building an AI-enabled workforce: hiring and retention strategies.

Building an AI-enabled workforce requires a complex blend of forward-thinking recruitment and strategic innovation. Many employers focus on the skill sets they need from candidates to harness AI effectively, but it’s also important to consider how AI implementations impact employee satisfaction and retention.

Rather than a singular focus on employee qualifications, it’s crucial to foster an organizational culture that supports employee satisfaction and growth with a balanced understanding of the benefits and limitations of AI. For some organizations, this may involve improving collaboration – while for others, it may require a rethink of how their organization uses AI in the first place.

Below is an overview of key strategies for successful AI adoption, and examples of the pitfalls and opportunities of building an AI-enabled workforce.

the building blocks of an AI-enabled workforce.

Our recent Workmonitor survey found that AI training was the most sought-after learning and development opportunity across Canada and the US. Workers have already made significant strides towards developing AI skills: 78% of US respondents and 68% of Canadian respondents felt ready to use AI in their roles.

But while it’s common to hear about the productivity gains that AI can offer, employee skills alone won’t guarantee increased efficiency.

One study by Upwork found that while the majority of C-suite leaders expect AI implementations to lead to higher productivity, 77% of employees reported that AI made them less productive. AI tools will enhance efficiency, but only when teams are empowered to make the most of them.

In the coming years, employers will need to maintain realistic expectations, foster collaboration, and invest in upskilling opportunities to build an AI-enabled workforce.

realistic expectations

When employers overestimate the capabilities of AI, they may unintentionally place unrealistic demands on employees – leading to burnout and high turnover. To harness the true potential of AI tools, it’s crucial to start with an understanding of the human effort required to manage them.

Realistic expectations are especially important at the beginning stages of AI adoption, since trial and error will lead to the improvements that spark real productivity gains in the future.

cross-functional collaboration

As HBR notes, rigid organizational hierarchies can limit the effectiveness of AI. When the teams building AI tools don’t collaborate closely with people who will use them, this can lead to a mismatch between what the tools deliver and what teams actually need.

Consulting with employees on their experience of AI tools can help improve AI innovation over time. Continual feedback can help employees and AI innovation adapt together to meet market demands.

training opportunities

In the North America, over half of Gen Z workers reported receiving training and development opportunities over the past year – yet only 29% of baby boomers in the US and 14% in Canada were supported to learn new skills.

Equal training opportunities can help employees collaborate, learn, and work more effectively. It also provides growth opportunities that help employees build their careers over the long term.

It’s important to note that what these strategies will look like on the ground will depend heavily on your industry. Below are a few examples of AI pitfalls and successes across manufacturing, healthcare, tech, and customer service sectors.

AI in manufacturing: investing in skilled workers

In the manufacturing sector, AI stands to speed up operations, maintenance, quality control, and delivery. However, a recent Deloitte survey found that 91% of manufacturing AI implementations didn’t meet expectations.

Consolidating the right data is a significant issue for 34% of manufacturers attempting to leverage AI. Sensors may detect performance and output data, but human workers understand the context of that data, which is crucial to high-level decisions.

As founder Nick Haase points out, data collected by machines is often stored in different locations than human-generated data, creating silos that prevent automated decision-making. To achieve success with AI, employers need to start by investing in the workforce that provides the foundation for accurate, integrated data.

In recent years, manufacturing employees have taken on additional responsibilities, but 40% felt unsupported in learning new processes – and 30% only saw partial salary increases. Adjusting salaries and providing training opportunities can help attract manufacturing talent up for the task of AI data initiatives.

AI in healthcare: choosing the best use case

Post-pandemic, 67% of healthcare workers felt more loyal to their employers as a result of the support they received. Amid an ongoing healthcare worker shortage, employers can turn to careful AI implementations that continue to support employees.

A recent AMA study found that AI use cases in healthcare had doubled since 2023, but many healthcare workers remain cautious about trusting AI outputs. Last year, nurses in California protested AI systems that generated false alarms when monitoring vital signs. The AI system also included chatbots which risked misinforming patients who needed urgent care.

However, AI-generated summaries of patient symptoms at Cedars-Sinai Medical Center have seen success in reducing manual note-taking efforts. AI is also widely used for appointment booking and clerical tasks, giving healthcare workers more time to focus on patients in the clinic.

This underscores the need for healthcare organizations to choose implementations carefully. Introducing too much risk can spark safety concerns, but automating lower-risk tasks can take work off the plates of overtaxed healthcare employees.

AI in software development: building the teams of the future

Earlier this year, Meta’s plans to replace mid-level engineers with generative AI caused controversy among the tech community. Generative AI can write code, but it still needs human oversight to review and test its output.

This presents a problem for entry-level developers, as it removes an important rung from their career ladders. It also presents an issue for employers, as 70% of Fortune 500 companies still rely on legacy software – and upgrading these systems will rely on skilled developers.

Rather than looking for developers with extensive experience, employers can turn to new grads with a strong capacity for problem-solving to meet talent demands. These skills-based hiring initiatives can use AI to support talent in learning new tools and processes.

For example, generative AI is excellent for generating summaries of code, and can be used for documentation that outlines different functions for new developers. AI can also be used to scrape communication data from Slack channels to help developers get up to speed on company initiatives.

AI in customer service: preserving the human touch

Recently, generative AI has made significant inroads in customer service, with chatbots that automate responses to basic queries. However, the recent failure of the Automated Order Taker at McDonald’s drive-thrus highlights the importance of the human touch in customer service.

The technology enabled customers to place their order with a chatbot instead of a human worker, in an effort to increase efficiency. However, the chatbots frequently misunderstood orders, causing McDonald’s to cancel the project after expanding it to 100 restaurants.

CNBC reported that the AI had difficulty interpreting accents, and that weak WiFi connections and background noise can also contribute to errors in voice recognition technology. Although the technology will continue to evolve, its current failure underscores the need to keep humans in customer service loops.

While customer service agents may not be replaceable, AI implementations can enable more of them to work from home, giving them the flexibility that office workers have long enjoyed. Customer service tools that incorporate AI-generated summaries of longstanding issues can help service teams avoid asking customers the same questions twice.

leading the charge: hiring and retention in the AI era

Organizations that consider how AI impacts their employee experience will be much better positioned to achieve productivity gains. An AI-enabled workforce relies equally on human teams and AI augmentation; considering how they can complement each other will create the flexibility and growth that top talent is looking for.

As AI evolves over the coming years, employers will need to keep a finger on the pulse of AI and automation strategies to ensure they continue to align with the best interests of their business, employees, and customers.