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Can Integration of AI in Learning Platforms Lead to Career Mobility?

  • Published on: June 25, 2024
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  • Updated on: June 25, 2024
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  • Reading Time: 4 mins
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Authored By:

Sudeep Banerjee

SVP, Workforce Solutions

The business case for corporate learning platforms is clear. They help L&D stakeholders manage, distribute, and track employee training. Yet, this is of little use if the organization is unable to make the most out of it.

Your training programs may fail because they are not backed by the right technology, detached from the on-field reality, or simply unengaging for the employees.

Your biggest platform problem is typically a disconnect between learning and harnessing the learning for organizational benefit. The integration of AI in learning platforms can help overcome this. Personalized learning with AI can create truly efficient connections between training and development.

For L&D this is an attractive proposition. The possibility to analyze large data sets and predict outcomes can be used for driving internal mobility. Leaders can use AI in their learning platforms to track all their employees and figure out who, when, where, and how of internal mobilization.

A multiracial group of leaders discussing how the integration of AI in a learning platform can lead to efficient career mobility for employees.

There’s an added benefit here, it takes the onus of leading internal mobility efforts from one department to a platform through tangible reports. With AI integration, learning platforms can become leaders that drive internal mobility effectively and efficiently. Moreover, it can help organizations that don’t have any solid internal mobility program move forward, as they redefine their key performance indicators (KPIs) by learning overlooked skill sets along the way.

Here are four different techniques through which AI integration in online education can be done:

 

1. Employee confidence building

Only 1 in 5 employees have a strong confidence in their ability to make an internal move. A significant factor warranting internal mobility is employee upskilling. A growing number of companies are seeing higher internal mobility with strong learning cultures. Giving employees a space to develop skills first, can increase employee engagement and decrease turnover rates. However, only 46% of employees find value in their organizations’ available training opportunities. 85% say that they want L&D that applies both professional and personal development opportunities. After all, skills training for professional development can’t be the same as training for regular compliance requirements. Moreover, the learning style, pace, and progress are different for every employee.

A multiracial group of leaders discussing how the integration of AI in a learning platform can lead to efficient career mobility for employees.

 

2. Enhance the Quality of Bench Talent Data

The talent bench is akin to a catalog where the skills and experiences of employees are detailed. Three things are important here: the employee’s current role, improvement in the role, and future role prospects. By collating talent bench data, social media, professional networks, internal HR systems, and public records with the AI-driven learning platform, leaders can create a centralized platform for ensuring comprehensive talent profiles.

AI-based education tools can then help discover the underlying potential of employees who have been overlooked in favor of existing KPIs. It can identify and promote diverse talent by highlighting candidates from underrepresented groups who may have been overlooked due to unconscious biases. Moreover, leaders can use predictive analytics to forecast hiring needs and identify potential internal candidates for future roles.

 

3. Improve Mobility Processes

Internal mobility is effective in organizational transformation but creating the perfect mobility process can be tricky. How do leaders promote new roles, facilitate them for application, and manage the large candidate pool?

Re-enter AI. AI can continuously scan internal databases to identify employees who may be a good fit for upcoming roles based on their skills and performance history and notify them. It can simplify and streamline the internal application process by automatically parsing resumes and populating application forms, reducing the manual input required from employees. A step further, AI can also coordinate interview schedules by finding mutually available times for candidates and interviewers, reducing back-and-forth communication.

 

4. Establish an Internal Marketplace

About half of executives lack confidence in their L&D programs. One of the key factors driving this is the inability to practically apply the skills learned. This can be due to a lack of awareness and marketing of new positions. Oftentimes organizations do not have new positions at all. However, this should not affect providing employees with new opportunities. Creating internal marketplaces is an effective way to help employees gain exposure to different areas of the organization.

AI can identify and recommend internal projects, cross-departmental initiatives, or temporary assignments that align with employees’ development goals. Furthermore, it can match employees with mentors who can guide their development and provide opportunities to learn from experienced colleagues.

The integration of AI in learning platforms can allow employees to become more agile in their current roles and facilitate transfer to different internal roles. While your company may still be at the starting line to drive internal mobility, AI can help you make the leap faster.

 

Written By:

Sudeep Banerjee

SVP, Workforce Solutions

A future-focused and experienced executive offering more than 20+ years of experience serving as a tactical partner to globally recognized corporations—helping businesses reach next-level success by tapping into the power of human capital and technology effciency. Championed multi-faceted Edtech, Learning & Development (L&D) transformations, workforce solutions, AI-driven training, and learning automation. Leading with vision, strategy, design, and execution for corporations with large and complex ecosystems. Spearheaded enterprise growth with adept at leading high-performing teams, driving business growth, and delivering excellence in client service.

FAQs

The most effective AI technologies for enhancing personalized learning in corporate training programs include machine learning algorithms, natural language processing (NLP), and recommendation systems. Machine learning algorithms can analyze vast amounts of data on employee performance and learning patterns to tailor content to individual needs. NLP enables the creation of interactive and conversational learning experiences, such as chatbots that provide instant support and feedback. Recommendation systems, similar to those used by streaming services, suggest relevant courses and resources based on an employee's past behavior, interests, and career aspirations.

Companies measure the success and ROI of AI-integrated learning platforms through several key metrics. These include improved employee performance, higher completion rates of training programs, increased internal mobility, and reduced turnover rates. Surveys and feedback mechanisms can gauge employee satisfaction and the perceived value of the training. Organizations can also track the time and cost savings by automating administrative tasks related to training and development. By analyzing these metrics, companies can determine the financial benefits and overall impact of their AI-enhanced learning initiatives on both individual career growth and organizational performance.

AI integration into existing learning platforms presents challenges such as data privacy concerns, the need for ample upfront investment, and potential resistance from employees or management. To overcome these challenges, organizations should prioritize data security and compliance with relevant regulations to address privacy issues. They should also ensure a phased implementation plan, starting with pilot projects to demonstrate the value and ROI before full-scale deployment. Furthermore, providing training and clear communication about the benefits of AI can help mitigate resistance and encourage adoption.

Through AI integration in learning platforms diverse learning needs and styles can be leveraged by adaptive learning technologies that tailor content delivery to individual preferences and pace. For example, AI can analyze learning patterns and feedback to identify the most effective formats for different employees, such as videos, interactive simulations, or text-based materials. This approach ensures that learners from various cultural and educational backgrounds receive content in a manner that resonates with them. AI can also offer real-time translation and localization of training materials, making learning accessible to a global workforce.

Smaller organizations with limited resources can still effectively implement AI in their learning platforms by starting small and scaling gradually. They might begin by focusing on one aspect, such as course recommendations or skills gap analysis, rather than attempting a comprehensive AI integration. Leveraging cloud-based AI services can reduce the need for extensive in-house infrastructure and expertise. Smaller companies can also explore partnerships with AI education technology providers that offer scalable solutions.

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