptations resisted by high performers, and outlines what they chose to do instead. The first temptation many companies face is to limit the application of new technologies to quick-payoff, high-visibility processes. The higher-value choice is to reimagine business processes across the company, not just in customer-facing areas. The second temptation is to patch or “lift-and-shift” legacy systems. The higher-value choice is to see the cloud not simply as a data centre but as a catalyst for innovation. The third temptation is to experiment with “hot” technologies in isolation. The higher-value choice is to identify foundational technologies and adopt them based on their potential effect on the entire organization. The fourth temptation is to rely on standard classroom training programs. The higher-value choice is to take a more flexible, future-focused approach. The fifth temptation is to move responsibility for IT to business leaders. The higher-value choice is to embrace systems that break down boundaries constraining data, infrastructure, and applications.

The continued spread of AI has stoked fears of catastrophic job losses in which machines and algorithms run the world, but research by Accenture paints a more encouraging picture. Many old jobs are changing and new ones are emerging in the “missing middle,” where humans and machines work together. The authors see evidence of at least eight novel skills that draw on the fusion of human and machine talent within a business process to create better outcomes than could be achieved when working independently: 1) taking back your time and doing something positive with it (with AI systems taking over many rote tasks, workers must redirect their time to more distinctly human tasks like interpersonal interactions, creativity, and decision-making; 2) anticipating unintended consequences and normalizing the use of technology and AI; 3) utilizing human judgment (AI can get many things right, but it still doesn’t know how to read situations and people well enough); 4) asking the right questions of AI; 5) helping bots help workers extend their human capabilities, business processes, and even careers; 6) understanding how machines work and learn and adapting accordingly (i.e., melding); 7) being a willing learner from AI and teacher to AI; and 8) reimagining just about everything. For years, the dream was to create an AI that could rival the intelligence of people. Today, we’re seeing that practical AI is becoming a tool to extend our own human capabilities at work.

What if, instead of perpetuating harmful biases, AI helped us overcome them? What if our systems were taught to ignore data about race, gender, sexual orientation, and other characteristics that aren't relevant to the decisions at hand? They can do all that -with guidance from the human experts who create, train, and refine them.

Artificial intelligence is transforming all sectors of the economy, but there's no reason to fear that robots will replace all human employees. In fact, companies that automate their operations mainly to cut their workforces will see only short-term productivity gains, say the authors. Their research, involving 1,500 firms in a range of industries, shows that the biggest performance improvements come when humans and smart machines work together, enhancing each other's strengths. People need to train AI agents, explain their outputs, and make sure they are used responsibly. AI agents, in turn, can assist people with information gathering, data crunching, routine customer service, and physical labor, thereby freeing them for higher-level tasks that require leadership, creative thinking, judgment, and other human skills. To get the most out of AI, companies need to redesign their business processes. After deciding what needs improvement--their operational flexibility, speed, or scalability; their decision making; or their ability to personalize products and services--they can devise appropriate solutions. That will mean not only implementing AI technology but also developing employees who can work effectively at the human-machine interface. The authors describe how a number of firms are already taking these steps and optimizing collaborative intelligence. But many more should follow their example.

This is an MIT Sloan Management Review article. A new global study finds several new categories of human jobs emerging. These roles are not replacing old ones. They are brand-new positions that complement the tasks performed by AI machines and will require skills and training that have never before been needed.

A look at the emerging field of "physiolytics"--the practice of linking wearable computing devices with data analysis and quantified feedback to improve workers' performance. Examples include the British grocery chain Tesco, whose warehouse employees have armbands that track the goods they assemble; Bell Canada, whose technicians enter data on wrist-worn PCs; and the French video game publisher Ubisoft, which developed a finger-clamp sensor that measures levels of stress.

The process of becoming self-aware has traditionally been based on intuition and anecdotal feedback. But that reality is changing, thanks to the growing discipline of auto-analytics--the practice of voluntarily collecting and analyzing data about oneself in order to improve. H. James Wilson, a senior researcher at Babson Executive Education, introduces some of the auto-analytics tools that are increasingly being used by employees at all levels and across many industries. Citing tools in three domains--the physical self, the thinking self, and the emotional self--he describes the practical results that specific people have achieved after taking power in their own hands to gather and interpret data about the nuances of how they work and feel every day. The best outcomes are realized when, Wilson argues, the enterprise of self-measurement is undertaken with a plan in mind. It's still the early days for auto-analytics, but two trends are emerging: The tools are becoming more precise, and the analysis they offer is becoming more holistic. Applied the right way, auto-analytics is beginning to provide the hard evidence that people need and want to make their work and personal lives more productive and satisfying.

Having analyzed more than 1,100 companies across a range of industries and geographies, the authors outline four strategies firms are using to make smart use of new forms of communication, depending on their tolerance for uncertainty and the levels of results sought.

There's an unsung hero in your organization. It's the person who's bringing in new ideas from the outside about how to manage better. This is the manager who, for instance, first uttered the phrase "Balanced Scorecard" in your hallways, or "real options," or "intellectual capital." Managerial innovation is an increasingly important source of competitive advantage--especially given the speed with which product innovations are copied--but it doesn't happen automatically. It takes a certain kind of person to welcome new management ideas and usher them into an organization. The authors recently studied 100 such people to find out how they translate new ideas into action in their organizations. "Idea practitioners," as the authors call them, begin by scouting for ideas. All of them are avid readers of management literature and enthusiastic participants in business conferences; many are friendly with business gurus. Once they've identified an idea that seems to hold promise, they tailor it to fit their organizations' specific needs. Next, they actively sell the idea--to senior executives, to the rank and file, to middle managers. And finally, they get the ball rolling by participating in small-scale experiments. But when those take off, they get out of the way and let others execute. In this article, the authors identify the characteristics of idea practitioners and offer strategies for managing them wisely.