Buried at the bottom of an an HBR post titled 8 Ways Machine Learning is Improving Company Processes, is a little nugget about the ways machine learning might soon affect career planning. Machine learning could help employees in navigate their career development by providing:
Recommendations (that) could help employees choose career paths that lead to high performance, satisfaction, and retention. If a person with an engineering degree wishes to run the division someday, what additional education and work experience should they obtain, and in what order?
Could this be a career coach in the future of work? It’s a fascinating idea and I’d love to see it in practice. We’ve already seen machine learning technology take over some parts of a career advisors job. There’s even a chatbot in development that’s trying to be a career coach (let’s hope they’re better than LinkedIn’s mediocre job recommendation algorithm.) IBM uses AI to guide job seekers through their search.
A good career coach will listen to you, help you work out ideas, guide you through an ambiguous process, support you emotionally, and reflect your own words back to you. Machine learning technology can’t do this yet, in answer to my clickbait title.
But there aren’t enough good career coaches to go around. And few people can even afford a good career coach. Moreover, not every organization offers career coaching that helps employees navigate their next steps. Tools that help people navigate a world full of increasingly ambiguous career paths are mighty helpful.
Like many jobs, career coaches won’t be fully replaced by robots or artificial intelligence anytime soon. There will always be people who prefer working with people over machines. But the role of career coaches will change as new tools and technology emerge. Career coaches need to be aware of these changes. The workplace and available roles are shifting rapidly. Career coaches need to be able to coach their clients through these changes. They need to rethink outdated career advice, especially given that our job search is becoming less human. University career departments in particular need to upskill.
Today’s post is brought to you by my half way mark to 50K words for #NaNoWritMo. I’m deep into a chapter on the future of work for my book and still finding a ton of good content to write about. The challenge of course is to write about it and not just read about it. Reading is not writing, I have to remind myself a bajillion times a day.
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I originally wrote this as a guest post on Switchboard, an alumni platform that connects students and alumni. Switchboard is one of the few ed-tech companies who understand the nuances of higher education transformation. Their higher education innovation fellowship and upcoming conference ListenUpEDU are models for professional development in higher education. And they kill it with good advice for the future of alumni relations.
By now we’ve all seen the headlines about the future of work. Beyond headlines about job-stealing robots, the reality is that machine learning and artificial intelligence technology are disrupting career paths. According to the World Economic Forum’s latest report, The Future of Jobs 2018, AI will create 58 million new jobs within the next five years. In a 2017 Deloitte report, Catch the Wave: The 21st Century Career, the authors note that only 19 percent of companies even have traditional career pathways. The future of work is filled with ambiguity and non-linear career paths.
With so much change ahead, career centers need to rethink outdated career training models. Career centers’ primary focus should not be to prepare students for linear careers anymore. Instead, they should prepare students for a lifetime of career changes. Navigating these ambiguous career paths requires students and alumni to embrace upskilling and lifelong learning. This same advice applies to careers services staff too.
20. That travel and hospitality schools, barring few exceptions, are training the young for jobs of the previous generation, instead of all the new types of positions opening up in travel and allied sectors. In fact, the deans, professors and teachers are more clueless about the current and future of the travel industry than the students they are teaching. – 21 Uncomfortable Truths That I Have Learned About the Travel Industry
The founder and CEO of Skift, a global travel intelligence company, wrote a killer post on the uncomfortable truths about the travel industry. Having worked several years in the travel industry, it was refreshing to read it. Most of these truths are usually confined to conference conversations after a few drinks. They’re rarely shared publicly. Judging from the shares and comments on the article, it’s clear it resonated with others too.
I would love to see this list for other industries.
Recently I came across a certificate in Higher Education Administration from Northwestern. For $19,975, I can “deepen (my) understanding of the field and expand (my) networks.” Details on career outcomes or paths are notably absent. Instead the page offers the basics of college career services: “access to ongoing professional development support, one-on-one career coaching, academic advising and networking opportunities.”
The certificate reminded me of a Northwestern ad I saw last year promoting a $10,000 global mobility online certificate. The ad was marketed towards future international education professionals. As someone who has worked in international education for over a decade, I got a bit riled up. Aside from the fact that you don’t need a $10K certificate to get a job in international education, the program’s career preparation promises were lackluster at best. The lack of testimonials from employers raving about the certificate, or explaining how the certificate signaled a candidate’s competitiveness, was telling.
Despite my frustrations with certificates with lackluster career promises, I recognize the role certificates play as career paths and institutions adapt to changes in the market. Certificates are revenue generating programs which help institutions shore up revenue from diverse sources. Certificates also provide an attractive option to employees who want to upskill or change careers. They usually take under a year to complete. Certificates are frequently associated with a university brand name. While affordability varies by institution and certificate program, they’re cheaper than a full degree and they qualify for financial aid.
However, data on career outcomes from non-degree credentialing – i.e. certificate holders – is hard to come by. Employers’ attitudes towards certificate holders are difficult to pin down, which makes it hard to know if certificates hold their value in the market or even determine the ROI on a $20K certificate.
Thankfully we’re a bit closer to understanding employer attitudes to non-degree credentials thanks to a new report by Burning Glass Technologies. A recently released report, The Narrow Ladder: The Value of Industry Certifications in the Job Market, examines how employers use certifications (not certificates) in the hiring process. Using their vast database of over 700 million job postings, Burning Glass Technologies examines the types of certifications that employers value, along with the skills and salary bump employees receive post-certification. It’s well worth a read for anyone who advises students or mid-career professionals about their upskill options.
“It’s not that the “non-degree” credentials are rare; more than a quarter of the employed U.S. population holds a license or certification, on top of any degrees they may hold. Certifications can be precisely tuned to industry needs, and they hold the promise of reducing the need for employers to rely on imperfect proxies, like college degrees. In certain occupations, certifications outline career ladders that define industries and give employers and job seekers alike guidance about what skills are necessary to advance.
Those occupations, however, are the exception, and if the nation is to close the skills gap, perhaps they should become the norm.”
Though the report focuses on certifications, its analysis provides material for examining certificate programs as well. Most importantly, it provides a clear difference between between certifications and certificates. The report examines employer attitudes towards certifications, which are “awarded by a certifying body, often an industry association or trade group, based on an examination process assessing whether an individual has acquired the designated knowledge, skills, and abilities to perform a specific job.” This differs from certificates, which the report defines as “short-term, professionally oriented credentials awarded by an educational institution (as opposed to an industry body) based on completion of specific coursework.”
This distinction is important since few people outside of mobility circles realize the difference. There is a critical difference between these types of upskilling. With such similar terms an employee looking to upskill could be forgiven for thinking a university certificate in higher education administration will provide the same signal to future employers and salary bump as a CISCO Cisco Certified Network Professional certification (it doesn’t). The former is a revenue generation program from a university with little focus on skill building and an unclear career trajectory. The latter is an industry-approved career training model with clearly defined career paths.
What struck me most from this report was the role that certifications played in outlining both the skills and career paths that job seekers and employers agree on. Certifications are built from industry needs. Here’s an example of the skills needed for a AMA Digital Marketing Certification:
Are university certificate programs mapping their content offerings to industry needs? Maybe but we don’t know. The report also finds that employers value certifications that improve technical skills. Do employers feel the same about certificates? Hard to know.
On top of that, the report finds that employers vastly prefer certifications over certificates.
In 2015, the demand for certifications is approximately 1.5 million job postings, whereas only about 130,000 postings ask for certificates.
Is it possible that employer demand for certificates aren’t as in-demand as universities promise? Again, we don’t know, but this stat and the lack of employee perspectives in program marketing for certificates is telling.
Among the most important takeaways from the report, however, is this nugget:
Relatively few certifications actually have market value, and there is a shortage of easy-to-find information to sort out which credentials are pathways and which are blind alleys. More transparency in the certification market can significantly improve the returns people receive on their certification investments.
Finding out which credential pathways are legitimate is difficult. I’d argue the same for certificate programs. Will a certificate in higher education administration make a candidate more desirable than a candidate with 5+ years working in higher education? Will a certificate provide a salary bump or launch a job seeker into a more senior role? Will a certificate ensure the skills learned are still relevant in the next five years? The lack of this data makes it tough to answer these questions.
Since we don’t have those answers yet, it’s up to the job seeker/future certificate student to ask the hard questions before taking on a certificate. So for job seekers thinking about getting any certificate – online or in person – ask yourself these six questions before committing:
Does this certificate add to or improve your technical skills?
Does this certificate put you on a path to a hybrid job?
Does this certificate map to industry needs?
Does this certificate frequently appear as a requirement in your future job posting?
Will this certificate give you a salary or title bump?
Will this certificate be relevant in five years?
If you can’t answer these questions on your own or through a Google search, ask admissions. You’re investing in a certificate; it’s perfectly fine to ask about career outcomes. Ask to speak to participants in the program (don’t rely on testimonials). Look at LinkedIn profiles of certificate holders to understand their career paths. If you don’t get a clear answer, consider other options that are either cheaper (i.e. MOOCs), bootcamps, or certificate programs that detail the results.
Employees will need to upskill throughout their career. Certifications and certificates are one of many paths to do so. To make sure they’re actually beneficial to job seekers, we need a lot more data like the recent report from Burning Glass Technologies.
Higher education should do their part by ensuring their certificate programs bring career outcomes data – or employer perspectives towards their certificate – to the forefront of their marketing and information websites. Because right now career outcomes from all these certificate programs basically look like this:
Here are two brutal quotes from an Axios post reporting on executives’ attitudes towards general pay raises and employee retraining. There were made during a conference for CEOs titled “Technology-Enabled Disruption: Implications for Business, Labor Markets, and Monetary Policy.”
“Executives of big U.S. companies suggest that the days of most people getting a pay raise are over, and that they also plan to reduce their work forces further.”
Damn. And then:
The moderator asked the panel whether there would be broad-based wage gains again. “It’s just not going to happen,” Taylor said. The gains would go mostly to technically-skilled employees, he said. As for a general raise? “Absolutely not in my business,” he said.
The CFO of AT&T also said that he doesn’t have a need for so many call center employees or guys that install their cables.
The message is pretty clear: employers don’t need you.
The idea that employees should be loyal to companies is a hold over from traditional career narratives. We’re still waiting for old school career narratives to catch up the present reality of work. But in the meantime it’s a good reminder that companies aren’t looking out for your best professional interest. Waiting for your employer to give you a raise, direct you to the next step, or reward you for your hard work – that’s not going to happen. Instead, it’s going to be up to you to figure out your next move and make sure you have the skills to get a pay upgrade. Don’t expect your employer to do it.
There’s a lot of bad career advice masquerading as good advice. Much of it stems from outdated notions about careers. Advice like “stick with a job at least two years” and “don’t job hop, it’ll hurt your resume!” is meant for old school careers where companies invested in employees. It was meant for a time when people stayed with companies 5, 10, even 15 (!) years.
This advice is dead wrong.
It keeps people in miserable jobs.
And there’s no need for it in the new world of work.
This perspective was most expertly summed up in the tweet thread below:
My first job in tech, I cried every day but stayed b/c I worried leaving at 4mos would look bad on a resume.
6 years later when it happened again, I left immediately.
Both times I moved to better companies that valued & supported me. Don’t stay somewhere shitty for your resume.
If you’ve got a bad manager or work in a toxic environment, leave. I don’t care if you’re two months into a new job, if you have the means to leave, gtfo. Don’t waste your time because it’ll look bad on your resume. Don’t stick with it to tough it out. It’s not worth your time or sanity, especially if you’re earlier in your career. It’s totally ok to make a mistake. (Note: not everyone has the means to escape; this is advice for those who do)
Instead, put all your energy into leaving asap. Build a story that explains the honest reasons why you left (bad work culture is a perfectly ok reason to leave). Build relationships with people inside companies that are known for having good work cultures. Learn what you like in a manager. Ask people what their managers are like during careful informational interviewing. Read Glassdoor reviews.
But don’t stay at shitty jobs just because of the fear of being perceived as a job hopper. With the number of workers who work in the gig economy, the increase of job seekers with side hustles, a tight labor market, new job types, there’s a lot more fluidity in your career. Employers can work with job hoppers. It’s not worth it to stay.
So hey, if you’re in this position, start plotting your escape.
Yesterday I presented to a group of undergraduate students at PSU about the future of work and the coming changes to the workforce. As someone who regularly talks about the future of work this was the first time I’ve stood in front of soon-to-graduate students and tell them they’ll need to become lifelong learners because artificial intelligence. It’s a bit of an awkward message to deliver. They’re in their last term, weeks aways from finishing up four years of learning, working, and preparing for their next career move. They are ready to take on the world with their new skills. And I’m telling them they’re going to need to keep learning, upskilling, post-college.
But the students were game for the discussion and asked solid questions.
The experience, however, highlights one of the biggest challenges I have right now. Everyone working in future of work spaces is working to educate employees and students about the coming changes to the workforce. Despite the blazing headlines about robots taking our jobs, the subject (or fear?) isn’t tangible enough to stick. How do we get people to shift from outdated career models and thinking to commit to lifelong learning and upskilling? How do we get people to see how artificial intelligence is changing the workplace and our jobs, if they aren’t yet feeling affecting by the technology?
Yesterday I asked if any of the students had experienced an interview using the HireVue platform. One had. I asked if she knew she was being evaluated by algorithms. She responded that she wasn’t, and the audible, “Whaaaat?” and gasps from the audience indicated most students weren’t aware either. Job seekers need to know about the technology that’s being used to evaluate them.
For yesterday’s talk I put together the resources to help students understand the coming changes, the technology, and how to prepare for an ambiguous career. If you’ve seen the headlines about robots taking our jobs and want to get beyond the headline hype, check out the resources below.
Start with the video below as an introduction to the subject.
BONUS WATCHING: Learn about the digital skills gap
It’s something that has been a bit of a mantra in the educational field. Everyone is going to have to be a student for life and embark on lifelong learning. The fact is right now it’s still mainly a slogan. Even within jobs and companies there’s not lifelong training. In fact what we see in corporate training data at least in the United States, is that companies are spending less. As we know right now people expect that they get their education in the early 20s or late 20s and then they’re done. They’re going to go off and work for 40, 50 years. And that model of getting education up front and working for many decades, without ever going through formal or informal training again is clearly not going to be the reality for the next generation.
Continuing on that theme is another article by McKinsey, Getting Ready for the Future of Work, which is worth reading if only for this shocking quote right here:
The time it takes for people’s skills to become irrelevant will shrink. It used to be, “I got my skills in my 20s; I can hang on until 60.” It’s not going to be like that anymore. We’re going to live in an era of people finding their skills irrelevant at age 45, 40, 35. And there are going to be a great many people who are out of work.
I’m a liberal arts grad. I love words and language. I teach soft skills. Qualitative data is my jam. I’m also obsessed with machine learning (ML) and artificial intelligence (AI).
In 2015 I tumbled down the AI rabbit hole after discovering a long read on the fabulous site Wait But Why. The site explains complex ideas paired with hilarious stick figures. The two part series on AI, The Artificial Intelligence Revolution, was my gateway article to the world of AI, and later ML as part of AI.
So far my self-directed learning journey has only included reading about AI and writing about its affectonhiring and the future of work. I can’t code in Python (with zero plans to do anything with R). My data background includes data analytics, cleaning data, and putting it into Tableau but nothing close to data scientist. I also have no interest in going that far professionally. As a non-tech person trying to access ML/AI, it’s been a challenge to figure out where I fit in. I’ve uncharacteristically avoided meetup groups or conferences on the subject since I don’t have the tech skills.
Not me.
Last month I changed that. I got tired of reading. I wanted idea exchanges. So I attended a ML/Al unconference in PDX. And hot damn I found my people!
An unconference is the opposite of the standard conference setup. Instead of corporate-sponsored keynotes paired with bland chicken and an abundance of shy speakers who read PowerPoints, the participants chose the content. We pitched and voted on what they wanted to talk about. The result was facilitated conversations about subjects we were curious about and a format that flowed. It was the ideal setup for idea exchange and learning.If you’re conference weary an unconference will restore your faith in professional development.
Many people at the unconference were data scientists or computer scientists, and some working on ML projects. A few were students or job seekers. I met one other person who is like me, a communications expert without a technical background who works for a machine learning platform, BigML (and they’re doing rad stuff).
In our sessions we covered a roving range of topics about ML/AI: novel data sets, making AI more accessible to the masses, establishing trust with users, data security, AI decision making re: self-driving cars and the Arizona accident, becoming a data scientist and machine learning engineer, the future of companies and jobs (my pitch!), learning ML/AI as a new person (do you learn the math, the code, or find a project first? plenty of debate on this!), and plenty more side conversations that spilled out of the main sessions.
As an non-tech outsider it’s a bit intimidating to participate in such a cutting-edge tech space. I think ML/AI people forget that at times. One of the guys I met at the conference noted that when you’re an expert it’s hard to remember how hard it is for others to start in your field. I’ll add that this goes double if you’re in a quant and code heavy field like machine learning. Luckily most everyone at the unconference made it easy to participate (as did the unconference format).
My main takeaway though is that you don’t need to be a software engineer, data science expert, or code wizard to understand ML/AI.
So for all the people who are curious about ML/AI but don’t know how to start engaging in these communities, here’s how.
Learn the basics: Know the difference between machine learning and AI; understand the difference between Artificial Narrow Intelligence, Artificial General Intelligence, and Artificial Super Intelligence; understand the basics of data science. There are no shortage of intro articles and videos on the subject (two examples below).
Prior to the uconference I was slightly worried I’d be left out of the conversation if it turned to technical. I prepared by returning to a set a YouTube videos I’d skimmed a while back: Fun and Easy Machine Learning. The YouTube list animates over 15 models to better understand machine learning.
Ignore the math and coding right now: Unless you want to become a data scientist or machine learning engineer, ignore it. You don’t need it to understand the basics or to explore products or impacts of ML/AI. For example, the Fun and Easy Machine Learning series sometimes dives into the math behind the models. Treat it as you would a foreign language; when you don’t the meaning keep moving forward and focus on what you do understand. Fill in the blanks later.
Read everything about ML/AI in the area you’re interested in. ML/AI for non tech people is a huge field. So narrow it down. Start with general articles about artificial intelligence and learn about it’s expected impact. The World Economic Forum has good articles with a global perspective. For business impacts, check out this history of ML/AI technology by industry/verticals. Then head over to CB Insights to study ML/AI companies (and subscribe to their newsletter as they’re cutting edge everything). Then pick an industry that interests you. Either one that you work in or one that you want to work in. Read everything you can about how machine learning is affecting that industry (it’s affecting all of them – right now finance, healthcare, and insurance are some of the industries talked about the most.) Explore products and platforms in that industry that use ML/AI. Read case studies. I study the future of work. So I read everything I can about ML/AI and it’s affect on workers and organizations: McKinsey, AXIOS, MIT, plus I play with HR Tech.
Avoid the hype. It’s easy to get caught up in the shiny promised of AI. Instead, pay attention to counter narratives, often published outside of the tech reporting ecosystem. Find the counter narrative about AI in your field. I read the amazing research and work by Audrey Waters at Hack Education for a counter narrative to AI edtech hype. Explore bias in ML/AI. Understand how AI isn’t neutral and that gender and race bias is coded into AI systems. Weapons of Math Destruction is an excellent book (and 99% Design has a good podcast on it). We need diverse perspectives and people in ML/AI fields to fight these bias, and non-technical people are part of that fight.
Take a course: FutureLearn, an online learning platform with a name after my own heart, offers an Intro to Data Mining course where you’ll learn the basics of classification algorithms. It’s a smooth intro to applied machine learning. They also offer an advanced course to build your skills further.
Go to an event and talk to people: This is the intimidating part. But get over it, embrace the awkwardness, and commit to asking curious questions. Remind yourself of the things that you know. Write down the things that you want to learn. Talk to people until you get the answers to your questions. Ask people how they got into their work, what impact they’re having, and how they’d explain their work to a non tech person. Tell them you’re curious. Some people will just talk at you. Others will teach you. Keep in touch with the people who teach you and simply move on from the ones who talk at you.
Get a project: This builds on not worrying about the math and coding. Instead, get a project. What problem do you want to solve? What problem does your organization need to solve? What data is available? What data is missing? How could ML/AI solve your problem? Starting there will help you lead you in the right direction. You might not have an answer right away. That’s ok. It make take a while to solve it. But that’s the point. You’re learning. Ambiguity is part of the process. So ask around your workplace. Visit the data science or computer science team in your organization (assuming you have one). Find a data scientist in your network or at ML/AI events and ask them how they’d solve your problem. Ask them to break it down. Ask a computer science student what they think.
Start with curiosity, ignore the part about not having a technical background, and see where it takes you.
The benefits of the comfort zone are appealing. Steady (though not always satisfying) incomes, “secure” jobs, relaxed routines, and predictable schedules are as comforting to humans as they are to animals. In this phase, people limit their learning to things they learn on the job, not knowing that yesterday’s lessons rarely solve tomorrow’s challenges… Without skill upgrades or a willingness to learn, people are caught in a rut. They are unable to see when the next trend is about to catch up or when the current one is about to die. For the few that can see the new trend, the pain of having to upgrade their skills far supersedes the pleasure of staying in the comfort zone.- How to stay relevant in today’s rapidly-changing job market
Since reading this article I’ve thought about the above paragraph multiple times. The last part about the pain of upgrading our skills nailed it.
No doubt, professional change is painful. I’m part of a generation where the narrative has always been college degree = career success, full stop. Two degrees and five professional jobs later and I’m wondering if I’m staring at irrelevance in five years if I don’t upskill. I quit my well paying, secure job at Yale last year because I was stagnant with little hope of gaining new, relevant skills that prepared me for the future of work. While I’m starting my own company, I’m concerned I’m not keeping pace with the technical skills needed to stay relevant. Should I take a side job designing chatbots? How can I fit in learning to code in python so I can get closer to working with AI systems? I’m not thrilled by self-paced learning, so what are my options? Where do I find the time?
Telling people they need to update their skills and #alwaysbelearning is the first step. But the next step is harder. How do we teach people reskill? How do we help them identify what to change and how to change it?
That’s what I’m setting out to change in my upcoming book. If you’re curious, subscribe.
Sixty-two percent of executives believe they will need to retrain or replace more than a quarter of their workforce between now and 2023 due to advancing automation and digitization.
As for solutions, 82 percent of executives at companies with more than $100 million in annual revenues believe retraining and reskilling must be at least half of the answer to addressing their skills gap. Within that consensus, though, were clear regional differences. Fully 94 percent of those surveyed in Europe insisted the answer would either be an equal mix of hiring and retraining or mainly retraining versus a strong but less resounding 62 percent in this camp in the United States. By contrast, 35 percent of Americans thought the challenge would have to be met mainly or exclusively by hiring new talent, compared to just 7 percent in this camp in Europe (Exhibit 3). – Retraining and reskilling workers in the age of automation, McKinsey
Two thoughts from this report:
Do executives think they need to upskill? Maybe their inability to see the training needs, new jobs, and workforce of the future is shaped by their inability to reskill.
Americans who think their companies are going to invest in them and their future career are mistaken.
