# Prepare for Critical HR Data Proficiency in 2023-2024
## Why HR Data Proficiency Matters Now
The next 12-18 months represent a critical window for HR teams to organize their data infrastructure. This urgency isn’t just theoretical—it’s a strategic necessity driven by rapid advances in **artificial intelligence** and predictive analytics. According to Justin Angsuwat, chief people officer at Culture Amp, HR leaders must develop a strong foundation of workplace data to navigate this transformative period effectively.
With his unique background in software engineering consulting and people leadership at tech giants like Google, Angsuwat brings valuable perspective to data-driven HR transformation. His message is clear: get your data in order now, or risk falling behind.
The pressure is mounting as more organizations adopt AI technologies. Deloitte predicts that 25% of companies using generative AI will launch agentic AI pilots this year, with adoption reaching 50% by 2027. This technological shift will fundamentally reshape how HR operates and delivers value.
## Moving Beyond Basic Automation
Most organizations currently use AI for simple process optimization—automating routine tasks to free up HR professionals’ time. While valuable, this represents only the first stage of AI implementation. The real breakthrough, according to Angsuwat, will come from:
– Personalizing organizational insights at scale
– Generating workforce insights previously impossible to extract
– Enabling data-driven decision making across HR functions
As McKinsey researchers noted in their January 2025 report, “To truly harness the potential of AI, companies must challenge themselves to envision and implement more breakthrough initiatives.”
Consider how recruitment teams currently use AI to screen resumes and schedule interviews. The next evolution will involve AI systems that can predict candidate success, identify skill gaps in applicant pools, and recommend precise intervention strategies to improve hiring outcomes.
## Building Your AI-Ready Data Foundation
The journey toward AI readiness begins with fundamental data hygiene. Angsuwat recommends starting with clear definitions of basic metrics. Even something seemingly simple as headcount requires precision—organizations need consistent understanding of:
– Total headcount versus full-time equivalents
– Active employees versus contractors
– Global versus regional workforce numbers
For recruitment data specifically, this means standardizing how you track:
– Time-to-hire across different roles and departments
– Cost-per-hire calculations that capture all relevant expenses
– Candidate source attribution with consistent parameters
Angsuwat emphasizes that HR data must become:
1. Structured consistently across multiple systems
2. Portable between different platforms
3. Free from potential bias
4. Capable of real-time integration and access
“The power of the HR team is no longer going to be finding data,” Angsuwat notes. “It’s about finding insights and knowing what to do with them.”
## The Four Stages of HR Data Maturity
According to McKinsey research, while most companies now invest in AI, only 1% consider themselves fully mature in implementation. Angsuwat outlines a progressive framework for HR data capabilities:
### 1. Descriptive Analytics
This stage simply describes current workforce conditions. For recruiters, this might include basic metrics like application numbers, interview-to-offer ratios, or acceptance rates. Most organizations operate at this level today.
### 2. Diagnostic Analytics
At this stage, HR teams understand why certain patterns emerge. Recruiters can identify why certain roles have higher decline rates or why candidate engagement drops at specific interview stages.
### 3. Predictive Analytics
This capability allows forecasting future outcomes. Recruitment teams can predict hiring needs based on growth patterns, forecast candidate availability for specialized roles, or anticipate salary requirements in competitive markets.
### 4. Prescriptive Analytics
The most advanced stage recommends specific interventions. AI systems might suggest personalized candidate outreach strategies, recommend interview panel adjustments to reduce bias, or propose targeted employer branding changes to attract specific talent profiles.
## Warning: Don’t Skip Steps
One critical warning emerges from Angsuwat’s insights: do not rush to purchase expensive AI tools before building a solid data foundation. “You can’t take advantage of these tools if your data is not ready yet,” he cautions.
Many recruitment teams have learned this lesson the hard way. One global manufacturing company invested heavily in an AI-powered candidate matching system, only to discover their job description data lacked the consistency and detail needed for effective matching. The result was frustration rather than efficiency.
## The Future of Data-Driven HR
Angsuwat envisions a future where HR becomes as data-driven as marketing or sales. Just as marketing teams use sophisticated tools to understand customer behavior, HR will develop equivalent capabilities to understand workforce dynamics.
“People leaders will demand that HR is data-informed,” he predicts. “Gut feelings will no longer work in HR.”
For recruitment teams, this transformation might include:
– Predictive models that identify high-potential candidates early in the process
– Algorithmic matching of candidates to team cultures beyond skill requirements
– Real-time labor market intelligence that shapes compensation strategy
– Automated personalization of candidate experiences at scale
## The Competitive Advantage of Acting Now
The technology is maturing rapidly. Organizations now have a critical window to prepare their data infrastructure. Those who wait risk falling behind competitors who have already begun building sophisticated data ecosystems.
AI presents a significant opportunity—nearly 90% of leaders expect it to drive revenue growth within the next three years, according to McKinsey. However, achieving that growth requires corporate transformation.
For recruitment leaders, this means:
– Auditing current data collection practices for consistency and completeness
– Investing in skills development for HR analytics capabilities
– Building cross-functional partnerships with IT and data science teams
– Developing clear ROI frameworks for HR technology investments
The message is clear: the window for preparation is now. As Angsuwat emphasizes, “Now is the time to do something about it.” Those who establish strong data foundations today will be positioned to leverage AI’s full potential tomorrow.
# Prepare for Critical HR Data Proficiency in 2023-2024
## Why HR Data Proficiency Matters Now
The next 12-18 months represent a critical window for HR teams to organize their data infrastructure. This urgency isn’t just theoretical—it’s a strategic necessity driven by rapid advances in **artificial intelligence** and predictive analytics. According to Justin Angsuwat, chief people officer at Culture Amp, HR leaders must develop a strong foundation of workplace data to navigate this transformative period effectively.
With his unique background in software engineering consulting and people leadership at tech giants like Google, Angsuwat brings valuable perspective to data-driven HR transformation. His message is clear: get your data in order now, or risk falling behind.
The pressure is mounting as more organizations adopt AI technologies. Deloitte predicts that 25% of companies using generative AI will launch agentic AI pilots this year, with adoption reaching 50% by 2027. This technological shift will fundamentally reshape how HR operates and delivers value.
## Moving Beyond Basic Automation
Most organizations currently use AI for simple process optimization—automating routine tasks to free up HR professionals’ time. While valuable, this represents only the first stage of AI implementation. The real breakthrough, according to Angsuwat, will come from:
– Personalizing organizational insights at scale
– Generating workforce insights previously impossible to extract
– Enabling data-driven decision making across HR functions
As McKinsey researchers noted in their January 2025 report, “To truly harness the potential of AI, companies must challenge themselves to envision and implement more breakthrough initiatives.”
Consider how recruitment teams currently use AI to screen resumes and schedule interviews. The next evolution will involve AI systems that can predict candidate success, identify skill gaps in applicant pools, and recommend precise intervention strategies to improve hiring outcomes.
## Building Your AI-Ready Data Foundation
The journey toward AI readiness begins with fundamental data hygiene. Angsuwat recommends starting with clear definitions of basic metrics. Even something seemingly simple as headcount requires precision—organizations need consistent understanding of:
– Total headcount versus full-time equivalents
– Active employees versus contractors
– Global versus regional workforce numbers
For recruitment data specifically, this means standardizing how you track:
– Time-to-hire across different roles and departments
– Cost-per-hire calculations that capture all relevant expenses
– Candidate source attribution with consistent parameters
Angsuwat emphasizes that HR data must become:
1. Structured consistently across multiple systems
2. Portable between different platforms
3. Free from potential bias
4. Capable of real-time integration and access
“The power of the HR team is no longer going to be finding data,” Angsuwat notes. “It’s about finding insights and knowing what to do with them.”
## The Four Stages of HR Data Maturity
According to McKinsey research, while most companies now invest in AI, only 1% consider themselves fully mature in implementation. Angsuwat outlines a progressive framework for HR data capabilities:
### 1. Descriptive Analytics
This stage simply describes current workforce conditions. For recruiters, this might include basic metrics like application numbers, interview-to-offer ratios, or acceptance rates. Most organizations operate at this level today.
### 2. Diagnostic Analytics
At this stage, HR teams understand why certain patterns emerge. Recruiters can identify why certain roles have higher decline rates or why candidate engagement drops at specific interview stages.
### 3. Predictive Analytics
This capability allows forecasting future outcomes. Recruitment teams can predict hiring needs based on growth patterns, forecast candidate availability for specialized roles, or anticipate salary requirements in competitive markets.
### 4. Prescriptive Analytics
The most advanced stage recommends specific interventions. AI systems might suggest personalized candidate outreach strategies, recommend interview panel adjustments to reduce bias, or propose targeted employer branding changes to attract specific talent profiles.
## Warning: Don’t Skip Steps
One critical warning emerges from Angsuwat’s insights: do not rush to purchase expensive AI tools before building a solid data foundation. “You can’t take advantage of these tools if your data is not ready yet,” he cautions.
Many recruitment teams have learned this lesson the hard way. One global manufacturing company invested heavily in an AI-powered candidate matching system, only to discover their job description data lacked the consistency and detail needed for effective matching. The result was frustration rather than efficiency.
## The Future of Data-Driven HR
Angsuwat envisions a future where HR becomes as data-driven as marketing or sales. Just as marketing teams use sophisticated tools to understand customer behavior, HR will develop equivalent capabilities to understand workforce dynamics.
“People leaders will demand that HR is data-informed,” he predicts. “Gut feelings will no longer work in HR.”
For recruitment teams, this transformation might include:
– Predictive models that identify high-potential candidates early in the process
– Algorithmic matching of candidates to team cultures beyond skill requirements
– Real-time labor market intelligence that shapes compensation strategy
– Automated personalization of candidate experiences at scale
## The Competitive Advantage of Acting Now
The technology is maturing rapidly. Organizations now have a critical window to prepare their data infrastructure. Those who wait risk falling behind competitors who have already begun building sophisticated data ecosystems.
AI presents a significant opportunity—nearly 90% of leaders expect it to drive revenue growth within the next three years, according to McKinsey. However, achieving that growth requires corporate transformation.
For recruitment leaders, this means:
– Auditing current data collection practices for consistency and completeness
– Investing in skills development for HR analytics capabilities
– Building cross-functional partnerships with IT and data science teams
– Developing clear ROI frameworks for HR technology investments
The message is clear: the window for preparation is now. As Angsuwat emphasizes, “Now is the time to do something about it.” Those who establish strong data foundations today will be positioned to leverage AI’s full potential tomorrow.