December 5, 2017 | By Jason Hayman
I'm the research manager at a major IT services and staffing organization. My professional passion—the reason I come to work every day—is managing our team’s fantasy football league.
Just kidding … maybe.
Actually, I love that I get to learn so much about tech, I have mountains of proprietary data to play with, I talk to people helping implement varied IT projects every day and part of my job requirement is reading a lot of fascinating (ahem) research about IT.
Because when you think about it, IT departments are on the forefront of world-altering changes coming at our tiny little ball faster than everything that came before.
So from my precarious perch as a research manager, I’d like to offer my five predictions for 2018. Or, as I like to call it, The Year Before the Robot Apocalypse.
Most enterprises are further away from a full-scale artificial intelligence revolution than the media hype would have you believe. Anyone who tells you The Next Big thing is about to change everything right away hasn’t lived through an ERP implementation and seen how deadline-shattering and painful systems integration can be.
But this won't stop enterprises from snapping up the limited supply of available AI talent. We saw this phenomenon a few years ago when new tech giants like Facebook starting grabbing all the talented developers they could find—before there were even projects to work on. The thought of getting Amazoned is making traditional enterprises jumpy, and they’re anxious to get their footing before the next disruption catches up with them.
The talent ware has already begun. Top-tier machine learning and artificial intelligence PhDs who also know how to code have found themselves (quite profitably) in the middle of NFL-style bidding wars. Machine learning engineers and data scientists will see their stock rise, as well, and I think software engineers plus anyone who knows their way around an algorithm are next.
I think these newer trends will bolster a classic skill set: enterprise architecture. There are lots of moving parts to make IoT and AI work: enormous data warehouses or expensive cloud options, computing bandwidth increases, systems integration at a level unheard of. And it all needs a tech architecture that's flexible enough to pivot with all the unknown change coming. Plus, someone has to figure out how to do it as economically as possible. In short, enterprise architects, gear up: Someone’s about to realize how critical your skills are.
The off-shoring of labor—already declining in fields like end user support—will fall further. U.S. companies are already facing stricter H-1B requirements, such as a higher burden of proof that candidates’ titles accurately reflect their skills. Political factors like negative public (i.e., customer) perceptions of offshoring and the uncertainty around H-1B caps and immigration standards have also made it riskier for companies to rely on this labor source.
This may actually help the near-shore market, either with companies taking advantage of outsourced labor in cheaper U.S. locations or close international neighbors, particularly Canada.
Talent shortages and the cost of IT labor mean that the people leading IT workforce planning—whether that’s IT leaders or HR—are going to become a lot more flexible about their staff portfolios. Instead of trying to hold workforce portfolios to certain percentages of contract, FTE and SOW, leaders will be granted more flexibility to choose whatever works from a variety of options, including contract labor, internal development, apprenticeship programs, setting up new work locations based on the availability of talent, plus, of course, hiring and contracting.
Gartner recently told IT leaders to start investing in AI, IoT and InfoSec in the coming year. But really, AI and IoT are probably not going to disrupt most businesses that soon. With IoT, there have been very successful limited-use cases, but it’s incredibly hard to scale the programs, especially when organizations have neither the computing bandwidth nor the data analytics expertise to harness IoT’s capabilities.
With artificial intelligence, the immediate challenge is simple: How do you make or save enough money to justify the extraordinary investment you’d make as an AI early adopter? Furthermore, how quickly can you create the organizational change needed to integrate AI into people’s jobs and mindsets? It behooves IT leaders to start thinking about these challenges, but AI disruption probably isn’t breathing down your neck quite yet. But it will be soon enough.
One place AI is likely to start making a difference sooner than later is in workforce development. It feels like I have this conversation every day: a manager is having trouble locating a Java developer and they don’t know why. I look at the job description and see everything from SDN to Hadoop in the required skills section. It’s clear to me that the pace of change has left employers in the dark about what they need, and it’s compounding the already significant shortage of talent.
As talent shortages and the pace of tech change force organizations to become more sophisticated about workforce development, augmented intelligence can help. New AI applications will help large enterprises identify in-house talent they could use better, create candidate success profiles, and develop in-house talent so it’s ready to fill new skills needs as they arise. Just imagine the implications to lineup optimization in fantasy football.
I’d bet my house on it. Well, maybe just my firstborn.
Thoughts? Comments? I’d love to hear your perspective. Leave me a message in the comments.