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6 ways recommendation engines can enhance staff development and deployability

July 17, 2020 juanjo.misis Comments Off

The arduous journey of learning and development. 

Staff development is a growing prerogative. A study by Training Industry, a learning and development consultancy showed that the training industry has grown US$100B over the last 10 years globally.

“Helping employees find ways to grow, help them stay connected to their individual and corporate purposes in the company.”

But often, that journey to skill mastery can be difficult. Knowing what to learn, where and how to learn is complicated. Orchestrating that for the individual for any global organisation is daunting. There are many variables to consider. Training a distributed workforce across markets will need special attention to cultural nuances that can limit training effectiveness. The current workforce contains at least three generations and line managers would need to cater for different learning habits and preferences. But thankfully, technology today particularly AI-driven recommendation engines can alleviate the planning load and augment the learning and development process in organisations.

How can recommendation engines improve staff development and deployability?

1. Detect skill gaps and recommend training resources

Skills are the modern currency of employability and are increasingly outweighing academic degrees. The Economist reported that ⅘ of CEOs surveyed worry about skill shortages. Employees at some junctions of their careers would also be keen to know how they should progress in their career and with that question, what are the missing skill sets they need to grow in their careers.

Learning management systems can incorporate extraction and recommendation modules that can read, identify skills in a resume and match it with those skills needed for a role and provide the areas to improve. This would also be useful to identify the calibre of candidates interviewing for a job, greatly reducing the time spent sifting through resumes without missing quality candidates. Besides skill gaps, these modules can also detect ‘skills at risk’ (E.g. certification expiry) allowing line managers to anticipate and advise employees accordingly.

2. Automate career pathway planning

It is common for employees to explore new opportunities in their line of work. With skill gaps detection as the foundation, learning management software can augment AI modules via APIs to automate career pathways for staff by matching the company’s existing career framework with the employee’s qualitative inputs like interest, skills and experience. With the addition of skill gaps analysis, the system can also identify skills to pick up for juncture of the pathway.

“A goal without a plan is just a wish.” Antoine de Saint-Exupéry


3. Facilitate operational knowledge discovery

There is a difference between information and operational knowledge. The latter is sought after to do a task at hand. People do not go to their shared drives or learning management systems to look for operational knowledge. The problem however is it stays in the heads of the subject matter experts or in conversations with colleagues. It is hard to document and therefore hard to find on an organisation’s search engines.

According to Harvard Business Review, 55% learn from their colleagues.

A knowledge management system or ‘ontology’, which is a system of connecting information by its relationship, can help to identify the right colleagues by identifying and tagging relevant and recent information from these subject matter experts or associated colleagues so they can be discovered and recommended. These information could be similar skills, recently departments worked with, document type and document author amongst others.

4. Explore mentor or peer-to-peer matching

Part of the career developing process is identifying the right mentors or peers that not only have the most relevant experiences and skills to guide employees, but the preferred coaching and sharing styles. Recommendation engines can identify different coaches or peers based on these user-centric factors.

5. Deploy mentoring bots

With the increased demand for online, self-paced learning to suit the hectic schedules of employees today, mentoring made bite-sized and available on demand in their micro moments, perhaps during their coffee breaks or water breaks, is an attractive option. A chatbot armed with a recommendation engine could serve as a facilitator to the right resources and connections or be a source of bite-sized advice. Add different personas to suit different types of users.

6. Redeployment engine

By tagging employees’ skills and experience so it’s identifiable by a search engine, an organisation can redeploy staff quickly in times of need without having to retrain or hire to fill the gap. Bank of America redeployed 3,000 staff with the right skills, to field an onslaught of calls from customers and SMBs. Redeployment could also be a useful strategy when served as an incentive for employees who are motivated by acquiring varied exposure.

Recommending the way the world learns

Recommendations are now the way the world discovers information and will soon be the way the way the world learns. The increasing hecticness of employees puts pressure on line managers and human resource teams to facilitate effective learning plans. Recommendation engines can play a key role in connecting the dots in career development. They can anticipate and provide handles to employees on what to learn, offer personalised learning preferences and connect people together to facilitate learning. These are all factors that will drive career satisfaction as well as productivity.