Intelligent Document Processing

Empowering talent management and employee onboarding with document AI

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Empowering talent management and employee onboarding with document AI

Recruiting, onboarding, and managing talent are an organization's most time-consuming and costly functions, yet they are also the most crucial components of a thriving work environment. 47% of employees looking for a job say they prefer working for a company with an established culture.

Disengaged employees in the US cost their employers $450 to $500 billion each year, so having structured and efficient onboarding workflows is essential. However, ensuring a smooth onboarding process poses several challenges with the extensive paperwork, processes, and approval cycles. Functions such as resume screening, candidate matching, scoring, etc., are labor-intensive tasks that create silos and prolong the onboarding lifecycle.

Let us examine how AI-powered intelligent document processing systems accelerate resume screenings, fast-track interview feedback, and improve the quality and objectivity of recruitment. We will also understand how it automates and streamlines workflows while overseeing employee engagement and compliance. By the end of this blog, you will also have a fair idea of the best practices for ethically implementing document AI in HR processes.

5 biggest challenges of manual document management in employee onboarding

Employee onboarding is a crucial process that sets the foundation for an organization's positive and productive employee experience. It ensures a smooth transition, drives engagement and retention, and aligns employees faster with the organization's goals. 

However, manually accomplishing its various phases, such as pre-boarding and training, poses several challenges that affect the efficiency and productivity of the HR function. Here are five significant setbacks to watch out for.

1. Onboarding delays

Manual document management involves piles of paperwork, such as printing, copying, and filing. These processes are time-consuming and lead to delays in completing the onboarding process. Additionally, manual document retrieval and verification can slow down the process, impacting the productivity of HR personnel and delaying the employee's integration into the organization.

2. Error-prone

Manual verification processes are prone to errors that lead to the potential hiring of the wrong candidates. For example, HR personnel hiring for a senior management position may misread the dates on a candidate's resume and fail to notice that they do not have the prerequisite years of experience.

3. Lack of visibility into an onboarding checklist

Onboarding is not a one-time affair. Rather than dumping all information and getting new hires to sign contracts and policies in a day, companies must space out the process. Recruitment CRM software can help streamline this process by automating and tracking each task's progress, ensuring timely completion. And hence, it is essential to have complete visibility over the onboarding checklist for well-paced knowledge dissemination. However, with a manual checklist, it is difficult to track each task's progress and ensure timely completion.

4. Departmental silos 

It is crucial to prioritize the education and engagement of new employees before their start date. This includes organizing, ordering, and testing all necessary IT support items and administrative essentials like laptops, company email or phone numbers, desk essentials, printable business cards, business cards, IDs, keys, and software subscriptions. Additionally, it is essential to communicate with department heads to ensure they are adequately prepared for the new hire's arrival. 

However, manually managing these priorities can create significant challenges, including silos and difficulties in collaboration tasks and information retrieval. It can lead to isolated departments or individuals handling different aspects of the process. This lack of centralized coordination can result in miscommunications, duplication of efforts, and delayed access to critical information.

5. Compliance and audit risks

Ensuring compliance with company policies, legal requirements, and industry regulations becomes challenging when relying on manual checklists. For instance, essential documents like the W-4 form, employment I-9 form, drug testing records, employment contract form, employee consent forms, health insurance forms, etc., demand robust management.

How document AI streamlines onboarding and transforms HR processes

Document AI is the application of AI technologies to streamlining and enhancing document-related tasks and processes. Organizations can automate extracting relevant information from various onboarding documents, such as resumes, identification cards, employment forms, and contracts. It improves the accuracy and efficiency of data entry by reducing manual efforts and the associated risks of errors. Nowadays, in-house and offshore AI engineers are working on approaches for automating routine tasks and reaching the goal of fast and efficient documentation management.

Moreover, the classification and categorization of various document types become effortless by implementing document AI. This categorization allows HR personnel to quickly locate specific documents when needed, improving efficiency and information retrieval. It also acts as a centralized repository for various data points and prevents departmental silos.

Let us understand its benefits in more detail.

1. Automated data extraction for screening and evaluation

Document AI leverages technologies such as optical character recognition (OCR) and natural language processing (NLP) to extract relevant information automatically from a wide range of onboarding documents.

For instance, it scans resumes and converts the text into a machine-readable format, automating screening. The ML algorithms then analyze the content, extracting essential details such as the candidate's name, contact information, educational background, work experience, and skills. This information is automatically populated into the appropriate fields in the HR system, eliminating the need for HR personnel to enter the data manually. Utilizing a CV maker can significantly enhance this process by ensuring that resumes are formatted in a way that optimizes compatibility with these advanced ML algorithms.

Through skills and competency mapping, document AI analyzes job descriptions, performance evaluations, and other documents. It fast-tracks screenings and assessments by measuring skill gaps within the existing workforce and enabling the identification of suitable candidates for specific positions.

2. Automated classification and retrieval

Using document AI, a system can automatically analyze and categorize each document based on its type. For instance, when a new employee submits their identification card or employment form, the system can identify them as such and assign appropriate labels accordingly.

This categorization enables HR personnel to organize and manage the onboarding documents systematically. They can quickly navigate different categories or use search functions to locate specific records when needed.

Related - Everything you need to know about Document Classification [Complete Guide]

3. Efficient review and approval cycles

Timely feedback is critical to efficient and faster onboarding. Document AI streamlines onboarding processes by automating document review and verification. It accomplishes this by employing advanced techniques such as cross-referencing information within documents with trusted sources or databases. 

Moreover, it intelligently routes documents, assigns tasks, and generates notifications, ensuring the relevant personnel complete each task within the desired timeframe.

4. Predictive analytics for candidate success and development

Document AI utilizes historical data on successful hires, performance metrics, and employee progression to contribute to predictive analytics models. These models identify patterns and characteristics that indicate the likelihood of a candidate's success within the organization. These are important metrics to track and should be documented in your employee record software to improve the ease of access to this key information.

5. Compliance management

AI-powered tools help HR departments automate compliance checks and mitigate the risk of errors and costly penalties associated with labor laws, regulations, and company policies. Pre-trained ML algorithms flag potential violations in various documents, including job postings and employee contracts. 

6. Performance management

Through the application of document AI, performance evaluations, attendance records, productivity metrics, project completion rates, feedback forms, and other relevant documents can be analyzed to uncover valuable insights. This analysis allows for identifying trends, patterns, and areas for improvement in employee performance. By leveraging this information, targeted feedback, coaching, and development opportunities can be provided to employees, fostering a culture of continuous improvement within the organization.

7. Diversity and inclusion

With AI-based document processing, identifying and addressing bias in draft advertisements is also possible even before they reach potential candidates. The advanced technology can flag language that may exhibit biases related to gender, age, or ethnicity. Consequently, organizations can rephrase and modify their job postings to ensure they appeal to a wide range of qualified candidates, promoting inclusivity and attracting a diverse talent pool.

A complete guide to implementing document AI in onboarding

1. Assess your onboarding processes

Conduct an assessment of your existing onboarding and talent management processes to identify suitable areas for integrating document AI effectively. Identify the specific documents, tasks, and decision-making processes within these workflows that can benefit from automation and AI capabilities.

2. Define your objectives

Develop a clear outline of your objectives for incorporating document AI into onboarding and talent management processes. These objectives can include minimizing manual administrative tasks, enhancing data accuracy, automating candidate screening, ensuring compliance, and optimizing talent matching and development.

3. Find a suitable document AI tool

Conduct thorough research to identify and select document AI tools well-aligned with your objectives and specific requirements. Seek out tools demonstrating proficiency in document parsing, information extraction, natural language processing (NLP), and machine learning. During the selection process, consider critical factors such as accuracy, scalability, integration capabilities, security, and user-friendliness.

4. Prepare documents

Collect the essential documents that are part of the onboarding and talent management processes, including powerpoint resumes, application forms, performance reviews, and learning materials. Transform these documents into electronic formats that can be processed by document AI tools by either scanning hard copies or converting existing digital files. 

5. Choose the right learning algorithm

Choosing the most appropriate learning algorithm depends on several factors, including the specific requirements of your onboarding process, the document types involved, and the desired outcomes. Both supervised and unsupervised learning algorithms can be applicable in onboarding, each serving different purposes.

Supervised learning algorithms are well-suited for tasks that involve classification or prediction based on labeled examples. For instance, they can be used to classify resumes into predefined categories or predict candidate suitability based on labeled training data.

On the other hand, unsupervised learning algorithms analyze the inherent patterns and structures in the data to discover insights or group documents without predefined categories. They are instrumental when no labeled training data is available and can cluster similar documents or detect anomalies within the document set.

6. Train and fine-tune the AI model

After choosing your algorithm, train the document AI models using relevant datasets specific to onboarding and talent management. This training enables the AI system to understand the documents' structure, content, and context, facilitating accurate information extraction and analysis. Label and annotate the data to guide the AI models during training. Leverage feature engineering and hyperparameter tuning to fine-tune and optimize various model parameters, such as complexity, learning rate, regularization, etc.

7. Integration and workflow design

Establish seamless integration between the document AI system and your onboarding and talent management workflows and software applications. This integration process may entail connecting APIs, applicant tracking systems (ATS), talent management platforms, or other tools. It is vital to ensure smooth data flow and compatibility across the interconnected systems to facilitate efficient document AI utilization in the onboarding and talent management processes.

Industry best practices for ethical document AI implementation and success

1. Data privacy and security

Ensure the protection of personal information and comply with relevant data protection laws and regulations. Establish strong security protocols to safeguard sensitive data from unauthorized access or improper use. When feasible, anonymize data to minimize potential privacy concerns.

2. Ensure data quality

It is vital to implement comprehensive validation checks to ensure the processed data's accuracy, completeness, and consistency. Verify the data's format, structure, and integrity. Data cleaning tasks are also necessary, and they involve eliminating irrelevant or redundant information from the documents. Examples of data cleaning tasks include removing duplicate records, correcting formatting errors, and standardizing data formats.

Another important step is data normalization, which involves standardizing data fields and values to ensure consistency across different documents. For instance, normalizing date formats or unifying naming conventions can help achieve this consistency.

3. Continuously monitor and evaluate

Continuously monitor and assess the effects of document AI onboarding processes to identify any unintended consequences or disparities. Establish feedback loops and mechanisms that let individuals voice concerns or provide input regarding performance.

4. Prevent biases 

Since the development of AI solutions demands human expertise in data collection and sample selection, it is crucial to regularly test datasets and model outcomes for biases. Adjustments should be made to address any potential discrimination. Maintain human involvement and oversight throughout the onboarding process. 

5. Train and support

Deliver training programs, like beginner AI courses, to employees responsible for implementing and utilizing document AI tools for onboarding. Promote awareness regarding potential biases, ethical considerations, and the reliable deployment of AI technology.

Concluding note

Implementing document AI in onboarding processes offers significant advantages but requires careful consideration of privacy, data quality, bias, and ethical considerations. Safeguarding personal information, ensuring data quality, and mitigating biases are crucial steps to maintaining the integrity and fairness of the system. Moreover, training and fostering employee awareness regarding responsible AI use helps ensure ethical practices. By embracing these principles, organizations can harness the power of document AI while upholding privacy, data protection, and ethical standards in their onboarding procedures.

Suggested Case Study
Automating Portfolio Management for Westland Real Estate Group
The portfolio includes 14,000 units across all divisions across Los Angeles County, Orange County, and Inland Empire.
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Pankaj Tripathi
Written by
Pankaj Tripathi

Helping enterprises capture data for analytics and decisioning

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