Author: Andrew Liew Weida
Hi guys, thank you for coming here. Today, I would like to talk about government care and the AI revolution. Before I go into that, I would like to help us to recollect about what was said in the last 2 talks [Art of AI and Automation] and [AI Revolution of human capital for the individual]. In the last 2 talks, we talk about what is AI ? what’s the implications of AI for human capital? What would be a world of people powered AI like? What can we as individuals do to support the ecosystem?
If you recollect, we will be superhumans in the next 5 to 10 years when AI empower us to do far more greater things than we can do the same things all by our own capabilities. We can achieve a lot of intellectual horsepower by working with them. One way to think about AI is that AI is a general-purpose technology. It would be like electricity and internet that impact our day to day lives without us even thinking about it. As such, it will create jobs and kill jobs at the same time. AI will kills jobs because of a huge component of those tasks in these jobs can be automated by AI. The new jobs created by AI require more complex skills and these skills take time to develop , to build and to work with AI and Automation. And because of that, there is a labor market mismatch.
This labor market mismatch gives us 2 phenomenons. We mentioned a classic example of a waiter that can serve 50 customers using pen and paper. When the boss implement digitization, the waiter needs to learn to use the ipad, use the software applications and make payment with these applications. These new processes mean that the cost of learning and cost of adapting to using new technological tools will cost the boss 25 less customers in the short run. As such, there is a potential short term loss or a recovering period to using digitization. This leads us to understanding the first reskilling deskilling paradox explains why companies are reluctant to hire talents with emerging skills , more reluctant to send workers for training.
When the worker realize that he or she does not generate return of investment in his or her job using the new skills, the worker will naturally be reluctant to invest in training as well. Then we have the second reskilling deskilling paradox that explains you can make more money using your current skills than your new skills if your utilization rate of your old skills is far greater than your utilization rate of your new skills. This is also known as the “Learn Less, Make More” dilemma.
Because of these 2 paradoxes, people feel anxious. New tech startups and Big companies will become leaner. We learn in my previous talk, automation leads to recomposition of jobs. If the job is repetitive, it can be automated. Jobs that will not likely to be automated are very complex. That tells us that the whole economy or the marketplace will transit into a transient workforce cycle.
The nature of transient workforce cycle will propel us to learn that the only risk in life is to not take risks in your work at all. At the same time, companies become more nimble at outsourcing and hiring more and more independent contractors and gigsters. The full time corporate talent might eventually become a gigster over time. Income and jobs are no longer certain. You have to constantly learn and constantly adapt. When the pace of change is rapid, people are lost, worried and anxious. This is why universal income will become a global norm.
We learn something. When that happens, universal income will make people feel assured and that enable a sense of inclusiveness and promotion of collaboration. We also observed the rise of philanthropic works. One of the reason for that can be the rich realize that hard work is only one of the contributing factor to their prosperity and the rich acknowledge the society plays a part in making them prosper. If the rich trust the governmental system in enabling universal income, then universal income can be a sustainable feature of a futuristic society. We also talk about another potential side effects of universal income in the society. We notice that the millennials are relatively more purpose driven workers. Universal income can get more millennials to work in the social sectors. This is especially if these sectors are located in countries with high cost of living and low wage offers, attracting bright talents will be enormously difficult.
Universal income supplement the income for these millennials to increase their participation in the social sector because these millennials no longer have survival anxiety. And because of that, the society as a whole is better off.
We also talk about the potential doomsday scenario that there will be rapid pace of change for people to adapt. Jobs will evolve over time. As such, how can we as a society build an ecosystem for a people powered world?
This leads us to this specific topic today: Government Care and the AI Revolution on Human Capital.
Introduction
So i'm going to share with you 4 different ways that any government needs to think about it. The four main ideas are showing metrics, enabling human capital statements in business registry, building an API for tech startups and enabling local companies to gear up. Let’s begin by examining the state of government intention , progress and hope in institutionalizing policies to grow human capital in their respective labor markets.
Government bodies are measuring progress.
The first one is to foster local government needs to enable accountability and clarity of human capital metrics at the national level, the company level and the individual level. Most of the government bodies in the world talk about economic growth and they measure economic growth using GDP and that stands for Gross Domestic Product. It can also be GDP per capita or Gross Domestic Product per person. By the same equation that they measure, they will focus on boosting the investment confidence via stimulating either monetary policies by Central Banks or fiscal policies by parliamentary bodies. These are in general financial allocation and spending. And because of these dollars and cents, the government place accountabilities on their spending and sharing that information on public websites. In the same way, there are regulatory bodies that ensure corporate governance for the companies and businesses. These organizations will also have to declare their company financial statements in a business bureau of registry. It's called ARCRA in Singapore and US Small business administration in the United States. In any country, there will be a central credit bureau for keeping records of the individual credit history or financial history. So you can see that there is public records of financial information at the individual level, at the company level and at the governmental level. As such, there is an ecosystem to debate and think about using financial information to improve the performance of the individual, of the company and of the government.
What about the display of human capital metrics at all of these levels?
Existing measurements for policies on human capital are inadequate.
Let's have a look. At the moment, we do see that government bodies do reveal information about the labor market conditions. The metrics are the unemployment rate, the number of new jobs added versus the number of people being retrenched, the number of people on payroll. However, this information only reveal information at the tip of the iceberg. It is not able to help the public understand how has government spending in improving labor market conditions.
Here are some suggested metrics for measuring human capital growth.
New metrics such as the transition time per employed individuals, the income transition per employed individuals, the placement rate to training subsidy per individual ratio will enable the public and the government to better understand the cyclical effects of their policies on transitional employment. It is a flow capture instead of a stock capture on the dynamic of human capital allocation and job creation-destruction state. There should also be new regulatory calls to collect information at the company level.
Improvement in measuring human capital is also needed at the corporate level.
At the moment, companies are only required to furnish financial information in their business registry records and government are still doing market research to understand the state of human capital development in their respective nations. This is ironical because we are living in the age of Big Data. Collecting information on human capital via market research is a poor cousin to getting companies to provide information on human capital metrics just as they did for financial information and corporate governance. This is because the former collect less data at the granular level and there needs to be more data driven policies to enable more personalised governance be it corporate governance or national governance. Without granular data, we are missing out insights to what drive the individual to work more? what drive the individual to learn better or learn more? what drive companies to spend more time and money to send its people for training? is there a ROI or return of investment in creating or subsidising training programs at the company level , at the individual level and at the national level? As such, I highly recommend government to consider making collecting human capital information a compulsory at the business registry. This small effort can have huge ramification for investment institution to assess the value of the company from the financial capital and human capital perspective. At the moment, human capital information at the broad level are available at public listed companies and even then, we have less clue about on the human capital development of the rank and file in the public listed companies let alone the small and medium businesses.
It’s better to start talking about it before it’s too late.
There will be a time when nations and government bodies will push for such legislation to mandating collecting human capital information and storing in the local business registry. It can be the time when robots or AI create a tremendous impact. It can be the time when the reignition of the luddites revolution. That time will come when human beings might unfortunately be visibly classified as those transienable ones or the non-transienable ones. The transienable ones are those can that jump from job to job, can learn very quickly and can work with different robots or AI applications. By that time, the great inequity might get us to think about the following questions? Why are some individuals having difficulties transiting? Should we tax companies with robots like what Bill Gates mentioned before? Before we get to that time, perhaps it is your responsibility or my responsibility to discuss now so that discussion can collective drive the state or the society to start collecting human capital information at the company level and to display that information in the business registry or a macro summary of these granular human capital statements.
Human capital metrics
Here are some questions when we start collecting these information. Have they been open and accountable to the following: the number of hours that the individual is studying or learning, the number of skills that the individual has learned and applied, the number of jobs that the individual can move because of their learning. how has the recent government human capital reach the respective policies outcome in enhancing human capital re-employability? Because if we don't have such metrics to put the respective agent accountable, then nothing move.
Management starts with measurements
It's like the management saying, if you want to manage anything, the first thing you need to measure. Your measurement will determine the outcome you want to achieve. In the same way, there is a lack of clear visible metrics that align government spending and human capital re-employability.
The analogy of Speed Limit Policy and Human Capital investments
Here is another analogy, if you want to have safe road, the government will need to install road cameras. When there is a speed camera and the speed limit is 90 kilometres per hour, the car drive more than 95 kilometres and the speed camera will take a snapshot of the car and issue a warning to the driver with a proof that he is driving above the speed limit. In the same analogy, if the government wants to restore labor market conditions or provide better jobs to companies, the government should measure the outcome of the individual learning per taxpayer dollar spent, the outcome of the individual time to transit into a new job per taxpayer dollar spent, the outcome of the company profit with reskilled individuals per taxpayer dollar spent. When the company human capital drop below the national standard for that specific sector, then the human capital agency can prompt the company to send its people for reskilling. When the company saw return of investment on reskilling its people, they will place more of their profits into H-exp (human capital expenditure) over Cap-exp (financial capital expenditure).
Ok, perhaps some want to see what kind of insights from collecting such information.
Business case of effective transparent metrics policies
Let me give you a numerical example. Suppose the government spend $100, $80 of that $100 goes to the individual learning and the other $20 goes to the administration of that policy, we can see the government is pretty efficient given 80% of each dollar go to the individual learning.
But wait, let's dig deeper in a situation if we have human capital information at the company registry.
How much does of that $80 of training goes to enabling the individual to apply and translate that into a new product or service that enable the company to increase revenue, decrease cost, increase profit or reduce risks?
Suppose there is a mandatory need to collect the working time, the learning time and the creative time. Now suppose that the individual is contracted to help the company for 8 hours. The company has a policy that states 2 hours of that 8 hours goes to learning and another 2 hours of that 8 hours goes to explore creativity. We might be able to know under the policy of giving $80 to the individual to learn, what is the profit impact of adding extra 10 minutes to learning ? what is the profit impact of adding extra 10 minutes to doing creative staff?
With such information we can construct smarter analysis into understanding how this $80 of government supported training goes into creating profitability for the company. Perhaps we might realize that the company is not going to change its current mode of operation, then we can study that the marginal profit impact of each dollar from government support training programs on a comparable sets of similar companies running on similar human capital operation. Perhaps we might realize the government can see extra $100 annual profit from pumping $82 instead of $80 subsidy. If that's the case, then the government can justify putting in $102.50 given it will be getting back in 4 years time at a 25% tax rate on that $100 annual profit that derives from the $82 of trickle down effect.
So you can clearly see why it is in the public interest to ensure that companies also put human capital metrics into the records of business registry. Human capital statements in Business Registry
Let's look at another example of what kind of human capital metrics that companies should put into the registry of business. The human capital balance sheet shows the contracted hours that any human being can work without dying from overwork, the number of headcounts that companies go into hiring, the type of working arrangement of getting human capital and the financial resources to paying people, to training people and to caring for its people.
Having these information can allow us to think about the following issues? Have more companies transiting into an ethical contingent workforce? Are the companies developing human capital while deploying human capital for profitability purposes? When should the government intervene to ensure market stability and enable companies maintain their corporate social responsibility?
These questions might seem like a compliance issues but they are more so about economic sustainability too. Think about it, when companies start hiring accountants, they started having clarity about their financial resource management and start thinking about optimizing financial performance of a company. Imagine how powerful will the aggregate effect be when companies start hiring HR engineers to quantify the impact of human capital resources and investors start asking questions about diversity? they ask questions about how can we improve the workplace? how can we care more about people?
These questions will no longer pertain to just for the big multinational organizations but across small and medium organizations. It is important because when companies want to go from where they are now to where they want to be, they need to be clear about the status of human capital position. Most will say it's easier said than done. How do we measure human capital? Where do we collect these data?
Build an API sandbox/testbed for HR tech startups
To enable data collection, we need to enable a way to collect these data at the national level and one way is to leverage a technology called Automated Programming Interface or API in short.
If a country want to be a smart nation, it must open up its API to collect human capital data. For example, the body that govern the social security fund and in Singapore, it's the Central Provident Fund or CPF in short needs to help companies to make easily from their existing software vendors to the CPF board.
By opening up an API infrastructure across the social security fund, the tax authority, the workforce agency, the ministry of manpower and HR technology companies can immediate help companies to digitize their HR processes. The most important implication out of this vision is to enable a central system for collecting human capital data just like what central banks are doing.
It always amazes me that financial information are easily available and standardise when each country will enable inter banking systems via API call while human capital information are often scattered everywhere and even harder to administrate.
With a centralized HR "banking" system, companies can have their own comprehensive view on their HR spending in terms of payroll, taxes, headcount and training impact. Only when everyone plays a part in advocating for a seamless HR experience for the individual , for the company and for the nation, will we be able to really use these human capital data to make smart decisions in an age which AI and automations are fast blurring the line between assisting human beings and substituting human beings.
There's an African proverb that it takes a village to raise a child. If you want a child to grow up and contributing back to the society, you need the teacher, the police officer, the uncle, the parents to train and guide the child to how the village want the child to be.
Similarly, it takes an ecosystem to build a smart nation. If we cannot enable local companies to gear up and the local government bodies to enable this API connection, then the freelancers or gigsters or local HR tech startups will not be able to fulfill their respective potential to enable a smart nation.
The lack of transparency does not boost labor market confidence for companies to invest in people when technology might seemingly do the job.
We might eventually see more unemployment and more chronically unemployed individuals as more AI and Automation are introduced in a labor market with lack of granular level of transparency about the return of investment on human capital.
No matter how much PR and marketing dollars are spent to send the message to the public to adapt and grow, to upskill, to reskill, giving them tough love without showing them a clear granular path of how they can see light at the end of the tunnel, they will eventually lost trust and possibly lost hope.
It's like telling a man with a broken leg to stop using a clutch and start running when that same man doesn't even know when he can regain his capabilities to run because the government is not providing this man an X-ray. Even if the government provide an X-ray, the guy might not know is he recovering faster than people in similar conditions because no one has collect data on the recovery period and benchmark.
In this analogy, the X ray is the tool to diagnose human capital. The data are the human capital metrics. It takes a lot of courage to open AI to HR tech startup to start collaborating, to enable companies to submit human capital data, to enable individuals to start measuring their own learning time and their skills, to enable government to use granular data to adopt a data driven policies in real time to enable personalized policies for the individual to reach its potential.
Successful campaigns have visible metrics to the target audience
Let's turn to another example on how measuring human capital can create an impact. This example derives the policies that the government are good at tackling the health and well being of the individuals.
In Singapore, we are trying to do our best to create a healthy nation. Everyone wants to be healthy. Let's look at the health related poster in Singapore from 2015 to 2017.
Let's look at the first poster. It tells you not to consume too much sugar. For example, if you take one teaspoon of brown sugar you will need to drop for 2.35 minutes if you take one teaspoon of white sugar you will need to jump for 2.42 minutes and if you take one teaspoon of honey you would need to job for 3.72 minutes. So here you can see the effects of taking different types of sugar and how much time you need to jog to burn those calories to stay healthy. So having these metrics in the first poster tells me that I can take up more time jogging if I can white sugar or honey relative to brown sugar. The other way is if I jog for the same amount of time, I can take brown sugar to take in less calories in order to stay healthy. Let's look at the second poster.
The second poster shows the calorie intake for taking different types of Mcdonald meals. If you take a grilled mcchicken wrap meal that consist of a bottle of water, a cup of corn and the wrap, you will consume 501 kilocalories. If you take a grilled chicken salad with the same bottle of water and a cup of corn, you will consume 256 kilocalories. The campaign name for the second poster is called the Delight 500 in that you will be healthy by consumer anything that have 500 kilo calories or less.
The first 2 posters give the individual a good indicator about the input of energy that one consumes while the third poster give the individual a good indicator about the time to quit something negative for the body.
The third poster shows the individual can quit smoking for good if the same individual can quit smoking for 28 consecutive days.
So you will be thinking have I seen any poster that indicate the return of investment for the individual to start learning. I don't know about other countries but I can tell you that there isn't any poster that relates to human capital return of investment on training or pay or leadership that indicate some specific numerical indicators in Singapore from 2015 to 2017. Existing HR campaigns lack visible metrics
In the next slide, you can see the posters in Singapore that encourages the individual to switch career, encourages the business owners to hire new people. There are posters on the public subway which is known as the Singapore Mass Rapid Transit that encourages individuals to reskill.
Why isn't there a poster that say the message similar to the public health posters like this: "learn this course in 30 days and you can most likely earn $2000 in your future income."? Why doesn't the workforce agency have the courage to stake a numerical claim? This is because that agency did not measure and collect data as the public health agency does. If you want to have a successful marketing campaign, showing the value proposition in numbers to ask for a call to action definitely works most of the time. When you see those human capital related posters, you will be thinking where are the return of investment metrics? where are these metrics?
When public agencies does not show the individuals the dollars and cents impact of taking their time to learn or taking their money to learn, they will be very skeptics. Everybody knows in general training is good like everyone knows investing is good too.
BUT everyone realize that training is as risky as investing!
They will be thinking like this: how much time will I be use for training? What is the opportunity cost of using that time for training? My time to take care of my kids? My time to take care of my parents? My time to rest my body? My time to build relationship with my wife? These other commitments are also important to any human beings. And if the government or the company or I want myself to learn, then I need to demand a clarity on the return of investment in learning a skill or a course or picking up a knowledge. What is that Return of Investment in learning?
In the same way, for an existing campaign, when the government wants to help the individual to transit one's career, the government needs to show them metrics. The agency needs to think like this: if we have 10 professionals that got retrenched, 6 of them come out of this program, 4 of them successfully transit. Among the 4 that transit, 3 of them double or triple their income 6 or 7 years down the road. Or at least this program maintain their income or prevent a downward spiral effect to their lifetime income.
Where are such metrics? Without these metrics, the individual will become very doubtful about these programs. What about the labor demand? What are they thinking about human capital policies without clear ROI metrics? Why are companies very reluctant to take up new programs and policies? The companies will be questioning the effectiveness of these policies.
If the policies are working then perhaps there can be greater transparency to show how effective these policies are at enabling companies to generate return of investment from using these policies. The business owners and companies are thinking like this. Ok , I can hire this person on this policy on a grant, I am still paying him and I need to train him. I need to get a training manager or a line manager or I need to train the person myself. And that means the opportunity cost of the business owners' time or the training manager or the line manager's time. And these time can possibly help the companies to collect more sales or revenue. Now I am taking a short bet on this candidate using this grant and I have no metrics to evaluate the return of investment on the candidate or the policy. what is the return of investment for this program that I am getting into?
Without a clear way of measuring and collecting data to illuminate the return of investment on these policies, the business owner will be reluctant and be skeptic about the program. It is a common sense to think in that way. When a business owner see an idea, that owner will go and test it. The business owner talk to his or her investor. The investor will ask what is this budget? what is the return of investment of taking this budget and putting it into marketing? similarly the investor if possible will also want to know the return of investment of HR policies like hiring key people, like training young people.
Adding clarity to the market
When we don't have a clear metric, then we cannot add clarity to the labor market dynamics, the public officers will want to know the following questions: what is the estimated increase in payroll for companies that take up training initiatives? what is the impact of training on the company current revenue and future revenue within an 18 months period? which sector see the biggest impact on GDP growth via implementing the training policies for that year? which training institute offers the biggest dollar impact to companies and to individuals?
We are currently observing an imperfect labor market. When we don't have clarity on the labor market and human capital dynamics, everybody will be like the headless houseflies running around in the air figuring out they need to get some training without realizing the danger of being smack by the tides of AI and automation. so the public agency has clearly seen that there is a lack of quantifying the human capital investment.
Why should we care? If an investment today doesn't generate a return tomorrow at a national level, then I better write off my investment as bad debt and quickly change my investment strategies. I can only change if I can measure them, right? So can we quantify human capital investment? Allow me to give you an example of how we can quantify the human capital investment and how this concept indicate the labor market dysfunctioning.
Lack of transparency on quantifying human capital investments
Let explain the effects of information asymmetry of human capital investments in the labor market.
There are 2 lines in this chart. The chart has 2 axes. The horizontal axis represents time while the vertical axis represents resources like effort and money.
This is the situation. The economy is not doing well and many companies are retrenching and restructuring. As a result, many people are out of job for a chronic period of time. High unemployment might trigger social unrest or political overthrow of the existing incumbent party. At the same time, there are new sectors seeking to hire people to take on new jobs with new skills requirements.
One possible reason why the retrenched group are not able take up these new jobs is because they do not have the skills for these new companies to hire them. Companies that are risk averse in human capital investment will generally hire experienced hires for the new jobs and pay them a premium. In the same fashion, companies that are risk averse in human capital investment will be more likely to offer a discount on the market rate wage to hire someone with no demonstrated way of showing skill utilization of the new skills demanded for the new job.
At the same time, these retrenched workers are so used to getting their previous earning wage that might be higher than what the employer of the new jobs are willing to offer to these retrenched group without these new skills.
This reveals a gap in labor mismatch. Without any intervention, the employer will not find workers unless they seek out foreigners and retrenched group will not get jobs unless they go overseas.
If the local government start opening influx of foreigners under the nose and direct observation that the retrenched workers cannot find local employment and are unwilling to relocate, then there will be political consequence of anti migrant effects and labor market crunch.
If there is continuous labor market crunch, the economy will go stagnant. If there is anti migrant effects, the incumbent party will lose their jobs. Therefore, the government decides to offer government subsidy to allow companies to take risks to training this retrenched group and eventually hiring them. The government subsidy is intended to reduce the risks of job placement. However, companies are not willing to take up.
Why is that the case?
One possible reason is the companies do not know whether the investment in this case, training this retrenched worker will lead to a payoff in the company's profit. This question arises because the individual can learn but not necessarily able to utilize that new skills. Skill utilization is key to applying knowledge into productive output that the company can use to grow its profitability. The fact that everyone has one's unique time requirement for learning and utilizing new skills, couple with the fact that companies assimilating AI and Automation are catching up with incoming competitors accelerate the urgency for the candidates to quick hit the ground and quickly apply the skills as they learn.
A good training can enable the specific candidate to quickly learn the skills and a good environment can enable the same candidate to apply the new skills. The candidate also need to have a good degree of learnability to learn complex skills. 3 of these elements are necessary for the companies to see the return of investment from taking chances on candidates without experience.
While the company can create a supportive work environment to satisfy one of the necessary conditions for obtain ROI from taking the government subsidy and putting the candidate for training, the company needs clarity to assess the quality and quantity of training as well as the learnability of the individual in order to assess the ROI or return of investment of taking up the scheme.
At the moment, we have seen that the existing agency has not been able to provide the return of investment on its existing training programs and the learnability of the individual. That is why the companies are worry that the worst case scenario can happen.
The worst case scenario is that the company or employer commit to the scheme only to find out that the individual is not able to learn or not able to utilize the skills to turn knowledge into products or services and eventually creating a loss from the employer initial investment on training and hiring a lost cause.
If training is not effective and cannot be indicated by a metric, then there will always be labor market failure in the age of AI and Automation. Most often economic recession is often signal with poor stock market performance with the local trading index like the Dow Jones index or the Straits Times index. The stock market is often an indicator of investor confidence about the economy.
Similarly, there can be a lack of company confidence and a lack of job applicant confidence in the labor market. The lack of company confidence in the labor market is the skeptics about the learnability of individuals without prior experience and the return of investment from human capital investment while the lack of job applicant confidence is the skeptics toward job search and learning given the prolonged search and effort to possibly land jobs or gigs over time.
I want to talk about another interesting phenomenon in the labor market and this phenomenon will either get more prevalent because of AI or diminished over time because of AI.
Let's recall one of the mentioned statement that companies that are risk averse in human capital investment will naturally want to hire experienced hires. These companies will look for experienced hire whose resume or profile meet at close to 100% of the job scope as much as possible.
Yet the ironical truth is experienced hire also want to learn, grow and expand their skill sets so they will not likely take up the same job with the same scope of work. If they do, then the incentive for doing so is milking as much salary as possible.
On top of that, individuals that take up jobs that matches almost 100% of the job scope will experience boredom or lack of engagement. The lack of engagement will most likely not able to enable the individual to fully utilize one skills given that individual's attention at work is probably waning or being diverted to some side projects.
That individual know that in the age of AI and Automation, the same set of skills and job scope will decline in value as new technology and new skills are emerging to replace the existing ones. They will be thinking: I need to take risks in this modern age so why do I have to take the same old job again and again in the corporate environment? Why don't I be a gig consultant and earn a premium as much as I can? By sticking to the same job scope, I am in fact increasing my risks of getting my next job. And so if I am increasing my risks of my next next job and this job wants me to do the exact same job in my previous job, I better ask for a premium on my new pay to compensate for that future risks.
As such, companies need to review the return of investment on candidates whose profile matches 100% of the job scope. The truth is it is often a challenge to know what is percentage match that the candidate can match the job scope without have a common way of assessing that percentage. This is because no one is measuring human capital as clear as what the market is measuring financial capital.
Gurus say add metrics to initiate impact
So what does all these examples and ideas boil down to for the government sector? It boils down to the following quotes.
The father of management theory, Peter Drucker said "If you can't measure it, you can't manage it." This means that if the government want to restore labor market failure in the age of AI and automation, it has to take a rigorous approach to adopting granular measures just as it has done it for central banking and fiscal spending.
Gray Becker who won the Nobel Prize in Economics for his contribution to using empirical analysis and theoretical calibration to explain the return of investment on human capital in education. At the moment, most have taken his rules but not his methods to collecting and analyzing human capital issues. His rules might changed in the age of AI and Automation but his empirical approach remains relevant till today thanks to big data and analytics.
Another Nobel Prize winner in Economics, Daniel Kahneman who study the psychological behavior of human being in its rational mode or irrational mode. In his book, Thinking Fast and Slow, he mentioned that we, human begin has 2 modes of thinking, the fast thinking mode and the slow thinking mode.
When the business owner is given a choice to take a grant, he will most likely take a rational thinking mode which is the slow thinking mode. And if the government is unable to provide indicator to the return of investment on that human capital policy, then the business owner can either take a chance or reject the offer. If the business owner reject the offer, then the government is not able successfully use the above policy to restore labor market confidence.
If the business owner takes a chance and make a lost in the first instance, then the business owner memory will automatically locked into memory for system 1 thinking or the fast thinking mode. The government re-introduces the scheme with more subsidy but still lack clarity on the return of investment on the human capital policy.
As such, the business owner will automatically go into system 1 thinking and reject the scheme. In the same way, the government will incur a lost of trust because a policy failure without clear data driven explanation will lose the confidence of the businesses in taking up new schemes with ambiguous outcome on the return of investment on human capital investments.
By having clear granular data to explain the return of investment on each policy, the outcome of new human capital policies will most likely not create a dependency on any subsequent possible failure of past human capital policies. This is because business owners constantly take information on the return of investment of human capital investment into its rational thinking. Companies to start sharing and discussing human capital insights like financial capital
Ok let's look at how companies can make better decision on human capital investment in the age of AI and Automation.
The company records the number of headcount deployed in 2015 and 2016. This is further classified into leaders, professional and rank & file.
We can see that there is no change in the number of leaders in 2015 and 2016. The number of leaders represent 10% of the company total headcount. Now let's look at the professional headcounts. There is a 33% increase in the number of professional from 2015 to 2016 given we observe that the headcount for professional increase from 30 to 40. In addition, we notice that the group of professional has increase its representation in the company workforce from being 30% of the company workforce in 2015 to forming 40% of the company workforce in 2016. At the same time, we notice that the rank and file workers has reduced in size from 70 in 2015 to 60 in 2016.
At first sight, most will be thinking oh no! the company is not doing well and so is retrenching people. But when you look at the human capital statement, you can clearly see that the total number of workforce remains the same at 100 workers in 2015 and 2016.
One possible story is that the company is doing well and it is getting ready for its next phase of growth as such it has increase more professionals to beef up its future capability and reduce the size of the rank and file workers to keep its running cost efficiency and sustainable.
With such a clear picture, government is able to better assess its macro policies working at the micro level on the ground.
Saving time and effort to get opportunities
Let's look at how the government helps the individual to restore confidence to learn and to job hunt. Recalling that it takes a village to raise a child, it also takes an ecosystem to build a smart nation.
We form the society and we want a society to help the individual to save time and effort to get opportunities. And right now what we are observing is that getting a job is a job by itself and the process of job hunting is not a trivial task. It is a job to get a job. There are a lot of steps that you need to do to get a job.
One of my personal opinion is that if Linkedin is seriously about helping its members to gain more economic opportunities, Linkedin should do some corporate socially responsible activities and one of the CSR is to provide "one click" to populate the candidate information into the company job application tracking system. The current one click is currently discriminating against those that paid for this feature and those that don't. This one simple gesture can save the individual enormous amount of time. The individual government from the respective country should enable a 1 click job population for its citizen by opening up its API infrastructure. If the Singapore is aiming for a cashless society, why not a frictionless job application world for its people in a fast dynamic labor market in the age of AI and Automation? This is a public good to ensure the efficient allocation between individuals and companies. This is no difference from building an extra bridge between Singapore and Malaysia to enable the efficiency transportation flow between 2 markets. I see this as a public virtual bridge between the individuals and the companies.
The other idea to help the individuals is to create awareness for companies to hire for talent potential over using traditional methods to assess job match. Resume is becoming obsolete given the changing dynamics of tasks and experiences that can be captured in a public profile like Linkedin. Recruiters or business owners can adopt the following thinking: Can we evaluate this guy ? Are there indicators for talent potential and learnability? Has the individual cover similar experience that is not exactly matching the job description but is what we are seeking for in the same settings or different settings? If the answer is yes, this profile seems to have match more than 50% of the job description, let's have a chat about it. It is through discovering the individual story that we can evaluate whether this person can add value to the company.
One of the famous movie about measuring the return of investment in human capital is the Moneyball. This movie is basically about this baseball manager whose name is Billy Beane played by Brad Pitt trying to change the game of winning baseball against the former winner club, which is the New York Yankees with 10 times less resources. Billy, the general manager of the Oakland Athletics do so by hiring a new assistant Peter Brand played by Jonah Hill to use econometric towards analyzing and scouting players. In the end, Billy won the game by using a data driven approach over the traditional approach of over-relying on his scouts in 2002.
So Peter Brand key message to Billy Beane is to hire players to achieve the wins instead of hiring players to play the game. In the same way, we can enable companies to win like the Oakland Athletics only if we start to quantify human capital and evaluate the return of investment on human capital investments like training, compensation, benefits and leadership succession.
Once we can do that, we will be able to identify different companies with different risk appetite on their human capital investment decisions. The current traditional approach is to get a headhunter to convince the super experience people and pay both the headhunter and experience people a ton of money. There is nothing wrong with using a headhunter or hiring experience hired and the traditional approach worked and still works today.
However, as we are moving into the age of AI and Automation, this traditional approach will results in increasing rate of attrition, decreasing the level of job engagement and perhaps increasing the cost of running a business.
The new way to do is to market the job, take job applicant whose profile fit 50% or less on the job description, assess the company risk appetite in hiring, training and paying this candidate on the job alongside with the return of investment of taking this candidate with a modern headhunter that also uses modern AI and Automation to recommend this candidate.
By doing so, the company adapts to the age of AI and Automation when corporate job holders are transiting in and out of being gigsters and when companies are constantly transiting between the state of innovation and the state of optimization. Again, there is high risks in hiring high potential but there is also higher return too.
API : be connected, be the hub for spokes
Now the human capital investment decisions for the companies and for the individuals will increase with clarity if everyone including the government to work on a common API infrastructure to communicate, engage and activate human capital activities just like the Central Bank working on the cashless infrastructure in Singapore or in other parts of the world.
So why is this more important and what are the implications of doing so?
Imagine that there is a central HR databank is seamlessly connected to the business registry like ACRA in Singapore, the social security fund like the Central Provident Fund, the Training bank like the Workforce Skillsfuture Agency in Singapore, the Job posting bank like the Singapore Job bank and the Physical and Mental health bank like the Health Science Authority in Singapore and this HR data bank, which is known as the HR Nexus, is the information highway for enabling efficient and effective information transfer and services between the respective agency and the public that consists of the locals, the companies, the gigsters, the foreign workers and investors.
This enables investors to value the company based on not just financial capital but also human capital. Freelancers and gigsters are able to differentiate the companies with agile workforce needs against companies with rigid workforce arrangement. Companies can reduce the cost of making erroneous mistakes in administering HR paperwork given there are seamless HR technology products constantly using AI and Automation to check and administer with all of the government agencies without having to call each agency to make inquiry every time. This dramatically reduces the cost of HR compliance for companies if the government can implement the HR Nexus project.
This HR Nexus project will eventually be the first Human Capital Central banking system that enables transparency and standardization of metrics to enable the first push towards using data to evaluate the return of investment on human capital matters.
This gets us to think about big companies to think about adopting the HR Nexus project for their global workforce. If so, let me share with you a way of thinking about managing the agile workforce and a way to think about human capital investments. The traditional way is using psychometric metrics for hiring, using traditional performance appraisal for rewarding and using engagement surveys for retaining people. These metrics are subjective data and so have inherent biases. If we only use these subjective data to make human capital investment decisions, then we run a very high risk of making bad bets about people, about a team and about the workforce into the future.
So, how can we avoid these potentially costly mistakes? This gets us to think about the next question. Why don't we consider using a quantitative perspective? How can we better enable human capital to be more efficiently deployed? How can we bring in operational science, statistical thinking alongside with the human behavioral study and the human psychological study to form the basis for our human capital investment decisions?
By combining these approaches, we can use machine learning techniques to better analyze and better predict the future outcome of companies profitability from the human capital investment decisions we make today.
Thank you for listening to this talk or reading this script.
We have come to the end of this talk.
Have a good day ahead.
Note: this post is being constantly reviewed, re-edit on new researches. If you have good sources of information or insightful opinions, please write to me or tweet me.