The cornerstones of large-scale technology transformation

By Michael Bender, Nicolaus Henke, and Eric Lamarre

Source Link

A clear playbook is emerging for how to integrate and capitalize on advanced technologies—across an entire company, and in any industry. 

How does your company use advanced technologies to create value? This has become the defining business challenge of our time. If you ignore it or get it wrong, then anything from your job to your entire organization could become vulnerable to rivals who get it right. The new technologies come with many labels—digital, analytics, automation, the Internet of Things, industrial internet, Industry 4.0, machine learning, artificial intelligence (AI), and so on. For incumbent companies, they support the creation of all-new, digitally enabled business models, while holding out the vital promise of improving customer experiences and boosting the productivity of legacy operations. Advanced technologies are essential to modern enterprises, and it’s fair to say that every large company is working with them to some extent. 

The cornerstones of large-scale technology transformation

In private discussions over the past year, we’ve asked more than 500 CEOs whether they think technology can improve business growth and productivity sufficiently to lift profits and shareholder value by 30 to 50 percent; a great many have said yes. So far, though, that prize has remained elusive for a lot of companies. Consider, for example, McKinsey research highlighting the large number of digital laggards, and the wide gap between them and leaders: digitally reinvented incumbents—those using digital to compete in new ways, and those making digital moves into new industries—are twice as likely as their traditional peers to experience exceptional financial growth. 

Most senior executives recognize the magnitude of the task before them. Although incumbents possess advantages such as hard assets, customer relationships, and valuable brands, those strengths—and the scale that accompanies them—also vastly increase the complexity of digital transformation. Some enterprise-wide technology transformations come up short simply because leaders have a difficult time creating coherent strategies that stitch together their digital priorities with other major business objectives. 

What’s more, even companies that devise sound strategies are likely to encounter two formidable obstacles to using advanced technologies at a transformative scale. The first challenge is the sheer number and breadth of technology solutions required to truly transform an enterprise, often in the hundreds. The second one might be called the “last-mile” challenge: redesigning a company’s processes to capture the value of new technologies, in line with their strategic goals. Both sound technical, but they play out far from the traditional IT organization and create headaches for the business leaders who will need to guide their people toward new patterns of thinking and operating. 

A playbook for overcoming these challenges is starting to emerge across industries. In this article, we’ll explore five cornerstone practices underpinning the progress of successful companies: 

Develop technology road maps that strategically focus investments needed to reinvent their legacy businesses and create new digital ones. 

Train managers to recognize new opportunities and build in-house capabilities to deliver technologies. 

Establish a modern technology environment to support rapid development of new solutions. 

Overhaul data strategy and governance to ensure data are reliable, accessible, and continuously enriched to make them more valuable. 

Focus relentlessly on capturing the strategic value from technology by driving rapid changes in the operating model.

Distributed opportunities 

The first scaling challenge is rooted in the sheer number of solutions that a company typically needs to carry out its digital strategy successfully. Consider, for example, a global mining company seeking dramatic productivity improvement through technology. Boosting the productivity of a mine would typically involve deploying solutions in a half-dozen broad domains such as “better ore-body management through advanced analytics” or “predictive maintenance to reduce maintenance costs and increase uptime.” Each domain, in turn, might contain dozens of more specific opportunities. Predictive maintenance, for instance, can be applied to drills, shovels, and heavy-hauling trucks. For hauling trucks, specific solutions might be needed to deal with operating conditions, drivers’ behind-the-wheel behavior, and the reliability of truck components and systems. All told, we estimate that it takes more than 100 technology solutions to maximize the productivity of a mining operation (Exhibit 1). In industries as diverse as banking, electric power, and retail, we have found that the benefits of technology are distributed among a similarly large number of opportunities. Across the business landscape of AI alone, McKinsey has inventoried more than 400 meaningful use cases. 

Exhibit 1 

 

While some solutions deliver more bottom-line impact than others, none will typically be a “silver bullet” that makes a genuinely transformative impact on its own. And since many technology innovations can be replicated by rivals within a year or two, the advantages they confer seldom last for long. Enduring advantages are more likely to accrue to companies that can sustain a high rate of innovation, consistently introducing new solutions and improving them with proprietary data. 

Creating a few pilot solutions is relatively straightforward, and many companies have done so. During an initial experimentation phase, it’s normal to use technology contractors and vendors to create solutions. But relying on third parties becomes impractical once a company establishes a digital strategy that calls for building a hundred or more solutions. Technology solutions must be tightly aligned with business needs, and as users try them out, they’re likely to discover shortcomings—necessitating progressive refinement. The many handoffs that take place with external providers over multiple revision cycles make this iterative mode of collaboration expensive and inefficient. Scaling up effectively therefore requires ample in-house technology-development capabilities—capabilities that few companies possess. 

The ‘last-mile’ challenge 

The second challenge that arises in technology transformations is capturing the business value of new solutions. Consider the predictive-maintenance opportunity for the mining company described earlier: technology makes it possible to boost productivity by performing maintenance only when a truck’s condition warrants it, rather than adhering to a schedule of preventive measures that are sometimes premature.

But the mining company won’t spend any less on labor and parts or keep its trucks in service longer, unless it changes the work routines of many maintenance-related experts. The reliability engineer minimizes excess effort by learning to triage predicted maintenance events. To prevent the downtime that can occur when trucks no longer come in on a known schedule, the maintenance-planning team creates a new scheduling procedure, and the inventory-management team finds a way of restocking that ensures the right parts are on hand when trucks are brought in. The maintenance team accelerates repair work based on new diagnostic information. And finally, the financial-planning team reallocates the money saved on maintenance to other activities or additional profits. 

This example illustrates a decisive, often overlooked fact about technology transformations: the value of advanced technologies largely comes from performance gains beyond the operating unit or process where a technology is applied. To realize this last-mile value, companies have to train people in R&D, procurement, operations, marketing, sales, support, and other areas to work in different ways. Incumbents routinely underestimate the effort required—if they think about it at all. And the last-mile journey may be even more challenging when the goal is to build entirely new businesses with advanced technologies. 

When a business commits to transforming itself with technology, the cost of changing its operating model can easily exceed the cost of developing the technology solutions. McKinsey has learned that businesses with highly successful analytics programs, for example, are four times as likely as other companies to devote more than half of their analytics-related spending to embedding the use of analytics in their workflows and decision-making processes. A company must therefore look at the release of each technology solution not as the final act in a project but as a turning point that sets up a new phase of operational changes.