In the last few years, as the labor market tightened, companies increasingly turned inward, searching within their ranks for hidden gems of talent.
The strategy, although well-intentioned, often fell short of its potential. HR managers and department heads rolled out skills assessments, performance evaluations and career development programs in a bid to squeeze out more productivity and promote from within.
A knock-on effect was, in the best of cases, a more spirited corporate culture and a boost in loyalty and retention, as employees felt seen and valued.
However, let’s be honest: Little headway was made. Rather than identifying internal prospects for promotion, these approaches were best suited to measuring what employees did well in the jobs they were in.
On top of that, these efforts to advance worker mobility were costly, time-consuming and challenging to scale throughout the organization. Frustrated leaders often resorted to a default tactic: hiring externally. Place job ads and watch the resumes roll in.
The grow-your-own push seemed to be fading. Data showed that worker mobility has been waning for years, partly because companies fail to benchmark metrics that produce better outcomes.
But there’s a more promising approach to finding these hidden gems: leveraging technology—specifically the hot new capabilities unlocked by artificial intelligence, machine learning and large language models.
This tech combination could be a perfect fit for companies—especially medium-sized ones —seeking to boost their workforce’s productivity. What’s key is that the AI combo can process reams of valuable information produced by workers in a variety of interactions that we tend to overlook: presentations, video conferences, reports and emails.
Leaders can discover talents and capabilities obscured by what a worker is currently being asked to do. Data is produced in real time, continuously updated and can be analyzed on the spot.
With AI engines capable of crunching that large volume of data, comparisons among employees become easier for managers. Some workers may need more training. Others are ripe for promotion. As a workforce development tool, it is very empowering for employees.
Here are examples of what AI can uncover on the job:
- Technical skills. In IT development and engineering, how many bug corrections were made among a set of computer programmers, which reveals their skill level. AI can help those programmers make fewer mistakes and identify the exceptional ones for other opportunities.
- Data analysis. How closely does an analyst’s prediction measure up to the final product’s outcome? AI can discover gaps and mistakes in quantitative analysis at scales unmatched by humans. And the technology can quickly identify who needs more training and who’s ready for the next step.
- Efficiency. In a call center, how often does a company representative solve a customer’s problem quickly or answer a customer’s question with accuracy and efficiency? A star in those areas might be easily groomed for leadership elsewhere in the company. The data is there to discover hidden gems because all calls are recorded, but only a fraction are usually monitored and often done in an inconsistent manner.
In addition to analyzing hard skills, AI can also hone in on soft skills, which are in high demand. This technology can scrutinize and benchmark problem-solving, collaboration, critical thinking and communication skills. Recent developments in LLMs, particularly with omnichannel models, show new applications in scaling coaching for employees in these essential skills. By leveraging personalized engagement and AI-driven insights, organizations can provide tailored coaching and development opportunities that were previously impractical at scale.
Because AI, machine learning and large language models can review most work done on the job, employees’ true skill sets are uncovered. These are the hidden gems that can bring overlooked value to companies. It might turn out that a reticent programmer has the gift of persuasion. An entry-level worker may possess gifted math skills. And so on.
Companies can also discover new information in their industry. For example, AI might reveal an uptick in the demand for prompt engineers in job ads or indicate a decline in capital expenditures in quarterly reports, potentially signaling a recession.
A challenge for companies considering using this AI combo is how to implement it. Up-front effort is required. It works like this: An organization starts with a pre-trained model with generic data and then spends four to six weeks calibrating it so the information captured is specific to that entity. Once the knowledge engine starts learning the company’s data, leaders can expect a high level of accuracy in picking up the presence and proficiency levels in the skills of each employee.
A misunderstood part of AI that I consider good news is the fact that it will end up helping, not hurting, workers. After all, it can pinpoint hard and soft skills, leading to their development and promotion within a company.
In that way, AI benefits both employer and employee. As I like to say: “The employer is sitting on the best evidence of what an employee is capable of.”
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