Artificial intelligence (AI) is transforming the way we live and work, and the pace of that change is accelerating across every aspect of the business landscape, from operations to the customer experience. As its capabilities expand, investment in AI continues to grow, reaching $67.9 billion in 2020 – a 40% increase from the year prior – according to the Stanford AI Index Report 2021.

Organizations that want to stay ahead know they face fierce competition for talent with in-demand skills like data scientists, data engineers, cloud engineers, and solutions architects. But too many have a blind spot to the need to develop AI acumen outside the technical space. And it’s the companies that do both that will ultimately win in the marketplace.

A War for AI Talent

To develop, deploy, and manage big data and AI solutions requires recruiting, training, and cultivating a highly skilled AI workforce. But with demand outpacing resources, to compete for top talent means an organization’s leaders have to create the right culture to attract these individuals and the opportunities to keep them there. Among the key components of that culture:

• Support for their development and lifelong learning: AI talent is drawn to environments that enable them to hone their skills through rich learning opportunities and progressively more challenging projects.
• The resources to see projects through: They want to know that when they work on meaningful challenges, the resources are in place to shepherd those projects through to production, so they can see the impact of their efforts. 
• An ethos of giving back: Beyond skill development, data scientists and other AI professionals increasingly have a desire to contribute not just to profits but to a higher order of things. A well-articulated mission to drive greater good can help tip the recruitment scales in your organization’s favor. 
• Flexibility: The AI field is full of self-motivated people who are driven to learn more and embrace hard work. But they want flexibility in how they get that work done and will have greater job satisfaction in an environment that enables it.

Developing AI Proficiency on the Business Side

Businesses need data scientists and other technical resources to build AI. But they also need product managers who know how to employ AI’s predictive power in their solutions, operations managers who can work with autonomous and semiautonomous processes, and client service representatives who are comfortable using AI to guide interactions with customers. And that means raising awareness outside the tech space of what AI is – and what it isn’t.

Employees outside the tech space can be reticent to the introduction of AI in their work roles. Often chief among their concerns is a fear that it will take away the things they need to feel fulfilled at work—or, worse, replace them. Education can help them see that AI is not driving a wholesale replacement of human talent. Rather, it automates analytically addressable – often mundane – tasks and functions, enabling employees to spend more time on tasks that require their uniquely human skills and talents. If AI can make processing customer transactions faster, for instance, that frees up more time for human interaction, making customer-facing employees more effective at their jobs and driving up job satisfaction.  

Engage Them Early in the Process 

When workers are brought in early in the solution development stage, training on AI models and giving feedback about the solution and being provided with insights on the way it works, they feel ownership and see opportunity. And input from the people who will be using the AI ultimately results in better AI.

Invest in Their AI Acumen

It’s the people who are closest to customers and business operations who have to apply the solutions the AI talent builds. They need a baseline understanding of what AI is, how it works, the types of problems it can solve, and how to manage it. They need to understand the composition of the team and the life cycle of AI. And they need an eye to the market to determine how competitors and industries are using AI in new ways.

The opportunities of a data- and information-driven business model belong both to those who build the AI and those who can employ it. And business value can only be achieved through AI if the science is integrated into an organization in a way that gets implemented and gets results.