Sara Alchin, Global IT Category Manager
Jeffrey Gaguzis, Global IT Category Manager
The time for Artificial Intelligence may now be at hand during this critical period of Procurement transformation. Although the technology has been around for nearly 60 years, the quality of AI tools and platforms, coupled with the quantum increase in raw computing power, has progressed AI from processing simple algorithms to much more complex functionality. AI now powers many real-world applications, ranging from facial recognition to language translators to virtual assistants like Siri and Alexa. Along with these consumer applications, enterprises across sectors are increasingly harnessing AI’s power in their operations. Embracing AI promises considerable benefits for Procurement enterprises (and economies) through its contributions to productivity growth and innovation. However, with this vast promise comes uncertainty as many enterprises are trying to determine how to lead with AI technology and leverage this power. While enterprises rush to unleash the power of AI, these same organizations often lose sight of the operational challenges that deploying AI can create. IT leaders responsible for AI’s implementation can structure their efforts with three basic pillars to effectively mitigate these operational challenges: People, Process/Policy, and Technology. In this post, the start of a three-post series, we will discuss the People pillar.
The demand for AI skills has greatly increased within the last several years, but that demand continues to outpace supply. The traditional way to locate this talent is proving ineffective at best. Common missteps that enterprises make when trying to identify talent include:
Not identifying or illustrating the non-tech related differentiators and factors which will appeal to AI workers.
Using a generalist recruiting process to find and recruit AI workers.
Overlooking or neglecting internal workers that can be upskilled and/or not developing an upskilling/reskilling program.
Not developing an overall strategy to recruit or reskill workers.
When identifying talent or developing the talent you already have, take a three-pronged strategic approach.
Understand supply and demand.
Enterprises need to first assess what they need vs. what they have in terms of workforce skill level. Many sophisticated enterprises implement a skills taxonomy. These taxonomies can be developed from scratch or leveraged from a 3rd party. The World Economic Forum has developed such a taxonomy:
Identify current and future skills needs and gaps and map skills to work tasks.
Articulate skills needed in job descriptions and leverage and recognize innovative skills assessment methods.
Co-develop and co-deliver skills-based training programs with industry, learning providers and government.
Boost lifelong learning and access to skills-based learning opportunities.
Create skills-based pathways for development and redeployment.
Develop a training strategy.
Once a skills taxonomy is established, design a curriculum that closely aligns with it. Training can fall into several main categories.
Foundational training. This training is required for the entire enterprise and address general topics and questions:
What is AI?
How does it work?
What are AI’s limitations?
What are language models?
What is responsible AI?
Role-based training. This approach focuses more on how AI supports different functions within the enterprise (sales, operations, accounts payable, customer service). Role-based training builds on foundational training, applying their previous AI learnings to their areas of work. By providing specific scenarios for AI use, your training shifts AI implementation from an abstract concept to a concrete reality. Successful role-based training will also teach employees how to create new applications for AI in their work.
Upskilling/reskilling program. Traditionally, workers are identified for internal roles based on their degrees or relevant work experience. In the context of reskilling a workforce, this approach does not work. Leveraging a well-developed skills taxonomy allows enterprises to think critically about who to select for upskilling based on how closely the worker’s current skillset align to what is needed. Meeting candidates where they are will facilitate the move from one skillset to another.
Develop a recruitment strategy.
Determine the need. If it is determined that the skill gap cannot be filled by upskilling or reskilling current workers, then you’ll need to look externally.
Adapt your recruitment strategy to your AI maturity. If your AI maturity is low and you need to look outside the enterprise for the necessary skills and adjust your sourcing strategy. Think in terms of building your AI team – start with the strongest resources that you can find and then supplement your search with other workers who have good skills and can continue to grow in the environment. The ability to grow and develop skills is a symbiotic value proposition to offer to a prospective AI candidate.
Articulate the vision. AI workers have a different set of values and expectations regarding potential roles. They want to be challenged, work on new and exciting projects, and see a path for advancement. A proper recruitment strategy doesn’t focus only on your wants; it also focuses on those of the candidate. Through your job description and onboarding process, it should be clear to a candidate why they should work for you rather than a tech company. Strong strategies will, at minimum, address candidate interests such as:
What is the path to advancement?
How can you grow the candidate’s skills and experiences?
What are the products and technologies that the candidate would be working on?
Tailor the recruitment process. The progressive mindset required to implement AI in an enterprise necessitates a departure from standard recruiting processes. As highly sought-after assets, AI candidates likely have multiple interviews and offers in process by the time they speak with you. In essence, the interview and offer process needs to respect the time of the candidate, moving much faster and including hiring managers who can articulate what the candidate will work on. To this end, here are a couple of other tips to ensure the efficiency of your recruitment process:
Add interviewers to the process who understand the AI vision in the enterprise and can articulate how the candidate’s skills will complement (or drive) that vision. These interviewers should be very well equipped to assess how a candidate may fit and judge their talent. Adding interviewers to the process who have little knowledge or expertise will dampen the candidate’s enthusiasm while jeopardizing the qualification of your hire.
Ensure the recruiting process specific to AI candidates is reviewed and streamlined. The time from final interview to offer should be minimized to the greatest extent possible. Remember that AI candidates most likely will have had multiple interviews and offers coming from other potential employers. Speed in the process is more important now than ever. Consider standups, daily meetings, or even a dedicated hiring team for AI candidates.
Talent scarcity in the AI world continues to be a challenge for enterprises and their recruiting teams. But, as results have shown, the efforts are well worth it, especially when pursuing Procurement excellence and optimized solutions.
Investing in your current employees and discovering new talent requires enterprises to be thoughtful, responsive, and collaborative. Human intelligence is still the driving force behind AI implementation so above all, take care of your people and put their interests at the top of your list. That will shine through in your training programs and interview process, and will attract top talent to your AI team, whether they are in your organization or waiting to be discovered.
Sara Alchin is a global IT category manager for WNS Denali. Sara has over 20 years in Procurement plus 10 years in various IT roles. Sara has previously worked with companies focusing on IT software procurement, hardware asset management and supplier management. Sara has a BSE in Computer Engineering from University of Michigan.
Jeffrey Gaguzis is a global IT category manager for WNS Denali. Jeff has over 20 years in Sourcing and Procurement with over 15 years focused on the IT category. Before joining WNS, Jeff worked for Fairmarkit, a pioneer in tail spend management technology. Prior to that, Jeff held similar senior positions at Accenture, TD Ameritrade, and FreeMarkets/Ariba. Jeff holds a BS in Microbiology from Penn State and an MBA in Marketing/General Business from Robert Morris University.
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