Study Notes on the Deployment of AI to Infer Employee Skills at Johnson & Johnson

Title: The Deployment of AI to Infer Employee Skills: Insights From Johnson & Johnson's Digital-First Workforce Initiative
Authors: Olgerta Tona, Dorothy E. Leidner, Nick van der Meulen, Barbara Wixom, Juliana Nunes, Doug Shagam
Affiliation:
- Department of Applied IT, University of Gothenburg, Sweden
- Information Technology and Innovation, University of Virginia, USA
- Center for Information Systems Research, MIT Sloan School of Management, USA
- Johnson & Johnson Human Resources, USA
DOI: https://doi.org/10.1111/isj.12594
Received: 29 June 2024 | Revised: 28 February 2025 | Accepted: 1 March 2025
Keywords: AI, digital transformation, digital workforce, employees, personal data, skills inference

Objective: Report insights from Johnson & Johnson’s (J&J) development of an employee skills inference platform using AI.
Importance of Digital Transformation: Organizations must develop their workforce to continuously adjust skills to thrive amidst digital innovation.
Challenges Faced: Deployment challenges of the skills inference platform.
Identification of Practices: Three key organizational practices for successful AI deployment:
- Blueprinting the future workforce
- Managing ethical data work across borders
- Compensating for AI blind spots
Lessons Learned: Insights applicable to other organizations employing AI to foster a digital-first workforce.

Digital Transformation Defined: Continuous digital innovation aimed at creating value and defending against competition (Hanelt et al., 2021).
Significance: Critical for organizational performance but often hindered by inertia and failure.
Challenges: Reinvention of organizational identity, which impacts all stakeholders, particularly employees.
Employee Skills: The prepared workforce is a key differentiator between high and low performers in digital transformation (Gurbaxani & Dunkle, 2019).
Focus on Existing Workforce: Organizations benefit from enhancing current employees' skills rather than solely attracting new talent (Braojos et al., 2024).

Johnson & Johnson Overview: Founded in 1886, operates in over 60 countries with 150,000 employees, developing medical devices, pharmaceuticals, and consumer health goods.
Objective: Transition to a digital-first workforce where employees can utilize technologies to enhance business processes and offerings.
Technology Group’s Role: Led by Jim Swanson focusing on developing digital competencies across the workforce, enhancing both commercial and scientific expertise.
Skills Assessment: J&J Technology decided to use Natural Language Processing (NLP) and machine learning to infer skill proficiencies based on existing employee data.
Proof of Concept (POC): A successful POC confirmed sufficient data richness for AI to infer skill levels.
Scoring Method: AI engine assigns proficiency scores from 0 (No Skill) to 5 (Thought Leader).
Validation Process: Results validated by employees and managers, emphasizing privacy.
Launch Timeline: Officially launched in August 2020, resulting in improved metrics across various talent management areas.

Methodology: Conducted 26 interviews with key stakeholders and analyzed internal documents and presentations.
Focus: Identify challenges J&J faced during deployment and practices employed to address them.

Deployment Waves: The Digital Talent Platform launched in waves, each expanding skills assessed, employee numbers, and geographical reach.

Phase 1: Rolled out to assess 41 skills among 3400 employees.
Phase 2: Expanded to include all J&J Technology employees across regions, adding more skills based on leadership capabilities.
Facilitating Factors: A centralized HR data repository facilitated rapid integration of the AI solution.

Definition: Creating an envisioned workforce profile using strategic planning to identify required skills for digital transformation.
Process:
- Analyze existing strategic business plans.
- Engage over 100 senior leaders for validation.
Evolving Needs: Lists of skills and capabilities updated across deployment phases.

Importance: Engagement with local data regulations and ethical considerations.
Compliance Measures: Collaboration with J&J’s privacy organization to ensure GDPR compliance and conduct risk assessments on data sources.
Engagement with Workers’ Councils: Ensured employee data protection and outlined benefits versus risks of using data for talent management.

Objective: Address and minimize opacity in AI working mechanisms and build trust among the workforce.
Key Initiatives:
- Employees encouraged to influence AI data inputs and outputs by modifying personal inputs.
- Transparent communication on how AI outputs are utilized for employee development.

Dashboard Utility: Aggregated data on skills proficiency helps leaders monitor talent needs across different regions.
Development Opportunities: Upskilling initiatives offered to employees based on AI insights, emphasizing voluntary participation.
Metrics of Success: Internal placements increased, time to fill digital roles improved, and lower attrition rates observed in digital positions.

Key Practices Enacted: Derived from J&J's experience during the deployment of the Digital Talent Platform:
- Lesson 1: Adopt a strategy-driven approach leveraging expertise to identify emerging skills.
- Lesson 2: Engage in context-sensitive data work to overcome local legal and ethical challenges.
- Lesson 3: Work toward transparency of AI processes through employee engagement with input and output data.

Digital transformation requires constant adaptation of skills across the workforce. AI-enabled solutions can streamline identifying skill gaps and recommending development. Organizations should implement practices to facilitate AI deployment effectively, benefiting employees and enhancing overall organizational performance.