Time to get rid of traditional Job Descriptions and Resumes

Traditional job descriptions and resumes have long been the standard for identifying and hiring job candidates, and the origins are a hot debate .?

If we break down the components and strip away Job boards, Linkedin, Social Media, and the plethora of online platforms pushing the greatest and latest to find candidates, there hasn't been any dramatic change because it will be a 3 Billion dollar industry by 2027 ?

Companies and talent are behind a paywall. What an incredible opportunity to decentralize and democratize the process. Is it feasible for organizations to build an extensive suite of modularized products to do the same? Of course not! However, there is a light at the end of the tunnel, and a potential solution can be adapted and integrated with maximum ROI without breaking the bank and with the right strategy.?

We learned that natural language processing (NLP) advancements enable companies to move away from these outdated methods and toward a more practical approach . Here are some of the top reasons why we need to eliminate traditional job descriptions and resumes and use NLP to align human capacity with business objectives.

1. Traditional Job Descriptions Can Be Limiting: Traditional job descriptions often limit the scope of a role by providing a static list of duties and responsibilities. It can lead to candidates who are not fully utilized or don't have the opportunity to contribute to their full potential. [Often overblown and biased]
2. Resumes Are Incomplete: Resumes often fail to fully capture a candidate's experience, skills, and potential. They also do not provide insights into the candidate's personality or work style, which can be crucial in determining the right fit for a particular role or team. [Often overblown and biased]
3. NLP Can Better Align Skills with Business Objectives: NLP technology can help identify candidates whose skills and experiences match a company's business objectives rather than simply aligning them to a specific job description, leading to more effective hiring decisions and tremendous overall business success.
4. NLP Can Reduce Bias: Traditional job descriptions and resumes can be biased based on race, gender, or educational background. NLP can help eliminate these biases by focusing on the skills and experiences of candidates rather than their characteristics.
5. NLP Can Save Time and Resources: NLP can automate many hiring processes, such as screening resumes and conducting initial candidate assessments. [We have proven that it saves time and resources while improving the overall quality of hires].
6. NLP Can Help Identify Hidden Talent: NLP can help identify candidates who may not have the exact qualifications listed in a traditional job description but possess transferable skills and experiences that make them an excellent fit for a particular role or team.

In short, the traditional hiring approach based on job descriptions and resumes must be updated and expanded. NLP technology offers a more effective and efficient way to align human capacity with business objectives while reducing bias and identifying hidden talent. Companies that adopt NLP-based hiring strategies can gain a competitive edge by attracting and hiring top talent aligned with their goals and objectives.

Cost Effective Opportunities

Implementing NLP into business operations can be complex and costly, especially when custom solutions are required. However, organizations can use several cost-effective strategies to add NLP to their business operations without purchasing off-the-shelf services or online tools.

One approach is to leverage open-source NLP libraries and frameworks such as Natural Language Toolkit (NLTK ), spaCy , and Apache OpenNLP . These libraries provide a range of NLP tools and functionalities, including text classification, named entity recognition, and sentiment analysis, that can be used to build custom NLP solutions. Organizations can save on licensing fees and development resources by using open-source libraries.

Another cost-effective strategy is to leverage cloud-based NLP APIs such as Google Cloud Natural Language and Amazon Comprehend . These APIs provide pre-built NLP models and functionalities that can be integrated into business operations without extensive development or infrastructure. This approach can be more cost-effective than building custom NLP solutions from scratch since it reduces the need for in-house expertise and infrastructure.

Lastly, organizations can leverage data sources such as customer feedback, chat logs, and social media to train custom NLP models. Organizations can save on data collection and labeling costs by using existing data, which can be a significant expense in developing custom NLP solutions.

In summary, leveraging open-source NLP libraries, cloud-based NLP APIs, and existing data sources are all cost-effective strategies organizations can use to add NLP to their business operations without buying off-the-shelf services or online tools.

Feel free to connect and DM me to chat about our workaround. We can challenge the status quo together! (No vendor spam)