At the law firm Allen & Overy, the idea of replacing traditional, annual performance appraisals with a technology-enabled continuous feedback system did not come from human resources. It came from a leader within the practice. Wanting something that encouraged more-frequent conversations between associates and partners, the senior lawyer read about what companies like Adobe were doing, and then asked his firm to help him create a new approach. When the new system, Compass, was rolled out to all 44 offices, the fact that it was born of a problem identified by internal staff helped accelerate the tool’s adoption across the firm.
In an era of transformative cognitive technologies like AI and machine learning, it’s become obvious that people, practices, and systems must become nimbler too. And because organizational change tends to be driven by those who most acutely feel the pain, it’s often line managers who are the strongest champions for “talent tech”: innovations in how firms hire people, staff projects, evaluate performance, and develop talent.
But as we have observed in our research, consulting work, and partnerships with dozens of Fortune 500 companies and top professional services firms, the transition to new and different ways of managing talent is often filled with challenges and unexpected hurdles. Gaining the most from talent tech, we find, depends on the adopting firm’s ability to confront, and ultimately reinvent, an often outdated system of interlocking processes, behaviors and mindsets. Much like putting a new sofa in the living room makes the rest of the décor look outdated, experimenting with new talent technologies creates an urgency for change in the rest of the organization’s practices.
While the jury is still out on the long-term impact of many of the talent tech experiments we have witnessed, we have observed five core lessons from those firms that seem to be positioning themselves most effectively to reap their benefits:
- Talent tech adoption must be driven by business leaders, not the C-suite or corporate functions.
- HR must be a partner and enabler — but not the owner.
- Fast-iteration methodologies are a prerequisite, because talent tech has to be tailored to specific business needs and company context and culture.
- Working with new technologies in new and nimbler ways creates the need for additional innovation in talent practices.
- The job of leaders shifts from mandating change to fostering a culture of learning and growth.
Let’s look at these one by one.
1. Talent tech adoption must be owned and driven by business leaders.
Many business leaders we have spoken with have stressed: It’s not about the technology, it’s about solving a problem. It’s no surprise then, as we have observed, that talent tech projects have a greater likelihood of succeeding and scaling when they are driven by the business line — and not by top management or functional heads in HR or IT. Because operational managers are closer to the action, they have better insights into specific business challenges and customer pain points that can be addressed by new technologies.
As a VP charged with talent tech innovation at a large consumer products company told us: “We started our digital transformation top-down, creating a sense of urgency and cascading it down. Now it’s much more bottom-up because you have to experiment, you have to do things that are relevant in the field. The urgency has to come from inside the individual instead of top management.” The company organized a series of road shows that exposed high-potential managers to new developments in AI and enabled them to propose and run with projects of their own.
Putting responsibility for innovation in the hands of those who are closest to customers, and reducing layers of control and approval, increases the likelihood that the talent technologies will be fit for purpose. But for a generation of senior managers and functional heads raised on a steady diet of “visionary leadership,” this more adaptive approach does not always come naturally.
2. HR must be a partner and enabler — but not the owner.
Not only are line managers closely connected to business imperatives, but they are also eager to move fast in technology adoption. They want to seize on the promise of AI, machine learning, and people analytics to improve business results and enhance their career prospects. But their priorities can conflict with other parts of the business.
At one of the companies we worked with, a young, ambitious manager experimented successfully with an on-demand talent platform for staffing employees on projects. But the experiment raised questions, for example, about what latitude bosses had in deciding who’d be allowed to take on extra projects and about whether performance on these extra projects could or should count toward an employee’s annual appraisal and compensation. HR was not involved early enough, was more attuned to the risks than the opportunities, and opposed scaling the project further. Only after a lot of stakeholder management and leadership intervention did the pilot get back on track.
The ramifications of reimagining work are far-reaching, necessitating talent strategies built on the ability to access the right people and skills at the right time and then put them to work in flexible ways for which they will be coached and rewarded. But if middle managers wind up caught in bureaucratic procedures and rule-enforcement mindsets, implementations will falter. That’s why getting buy-in from HR early in the process is so important — and necessary for scaling up when pilots yield promising results.
3. Knowing how to use lean, self-managing team methodologies is a prerequisite.
Because AI-powered tools like on-demand talent platforms and project staffing algorithms are not simply “plug and play,” it can be helpful to use methods such as rapid prototyping, iterative feedback, customer-focused multidisciplinary teams, and task-centered “sprints” — the hallmarks of agile methodologies — to determine their usefulness.
For example, one large industrial company needed a better way to get people on cross-functional projects. Information about people’s skills and capabilities was dispersed across siloed business lines. Rather than attempting to build out a comprehensive system to identify and match employees across all the projects (and the silos), the company piloted the idea with only a few projects and a carefully selected pool of employees. Starting small allowed extremely fast learning and iteration, broader scaling, and more-complex uses of the system.
We have worked with a range of companies that are experimenting with technology platforms that catalogue projects that need doing, match project needs to skill supply, and then source appropriate talent. In each case, significant modifications were needed to adjust to specific requirements. And in most cases the data necessary to run the new systems existed in different formats residing in silos. Companies that lacked experience with lean methodologies had to be trained to operate as agile teams in order to define a specific use case for the technology. This learning curve is often the culprit behind implementation processes that take significantly longer than managers expect.
4. Talent tech raises urgency for further talent innovation.
Much has been made of the scarcity of AI engineers, along with the fact that the precious few are quickly snapped up at huge salaries by the usual suspects — Amazon, Apple, Google, and Facebook. Beyond the hype, many firms are finding that they cannot hire the talent they need (because the top experts prefer to be free agents or already work for competitors) and that the skills and capacities they need evolve rapidly or are best sourced externally. These trends are fueling a strategic shift from acquiring talent to accessing talent on an as-needed contract basis; yet the cultural hurdles to staffing externally can be as, if not more, challenging than the technological ones.
One organization we worked with did not have a good mechanism in place for prioritizing the work requested of its shared internal consulting services. Its highly skilled consultants were responding on a first-come, first-served basis and fielding more demands than they could handle. Often they were also the wrong demands. When the team didn’t have the right people on the team for the work, they’d either do their best to complete it themselves or abandon it altogether. An analysis revealed that a good portion of the work could be better done externally by highly skilled contractors, and in fact the team could dramatically increase its ability to provide value across the organization if it could access a specific set of external expertise. But implementing the change was a challenge because the unit’s internal clients felt “safer” working with internal employees.
Once one part of the people system changes significantly, the pressure is on to change related processes. Companies that have shifted to more-agile ways of working have also found that they can no longer evaluate people once or twice a year on their ability to hit individual targets; they now need to look at how people perform as team members, on an ongoing basis. All of this is driving a shift from annual performance assessments to systems that provide feedback and coaching on a continuous basis, as firms ranging from Allen & Overy to Microsoft have found.
5. Leaders must foster a culture of learning.
One CTO we spoke to tells a story about an AI project that “hit the wall” despite a sequence of green lights. “It was over-administered,” she explained. “We had specified detail into 2019.” As reality on the ground began to diverge from the plan, the people in charge of executing the plans failed to speak up and the project derailed. Without people who feel an “obligation to dissent,” she concluded, it’s hard to innovate.
Across industries and sectors, practitioners and academics seem to agree on one thing: Successfully piloting new technologies requires shifting from a traditional plan-and-implement approach to change to an experiment-and-learn approach. But experiment-and-learn approaches are by definition rife with opportunities for failure, embarrassment, and turf wars. Without parallel work by senior management to shift corporate cultures toward a learning mindset, change will inch along slowly if at all.
When Microsoft CEO Satya Nadella took charge, for example, he saw that fear — and the corporate politics that resulted from it — was the biggest barrier to capturing leadership in cloud computing and mobility solutions. A convert to Carol Dweck’s idea of a growth mindset — the belief that talent is malleable and expandable with effort, practice, and input from others — he prioritized a shift from a “know it all” to a “learn it all” culture as a means to achieving business goals. Today, not only does Microsoft rank among the top firms in cloud computing, but the company is also “cool” again in the minds of the top engineering talent it needs to compete.
There is a lot of fear about the speed and scope of technological change, and it’s perhaps most acutely felt by the middle-management survivors of years of corporate layoffs. Fear does not make people more open to experimenting; rather it leads us to put all our energy and ingenuity toward protecting ourselves — and that is lethal for innovation. That’s why the critical task for leaders in a world in which machines will do more and more of their routine work is to enable a shift, from valuing being right, knowing the answers, or implementing top-down changes, to valuing dissent and debate, asking good questions, and iterating to learn.
This article first appeard on hbr.org.