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Scientific studies have long suggested that investing in the right people will maximize organizations’ returns. In line with Pareto’s principle, these studies show that across a wide range of tasks, industries, and organizations, a small proportion of the workforce tends to drive a large proportion of organizational results, such that:
- the top 1% accounts for 10% of organizational output
- the top 5% accounts for 25%, of organizational output
- the top 20% accounts for 80% of organizational output
It is also noteworthy that talented employees are “force multipliers”, raising the performance bar for their colleagues, and particularly for their direct reports. By word and deed, they model and teach winning behaviors that shape high-performing cultures. Simply adding a star performer to a team boosts the effectiveness of other team members by 5-15%. No wonder, then, that study after study shows stronger financial performance in companies that make proportionally greater investments in identifying and developing top talent.
If we are going to invest in the right employees, what are the key indicators that signal star potential?In our view, interventions should focus on predicting who is likely to become a key driver of organizational performance. That is, they should define future stars as the people who will “consistently generate exorbitant output levels that influence the success or failure of their organizations.’’ Fortunately, science reveals that regardless of the context, job, and industry, such individuals tend to share a range of measurable qualities, which can be identified fairly early in the process.
Ability
The first category concerns indications that an individual is able to do the job in question. In forecasting potential to excel in a bigger, more complex job at some point in the future, the question is how likely an individual is to be able to learn and master the requisite knowledge and skill. The single-best predictor of this is IQ or cognitive ability. Learning ability includes a substantial cognitive component but also the motivation to pick up new knowledge and skills fast and flexibly. Potential for performing in a leadership role at the executive level requires strategic thinking and the ability to adapt an organization for the long-term future. In addition to raw intellectual horse power, this involves vision and imagination, as well as an entrepreneurial mindset. Thus early indicators of the ability for senior organizational leadership would also include creativity and a knack for systems thinking.
Social skills
The next big category reflects the growing significance of team work and collaboration in modern organizations. At a basic level, employees have to be able to get along and earn the support of supervisors and coworkers. Social skills involve two fundamental abilities: Employees likely to succeed in bigger, more complex jobs are first able to manage themselves — to handle increased pressure, deal constructively with adversity, and act with dignity and integrity. Secondly, they are able to establish and maintain cooperative working relationships, build a broad network of contacts and form alliances, and be influential and persuasive with a range of different stakeholders. And for senior roles, they have to be able to develop sophisticated political skills — the ability to read an audience, decode the unspoken rules, and find solutions that satisfy the often competing interests of key power brokers.
Drive
The third category concerns the will and motivation to work hard, achieve, and do whatever it takes to get the job done. It is easily identified as work ethic and ambition. Ability and social skill may be considered talent; but potential is talent multiplied by drive as this will determine how much ability and social skills get put to use.
In sum, most organizations could probably upgrade their talent identification processes if they keep things simple and focus on these three generic markers of potential. Not many employees are highly able, socially skilled, and driven — but if you bet on those who are, which involves evaluating these qualities as accurately as you can, you will end up with a higher proportion of future stars who will contribute disproportionately to the organization.
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Senior executives need to understand the tactical as well as strategic opportunities, redesign their organizations, and commit to helping shape the debate about the future of work.
Senior executives have two critical priorities in this world. First is to gain an appreciation for what automation can do in the workplace. While cost reduction, mainly through the elimination of labor, attracts most of the headlines and generates considerable angst, our research shows that automation can deliver significant value that is unassociated with labor substitution, for example, helping companies get closer to customers, improve their industrial operations, optimize knowledge work, better understand Mother Nature, and increase the scale and speed of discovery in areas such as R&D.
As leaders consider this wide range of possibilities, they have a second priority, which is to develop an action plan. That plan should include a view of both tactical and strategic opportunities for their companies, a blueprint for building an organization in which people work much more closely with machines, and a commitment to helping shape the important, ongoing debate about automation and the future of work.
Automation is enabling companies to make the following far-reaching set of moves:
Get closer to customers.
Affectiva, a Boston-based company, uses advanced facial analysis to monitor emotional responses to advertisements and other digital-media content, via a webcam. Citibank works with Persado, a start-up that uses AI to suggest the best language for triggering a response from email campaigns. The results are a purported 70 percent increase in open rates and a 114 percent increase in click-through rates. And Kraft used an AI-enabled big data platform to reinvent its Philadelphia Cream Cheese brand by better understanding the preferences of different consumer segments.
Improve industrial operations.
GE uses machine-learning predictive-maintenance tools to halve the cost of operations and maintenance in certain mining activities and so extend the life of its existing capital. Rio Tinto has deployed automated haul trucks and drilling machines at its mines in Pilbara, Australia, where it says it has seen a 10 to 20 percent increase in utilization in addition to lower energy consumption and better employee safety.
Optimize knowledge work.
It’s becoming more common for a software robot to receive a user ID, just like a person, and then to perform rules-based tasks such as accessing email, performing calculations, creating documents and reports, and checking files. Besides scalability and higher throughput and accuracy, the results include built-in documentation of transactions for audit, compliance, and root-cause analyses. Meanwhile, numerous financial institutions and other companies deploy robotic process automation to collect and process data.
Harness the power of nature.
Land O’Lakes’ WinField United compiles data on US crops to help farmers make key decisions throughout the year, including which seeds to purchase, soil and nutrient requirements, and yield potential. Meanwhile, the Coca-Cola Company’s Black Book model uses algorithms to predict weather patterns and expected crop yields to inform procurement plans for their Simply Orange juice brand, so that no matter what the quality and quantity of the crops, they can be blended to replicate the desired taste. The model also enables the company to overhaul its plans within minutes if weather conditions threaten to damage crops.
Increase scale and speed.
The potential for AI-enabled automation to create scale, boost throughput, and eliminate errors creates a range of opportunities for discovery in R&D. For example, GlaxoSmithKline’s machine-learning-enabled model-selection process helps the company analyze many times more models in a matter of weeks as it could in several months using traditional processes. In the automotive industry, Nissan has cut in half the time it takes to move from final product design to production, thanks to digital and automation. And BMW has reduced machine downtime significantly in some of its plants through AI-enabled condition-based maintenance, effectively generating fresh economies of scale with minimal investment.
This dizzying array of possibilities makes it critical for today’s CEO to develop an automation action plan. A good one will include the three following components.
A tactical and strategic view of the opportunities
As leaders seek to plan and prioritize what they might achieve with automation, they must grapple with two imperatives. First is to examine their current business systems to identify which components will benefit not just from labor savings but from improvements in speed, quality, flexibility, and service.
The second imperative is for leaders to look beyond their current business processes and start imagining how automation will enable them, and others, to make bolder moves. The question to ask: How could a disruptive competitor or a player along the value chain use automation to upend your business model?
A plan for integrating automation into the workplace
The workplace norm for years to come will be people working alongside machines, with profound implications for the way the workforce is structured and organized. Companies will of course have to recruit automation-savvy talent, from experts in sensory or pattern-recognition technologies or natural language processing, to data scientists able to interpret and integrate massive amounts of information, to roboticists who can build, train, and repair intelligent machines.
Simultaneously, however, many workers will need retraining to acquire new skills, focusing on those activities that machines have yet to master, and learning to work more closely with machines. Frequent redeployment, with people shifting to new roles and tasks, will also be a feature of the workplace as automation gathers pace and processes are transformed.
A commitment to participating in a broader dialog on the future of work
For all the positive effects, many questions about the impact of automation on society remain unanswered, particularly regarding employment and incomes. In the past, technological progress has not resulted in long-term mass unemployment, because it also has created additional, and new, types of work. We cannot know for sure whether these historical precedents will be repeated. But we do know that business leaders will be at the forefront of what is afoot as they move to embrace automation. They will be drafting the blueprints of the automated workplace, the first to understand which new skill sets will be needed, which old workplace orthodoxies will be obsolete, and how machines and humans will work together. It falls to them, therefore, to take what they have learned beyond their corporate walls and engage in a broader dialogue to help shape the future.
That may mean pressing home to policy makers the urgency of investing more, not less, in human capital at the very time that machines are taking on more activities. It may mean working alongside educators to pinpoint skill gaps and help establish priorities, as well as funding mechanisms, for lifelong-learning programs that address the needs of workers changing employers more frequently. It may even mean helping to assess the need for new mechanisms that support transitions between employers, and help workers whose wage levels are threatened by automation.
The point is, executives’ vantage point gives them an important voice in the future-of-work debate that needs to be heard if the value of automation is to be captured at the same time as its challenges are addressed.
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Richard Thaler, the University of Chicago professor who just won the Nobel Memorial Prize in Economic Sciences, has inspired scholars across different disciplines and fundamentally changed the way we think about human behavior. He is considered the father of behavioral economics — a relatively new field that combines insights from psychology, judgment, and decision making, and economics to generate a more accurate understanding of human behavior.
Among his many achievements, Thaler inspired the creation of behavioral science teams, often call “nudge units,” in public and private organizations around the globe. Nudges can solve all sorts of problems governments and businesses alike consider important. Here are some examples.
A few years ago, for instance, General Electric’s leaders wanted to address the issue of smoking, believing that it impacted its employees negatively. So, in collaboration with Kevin Volpp and his co-authors, they conducted a randomized controlled trial (think: field experiment). Employees in the treatment group each received $250 if they stopped for six months and $400 if they stopped for 12 months. Those in the control group did not receive any incentive. The researchers found that the treatment group had three times the success rate of the control, and that the effect persisted even after the incentives were discontinued after 12 months. Based on this work, GE changed its policy and started using this approach for its then-152,000 employees.
In research that Thaler himself conducted, defaults were used to increase employees’ savings rates by automatically increasing the percentage of their wage devoted to saving. This is a program called “Save More Tomorrow” (SMarT). SMarT program participants increased their saving rates from 3.5% to 13.6% over the course of 40 months, on average, while savings rates remained stagnant for those who did not participate in the program.
Smoking, savings, honesty, and healthy eating may not be items on your list of problems to address or areas where you’d like to see improvements in your own behavior or the actions of people you manage or lead. But no matter what concerns you, adopting a nudge, as Thaler and the many scholars who followed his approach to research tell us, may lead to a powerful change for the better. It just requires an acknowledgment that human behavior is full of anomalies.
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