What exactly is technical debt? When discussing your organization’s technical debt - and possible changes to it - with various audiences, you need to articulate the key issues in plain terms. Here’s expert advice on how to do that.
AI skills: 5 ways to build talent internally
Artificial intelligence skills are hot. It’s time to stop bemoaning the lack of AI talent and start training up existing staff
As artificial intelligence use cases expand inside all types of businesses and industries, you can expect another sequel to a timeless IT hiring story: The Skills Shortage, AI edition – coming soon to a team near you.
As Pat Calhoun, CEO of Espressive, told us recently: “Most organizations want to embrace AI as part of their digital transformation but do not have the developers, AI experts, and linguists to develop their own or to even train the engines of pre-built solutions to deliver on the promise.”
That’s a challenge, but it’s a familiar one for most IT leaders. As new technologies emerge and especially as their adoption begins to spike, there’s commonly a gap between an organization’s goals and the technical skills necessary to achieve those goals. So while AI might sound exotic and fancy, you don’t actually need a newfangled playbook to bridge the gap.
“Despite its evocative title, AI is remarkably similar to other fields in information technology, in that success comes through continuous learning, training, and great processes,” says Zachary Jarvinen, head of technology strategy for AI and analytics at OpenText.
Unless you’re going to sit around patiently waiting for years for the labor market to catch up, IT leaders and their hiring managers must build the AI skills they need in their existing teams.
That’s not to say you can’t or won’t bring new people on board; it’s simply an incomplete strategy, especially if you can’t attach a blank check when you make a job offer.
[ What's coming next in AI? Read AI in 2019: 8 trends to watch. ]
Stop hunting purple squirrels
Jeff Reihl, EVP and CTO at LexisNexis Legal and Professional, expects this to be one of the key AI trends in 2019: IT shops investing significantly in developing AI skills internally, rather than hunting for purple squirrels – candidates who may not even exist – on the job market. This emphasis on training, sometimes referred to as upskilling or reskilling, is part of a broader pattern in technology skills development.
Reihl and other execs who are already doing this in their organizations describe a common foundation for success in building AI skills in-house: Give enthusiastic people opportunities to learn, and then give them opportunities to actually practice what they’re learning.
“Managers need two ingredients to begin upskilling their tech teams – developers with an appetite to learn and projects for them to work on,” Reihl says.
We asked Reihl and other experts for some actionable approaches to developing AI skills in your existing teams. Consider their top advice:
1. Programmatically invest in learning
One reason you can encounter some cynicism about corporate training initiatives: Too often company leadership talks a nice game about continuous growth and learning, but they don’t tangibly carve out the time and resources for people to actually do so. The subtext, intended or not, that comes with this laissez-faire strategy: We’d like this continuous growth and learning to happen on your own time and your own dime.
That’s not going to cut it for robust skills development in a field as complex as AI, nor with overlapping technologies and disciplines such as machine learning or natural language processing.
You can certainly start small if you want, but the leadership team needs to actually create the time and resources people need to really commit to learning AI skills and technologies. Ad-hoc opportunities won’t likely produce the results you want. Moreover, someone who’s worried that their manager is looking over their shoulder and wondering why they’re not doing their “real” job can’t well focus on meaningful learning.
“Managers can offer workshops, classes, and online courses and pull together a motivated team of volunteer developers to experiment or develop an AI-related project – [that] would be a good first start,” Reihl says.
Sofus Macskassy, VP of data science at HackerRank, notes that some organizations might not feel they have the right training resources close at hand. There’s plenty of help, and it’s OK to encourage people to see out opportunities from outside the firm – again, just make sure the leadership team is clearly sponsoring (and ultimately rewarding) the initiative.
“Skill development is most effective when the teachings are grounded in real projects, but many times, businesses aren’t equipped to train their employees in emerging technologies,” Macskassy says. “Managers should encourage employees to seek their own education through platforms like Coursera, Udacity, Datacamp or Kaggle, and they should support these endeavors by giving employees time to learn the skills they need.”
2. Partner with local universities
Conferences, online courses, certification programs, and similar resources can be wonderful options, especially in the initial phases of an upskilling program. But if you want to take your internal skills development to the next level, consider essentially launching a school of your own. Don’t worry, it’s a lot less scary than it sounds.
LexisNexis essentially did just that to double-down on its AI skills development.
“We worked with N.C. State University to develop a proprietary in-house curriculum to develop staff more quickly,” Reihl says. “Courses in data sciences, data engineering, machine learning, NLP, and Python are offered regularly and always sell out. More than have our tech staff worldwide is now skilled in AI-related technologies.”
Jim Johnson, senior VP at Robert Half Technology, notes that in addition to high-quality online courses in AI, in-person learning (including partnered with a local school) can happen both on-site or by sending people off-site for coursework.
“Help your employees get the technical skills they need by providing training resources for them, whether that be a skills development course at a local university or bringing someone in-house to train your team,” Johnson says.
3. Launch actual projects to apply learning
“It’s one thing to get the training, and it’s another to actually use it,” Johnson adds. “Give your team the opportunity to apply what they’ve learned with different AI projects, and where it makes sense, invest in new AI technology.”
One of the best ways to do this is to identify pilot projects that can actually improve a process or solve an existing problem, rather than doing projects that have no potential longer-term value or simply tinkering with new tools in a sandbox without any goals attached to the team’s experimentation. Bonus points if the team members who are developing new AI skills get the opportunity to identify the potential areas of the business that might benefit from an AI application.
“They should identify AI pilot programs that will improve some aspect of the company, whether it be the product, customer support, sales, or marketing,” says Macskassy from HackerRank. “External education efforts like these work best when they are paired with an internal project that lets employees test their new skills.”
Be sure to celebrate and reward the progress and success of those projects, too.
Rahul Kashyap, CEO at Awake Security, notes that this approach also helps organizations implement AI in meaningful, thoughtful ways while avoiding misguided stereotypes that assume AI will replace human talent rather than augment it.
“IT execs and managers should encourage critical thinking and problem solving whenever possible,” Kashyap says. “IT managers should encourage teams to explore what interests them most about AI.”