Leveraging AI and People
Analytics to Unlock Workforce
Productivity and Performance
Page 1
Table of Contents
0. Rationale
1. Intro
► The
Current Landscape of People Analytics
► Types of People Analytics
2. Body
► How AI
enhances People Analytics
► Employee Wellbeing & AI
► The Role of AI in Talent Acquisition
► Impact on Workforce Productivity
► Predictive Analytics for Employee Retention
► Case Studies and Success Stories
► Ethical Considerations
3. Conclusion
Page 2
Rationale
Key Terms
People Analytics:
Artificial Intelligence
The collection and transformation of HR data and organizational data
into actionable insights that
improve critical talent and business outcomes
The ability of a digital computer or computer-controlled robot to
perform tasks commonly associated with intelligent beings
Artificial Intelligence + People Analytics
Combines AI's predictive capabilities and automation with people analytics' workforce data foundation.
Source: Brtiannica, Visier
Page 3
Rationale
Rationale: “To demonstrate how integrating AI with people analytics can provide deeper and
predictive insights about employees and the workforce is the main objective.”
1
2
3
•
•
Endure success in this fast-changing world
•
Conduct right strategies
•
Innovate front-line workers
•
Pursue cost leadership and productivity by leveraging fixed costs
•
How merging AI capabilities with people analytics unlocks a new level of workforce intelligence
•
How AI and people analytics allows more nuanced management, ultimately powering productivity
•
How AI and people analytics performance gains that translate into competitive advantage.
•
Optimizing the efficiency and productivity of HR processes\
•
Gaining predictive insights into future scenarios
•
Personalizing and enhancing the employee and candidate experience
Source: AIHR, The Chartered Institute of Personnel and Development
Page 4
Intro
Page 5
Intro
AI automates and enhances people analytics for deeper
workforce insights
The Current Landscape of People Analytics
1
► Using
2
data and analytics to guide decision making around HR, talent, and the workforce
•
People analytics has evolved from simple reporting of workforce metrics to dynamic analysis using statistical models, predictive
algorithms, and machine learning.
•
It enables data-driven decision making related to recruiting, retention, talent development, performance management,
compensation, workforce planning, and more.
► Transforming
•
3
Rather than being siloed in HR, people insights are increasingly integrated into broader business strategy and operations via
workforce planning, capacity modeling, and people data-informed decision making.
► Making
•
people analytics from an HR function to integrating it into core business processes
programs and processes more data-driven and effective
•
Analytics identifies opportunities to improve existing talent programs using experimentation, optimization, and personalization
powered by workforce data.
•
For example, analytics can inform targeted learning interventions, the candidate screening process, predictive performance
management, and tailored employee experience enhancements.
Source: Insight222, Sage Journals, AIHR
Page 6
Intro
People analytics leverages data and analytics to provide descriptive,
predictive, and prescriptive insights about the workforce
Types of people Analytics
1
► Descriptive Analytics
•
Retrospectively analyzes workforce data to identify trends and
patterns
•
•
•
3
: What happened?
2
► Diagnostic Analytics
: Why did it happen?
•
Metrics analyzed cover compensation, diversity, attrition, costs,
productivity, and satisfaction
Diagnostic analytics is an HR analytics that goes beyond the
descriptive analysis of past events to identify the root cause of
workforce problems or issues.
•
Statistical techniques involve data visualization, cohort analysis,
benchmarking, and employee journey mapping
Involves analyzing and extrapolating data to determine why certain
trends or patterns are occurring in the workforce data.
•
Valuable tool for HR professionals which helps them identify and
address workforce issues before they become more serious problems
Enables insights like average promotion rates by department over 5
years, manager-team gender ratios, or monthly sales per employee
► Predictive Analytics
: What is likely to happen?
4
► Prescriptive Analytics
: How will it happen?
•
Leverages machine learning and statistical models to make talent
forecasts
•
Applies optimization, statistical techniques and talent data to
prescribe best workforce decisions
•
Techniques used include regression analysis, simulation modeling,
risk algorithms, survival analysis for attrition, neural nets
•
Mathematical programming and simulation used to show optimized
talent investments and strategies
•
Key outcomes predicted cover future resignations, high potential
talent, manager burnout risk, hiring needs, skill gaps
•
•
Enables proactive planning and intervention rather than reacting after
outcomes occur
Provides specific recommendations to HR leadership on number of
hires needed, compensation policy changes required to meet 5-year
business goals given budget constraints and talent market dynamics
•
Drives strategic workforce decisions aligned to business objectives
for peak human capital impact
Source: CHRMP, AIHR
Page 7
How AI enhances People analytics
Page 8
How AI enhances People Analytics
Leveraging AI's capabilities for automated data aggregation, insight discovery,
forecast modeling, and continuous learning to elevate workforce analytics
Automated Data
Collection
•
•
•
•
HR Information Systems (HRIS) – Employee Data
Applicant Tracking Systems (ATS) – Recruitment Process Data
Employee Surveys and Assessments – Employee Engagement
Learning Management Systems (LMS) – Tracking Employee Development
Insights and Pattern
Discovery
•
•
•
Descriptive analytics: Data Aggregation, Data Visualization
Diagnostic analytics: Correlation Analysis, Regression Analysis
Predictive analytics: Time Series Analysis, Machine Learning Algorithms
Scenario and Risk
Forecast Modeling
•
•
•
Predictive Analytics: Natural Language Processing (NLP)
Use of historical data to forecast future trends and outcomes
Resource Optimization and Risk Scenarios
•
•
•
Adaptability ensures that analytics models remain accurate and relevant over time.
Incorporate Feedback loops to enhance the system,
Develop Dynamic Models to adapt to organizational shifts over time
1
2
3
4
Continuous Learning
for Improvement
Source: Betterworks, Society for Human Resource Management
Page 9
Employee Wellbeing and AI
Page 10
Employee Wellbeing & AI
IntegratingAI to facilitate streamlined, unbiased recruiting while promoting candidate care
through communications, transparency, and advanced employee benefits
1
•
•
•
Automation of applicant screening and ranking.
Minimized unconscious biases, and fairer evaluations.
Use of Chatbots to incorporate instant response, scheduling, and feedback.
Improve Employee
Attitudes towards AI
•
•
•
Engage in Clear Communication – transparency, open dialogue, and continuous feedback
Incorporate training programs – skill development in AI and accessibility
Demonstrate positive AI outcomes: showcase success stories and quantifiable results over time
Personalized
Benefits/Healthcare
through AI
•
•
•
Personalized Benefits Packages – AI Analysis and Customization
Healthcare Predictive Analytics – Risk Assessment and Preventative Measure
Cost-effective Healthcare solutions – Expense Analysis and Budget Optimization
Role of SCR in Balancing
Tech and Workplace
•
•
•
Ethical Tech Implementation - Guiding Principles and Avoiding Harm
Balancing Automation and Human Work – Human-Centric Approach and Job Security
Global Reputation and Brand Image - CSR Impact in Org. and attracting talent
Bias reduction to enhance the
candidate experience.
2
3
4
Source: Forbes(Bryan Robinson), Wellbeing Ai, Pocked HRMS
Page 11
Employee Wellbeing & AI
Real Life application of AI on employee wellbeing
➢ Virgin Pulse: Employee Well-being Platform
➢ Headspace: AI-Powered Meditation and Mindfulness
Source: Virgin Pulse, Headspace
Page 12
How is AI transforming the talent acquisition process?
Page 13
The Role of AI in Talent Acquisition
AI interjects amplified data, personalization, automation, and neutrality into hiring.
Speed, Scalability, Objectivity, Personalization, Auto Improvements embedded into recruitment
Roles
1
Leverage data to
understand workflow
patterns and
productivity drivers
2
The Role of AI in
Talent Acquisition
Identify
opportunities to
reduce waste, bias,
and variability
3
Offer personalized
recommendations to
optimize productivity
4
Continually audit
and refine insights
per emerging data
Details
►
Sophisticated AI algorithms can discern insights from historical hiring data such as source of recruitment, time-to-offer, and recruited talent performance to delineate the most fruitful talent pipelines, streamline requisite-to-offer
procedures, and prognosticate superior contributors. This engenders
augmented recruiting efficacy and efficiency.
►
AI-driven process mining can scrutinize incumbent talent acquisition workflows to
red-flag redundant components that prolong hiring cadences. Additionally, it can
detect implicit biases that could skew the screening of applicant credentials and
interview performances, proposing remediations accordingly. This curtails waste and
partiality.
►
AI-enabled advisory tools can tender bespoke guidance to both talent
acquisition teams and applicants regarding optimal subsequent courses of
action to expedite and enhance hiring decisions - such as prioritizing
particular prospects for outreach or underscoring specific competencies on
candidate profiles.
►
As augmented hiring data emerges, AI can repeatedly analyze such inputs to
gauge the impact of past recommendations and fine-tune forthcoming
suggestions via a feedback loop, enabling continuous optimization.
Source: Forbes (Danielhenkin), Talent Insight Group, Sertifier
Page 14
The Role of AI in Talent Acquisition
Real Life Application of AI in recruitment
Artificial intelligence expedites and enhances hiring processes through automated workflows, data-based insights, and reduced manual tasks.
Roles
1
Leverage data to
understand workflow
patterns and
productivity drivers
2
Identify
opportunities to
reduce waste, bias,
and variability
3
Offer personalized
recommendations to
optimize productivity
4
Continually audit
and refine insights
per emerging data
Details
►
Google - Leverages neural networks to learn from piles of internal Google
data on role requirements, project timelines and team productivity. It then uses
this to recommend optimal hiring requisitions and candidate profiles that align
with business priorities.
►
Unilever - Uses AI video interviewing platform HireVue to standardize its
global college graduate screening with game-based assessments. This
minimizes localization biases and ensures parity. Using AI, the team Unilever
was able to save £1 million in one year on their recruitment strategy, as well
as cutting time to hire by as much as 90%
►
IBM - Uses its internal AI solution, IBM Watson Recruitment, to match
applicant resumes with optimal open positions across business verticals. This
has enhanced placement rates by over 10%
►
Oracle - Has an Adaptive Intelligence app that audits Oracle's talent
acquisition patterns every 90 days based on latest hiring data. It spots
inefficiencies, revamps processes, and provides fresh success benchmarks to
recruiters.
Sources: Google, Unilever, IBM, Oracle
Page 15
How does the integration of AI and People Analytics
impact workforce productivity and efficiency?
Page 16
Impact on Workforce Productivity
AI transforms data into actionable workforce insights and organizational intelligence.
powering more effective planning, skill development and programs that boost productivity.
Roles
1
Details
▶
AI can analyze company growth plans, product roadmaps, and market
trends to determine the most critical skills and competencies needed - the
roles, location, experience levels, and more. People analytics teams can use
this insights for more aligned workforce planning and 'future-proofing' the
workforce.
▶
Advanced simulations powered by AI and machine learning can map out how job
roles will change and be impacted as automation and AI are introduced. Based on
these models, training needs are predicted, allowing proactive re-skilling or hiring
for in-demand skills.
▶
People Analytics algorithms can crunch engagement, performance, and
other workforce data to identify 'at-risk' employees or consumers. By
proactively addressing concerns of such talent and providing supportive
interventions, retention is improved - saving costs and preventing
productivity declines associated with attrition.
►
Chatbots and intelligent assistants can handle common employee queries
and requests related to payroll, leaves, expenses, freeing up HR staff to
focus on more strategic priorities.
Forecast future skills
demands based on
business strategy
2
Model the impact of
automation and AI
on workforce needs
Impact on Workforce
Productivity
3
Predict retention risk,
allowing proactive
intervention
4
Automating
administrative HR
workflows
Source: Zoho, Peoplelogic, HBR
Page 17
Impact on Workforce Productivity
Real Life Application of AI enhancing workforce productivity
Artificial intelligence enhances workforce productivity through predictive analytics to forecast talent needs, model automation impacts, and identify retention risks.
Roles
1
Details
►
Predictive Analysis: Supply chain optimization of Walmart
Walmart takes data instantaneously from its point-of-sale systems and
incorporates this within its forecasts to assess which products are likely to sell
out and which have underperformed. Combined with online behavior patterns,
this provides a huge amount of data points (over 40 petabytes of them) to help
Walmart prepare for workers to be allocated for a fall in product demand.
►
Predictive Analysis: Predicting the demand for perishable products : Metro Inc
AI solution allows Metro store managers to order the right quantity for each product,
minimizing waste, a better plan the labor required to process and prepare the products,
and a data-driven decision tool to order the right quantity of product to meet
customers’ ever-changing needs (Doesn’t forecast jobs needed at the moment)
►
Predictive Analysis: Parking issues of a Zoo (Point Defiance) in Tacoma
By incorporating the National Weather Service’s data into IBM AI, the zoo was able
to pinpoint exactly which conditions caused more people to make a visit. This
knowledge was then used to model future visitor patterns, using historical
attendance figures and projected weather statistics. Now it predicts attendance
figures with greater than 95% accuracy, allowing managers to staff the park.
Forecast future skills
demands based on
business strategy
2
Model the impact of
automation and AI
on workforce needs
Impact on Workforce
Productivity
3
Predict retention risk,
allowing proactive
intervention
4
Automating
administrative HR
workflows
►
Chatbots for handling administrative HR workflows : IBM Watson
Rather than ask a manager or HR about vacation policy or dig through a
maze of portals, employees can query Watson, which responds using data
based on their tenure, location, and days already used. IBM HR team was
able to save 12,000 hours in 18months using Watson
Source: Walmart, Metro, IBM, Point Defiance
Page 18
Predictive Analytics for Employee Retention
Page 19
Predictive Analytics for Employee Retention
Predictive Analytics for HRM
AI-HRMS
Major Attributes
►Information
system using artificial intelligence-based
technology
►Integrate
and manage data related to employee
personnel, salary, performance evaluation, education
training, and service
►Establish
scientific and rational personnel policies
►To
minimize heuristic errors
►Machine
learning and the rapid development of AI
►Break
down the boundaries of tasks that can be
automated
►Efforts
and competition to secure lots of learning data
Source:AIHRMS
Page 20
Success Stories (Performica)
Page 21
Case Studies and Success Stories
Success Stories: Performica
Performica
► Began
► Data-driven
Source: Performica
► Unlock
in 2022
approach to HR Decision making
the full potential of their workforce
Page 22
Case Studies and Success Stories
OrgGraph
OrgGraph : groundbreaking foundation
for performance management
1. Build the OrgGraph
►Performica
creates a detailed OrgGraph using digital
trace data from communication and collaboration tools,
and surveys, or both.
2. Collect Bias-Free Feedback:
►Employees
indicate their go-to colleagues for assistance
and support through Performica’s simple, non-intrusive
feedback method.
3. Comprehensive Picture:
shows a clear view of each employee’s
strengths and weaknesses aiding managers and teams in
alignment.
►Performica
Source: Orggraph
Page 23
Real Life application cases
➢ Increasing Trends
➢ Implications
✓ Essential for organizations to consider specific needs,
industry requirements, and the scalability of the chosen
solution
✓ Collaborating with vendors, staying abreast of
technological advancements, and continuously refining
strategies based on data-driven insights
✓ Significant in helping teams leverage ai to support
different workforce-related tasks
✓ Create tools to reconstruct cognitive hr processes
through a conceptual framework
Source:Juicebox, Fetcher
Page 24
Ethical Considerations
Page 25
Ethical Considerations
Risks: Ethical considerations and challenges of using AI for People Analytics
Main Concerns
►
Privacy and Data Protection
•
Working with sensitive data leads to privacy concerns
►
Bias and Fairness in Data and Algorithms
•
Can lead to unfair outcomes, especially when making decisions
about individuals.
►
•
•
Transparency and Explainability
Complexity of data processes
Can be difficult to interpret AI models and conclusions
Source: AIHR, myHRfuture, Society for Human Resource Mangement
Page 26
Ethical Considerations
Addressing the concerns of using AI for People Analytics
Addressing Methods
►
•
►
Communication with employees
“Fair exchange of value”
Implementation of clear policies
•
Clear policies on data collection, storage, & usage
•
Obtaining informed consent from employees
•
Ensuring compliance with data protection regulations
►
Controls to reduce bias and unfair outcomes
•
Identify, mitigate, and prevent biases in data and algorithms
•
Regular audits of AI systems
•
Diversifying training data
►
Usage of interpretable models
•
Provides clear understanding of how decisions are reached
Source: AIHR, myHRfuture, Society for Human Resource Mangement
Page 27
Conclusion
Page 28
Conclusion
Benefits and Limitations of Artificial Intelligence + People Analytics
Benefits
Limitations
Predictive Analytics and Talent Management:
➢ By leveraging data-driven insights, organizations
can make informed decisions about talent
management.
Data Quality:
➢ If the input data is of poor quality, the analysis
results may be inaccurate, affecting effective
decision-making for employees and the organization.
Intelligent Recruitment and Candidate Screening:
➢ Automatically screen resumes and improve
matching to optimize the candidate experience and
shorten hiring cycles.
Bias in Algorithmic:
➢ Algorithms may be affected by biases in historical
data, resulting in unfair treatment of certain groups
of employees.
Transparency and Fairness:
➢ Promote trust, ensure fair practices and reduce bias.
High maintenance cost:
➢ The maintenance cost of AI is unaffordable for some
companies.
Page 29
Conclusion
Application settings and constraints for certain settings
➢ Privacy protection:
Ensure necessary measures are taken to protect employees' personal data
➢ Transparency:
Design applications to provide transparent operational and decision-making processes,
explain to employees the purpose and methods of using people analytics tools, and build trust
➢ Fairness and non-discrimination:
Avoid AI applications introducing or amplifying discrimination
and take steps to ensure analysis results are fair to all employees.
Page 30
Conclusion
Future
➢ Clear goal:
Ensure clear goals so that the application of people analytics and AI aligns
with organizational strategy and solves real problems
➢ Continue learning:
Regularly review and update AI models to ensure they remain consistent
with changing business circumstances and workforce needs.
➢ Communication with employees:
Obtain employee feedback and involvement to ensure actual requirements are met.
Page 31
Conclusion : Suggestions for Executives
Suggestions| 2-year pilot development timeline for implementing AI-enhanced people analytics
➢ Top industry rivals are already gaining an edge from AI-enabled people analytics
✓
✓
✓
Adopting similar solutions enables more advanced talent management programs to attract and retain top talent.
Advanced analytics and AI allow more personalized, tailored employee support.
Leverage people analytics insights on departmental productivity, diversity impacts, predicted attrition risks, make plans aligned to corporate goals.
19-24M
13-18M
7-12M
4-6M
•Get executive buy-in on pilot goals, timeline,
expected outcomes
•Procure AI software and needed data/analytics
talent
•Integrate software with HR systems; customize
analytics models
•Socialize positive ROI metrics and user
testimony to build momentum
•Identify employee pain points via surveys;
communicate pilot objectives
•Plan initial enterprise-wide rollout to new
functions based on priority
•Run controlled pilot focused on key use cases
like hiring analytics
•Develop policies for standardized tracking and
governance of AI systems
•Collect user feedback iteratively; refine tools to
address adoption barriers
•Create long term product roadmap for
advanced capabilities
•Expand availability of analytics dashboards
and AI advisory tools
•Drive adoption through training programs on
data literacy and use cases
•Set targets for next wave of value creation
focused on automation, personalized retention
etc.
•Track pilot KPIs related to process efficiency,
costs, productivity etc.
1-3M
•Assemble cross-functional pilot team including HR, IT, business unit heads
•Define scope focusing on 1-2 top priority workforce challenges
•Audit existing data sources and infrastructure; address gaps
•Research AI tools and vendors; develop business case
Page 32
Thank You
Page 33