The Emerging Artificial Intelligence Managed Application Recruitment Process

Where we are Today

From the applicant, the recruiting agency, the hiring manager, the candidate representation businesses, and within the internal hiring process, many within the current application process point to complexity with the multiple stakeholders, evolving needs, and multiple moving parts with an overall lack of quality assurance and transparency. The hiring process has already changed around technology including the reduction in newspaper adverts, the revised role of recruitment agencies, less direct contact with hiring managers, the volume of applicants changing (a short summary of aspects of the hiring process is: If Everyone Is Hiring, Why Can Nobody Get A Job?  - https://www.youtube.com/watch?v=HWXmfrXNwqw), and the application process is set to change further around artificial intelligence (AI). The issue is no longer the change and how fast, rather it is the impacts, how it is managed, and will wise outcomes be seen?

Beyond the economic cycle and market influences, themes inherent within the application process today include:

1. Application Volume – The volume of applications can be overwhelming for those screening (agencies and internal hiring).
2. Application Quality – The quality of applicants can be less than required. Some of this reflects client expectations, the desire by applicants, and the ease of submission with varying technologies.
3. The Value of Customisation – Applicants have pride in who they are and what they do. Some go to considerable efforts to tailor cover letters, provide selection criteria matching matrices, and custom resumes. Others provide case studies and/or worked examples which show value, and effort to stand out, as well as to help those in in the screening process. These efforts show how much they care, yet this is often not seen or reciprocated. While some in the screening process value these efforts, there is an increasing trend for applicants to send in generic resumes without covering letters or any customisation because (i) the sense that these value-adds will be ignored by those in the screening process so why bother?, (ii) the experience is that many of these value added parts are taken out and are unseen by hiring managers or even those in the screening process, (iii) they add complexity to the screening and readers can be overwhelmed, and (iv) they confuse the systems in the hiring process. The irony of the reality is that by ignoring and discouraging the customisation of applicatio0ns, the work in the screening process is increased
4. Screening Skills and Expertise – The skills and expertise of those within the screening process needs to be higher and assured across the operations. The drive to lower cost using less skilled resources provides short-term operational saving, but often costing more in the medium and longer term. Recruiters and applicants alike speak frequently of the need for better understanding of roles, business expertise, as well as subject matter and specialisation expertise and experience. Those within the screening process often identify the need for skills and experience as well.
5. Appropriate Keyword Screening – The use of keywords for screening is part of the process, but it is a matter of priority and use of keywords appropriately, as well as the oversight. Having defence clearance is a must for have some roles, so why not screen on this first and have it as a major requirement in any job adverts?
6. Appropriate Position Descriptions – Having quality position descriptions, selection criteria profiling, and acceptance statements (whether in systems and tied to keywords or in less automated processes) is required (please see Related Links below). Having advanced spelling and grammar or advanced PowerPoint skills for senior management and executive roles as selection criteria may not form the best criteria. Stating things like for a Business Analyst to gather requirements and engage stakeholders is another common oversight in roles descriptions because aspects like this just state what a business analyst does rather than what is needed for a specific role.  
7. Professional Skills – The professional skills are required in the roles for delivery but are often of a lower priority in the screening process.
8. Reading Applications – Some in the process are highly conscious of the need to read and not just browse. Reading takes time and requires effort. There is a responsibility on applicants to prepare quality applications as well as those in the hiring process to respect this.

While there are more aspects for consideration (please see Related Links below) one area of note is feedback in the application process. While applicants seek feedback, the hiring managers and recruitment agencies are under no obligation to provide it. Providing feedback can take time, lead to disputes and prosecutions, and the volume to manage also presents operational issues. One of the issues for applicants is how having a lack of success in an application presented without undermining the skills of an applicant. Telling someone that “had good skills” or “many high-quality applicants” or “impressed with skills” then saying “others with better suited to the role” is insulting. A simple but polite statement that they were unsuccessful should suffice.

Artificial Intelligence within the Hiring Process

As with any rapidly evolving technology, it is the business application of the AI, which is the issue, requiring the operational changes, processes, governance, policies, procedures, and management on a risk-based approach with the required skilling (ability to do) and training (how to do). Areas for consideration include:

• AI-to-AI Cycle – To increase success within the application process, candidates make use of AI. Similarly, those within the hiring process are using AI to help with screening the volume of application. This creates a cycle of AI talking to AI with a lack of benchmarking, human intervention, and a sense of caring which can lead to a series of operational management issues and frustrations across parties.
• AI Generated Responses – The shortcoming within AI, especially Generative AI, are well documented. Already there are legal cases and extended adverse publicity to standard AI responses or custom AI responses being sent out without controls.
• AI Decisions – AI is being used to narrow down to three applicants without human interaction. From an applicant no longer being available being asked to interview to the wrong applicants being asked, the impacts of automated AI decisions are occurring. People are already asking if having the required controls, intervention points and exceptions management, is beyond the ability of those in the hiring process?
• Data Quality for AI– The effectiveness of AI is dependent upon the quality, accuracy, and reliability of the data provided. The need for data governance and data practices across the hiring process, and wider organisations has been noted by many. The cliché “rubbish in rubbish out” is even more applicable to AI with the derived and blended product dependency and the operation at scales (please see Related Links below).
• Benchmark Testing of AI – The screening process operations and services will need to ensure that algorithms are learning in the right way. Operational changes with the required processes will be required for this (please see Related Links below).
• Professional Skills Required- Making greater use of professional skills within the hiring process to enabling the crafting and shaping of capacities and capabilities for adoption and adaption are required more than pure technical and specialist skills. The use of AI for the professional skills is a gap within many current systems and operations.
• Fakes and Plagiarism – AI can be used for fakes and plagiarism which is part of the hiring process now. AI is also evolving a role in determining fakes and plagiarism. One example is the role descriptions on an application to be validated with other sources where required systems integration occurs and regulatory compliance allows. This includes the profiling of both those accepted for further screening and those unsuccessful.
• Applicant Validation – Related to fakes and plagiarism is the role of AI in applicant validation. With the management of aspects around privacy and other related considerations, AI can be used in profile validation. The obvious one is the matching of a LinkedIn profile or similar to a resume submitted as a Word document or equivalent.
• Career Changes, Gaps, Returning to Workforce, Overseas Migrants – Major areas for management within applications are career changes, the moving between contract and permanent, having run your own business and retuning, taking time out, especially women returning to the workforce, caring for family, and being an overseas migrant. These areas are a source of contention within the screening process. Do we use AI to be selective on how these are managed and make improvements or do the incumbent practices carry across with the short comings intensified because of the AI?
• Agism and Other Discriminatory Practices – While some regimes legislate against discrimination, those in the hiring processes often experience these discriminations. The use of AI can further enhance these discriminations because of the way it is used and how people intervene in the process.

Many of these practices can be implemented for better returns now as well as for AI.

Areas for AI within the Hiring Process

With much of the focus on cost savings and knowledge worker transformation, what are the other areas for the application of AI:

• Routine Questions – Within the application process are many routine questions from previous applications or previous employment at an organisation through to qualifications and licences. AI can be sued to pre-populate these for validation by an applicant.
• Quality of Data – The use of AI for the validation of data and help to address the quality of data.
• Data Protection – Application processes often collect more data than are needed for an application. A common area is the keeping of data after the application is completed, with associated risk of data breaches. Varying with the regulatory regime, AI can be used to manage data protections.
• Process Assurance – AI can be used to track the performance of the hiring process, reminders on closures on role remaining open and which are progressing too slowly.
• Assessments – Undertaking assessments with the use of AI and without the use of AI to assure the necessary skills and expertise. With the results being tracked, compared, and managed with AI.

The driver to improve the hiring process is often identified and AI brings the opportunity to revise the experience and realise better business outcomes.

Related Links

A Gateway

• Business Transformation – An Information Gateway (https://www.linkedin.com/pulse/business-transformation-information-gateway-keith-sherringham-hk0bc)

The trilogy may assist with hiring:

• Outcomes Based Position Descriptions with One Small Step for Better Recruitment - It is Worth a Try (https://www.linkedin.com/pulse/one-small-step-better-recruitment-worth-try-keith-sherringham-lawoc/)
• Selection Criteria Based Profiling with Another Small Step for Better Recruitment – Also Worth a Try (https://www.linkedin.com/pulse/selection-criteria-based-profiling-keith-sherringham-e3pdc/)
• Skills and Expertise Acceptance Statement - One further Small Step for Better Recruitment – Maybe Worth a Try as Well (https://www.linkedin.com/pulse/skills-expertise-acceptance-statement-keith-sherringham-li0xc)

Associated Link

• Prioritising Skills for Roles, Whether Business or Technology, Around Artificial Intelligence (https://www.linkedin.com/pulse/prioritising-skills-expertise-within-roles-whether-keith-sherringham-qdluc/?trackingId=zychRQyMCJdn%2FyBqgCyGLQ%3D%3D)
• Hiring in a Changing World (https://www.linkedin.com/pulse/hiring-changing-world-keith-sherringham-0fvwc)
• Technology & Opportunity Reflections – Bridging for AI Resources, Skills, Experience, Expertise (https://youtu.be/ypqTvr6m7kE)

Supporting Links

• Power of Data Within Artificial Intelligence – Being Ready to Realise Opportunities on a Risk-Based Approach (https://www.linkedin.com/feed/update/urn%3Ali%3Aactivity%3A7224546336803540992/)
• Implementing Artificial Intelligence – The Proven within Environment of Uncertainty (What We Can Do Now) - (https://youtu.be/sqwWG2Wl_go)
• Technology & Opportunity Reflections – Data for Assured AI Services (https://youtu.be/0zTOsfSfSc0)
• Technology & Opportunity Reflections – Benchmark Testing within AI (https://youtu.be/6C525tXw0t4)
• Technology & Opportunity Reflections – Risk Based Data Practice for Artificial Intelligence (https://youtu.be/nytaG59uCTg)