I’m glad to be back with SourceCon, where the fusion of human intuition and technological innovation shapes the future of talent acquisition. After a hiatus, I’m diving back into the heart of sourcing with a perspective freshly honed by the latest advancements in artificial intelligence (AI). This isn’t about distant futures or speculative technologies; it’s about the tangible, powerful tools at our fingertips today.
As we navigate this era of digital transformation, let’s explore how AI, as it stands, amplifies our sourcing capabilities, making every search not just a hunt for talent but a journey into the potential of human-machine collaboration.In the world of talent acquisition, AI has transitioned from a buzzword to a critical ally. It’s not about replacing the human element; rather, it’s about enhancing our innate capabilities with its computational power. Here’s how AI serves as a linchpin in modern sourcing strategies:
1. Precision-Driven Candidate Search
AI systems are already able to API data from external sources as well as search the web. Sourcing GPTs (AI Agent assistants designed for sourcing) will allow Sourcers to prompt their way to finding talent online and in the ATS. If you have your GPT connected to external sources, like your CRM, you can use an AI to either craft the search or search sources for matches based on criteria.
Example Prompt for AI-Assisted Precision-Driven Candidate Search
Imagine you’re seeking a software developer with a specific set of skills and experiences. Here’s a detailed prompt you could theoretically give to an AI system like me, designed to perform a precision-driven search:
Prompt: “Generate a list of candidate profiles for a software developer role requiring 5+ years of experience in full-stack development, with a focus on JavaScript and Python. Candidates should have contributed to open-source projects and possess a bachelor’s degree in computer science. Preference for those who have worked in agile development environments and have a track record of innovative problem-solving. Location: Seattle area.”
How AI Processes the Prompt
1. Keyword and Criteria Extraction:
- The AI begins by extracting key criteria from the prompt: experience level, skill set (JavaScript, Python), open-source contribution, educational background, agile experience, problem-solving skills, and location.
2. Data Parsing and Matching:
- Utilizing its algorithms, the AI scans through available data sources that could include online professional networks, resume databases, and social media platforms. It looks for matches based on the extracted keywords and criteria.
3. Evaluation and Ranking:
- Candidates’ profiles are evaluated against the search criteria. The AI uses a scoring system to rank candidates based on how closely they match the comprehensive list of requirements. This includes analyzing the depth of their technical skills, the relevance of their work experience, and their contributions to open-source projects.
4. Highlighting Key Attributes:
- For each top-ranked candidate, the AI highlights key attributes that make them a strong match. This could include a summary of their technical expertise, notable projects, and achievements that align with the role’s demands.
5. Generating a Curated List:
- The AI compiles a list of the most promising candidates, complete with links to their profiles and a brief overview of why they were selected. This list serves as a focused starting point for further human-led exploration and engagement.
Output Example
Given the theoretical capabilities, here’s what an output might resemble:
- Candidate 1: Jane Doe, a seasoned full-stack developer with 6 years of experience, a strong portfolio of open-source contributions, and a B.Sc. in Computer Science from the University of Washington. Known for her innovative solutions in agile projects at XYZ Corp.
- Candidate 2: John Smith, an agile software developer with 7 years of experience specializing in JavaScript and Python. Holds a degree in Computer Science and has made significant contributions to open-source projects. Demonstrated problem-solving prowess at ABC Tech.
2. Engaging Passive Candidates
One of AI’s standout capabilities is its ability to engage passive candidates—those not actively seeking new opportunities but open to the right offer. You can now use a GPT to create personalized outreach in seconds. Here are some examples of the types of options you have.
A. Tech Industry: Senior Data Scientist
“Craft an outreach email targeting a senior data scientist. Begin with an intriguing fact about data science, use two emojis to keep the tone friendly, include a short rhyme about data analysis, insert a pun related to their expertise, and conclude with a compelling call to action inviting them to discuss how they can make an impact with their skills.”
B. Healthcare: Experienced Nurse Practitioner
“Compose an email to an experienced nurse practitioner. Start with a recent statistic on pediatric care advancements, incorporate emojis related to healthcare, share a heartfelt poem about nursing, include a light-hearted joke on healthcare, and end with an invitation to explore new opportunities in pediatric care.”
C. Finance: FinTech Project Manager
“Write an email to a FinTech project manager. Begin with a fascinating fact about blockchain’s impact on finance, use emojis to underscore financial growth and innovation, craft a clever rhyme on digital payments, integrate a finance-related pun, and conclude with a call to explore transformative projects together.”
D. Marketing: Digital Marketing Specialist
“Draft an outreach email for a digital marketing specialist. Start with an impressive SEO success story, use emojis to highlight creativity and digital trends, add a verse about marketing strategies, insert a marketing-related pun, and finish with an invitation to discuss cutting-edge marketing techniques.”
E. Engineering: Civil Engineer
“Prepare an email for a civil engineer involved in sustainable projects. Lead with a recent breakthrough in green infrastructure, incorporate nature-related emojis, write a short rhyme about building the future, add a construction-related pun, and end with a call to join your team in shaping sustainable cities.”
3. Data Driven Sourcing
Have you been in a meeting where you’ve left having to spend the next two weeks validating to your hiring partner something you told them. Do you want to even venture a guess at how much of your time you’ve wasted searching for unicorns and purple squirrels? If sourcing is to survive as a function it must become more data driven. We don’t have weeks to spend validating our knowledge. GPT now makes it possible for us to take free labor market data (from the BLS, Eurostat Indeed..) and have the AI do the analysis for us. Take free data, learn some prompts and start bringing data. Below is a step by step guide I worked with GPT to help you learn how to bring data to the table and the conversation.
To perform basic analytics with GPT using Bureau of Labor Statistics/Job Openings and Labor Turnover Survey data for insights into the talent market and competitor activities, follow these steps:
- Define Objectives: Decide what you want to analyze, such as trends in job openings, hires, and separations across different industries and regions. For example, you might be interested in understanding which sectors are experiencing growth or decline.
- Data Collection: Access the latest JOLTS data from the Bureau of Labor Statistics website. For instance, you can find detailed statistics on job openings, hires, and separations by industry and region, as provided in their December 2023 report.
- Data Processing: Organize the collected data. You may categorize it by industry (e.g., tech, healthcare, finance) or by job function (e.g., data science, nursing, project management). The JOLTS data provides a comprehensive breakdown, including changes from month to month and year to year.
- Analysis: Use GPT to analyze patterns within this data. Ask it to identify trends such as which industries have the highest growth in job openings or which regions show significant movement in employment separations.
- Interpretation: Interpret GPT’s findings to deduce how these trends can impact your recruitment strategies. For example, an increase in job openings in the tech industry could indicate a growing demand for tech talent, suggesting a focus area for sourcing efforts.
- Strategic Planning: Based on these insights, develop a recruitment plan that aligns with the observed market trends. This might involve targeting specific industries with growing job openings or regions with high candidate mobility.
- Implementation: Implement your recruitment strategies by tailoring your outreach and sourcing methods to the identified opportunities within the talent market.
- Monitoring and Adjusting: Continuously monitor the effectiveness of your strategies and make adjustments based on new data and emerging trends. Regularly revisit the JOLTS data for the latest insights.
By following these steps and utilizing the detailed job market data provided by JOLTS, you can gain strategic insights into talent market dynamics and refine your recruitment approach accordingly. For more detailed information and to access the data, visit the BLS JOLTS page and the Data Catalog of the Job Openings and Labor Turnover Survey.
Sources:
- Bureau of Labor Statistics, Job Openings and Labor Turnover Survey (JOLTS), December 2023 report.
- JOLTS 2022 Benchmark Article, Bureau of Labor Statistics.
- Job Openings and Labor Turnover Survey – Catalog, Data.gov.
As I make my return to SourceCon, I’m invigorated by the possibilities that lie at the intersection of human insight and AI efficiency. Let’s embrace these tools not as futuristic novelties but as current partners in our mission to discover, engage, and hire the talent that will drive our organizations forward. Together, we’re not just filling positions; we’re crafting the future of work, one hire at a time.
