man paused on bicycle part of the way up snow covered mountains in India
PHOTO: Mohit Tomar

The Fourth Industrial Revolution is upon us, and we’re already seeing massive changes in workplace connectivity, collaboration, productivity and (of course) technology.

One of the biggest changes is the emergence of artificial intelligence (AI). From automation and virtual assistants to systems capable of analyzing massive data sets to find trends, AI represents one the biggest opportunities that business leaders can take advantage of now to ready themselves for the future. 

The number of enterprises implementing AI has already grown 270 percent in the past four years, according to a Gartner survey of more than 3,000 CIOs. So “if you are a CIO and your organization doesn’t use AI, chances are high that your competitors do and this should be a concern,” said Gartner analyst Chris Howard.

Savvy leaders understand that AI is not some magic dust that can be easily sprinkled on an organization. While organizations are developing AI and machine learning expertise as part of their larger digital strategies, they may face trouble actually implementing AI because the tools are so new and their employees have limited hands-on experience with them.

Here are three practical tips that can help you prepare your organization for the AI-first future.

Related Article: Confused By AI Hype and Fear? You're Not Alone

1. Be Clear About Your Business Use Cases for AI

Discussions about AI often focus on the technology itself and not on practical use cases. You can avoid building the dreaded “bridge to nowhere” by finding the right machine learning algorithm to address a real pain point. Whether it’s personalizing customer service through a chatbot or developing improved data analytics, focus on finding the best use cases connected to your organization’s value drivers, and prioritize AI investments there.

In a Harvard Business Review article titled “Artificial Intelligence for the Real World,” Thomas H. Davenport and Rajeev Ronanki argue that there are three primary AI use cases: process automation, cognitive insight and cognitive engagement.

You can integrate AI-driven process automation to handle repetitive, data-driven tasks that employees do frequently, or even to manage entire workflows. AI systems that yield cognitive insights can help you analyze a large amount of data quickly, using algorithms to help identify trends that will have predictive power to, for example, personalize offers. Finally, AI-driven cognitive engagement tools can help you improve your direct communication with customers, such as collecting information to create more personalized experiences through chatbots.

Get started quickly by testing out your AI use cases as pilots or prototypes, iterate, and get feedback early before committing to a large-scale investment.

2. Train Existing Employees as You Recruit New AI Talent

With employer demand for AI-related roles more than doubling in the past three years, it’s no surprise to learn that people with AI expertise are scarce. In a recent Gartner Research Circle survey, 54 percent of the respondents said that they viewed the AI skills shortage as the biggest challenge facing their organizations. Hiring the right technical talent will continue to be a challenge over the next few years, until supply grows to meet the demand, so it is best to respond to the AI talent shortage by offering AI training to current employees in addition to recruiting people with AI expertise.

Start building AI expertise within your organization now by investing in online training programs for current employees who have backgrounds in statistics and data management. Seek out continuing education courses at local universities or online mentoring platforms to gain access to expertise outside of your local area. 

When it comes to hiring AI talent, consider tapping into the worldwide talent pool and hiring programmers who can work remotely. You can always have your own internal team focus on statistical and mathematical modeling, rather than spending time on pure programming in languages like R and Python, which can potentially be handled by more junior team members or contractors.

Related Article: What Will Drive AI Adoption in the Coming Year?

3. Test Smaller-Scale (and Less Costly) AI Solutions

Many organizations want to start taking advantage of artificial intelligence but simply don’t know how.

Out-of-the-box solutions are a good way to get started quickly and avoid unexpected expenses. In its second “State of AI in the Enterprise” report, Deloitte notes that a popular, and easy, path to AI is to implement cognitive tools with existing enterprise software, such as ERP or customer relationship management systems. While potentially less flexible, such “first step” approaches help you take advantage of your own customer data sets, which your team can use without specialized technical knowledge.

One example of this type of ready-made AI solution is Salesforce Einstein, which is designed to help users determine which of their prospects are most likely to convert to sales.

In addition, instead of investing heavily right away in training algorithms, tap into the technologies you need and pay only for what you use. Many big cloud-based deep-learning services have such AI-as-a-service offerings, which relieve you of the need to set up your own infrastructure.

Related Article: Dream Big, But Start Small With Artificial Intelligence

The Future of Work Is Now

Implementing artificial intelligence will require focus and alignment across your entire organization, but not pursuing AI will be costly. As you move to take advantage of all that AI has to offer, adopt an approach that focuses on realistic use cases, is tailored to your organization’s needs, involves a process of iterating smaller-scale AI deployments, and addresses the AI skills gap by mixing training of current employees with efforts to recruit new people with AI skills.

With the right strategy, you can put your organization on a path to success during the Fourth Industrial Revolution.