AI Talent Hiring Guide

Please share this if you found this useful so that others can benefit from this. Please comment if you felt something is missing, so that it gives an opportunity for the readers to consider them. Thank you.

AI Talent Hiring

As companies are formulating strategies for AI, a relevant and contemporary topic to consider is hiring the right talent for AI initiatives. Well, hiring has been around since the caveman days (pun intended), so is there anything new to consider when hiring the AI talent? Lets discover this.

Hiring process

Hiring is a very complex process with lot of uncertainties. This falls under the talent management function of the human resource management which includes work design, attracting talent, employee engagement, learning and development and retaining talent. When hiring, we just don't consider one aspect. We look at a basket of knowledge, skills, abilities, experiences, values, habits and behaviors. Attrition is very expensive.

Considering the complexities of AI, we need to rethink if continuing the existing processes of hiring without making changes may be sound or not.

AI complexities in Hiring

Supply side complexities

To begin with, AI is a very generic term that encompasses a large umbrella of skill sets. Next, AI is interpreted in different ways by different people. Third, a number of professionals repurposed their previous skills into AI skills as there is a growing demand in this field.

Demand side complexities

The probability of a successful hiring increases with an increasing clarity of the job description. For many firms, defining a job description for some of the AI roles could be similar to changing a wheel of a running vehicle. Why? Because, the previous initiatives and projects may just be producing results and the teams may be catching their breath for the next round of initiatives. However, the speed of AI adoption among the competitors may increasing the sense of being left behind.

Strategy related complexities

A joint committee of core business teams, supporting functions and technology need to look into the future map of the business and identify where AI could play a role in their business growth. Many firms do this and they succeed because of that. But, there will be firms that will struggle to bring the stakeholders together to i) define the map ii) execute as per the map and iii) evaluate in regular intervals. It is effort intensive. Hence, it is easy for firms to slip into the urgent priorities of the present and push the discussion on the strategy to a future date. This should show somewhere. One of the possible victims could be the siloed hiring decisions with the best-understanding job descriptions of AI that may or may not tie to what the firm may actually do in the future.

AI impact complexities

AI is expected to impact almost every part of the business. Finance, Human Resources, Information Technology, Operations, Customer Support, Sales and Marketing, Governance, Compliance, Audit, Security, Research and Development, Projects, Vendor management, etc. Who would lead a coherent effort across the organization to identify the needs and create a comprehensive talent requirements in an efficient manner benefiting the firm?

Organization structure complexities

Where does AI fit in the organization? Is it an extension of Information Technology (IT) or is it an extension of Data Analytics tightly coupled with IT or is it an extension of Business Analytics closely associated with business or is it a separate support function pillar sitting at the intersection of IT, business, legal and compliance? Not having a firm decision on this could lead to potential conflicts, duplication of hiring and misrepresented job description.

Analogy

Lets consider an analogy that occurred in the recent times. Supermarkets shifted from checkout lanes to self-checkouts. The skill sets and the job description of the cashiers who handle the checkout lanes were different from the cashiers (they are not cashiers anymore, they are more of self-checkout associates) who champion the self-checkouts. Checkout lane cashiers had to acquire knowledge on the steps in the point of sale transaction, balancing end of day operation, have a knowledge of item look-ups when barcodes do not scan and possess enough knowledge to answer pricing and promotion questions, among other skills. The self-checkout assistants/associates, though helping customers in the checkout, their job duties are redefined to assisting multiple customers, mostly help in item look-ups, manage overrides and create a warmth when customers use self-checkout, among other functions.

Therefore, the supermarkets would have rewritten the job descriptions to attract different personalities, with skills appropriate for self-checkout as compared to the regular checkout lanes.

This shift does not involve a higher order of dimensions like in the case of the complexities in AI. It is partly so, because, the change (from regular check-out to self-check-out) is within the same operational unit. However, the job descriptions and hiring pattern would have changed to address the new pattern of self-checkout.

Next Best Action(s)

I'm curious to hear - I'm sure others will also be curious to benefit from - others views on what would be the next best action(s) to address the AI talent requirements. Would you continue with the current patterns of hiring? Would you make small tweaks? Would you encourage and/or lead some focus groups or workshops in your organization to influence or update some policies for hiring AI talent? Would you use some discussion boards to create a free-flow of thoughts across the organization to generate inputs to funnel them for a board meeting? Would you hire some external consultants to prepare an actionable plan for your organization? Or would you consider something else?

What should aspiring applicants of AI jobs showcase in their resumes and interviews to make it easier for the hiring teams?

Are there any AI tools that address the AI talent needs?