There’s no doubt that machine learning is opening up exciting opportunities for enterprises and startups around the world. But for most organizations, there are some big obstacles to overcome before they start seeing the fruits of their labor.
So, the real question is how do you get past these roadblocks? How can you make the most of machine learning faster and more effectively? Well, it all begins with an investment in people and technology.
Barriers to a New Frontier
From an operational standpoint, there are three main factors impeding progress in building machine learning applications that are ready for widespread deployment.
We’re In the Experimental Era
The first is the current state of the technology. Right now, machine learning is still extremely experimental. Often, quite a bit of exploration is needed before organizations start making sense of their data.
This, in turn, informs the selection of algorithms to employ. This unpredictability often results in too much guesswork and trial and error—which ends up making machine learning implementation even more daunting.
Data is the fuel of machine learning. And access to high-quality data makes the difference between a successful project and a failure. Many companies today are having trouble striking a fine balance between adhering to security protocols and privacy policies while maintaining ongoing, efficient access to data.
Consistent and up-to-date access to data is an essential ingredient to effective algorithms. Unfortunately, providing it is easier said than done. Even when businesses allow for easy real-time data access, bringing all this information together, organizing it, and maintaining flow are difficult to do. But it’s necessary. To evolve and adapt, machine learning models must be continuously trained on changing data. Static sets only result in the decay of model efficacy.
Out with the Old Tech, in with the New
Tons of organizations are still relying on legacy technologies without any plans to invest in advanced ones. But when it comes to tech, as we all (should) know, businesses only really have two options: Disrupt or be disrupted.
Investing in new technology offers many benefits that businesses can use to stay ahead of the competition. It helps foster improved experiences, more efficient use of resources, a better understanding of users, and more actionable insights. On the other hand, there’s no faster way to become obsolete than to let your tech do the same.
The state of your tech goes hand-in-hand with the previous factor we mentioned. There are far too many tech professionals dedicating their time to solving data issues due to poor infrastructure or equipment. This could be time spent on higher value tasks more suitable for the tech talent you invest in. Speaking of that…
Solving These Issues Starts With Your People
These factors are all connected by one bigger problem soon to plague every industry as the demand for AI grows: a short supply of qualified talent. To solve each of the technical issues we’ve discussed, businesses should also invest in the training and upskilling of their employees.
The number of data scientists and machine learning experts is definitely growing. In fact, they were first and second, respectively, in the World Economic Forum’s prediction of the top ten emerging roles in 2022’s job landscape. But to stay on track with this, time is needed to develop the skills required. This is a big reason why the AI expert supply can’t meet the current demand.
It’s important to note that the onus of training AI experts of the future doesn’t only fall on businesses alone. Academia obviously plays an integral role too. Many academic institutions have already stepped up by adding more data science classes and creating machine learning curriculums. It’s a good start. But more work is needed to be done.
Many businesses are only hiring these recent university graduates for AI and data science roles. But they’re leaving a ton of potential on the table by doing so. Training existing workers with other skills to offer not only bridges the skills gap; it also results in unique value and insights. And with the availability of low-cost (and sometimes free) resources available online, getting employees up to speed on new technologies has never been easier.
Business organizations and universities could also help each other more by making work experience a requirement. Some progressive academic institutions already do this. But it’s even more necessary with AI. Without practical, on-the-job experience, many students are entering the workforce and asked to tackle real-world problems with only theoretical training. This is a spell for disaster.
With job experience, students can broaden their horizons and seamlessly transition into a field they know they’ll like. As a result, this would shorten the recruitment and hiring process for businesses, something that’s much needed when it comes to AI talent.
Two Steps to Success
For AI and machine learning to reach their true potential, organizations must invest in both people and technology. This is the only way the technologies will expand outside of San Francisco and other tech hubs to reach widespread adoption. And this is when all industries will start benefiting.
Businesses that start taking these steps now significantly increase the chances they not only survive but thrive in the near future. What is your organization doing to embrace machine learning? Let us know in the comments!