Throughout history, new technologies have demanded step shifts in the skills that companies need. Like the First Industrial Revolution’s steam-powered factories, the Second Industrial Revolution’s mass-production tools and techniques, and the Third Industrial Revolution’s internet-based technologies, the Fourth Industrial Revolution — currently being driven by the convergence of new digital, biological, and physical technologies — is changing the nature of work as we know it. Now the challenge is to hire and develop the next generation of workers who will use artificial intelligence, robotics, quantum computing, genetic engineering, 3D printing, virtual reality, and the like in their jobs.
The problem, strangely enough, appears to be two-sided. People at all levels complain bitterly about being either underqualified or overqualified for the jobs that companies advertise. In addition, local and regional imbalances among the kinds of people companies want and the skills available in labor pools are resulting in unfilled vacancies, slowing down the adoption of new technologies.
Before organizations can rethink how to design jobs, organize work, and compete for talent in a digital age, they must systematically identify the capabilities they need now, and over the next decade, to innovate and survive. For more than 10 years, we’ve been studying the impact of digital design and product development tools on organizations, their people, and their projects.1 We’ve found that the competencies companies need most are business-oriented rather than technical. That’s true even for brick-and-mortar companies that are trying to become more digital.
And most companies are beginning to realize that they can’t just hire all-new workforces; there aren’t enough qualified recruits, and the expense would be enormous. Instead, they need to retrain and redeploy existing employees and other members of their communities, in addition to hiring and contracting new ones to fill their needs. However, rapid technological change has rendered skill cycles shorter than ever; key competencies of even a decade ago are passé today, and most of tomorrow’s jobs remain unknown.
Waiting for the fog to clear isn’t an option. Companies must identify and develop the core skills their employees will need going forward. Our interviews, surveys, and case studies have revealed that most companies focus on refining the skills their people already possess, which doesn’t prepare existing employees or new hires for the business challenges they’ll face when using emerging technologies in their jobs. We’ve also found that young digerati, many of whom come into the workforce from narrow academic streams, are typically more captivated by digital technologies than they are by business problems. And yet, given the sweeping changes that the new technologies are likely to bring about, companies would do well to cultivate four broad business-oriented competencies in tomorrow’s innovators.
1. Omniscience
To know it all may be a godlike, even insufferable, goal. But tomorrow’s talent must aspire to understand everything — or at least much more than they currently do — about their businesses. Employees must grasp key connections: links between physical machines and digital systems, between each step of the value chain, between the company’s current and future business models.2 And they must know their customers’ businesses — how and when their customers’ products and services are used, how their customers’ organizational processes work, and the related challenges and opportunities. That’s the only way companies will be able to evolve from selling products and services to delivering outcomes — a process that will likely change the very businesses they’re in.
For instance, a major medical device manufacturer we studied has moved from developing R&D-driven solutions to delivering patient outcomes, which has become possible because of new technologies and big data. The company needed to quickly employ more people with a systemic understanding of everything it does, including patient care and rehabilitation and treatment efficacy. To move the needle on patient outcomes, it’s critical to understand all those aspects of the system and the associated variables. Thus, the business will demand that existing and new employees have a broader understanding about the underlying science, the delivery technologies, and the industry than almost all of them, other than top management, currently possess. Breadth of knowledge cannot substitute for depth, either; employees must also be able to make deep dives into the vertical aspects of the business when necessary.
Let’s consider another example: The Canadian company Dental Wings is using recent advancements in digital design, digital imaging, and additive manufacturing, as well as a collaboration platform, to rethink its dental implant business. From the dentist’s initial assessment to patient recovery, the company has started adopting new technologies to improve its processes and provide better care. For instance, all-new imaging capabilities provide more accurate pictures of the dental site that can be used not only to create digital models for implants, but also to develop tools to help surgeons define the optimal surgical paths. That reduces exploration of the implant site, which helps reduce recovery time and lowers the risk of infection. To innovate at each step, Dental Wings’ employees need to understand how the new processes and systems connect and work together.
The need to know more holds true for people in every function, but especially so in R&D and product design. In the not-too-distant future, product designers who are designing new earth-moving equipment will have to use AI and internet of things (IoT) sensor data to model, analyze, develop, and modify features in near real time. Once in the field, each prototype and its digital twin will operate simultaneously so that the designers will have access to data 24-7. They must be trained to use it to develop improvements for the current model on the fly as well as to better design the next generation of equipment.
In almost every brick-and-mortar company, dozens of digital platforms will have to be coordinated, the data mined, and the insights used in a harmonized effort between the human team and AI systems. Orchestrating all that data, whether from design outcomes or field performance, will require people who understand the value of each data point and how all the pieces fit together. It will also require knowledge across myriad disciplines, such as mechanical and electrical engineering, computer sciences, and product development, because the variables in a complex system interact in many ways. For instance, the location of a sensor on a suspension lever (a mechanical issue) will affect the data that the sensor electrically measures, which will in turn affect the mathematical algorithms that determine the lever’s accuracy. Companies whose employees can manage and navigate complex data-based systems will be best equipped to improve the performance of their products, reduce maintenance costs, and attract and retain customers.
2. Entrepreneurial Mindset
Although it may sound obvious, innovation teams will need to become more enterprising to succeed. They must become boundary pushers in terms of not just the products they wish to develop, but also the processes they use. The two are closely linked.
In large businesses, R&D and product development teams are organized like most other functions. They must follow the company’s guidelines about sourcing hardware, materials, and technologies to do their work and can use only IT-approved tools. R&D must adhere to time-tested procedures and rules for sharing information about or testing prototypes and product designs. And traditional R&D teams usually work in a centralized way, relatively insulated from the outside.
All that works well when business is as usual, but these are extraordinary times. R&D is meant to push technical boundaries, so R&D teams must learn to redraw organizational boundaries to keep pace with technological change. Essentially, they must become digital intrapreneurs, using the latest tools or, if necessary, creating them. That involves experimenting with new software and systems outside those recommended by IT, and even
developing some solutions in-house.
For incumbents, that can be a shock to the system — most people are used to working on proprietary systems and tools, getting things “right” before launch, and offering better products over time. Moving toward open systems, beta versions, and constant iteration can feel like a clash of civilizations in established companies, but they need to do so to innovate for today, as well as tomorrow. Collaboration is central to this effort. One study of 152 managers found that companies that used digital tools for collaboration improved performance — as measured by the number of concepts and prototypes developed — during the early stages of innovation. And another study of 400 companies showed that more-innovative organizations, measured by similar yardsticks, used such tools more frequently than less-innovative ones. Since better collaboration leads to more innovation, the collaborative tools and processes that organizations use are critical. Figuring those out requires an entrepreneurial mindset as well.
For example, at a large company outside Boston, a new digital group is working on completely changing the way the organization designs products. This small team has asked for, and been given, the freedom to use any tools it wants, wherever they may originate. So the team has created a new system from scratch that allows it to test design structures in real time. The group also uses several digital platforms, most developed by unknown startups, to communicate and collaborate both internally and externally. It’s unlikely that IT approves or is even aware of what’s happening, but top management realizes that the company’s digital transformation will never occur if teams like this one are confined by rigid boundaries.
There’s a reason entrepreneurs in high-tech startups are risk-tolerant, and it’s time that intrapreneurs, or innovators in established companies, followed in their footsteps. Look at Proto Labs, which manufactures injection molds and machined parts and offers additive manufacturing services. To accelerate the time it takes to develop the first tooling cuts for its clients, the R&D group quickly developed some software on its own. The program could identify possible manufacturing problems in the digital-parts files sent by clients.
Through its automated platform, Proto Labs R&D communicates any possible glitches it detects directly to clients so that they can rectify those well before production starts. If such revisions were made after test production had begun (as they were in the old days, before the homegrown software existed), the process would have been deemed client-unfriendly and would have cost both the client and the company time and money. Proto Labs has also added downloadable tools and other materials to help clients design better parts, ensuring that everyone in the ecosystem benefits from the process improvements. These offers are the outcome of entrepreneurial actions of Proto Labs employees.
3. Bottom-Line Focus
In a data-driven world, employees need to be just as skilled at thinking about business models as they are at designing and implementing systems. Thanks to IoT and other technologies, companies’ value-capture strategies can be shaped not just by the marketing, sales, and business development functions, but also by R&D and product development. IDEO’s Tom Kelley describes people who look for business opportunities, beyond the current challenges, as cross-pollinators. Fostering that capability will be key.
Product engineers, for instance, must consider what kinds of sensors should be used, their placement, and the data types captured in light of possible revenue streams and cost savings. After all, big data poses as many challenges as opportunities. All hands must be on deck. The number of IoT-connected devices, estimated at around 2 billion in 2006, soared to 11 billion by 2019, and, according to Statista, is projected to touch 75 billion by 2025. Companies are capturing an enormous amount of data: IoT-generated data, estimated in 2016 at around 22 zettabytes (1 zettabyte equals 1 trillion gigabytes), reached 52 zettabytes by 2019 and is projected to hit 85 zettabytes by 2021.
While a company’s digital people may appear to be on the front lines of the data explosion, they also need to be able to figure out what all that data means for the business and how it can be monetized. They must go beyond checking where the data originated, how dependable it is, where it is stored, and whether it has a coherent sequence. All that is useful but has become mere hygiene.
In focusing on business relevance, data technicians should be trained to ask some key questions: Can the data be used to monitor our products’ performance and be offered as a service? Can that be done in real time? How else can the data be analyzed to generate insights about customers and their needs? For instance, can it be used to change the way customers schedule preventive maintenance for our products?
The need to be business-focused throughout the organization can lead to dramatically different customer-facing roles. One fast-growing company we studied develops sensor-based modules for the aerospace, automotive, and medical industries. It recently combined the roles of the product development manager and the product manager in all its lines of business — a radical step that immediately helped speed up cycle times.
To have a product position that is both inward- and customer-facing is unusual even today. Traditionally, the product manager would assess market trends and customer needs while developing working relationships with the company’s clients. He or she would then feed the R&D team — led by a product development manager — the information to develop new products, systems, and solutions, or improve old ones. Once the company combined the two roles, the speed with which new technical solutions were matched with prospects, and vice versa, rose dramatically.
Combing the two roles also created avenues for the cocreation of nontraditional solutions. For instance, by drawing on data from IoT sensors, the company was able to develop several new applications that reduced operating costs in areas that could not be assessed earlier, because the product development/product manager could now understand clients’ pain points as well as all the solutions the company’s technologies could provide.
4. Ethical Intelligence
Machines, overseen by smart humans, will make many design decisions. Though they are innately logical, they lack empathy. That will have consequences for companies, consumers, and society. Doing the right thing will become only more challenging as digital systems become increasingly complex.
People must examine machines’ choices through an ethical lens — and weigh in. Companies will have to figure out how design decisions and digital systems affect each stakeholder and factor in the likely unintended consequences. In industries such as aerospace, automotive, and medical device development, traditional engineering processes like risk analysis and failure mode and effects analysis (FMEA) should also be deployed during the development of digital platforms and products. For instance, when Twitter’s founders created the platform, they didn’t imagine it could be used to influence elections with the use of fake accounts and bots. However, a coder putting the platform through a design FMEA would have identified the possibility well before people caught a glimpse of the platform’s dark side.
Given AI’s potential, every company needs to consciously decide what good judgment looks like. Take the case of Boeing’s 737 Max 8, where, according to recent reports, pilots complained about an issue with the aircraft software while testing it years before 346 people died in two crashes.3 However, those concerns never made it to the Federal Aviation Administration — a tragic failure of ethics at all levels of the company. The countermeasures lie beyond the scope of this article but must include new codes of conduct, fresh corporate responsibility norms, KPIs that reinforce personal accountability, and specialized training.
To embed a watchdog mentality in the culture, companies should provide ethics training — and clearly define what ethical means in their specific context. Moreover, agility may be the norm, but companies still need to be disciplined in terms of process. That means a heightened emphasis on developing tools that improve quality and stop bad design from hurting people. Making processes more digital must not take away from the inherent value of techniques such as control plans and independent testing, whose importance should be engrained in tomorrow’s talent.
As ecosystems develop, companies must use ethical intelligence to consider implications for all their stakeholders. At one open innovation platform, we found ethical breeches by the participants as well as the platform’s management. The lapses affected the quality of ideas and input from the community as well as the trust among stakeholders. Companies must build guardrails into their platforms if they want to keep the faith of society, which already views corporations and intelligent machines with distrust. That could include more visibility into management processes and decisions, a clearer articulation of privacy policies, and better identification and reporting of anomalies in the system. Think of the impact on Facebook’s image if it had reported the issues it experienced with foreign bots in 2016 in real time.
Why Structure Matters
Traditional companies will have to experiment with new organizational structures to get the best out of their people. Otherwise, tensions between well-entrenched managers and digital talent may thwart transformation, and the digital folks may walk out the door.
In their restructuring, it’s important for companies to signal that digital transformation is critical for their futures. One radical approach is to replace the central R&D unit with a digital product design group. A well-known shoe company recently did this. The new group oversees the development of a new approach to product design, testing, and analysis, which will include customized generative design and analysis tools. Top management views this group as spearheading the company’s future product development process.
Another option is to form a digital group that floats from project to project across the organization, as one leading consumer electronics company has done. There, digital experts hover over projects in various businesses and countries, providing input whenever asked or needed. The flexibility reduces the number of digital experts the company needs, even as it helps retain them, because they enjoy the variety of opportunities and challenges the arrangement provides.
Some companies, like Apple, have internal venture teams to develop new products. Others are now doing so with a generational twist by creating new venture teams made up entirely of millennials and centennials to come up with new products and processes. A large pharmaceutical manufacturer we studied invited its youngest employees to conceptualize and implement a new way to connect patients, doctors, and the company during clinical trials for its products. Those employees used their native expertise in mobile technologies and social media to keep all stakeholders informed and involved. Top management let them run the show, without allowing the rest of the organization to interfere. Funded by an internal venture capital panel, the project was tested, and eventually the company rolled it out to a wider audience. All too often, such projects are killed after their conceptualization, but companies that institutionalize entrepreneurial ecosystems can substantially improve their ability to innovative.
To be sure, the goal isn’t to have a bifurcated talent pool in a company but rather an organization in which all the talent works together in a continuum, from hardware-focused experts to digital natives, from baby boomers to centennials. That’s how many design and innovation companies now function, with older designers using sketches and hand-formed foam prototypes while recent graduates go right to CAD software. Interestingly, the approaches can be effective if used together. At one design company we studied, the older designers, who preferred traditional methods, learned over time how the younger designers worked, and the younger ones gained a deeper sense of what they were doing from their older colleagues. It wasn’t long before all the designers, regardless of age, were using digital tools for project management, communication, and collaboration.
It isn’t easy for companies to change, especially from within. Kodak’s middle management was skeptical of digital technology, for instance, and internal inertia was one of the key reasons it failed to make the transition from physical film.4 However, identifying and bringing in the skills needed to move forward with innovation can help kick-start the transformation process. Indeed, doing so may make all the difference between success and failure.
Image courtesy of Jim Frazier/theispot.com & Unsplash
Source: MIT Sloan Management Review
