An easy way to Implement Agile in HR Today

Cropping of Archibald Thorburn's work Pectoral Sandpiper

Agile was designed as a set of values and way to focus, not as a strict ideology. As such, a lot of tools have been developed with agile ideas.

A co-worker at my last job rolled out a new project to rethink exit interviews, and he spent 90 minutes with a large team (many of whom are highly compensated) explaining the structure of his project management. It included Jira, a kanban board, and multiple supporting documents. I don’t think anyone really understood their role after the meeting, and as far as I’ve seen, no one did any work for the first sprint. I want to commend him for putting these tools together, but sometimes the administration of the tools takes away resources that could have gone into actually doing the work. Agile is a methodology to make a team more efficient and flexible, not just administration of a set of tools.

Prime Opportunity for a Change

Right now is a prime opportunity for HR to embrace agile. But the past (almost) two decades has also been a prime opportunity. Although agile specifically is relatively new, other ideas like flat organizations have existed for much longer. There are some important research areas that had blockers because we didn’t have the computing power (like machine learning) or the mathematics (like string theory). That’s not the case with agile; everything has been in front of HR for a while, and we’ve just decided not to pick it up.

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Agile Compensation Model

Section of Impressionist painting by Georges Emile Lebacq

Compensation is not just paying people to show up, it should be a signal to incentivize groups to work together for common goals. Organizations meet their goals when they have employees working together to achieve small tasks aligned to a larger strategy. Our compensation model should be designed to simply reinforce this idea.

Paying What Matters

The first goal of compensation is to align workers to the organization’s goals. One common tool in agile methodology is allowing employees to pick what they want to work on from a list of tasks. The tasks are created as steps to building a better product/service, so they are all in support of the organization. Allowing the employee to pick the area they want to help in (usually within their department) gives them a much larger feeling of ownership and autonomy, in addition to providing cross training and redundancy of skills.

Each task has a “story point” number indicating how much effort it would take to complete that task. A task also has a “priority,” which identifies its current level of importance. Connecting cash incentives to number of story points and priority level directly aligns work with compensation. The employee always gets to choose their task, but the company can shift compensation to encourage the order in which tasks should be completed. Employees see the direct monetary results of their efforts, clearly indicating that higher achievement results in greater rewards. This encourages more effort, creating a powerful feedback loop.

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Replace your CEO with A Bot

(The below thought exercise was designed by Tina Strout and John Kelly under the direction of Anna A. Tavis, Ph.D in the Human Capital Analytics and Technology masters program at New York University.)

What’s the Problem?

CEOs are expensive.  In the U.S., they can cost hundreds of times more than an average employee.  Computing power gets cheaper ever year.

CEOs don’t listen to you, they are usually type-A personality, and they can even be anti-social.  The CEO bot is programmed with a friendliness slider.

CEOs are big-picture people who sometimes miss details.  The CEO bot goes all the way down to the ones and the zeros.

Buy a CEO Bot to Replace Your Old Meat-Sack CEO!

He takes input from the team and approves decisions (often in a less random way then your current CEO).

Just like a normal CEO, he decides the success or failure of a project (and his projects are always considered a success).

He runs the budget (he’s very good with numbers, since he is numbers).

He’s great at making connections (APIs) and cascading goals (CSS).

He does his own fundraising: no venture capitalists needed (mining bitcoin).


How would a Robot Overlord be Superior?

He’s accessible to literally everybody, all the time.

He’s sociable but not chatty and will automatically adjust his speech to align with your personality assessment quiz to ensure a good fit.

He doesn’t boss anyone around to get coffee for him, and he’ll never sexually harass you.


When we talk about automation taking the place of human workers, we generally upend those lowest in the hierarchy.  For this exercise we wanted to create a dialog around the benefits of removing those at the top first.

How HR Can Leverage the S-Curve

S-curve analysis is used in a variety of business areas.  By borrowing this tool cross-functionally, HR can better plan an organization’s program management.

Very commonly, the S-curve is used to show how a new technology is accepted by a population. Everett Rogers’s book Diffusion of Innovations discussed the theory of four main elements in the spread of ideas: the innovation itself, communication channels, time, and a social system.  Different groups of people tend to be more or less accepting of the new ideas. Rogers grouped these populations along a bell curve: innovators (2.5%), early adopters (13.5%), early majority (34%), late majority (34%) and laggards (16%).

Normal Distribution

We can look at the same data, but instead of a bell curve, we can take a more additive approach.  If we map the total percentage of people that have accepted the new idea at any time, we’ll see an S-curve.

S Curve

In HR we inherently know that some people will buy into our work quicker than others.  Some people are very accepting of a new process at the start when it’s more radical, while others only support it once it’s been well established for a while and the only changes are incremental.  Mapping participation rates onto the S-curve could help us better identify where to allocate project resources.  For example, if a project already has buy-in from everyone except a small percentage of the Laggards, it might not be worth putting more resources into company-wide communications.  If a project has buy-in from half the population, a relatively small amount of effort might be needed to get much of the Late Majority of the population onboard.  Not all of our programs can be managed like this, but using S-curve analysis as a tool for communication decision making can help bring metrics to an often far too subjective area.

Sweet Spot S Curve

Product design leverages the S-curve analysis in a different way.  By looking at how product performance and engineering effort are related, we can better understand how efficient adding more resources would be on a project.

At the start of a project, a lot of effort is put into developing a product while performance increases slowly.  After a dominant design is selected, there is an increase in the return on investment.  This enters a sweet spot where there is a major increase in performance as more effort is put in.  This slows with technology fatigue, and eventually it takes more and more effort to make even small performance increases.  When looking at this from a product design viewpoint, the speed of the s-curve is not the speed of implementation, but the speed of improvement, which will be very different from one project to another. Continue reading

Sujith Papali, Founder of HireSeat, speaks to Compensation Science about Crowdsourcing Recruiting

Sujith Papali grew up in Freeport, New York, and studied economics and international relations at Boston University. For the past decade he’s worked and consulted for Wall Street financial institutions including Barclays, Bank of America, Goldman Sachs, and Merrill Lynch.

HireSeat looks to match candidates to interview seats using an auction system. Contract recruiters bid money on their candidates, creating a system that incentivizes submitting only high-quality candidates.

Compensation Science: Could you tell us about the impetus to start HireSeat and its underlying structure?

Sujith Papali: I started my career at Merrill Lynch right before the financial crisis, and I started HireSeat as a result of years of experience navigating the stormy post-recession Wall Street job market. I witnessed some technologies completely disrupt the banking landscape, and everyone wanted to be a part of the wave of innovation. However, I also saw many people overpromise the potential of “big data” and “machine learning.” I was quickly disillusioned and focused my energy on an older and less sexy concept: crowdsourcing.  Put simply, it is the approach of solving a problem by employing the independent inputs of a large group of people.  As I learned more about it, there seemed to be something intriguing about it.

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Irene Chung, co-Founder of StellarEmploy, speaks to Compensation Science about Data-Driven Recruiting in High-Turnover Markets

Irene Chung is the co-Founder and co-CEO of StellarEmploy, a company which delivers a data-driven tool to automate and optimize better hiring decisions.  Previously, she has been an executive recruiter in the biotech, financial services, and higher education industries and has supported strategy consulting to The Bridgespan Group.  She has a dual graduate degree in public policy and business from the Harvard Kennedy School and the MIT Sloan School of Management.

StellarEmploy provides recruiting technology for roles that may have 100% average turnover a year, such as food services, warehouses, and call centers.  Their talent acquisition platform leverages custom algorithms to optimize hiring decisions, successfully decreasing turnover by 30%, increasing revenue per employee by 10%, and saving customers over two million dollars in labor costs.  They have worked with small and large franchises including Burger King, Dunkin’ Donuts, and Domino’s Pizza.  Compensation Science spoke with Irene about how she leverages predictive technology in hiring decisions.

Compensation Science: Could you tell us a little about StellarEmploy and about what your focus is today?

Irene Chung: StellarEmploy is a SaaS tool for recruiting hourly workers in industries with average yearly turnover of 100%.  We enable companies with large workforces, like restaurants, warehouses and call centers, to hire staff who will stay and succeed.  Our talent acquisition platform uses custom algorithms to optimize hiring decisions.  Algorithms capture the complexity of people and jobs.

Our company has a strong research bent, and I spend most of my time mapping target industries and different career paths instead of just relying on the old-school rolodex. 

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Brian Trautman, Captain of the Sailing Vessel Delos, speaks to Compensation Science about How to Run a Ship (Part 2)

In our last article we spoke to Brian Trautman about his path to becoming a sailing blogger.   Now we speak to him about how he manages a small business while at sea.

Compensation Science: You’ve said that moving from the corporate world to sailing has “changed your DNA” and that you’ve “learned a lot about what’s important in life and I know it’s not doing the 9-5 rat race!”  What perspective can you give to people who only know the corporate world? What would you recommend to encourage people to take control of their own path, within or outside the corporate system?

Brian Trautman: I think if there’s anything I’ve learned from this endeavor its patience and the value of time.  Sailing and traveling teaches you both. I remember being very frustrated with people sometimes, feeling rushed during my day that that there was never enough time to get everything done.  I would constantly feel pulled in many directions and keep myself occupied hoping to accomplish more.  If I had a few spare moments I would be on my email trying to hammer out a bunch of responses.  Now I really think a lot about the value of time and how much more productive you can become if you give yourself the chance to quiet your mind.  I don’t think you necessarily need to drop everything and take years off, it can be something as simple as sitting outside and enjoying your lunch with no interruptions and no phone.  Just giving yourself that time can really help you focus, or making sure you take time on the weekends or your days off to do something that you truly enjoy, something that relaxes you, without feeling guilty that you are not being productive.  I remember having a 30-minute bus ride every morning on my way to work.  I would use that time to “catch up” on emails to get a jump start on the day.  I don’t think I would do that now, I would most likely listen to music, stare out the window, and enjoy the view.

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Brian Trautman, Captain of the Sailing Vessel Delos, speaks to Compensation Science about How to Run a Ship (Part 1)

Brian Trautman is the owner and captain of the Sailing Vessel Delos.  In part 1 of our interview with him, he speaks to us about the wide variety of jobs that led him to forge his own path to become a successful sailing blogger.  In part 2, he’ll discuss what it’s like to recruit and manage a crew and succeed in an emergent industry.

The Delos is a 53-foot Amel Super Maramu built in France in 2000.  Brian bought the boat in 2008 and eventually took on his brother, Brady, and met Karin while in New Zealand.  In the intervening years they’ve taken on crew, over 50 total, who help sail the ship and become the co-stars of their popular travel videos.

Compensation Science: Before managing a sailboat and a travel blog, you worked in a wide variety of roles.  What was your early career like?

Brian Trautman:  I had what I would consider to be a pretty typical American educational experience.  My Dad was self-employed and supported our family by running a small sandwich shop and deli in Flagstaff, Arizona.  We didn’t have a lot of money and lived paycheck to paycheck so I attended normal public schools.  Straight out of high school I started working for the phone company (U S West).  I was the guy in the truck with the spikes on my boots that would climb the poles and repair the lines after a storm knocked them down.  It was a cool job and I got to work outside which was nice.  I actually really enjoyed it.

One of the major benefits is that as a Union employee the phone company would pay 100% of my tuition to further my education.  I started taking night classes at a community college, and finally transferred to the University of Washington.  Working full time while attending the engineering college was tough, but I made it happen.  It took six years but I finally graduated with my Bachelors of Science in Electrical Engineering and got offered a job as a Program Manager by Microsoft just across Lake Washington in Redmond.  Microsoft was an interesting place to work, much different than the culture of the older style phone company I was used to.  The Redmond campus at that time had just over 35,000 employees, all very smart and at the top of their field.  I learned a lot there and got to work on the massive Windows team for almost two years.  One day during a meeting with my manager he closed his office door and said “Brian, look at this org. chart.”  On the back of his door was a massive chart starting with Bill Gates at the top, then hundreds of nodes from SVP to VP finally ending up at Director at the bottom.  If you weren’t a director (managing 100’s of people) you didn’t make the chart.  He continued “It’s my goal to be on this chart someday.  What’s yours?” This is what really started me thinking about my place in the world and what I wanted to do with my life.  Striving to appear as a node on someone’s org. chart really didn’t appeal to me.  Within a few weeks I put in my notice, along with two other friends from Microsoft and we started our own software consulting company.  As it turns out for the first few years Microsoft was actually our biggest customer as they were favoring contract work instead of hiring more full time employees at the time.

We ended up growing the company to about 35 employees, and it was a fantastic experience.  Very fast paced with a do-anything start up mentality.  I was able to learn a lot about managing and motivating people, and generating new customers.  It was a crash course in running a small company.  The business ran great and did well right up until the global financial meltdown in 2008/2009.  Within a period of 3 months about 90% of our business dried up.  By this time we had been running the company for a few years, and it was starting to mature and I grew restless.  It became more about growing the business and paying the bills than the technology, and I felt myself becoming less passionate about the day to day operations.  The last few years I had been fantasizing about traveling and taking some time off.  I think Americans are expected to go to school, graduate, get a house and mortgage, and work until you retire.  I didn’t realize it at the time, but taking time off to travel and explore really isn’t ingrained into Americans as it is other countries.  I could have easily ridden out the financial storm but the thought of doing the same thing for the next 20-30 years really didn’t appeal to me.  I had stashed some money away during the good years and with the down turn in the economy it was a perfect opportunity to take some time off.  So I sold everything, turned the business over to my partners, and bought Delos with the intention of going sailing for 1.5 or 2 years. At least that was the plan! Continue reading

What Human Resources Should Know About Machine Learning

Machine learning is a relatively new computing process that is very well suited to solve specific problems that other forms of computing struggle with. As technology advances, Human Resources needs to understand how to manage new machine learning departments and how to integrate new technologies as tools for better worker management.

Since it is still a new technology, machine learning is relatively limited in practical application. Others have compared this technology to a train a few miles away that is slowly building up steam. Like a train gaining speed, by the time machine learning gets to our station, it will already be moving fast and will be much harder to board.

To understand machine learning we need to consider the larger field of artificial intelligence and a common form of machine learning, artificial neural networks.

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