Software continues to “eat the world,” but often at the cost of user privacy and trust. In order to grow responsibly, companies must move from a “can we” to a “should we” framework when bringing products to market.

The art of bian lian — or “face changing” — is integral to Sichuan opera: A main character changes masks to avoid capture by foes. The transformation is quick and surprising, the new face clearly different. In the theater of business, Chinese performers are undergoing a rapid transformation of their own as they seek to evolve from backroom producers to the world’s leading face of innovation.

Emerging technology and new business models have created new ways of serving customers and allowed digital leaders to disrupt traditional companies. Disruption rarely comes out of nowhere, however: There are common patterns to learn from and three major signals to recognize in evaluating the risk for your industry.

As digital disruption expands, many legacy businesses seek partnerships with tech companies to maintain competitiveness in the digital sphere. But instead of a centralized “hub” partnership, some companies find greater success through an adaptive ecosystem model, where partners develop significant projects or innovations together. This type of strategy requires imagination and flexibility.

Using industry examples and findings from the McKinsey Global Institute’s research, presenter Jacques Bughin describes the current state of AI and offers strategies for maximizing its potential.

Companies used to spend years clarifying business requirements before they would even think of launching new software. Today, cheaper cloud-based apps mean that implementation decisions are made on the fly — and there’s no going back.

Mature companies often lack the vision and the commitment to fully commit to new technologies — even when consumers are ready for them. This leads firms to develop watered down products with limited capabilities and leaves them exposed to upstart competitors.

For more than a century, economies of scale made the corporation an ideal engine of business. But now, a flurry of important new technologies, accelerated by artificial intelligence (AI), is turning economies of scale inside out. Business in the century ahead will be driven by economies of unscale, in which the traditional competitive advantages of size are turned on their head.

The patent system and the standards system, which have together kindled a generation of unparalleled technological advancement, are being wrongly targeted as impediments to future progress. Public policies aimed at weakening the patent and standards systems risk stalling the pace of technological advancement.

Systems using artificial intelligence increasingly match or surpass human-level performance, driving great expectations and soaring stock prices. Yet measured productivity growth has declined by half over the past decade.

A wide range of evidence shows that U.S. research efforts are rising substantially, but at the same time, research productivity is sharply declining. Optimists hope for a fourth industrial revolution that will raise the bar again, while pessimists lament that most potential productivity growth has already occurred.

Collaborations between companies and universities are critical drivers of the innovation economy. As many corporations look to open innovation to augment their internal R&D efforts, universities have become essential partners. However, companies often struggle to establish and run university partnerships effectively.

As digital technology advances, the opportunity to use it to create a more sustainable, equitable world should not be overlooked. The first step: Define key terms and set up a framework for understanding how the digital revolution can also become a revolution for sustainable development.

It’s not smart to base any part of your strategy on what you see in the rear-view mirror — and that’s particularly true when you develop strategies for navigating modern, thorny environmental and social challenges. The norms and expectations about how companies manage sustainability issues are shifting fast: Just six years ago, only 20% of the S&P 500 companies produced sustainability reports, while by 2016, 82% did. Change is coming to business — and executives need to adjust.

Just when you have your data collection and analysis systems in place, technology changes mean that your company needs new, updated systems. This is a problem for many companies — but it can also be an opportunity. Organizations that work with data from old and new equipment can learn more about the shortcomings that modern techniques have in this context and can gain advantage in developing tools that no one else has.

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.

A global survey by the McKinsey Global Institute finds that AI is delivering real value to companies that use it across operations. C-level executives report that when they adopt AI at scale — meaning they deploy AI across technology groups, use AI in the most core parts of their value chains, and have the full support of their executive leadership — they are finding not just cost-cutting opportunities, but new potential for business growth, too.

The key problem facing organizations with respect to digital disruption is the different rates at which people, organizations, and policy respond to technological advances. The gaps between innovations and their adoption — and organizations’ ability to adapt — pose a challenge for companies.

Detailed and data-rich insights won’t help your company if your employees don’t know where to find them — but that’s a problem AI can solve. Machine learning can enable faster organizational learning by helping each employee quickly understand what others in the organization understand — forming a knowledge distribution network.

Thanks to emerging technologies like 3-D printing, manufacturers can offer consumers customized products and do so with unprecedented speed. Intrigued by a new product you saw in a YouTube video? Well, someday soon you may be able to personalize it, order it via the company’s website, and have it in your hands in a matter of days. But to enable this phenomenon at scale, an entirely new model of supply chain is required.