ITM President and HR Bartender blogger Sharlyn Lauby recently discussed the current war for skilled talent. “It’s a candidate-driven market,” she said. “Finding the right people for the job is becoming increasingly difficult. The worst situation is when a company gets a new piece of business, but doesn’t know if it can deliver the service because it isn’t staffed appropriately.”
Many HR professionals already recognized that the current state of performance management, which often involves forced rankings, is untenable. As Lauby said: “You can sell $10 million worth of business and still be the worst salesperson in the company. Instead of talking about performance in the past, we need to shift the conversation to the future.”
Twenty-first century performance management must be about tying employees to the business strategy. According to Lauby, there are several opportunities to create alignment, and they arise well before a candidate signs on the dotted line. “Setting performance expectations before a new hire joins helps with engagement, results, and retention,” she said. “You can facilitate this by keeping your job descriptions accurate and up to date, and having an interview process that is collaborative and uses behavioral-based questions.”
Alignment should occur at onboarding as well. HR professionals and hiring managers should use the onboarding process to educate employees on how their performance will be measured, and to provide transparency about the performance management process itself.
Employee goal-setting, particularly in the form of cascading goals in which individual objectives connect directly to bottom line objectives, is a critical piece of effective modern performance management. Best practices for setting relevant goals include getting buy-in, documenting what you intend to do, phasing goals in over time, monitoring progress, modifying them as appropriate, abandoning goals that no longer make sense, and celebrating those that have been achieved. This last one should not get lost. “People who accomplish goals want to set more of them,” said Lauby.
My company is scared of people analytics. What if the results show that something is really wrong?
People analytics involves the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. It’s now possible to examine data regarding recruitment, performance, employee mobility, and other factors, and use it to determine what does (and doesn’t) drive business results.
People analytics holds the promise of incredible value. It’s not hard to get started, and analytics implementations are known to pay for themselves far more quickly than other types of technology. Yet there is fear: fear of risk and compliance issues, fear of a lack of available talent to leverage data insights, and fear that data is simply too “all over the place.”
But the biggest fear of all is exactly what this participant articulated: that having accurate measurements of talent management practices will reveal weaknesses. This scenario reminds me of the time I asked a fellow HR speaker why she didn’t use evaluation forms. She said: “I’m afraid they will tell me I’m bad.”
By passing up the opportunity to read evaluation forms, the speaker could not learn from her experiences. She would keep doing things the way she’d always done them, and if her approaches weren’t effective, she’d never know. As other speakers got hired over her, she’d lose business.
People analytics is not a panacea (i.e. implement it, and your talent management practices will be immediately bulletproof). As a human-run organization, you are bound to make mistakes, and bound even to fail totally in some cases. But people analytics will help you identify problems and address them sooner, before they put your business in major hot water. People analytics offer rare opportunities to strengthen your organization, to make it a better place to work and a better place from which to buy products and services.
Laurano and her colleagues found that onboarding is the linchpin to everything related to talent management, and both employers and candidates are asking for a new hire experience that is continuous, dialogue-driven, and meaningful. Seventy-three percent of Aptitude’s respondents said that their top talent acquisition priority was a consistent experience for both candidates and employees, while 83 percent said their top engagement priority was a consistent experience.
Consistency, however, is exactly what is lacking. Sixty-seven percent said they currently have a consistent experience for candidates and employees, but less than 40 percent said they have improved their employee experience, while greater than 60 percent said they have improved their candidate experience. Those numbers speak for themselves.
Not only are our onboarding experiences relatively inconsistent, they are also far too complex. In the last four years, companies doubled their spending on HR technology from $400 million to approximately $2 billion. Fifty percent of respondent companies are using 3+ onboarding solutions, and 1 in 5 plans to increase their onboarding technology investment in 2017. Unfortunately, though, all of these systems have made the onboarding experience quite complicated, and respondents agreed that streamlining and simplifying it is essential. Fortunately, technology providers are now offering experience systems, which differ from traditional HR systems in that they span the entire employee experience, integrate various technologies, are optimized for mobile, and are analytics-based.
Most know that diminished engagement has reached a crisis point – only 30 percent of workers are engaged. Eighty nine percent of Glassdoor users are either actively looking for new jobs or would consider better opportunities. And 93 percent of workers that took a new job did so outside their current organizations.
According to analyst Ben Eubanks, our mistake as HR professionals lies in thinking that our leaders are focused on engagement as a unique outcome. But engagement is typically not the end variable. Rather, CEOs want to move the needle on productivity, customer satisfaction, retention, and revenue. The key is to understand how employee engagement impacts all of these things.
For example, per IBM, employees who score highly on the experience index, which is correlated with engagement, gave 40 percentage points more discretionary effort and were therefore far more productive. Per Aon Hewitt, teams of engaged employees saw an additional 25 percent growth in Net Promoter Scores. The Corporate Leadership Council found that engaged employees have the potential to reduce staff turnover up to 87 percent, while Hay Group discovered that engaged workforces lead to 2.5 percent annual revenue growth.
In building a business case to focus on engagement, you must make an effort to use the same language as your leaders. Eubanks suggested that you gather data on these metrics (productivity, customer satisfaction, retention, revenue, etc.) as they currently exist in your business. Plot them against your employee engagement data and note any connections or correlations. What are your strongest conclusions? For instance, is there a team that’s driving the greatest business value that you need to focus on engaging and retaining?
Until recently, professional service consisted of human providers having real-time, face to face interactions with customers that were rewarded according to the amount of time spent. In this traditional model, advice or products are highly customized, focusing on the needs of individuals.
But in the near future, disruptive new models for the production and distribution of expertise are emerging thanks to advances in digital technology. According to the book The Future of the Professions by Richard and Daniel Susskind, these models include the networked experts model, the paraprofessional model, the knowledge engineering model, the communities of experience model, the embedded knowledge model, and the machine-generated model. Let’s examine each in a bit of detail.
The Networked Experts Model
Like the traditional model, this model involves human service providers, but unlike the traditional model, where experts work alone or in relatively stable organizations and groups, when experts are networked they convene as virtual teams. Groups of specialists, often online freelancers, use online platforms to interact and communicate with each other, forming transitory affiliations to solve specific problems. Professionals might not know one another, and service is more likely to be ad hoc than in the traditional model. Online project management systems are an example.
The Paraprofessional Model
In this model, service is provided via consultation from one human being to another. However, the provider is a person with more rudimentary training in a discipline. The paraprofessional cannot provide the full professional service unaided, but rather is equipped with procedures, systems, and support tools that have been previously created by experts. Ownership of the intellectual property that results from collaboration between experts who provide the guidance and paraprofessionals who deliver it is shared by both. A junior teacher who supports her curriculum with world-class online lectures is an example of this model in action.
In a research paper, Understanding the Long-Run Decline in Interstate Migration, Greg Kaplan and Sam Schulhofer-Wohl of the Federal Reserve Bank of Minneapolis analyzed the secular decline in interstate migration in the United States between the early 90s and the early aughts. They found that while gross flows of people across states are about 10 times larger than net flows, they declined by around 50 percent over the study’s 20 year period.
The latest U.S. Census numbers support this decline. According to Richard Florida at CityLab, just slightly more than one in ten Americans (11.2 percent) moved between 2015 and 2016, almost half the 20.2 percent rate back in 1948, when the Census began tracking American mobility. “Mobility was once the cornerstone of the American Dream, but today Americans move less often than Canadians, and only a bit more than Finns or Danes,” he wrote.
Why Mobility Is Falling: The Research Speaks
It’s arguably easier to move out of state now than it was in the past, so why are fewer Americans doing so? After looking at microdata on the distribution of earnings and occupations across space, the researchers assigned this fall in migration to a decline in the geographic specificity of occupations, together with an increase in workers’ ability, before moving, to learn about other locations and assess how much they will like living there through information technology and inexpensive travel.
In other words, you don’t necessarily have to relocate to Silicon Valley to work in software development, and if you were thinking about such a move, you might decide through online research that it would be dumb to trade a 10 percent salary increase for a 60 percent increase in living costs. So you stay put, whereas before the Internet you would have had less information available, made the move, and potentially regretted it.
In a related CityLab piece from a few years ago, Plumer speculated that today’s economy actually mandates fewer overall changes in jobs and careers, meaning opportunities to move would simply come up less often. And, he wrote, “in our increasingly specialized labor market, people increasingly sort into the places and jobs that best fit them far earlier in their careers. Those who want to be in finance head to New York while they’re still young, just as those who want to be in film head to L.A.” After a while, it becomes prohibitively expensive to leave that geography, so people stay.
Depending on your professions, a move across state lines could actually negatively impact your career. For example, lawyers and doctors are licensed at the state level and might not want to go through the trouble of getting the necessary credentials in a different state, even if the state in question pays better or is experiencing greater demand. Similarly, a high school teacher who moves out of state after working for many years loses both his seniority and his pension eligibility.
Supply chains are complex operations. Inevitably, there are hundreds (or even thousands) of people involved in each one, and manual tasks that are prone to error often dominate the process. But because tasks are usually interdependent, one problem or mistake can throw the whole system into chaos.
Digital transformation, however, is playing a major role in supply chain management, changing the way organizations deliver products, fulfill orders, and conduct operations. To start, in a recent article for Supply and Demand Chain Executive, Ray Barratt explained the value of process robotics.
Backend Software Drives Process Robotics
“Process robotics works by automating the entire supply chain from end to end (not just individual tasks) – enabling all different sections to be managed in tandem. The adoption of software robotics allows professionals to focus less time on day-to-day processes and, instead, provides more time to drive value for the entire business,” he wrote.
Process robotics provides a centralized approach to procurement, shipping, warehousing, and inventory management. In essence, it involves teaching automation software how jobs are completed. Barratt called this embedded process know-how. “The tasks are completed on a job-by-job level, but coordinated as an entire unified process, allowing the interdependent sections to work in tandem,” he said. “For example, if the robotics solution detects that a warehouse is full due to a lack of inventory movement, it automatically alerts/halts procurement, or adjusts to a new storage location if one is available.”
HR used to be easy. Hand out cut-and-dry information, and make sure said employees consumed said information. But today, technology innovations impacting HR business processes have professionals simultaneously looking for ways to uplevel the function, and fearing that machines will take their jobs. Specifically, HR pros have had to contend with the following transformations, as the field moves from:
Transactional HR to Strategic HR
Uni-Directional Communication to Cross-Directional Communication
Legacy Human Capital Management Systems to Flexible HCM Systems
Technology Product Use by a Few to Technology Product Use by Many
Technology Product Use in the Office to Technology Product Use Anywhere
Digital Posting to Digital Relationship Building
Static Online Learning to Experiential Online Learning
The Rise of Disruption
Almost 20 years ago, Harvard Business School professor Clayton Christensen first coined the term disruptive technology, referring to technology that displaces an established technology and shakes up an industry or a groundbreaking product that creates a completely new industry.
Christensen separated new technology into two categories: disruptive and sustaining.
Sustaining technologies rely on incremental improvements to an already established technology, and large companies are designed to work with these types. They excel at knowing their market, staying close to their customers, and having a mechanism in place to develop existing technology. They also have trouble capitalizing on the potential efficiencies, cost-savings, or new marketing opportunities created by disruptive technologies.
Even in 1997, Christensen nailed the issue facing many HR organizations today: it’s easy to dismiss the value of a disruptive technology because it does not reinforce current company goals, only to be blindsided as the technology matures, gains a larger audience, and threatens the status quo.
Although disruption is everywhere, there are four specific technologies in which the modern HR pro must become proficient: cloud computing (HR as a service), mobile, predictive analysis, and virtual reality.
Are financial experts afraid of automation? Not exactly. If you talk to many of them, you’ll hear that technology has actually changed their jobs for the better and has positively impacted how they work in their organization by eliminating manual processes, increasing accuracy and efficiency, and facilitating valuable customer interactions.
I rounded up some of the best thoughts on automation from big timers in the industry. Here’s what they had to say.
“Automation will change how we insure property, loan money, invest money, deliver technology, write research reports, and what professionals in financial services do every day. Every week in the news we read about a new application for artificial intelligence, machine learning, neural networks, or robots — whether it is self-driving cars, AI assistants, predictive models, robots building (or printing) hardware, or how to invest our money. Put these all in the category of automation — and that is what will impact finance the most in the next decade.” David Reilly, CTO of Bank of America, as told to Tina Wadhwa, Business Insider.
“Open digital technologies will continue to support finance transformation. Transformation is accelerating in terms of companies and people needing investment decisions, as well as the development and implementation of new business models. This will require increased automation and simplification to drive process efficiencies, increased analytics to provide high-speed business insight to drive better business decision-making, and, finally, better collaboration so business connects in a much more seamless way.” Richard McLean, regional CFO, Asia-Pacific and Japan, SAP, as shared on the SAP blog.
“We developed a chatbot named Eno, an automated program that can communicate with the bank’s customers via text message to give them information on their accounts and help them make credit card payments from their smartphone. The gender-neutral virtual assistant uses artificial intelligence to respond to natural language text messages from users about their money. For example, customers might ask Eno to show them their recent account balances or pay off a credit card bill.” Ken Dodelin, vice president of digital product development, Capital One, as told to Anne Irrera, Reuters.
A recent MIT/Sloan survey of senior corporate executives showed they see artificial intelligence (AI) as the single most disruptive new technology, and nearly 70 percent said they already have AI investments underway.
Looking at these results, you might feel insecure. You might worry that you’re falling behind, and that your business will lose its competitive edge if you don’t get with the program (literally). And it’s tempting to march into your CEO’s office and demand approval for an AI strategy now. You might want to sleep on that, though, because in the project management domain, the timing might not be quite right.
We Don’t Have it All Figured Out Just Yet
As we talked about here last year, AI has made interesting strides in the last few years, particularly with respect to deep learning. To refresh your memory, deep learning involves training computers to recognize patterns in data and then classify and categorize them as a human brain could do instantaneously. This is cool to be sure, but in the enterprise, a lot of deep learning applications are still in their infancy. They have yet to solve specific business problems or notably increase bottom-line profitability.
One common problem is that most organizations still haven’t mastered big data and basic data analytics. While they’ve started collecting data, in many cases that data is just sitting around. And this data can’t necessarily be used for AI if it’s not in the right format and cleaned appropriately. So, before being able to realize the true potential of AI, you’ll need to first plan exactly how you intend to analyze the data you have and put it to use in the service of insightful business decisions.
AI is Tougher Than It Sounds
According to Brandon Allgood at Forbes, the complexity of AI is another reason to put off an implementation. “As the CTO of a company with a foundation in AI, trust me when I tell you that it’s harder to implement than you might think,” he wrote. “AI and machine learning are not commonplace today because we still lack a few essential building blocks, like a robust software infrastructure around core algorithms, or the interfaces to easily make use of those algorithms.”
In an article for Fortune.com based on the recent Brainstorm Tech event, panelists discussed the use of AI in small-to-medium sized businesses. “It probably makes no sense to dedicate limited resources to hire an AI expert, even if there were one available,” they concluded. “Maybe every industry needs an AI strategy, but not every business,” said George Kurtz, CEO of CrowdStrike, a cybersecurity specialist.
“AI is just beginning, so having a strategy around it is a problem because you have to define what you’re talking about,” added Norman Winarski, founder of Winarski Ventures. “You have to be incredibly careful in how you deploy an AI solution, you need to think about how people will react, and it takes a lot of resources.”