In our present age of digital disruption, the fact that digital technologies cause many of the challenges companies face doesn’t mean that technology is necessarily the solution to those problems. Where do companies focus their transformation efforts if not on technology? They focus on their most valuable assets: their people.

Most of us know Moore’s law, that the power of semiconductor chips grows exponentially, not linearly, over time. Moore’s law, though, is only part of the technology equation. There’s another critical law that needs more attention. It’s this: While technology changes quickly, organizations change much more slowly. That means digital transformation is as much a leadership challenge as a technical one.

It can be easy to get caught up in all the uncertainty and speculation around the future of automation, but when we focus on the future, we may fail to see what’s happening right in front of us. In this audio interview, MIT SMR editor in chief Paul Michelman revisits the work of Accenture researchers Paul Daugherty and H. James Wilson to discuss what’s changed in the AI landscape and what leaders should be thinking about next.

Many companies approach the transition from legacy to digital systems as a technology issue when it’s really a people and processes issue. This webinar featuring Gerald C. Kane and Anh Nguyen Phillips, coauthors of The Technology Fallacy: Why People are the Real Key to Digital Transformation, centers upon the strategic and leadership approaches that lead to success in digital transformation.

Data reveals the C-suite recognizes that technical debt — the “price” companies pay for short-term technological fixes — hinders their ability to innovate and adapt in the digital age. One strategy to combat technical debt? Digital decoupling.

Companies are adopting artificial intelligence at an accelerated pace — and learning that developing and deploying AI is not like implementing a standard software program. Before diving into AI systems, companies should consider three principles that can greatly improve the chances for a successful outcome. First, they need to recognize that humans and machines are in this together. Second, they need to teach the AI systems with a lot of data. And third, they need to continually test what the systems have learned.

The danger in using artificial intelligence to expedite inefficient legacy processes is that the technology may prolong the life of systems that limit competitive advantage.

In Part 5 of our eight-part video series, we look at production steps in digital additive manufacturing.

In Part 4 of our eight-part video series, we look at how digital simulation technologies like finite element analysis (FEA) make product testing more efficient and cost-effective in both short- and long-term contexts.

In Part 3 of our eight-part video series, we explore the technologies used for topology optimization and the role they can play in improving component and product performance.

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.

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

Managers may wonder what sets AI apart from all the other highly touted technologies that may (or may not) live up to their hype. The key difference: We now have sufficient processing power to make good on AI’s promise.

Poor communication between managers and technical experts is an obstacle to technology innovation that literally has been present for centuries. To overcome these issues, leaders need to absorb three key lessons about how to manage the inherent tensions between defining technical requirements and achieving valuable business outcomes.

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.

The 2017 Data & Analytics Report by MIT Sloan Management Review finds that the percentage of companies deriving competitive advantage from analytics increased for the first time in four years. Incorporating survey results and interviews with practitioners and scholars, the report finds that companies’ increasing ability to innovate with analytics is driving a resurgence of strategic benefits from analytics across industries. The report is based, in part, on MIT SMR’s seventh annual data and analytics global survey, which includes responses from 2,602 business executives, managers, and analytics professionals from organizations located around the world.

With the benefit of digital technologies, companies are using Big Data to identify supply chain risks and create early warning systems with much greater speed and precision. However, the ability to respond to these signals hasn’t advanced at the same pace.

As digital technologies evolve, managers and employees will need to learn three important skills: partnering with new digital “colleagues,” creating a mindful relationship with omnipresent digital technologies, and developing empathy for the varying technology preferences of their human coworkers. Organizations, for their part, will need to design processes to support these efforts, and managers will need to be both flexible and thoughtful in how they respond.

What’s happening this week at the intersection of management and technology: how to avoid digital black swans; partnering with early-stage startups; the trouble with wearables.

Artificial Intelligence is about to transform management from an art into a combination of art and science. Not because we’ll be taking commands from science fiction’s robot overlords, but because specialized AI will allow us to apply data science to our human interactions at work in a way that earlier theorists like Peter Drucker could only imagine.