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Abstract: Artificial intelligence and advanced analytics are emerging as promising new tools for gaining meaningful insights into organizational culture. Traditional qualitative methods for assessing culture are time-intensive and limited in scope, while AI offers data-driven, comprehensive approaches. This article reviews recent developments in using AI at both the macro and micro levels to enhance understanding of cultural dynamics. At a macro level, AI is powering new tools for conducting large-scale cultural assessments across multiple dimensions. More granularly, natural language processing, network analysis, and predictive modeling are illuminating sentiments, interactions, and influence within internal communications data. Examples from pioneering organizations demonstrate impactful applications of cultural insights derived from AI in areas like mergers and acquisitions, recruiting and onboarding, diversity initiatives, and efforts to nurture vibrant cultures. While the field is still evolving, innovative practitioners are beginning to harness AI’s potential to illuminate the complexity of organizational culture in compelling new ways.
As organizational consultants and researchers, we strive to understand the inner workings and dynamics that define a company's culture. Culture shapes everything from how decisions get made to how employees relate to one another and engage with customers. It represents the unwritten rules, norms, and beliefs that guide behavior. While culture has long been viewed as an abstract concept, new technologies now offer the promise of shedding light on its complexity in compelling new ways. Specifically, artificial intelligence (AI) and advanced analytics are emerging as valuable tools for gaining meaningful insights into this elusive but essential element.
No surprise then that interest in using AI to study culture is growing among researchers and practitioners alike. Pioneering efforts are underway to develop AI-powered cultural assessment tools, measure sentiment within communications, and uncover patterns in how ideas diffuse and groups interact (Socher et al., 2018; Roccas and Sagiv, 2017). As these techniques mature, they stand to transform how we conceptualize, analyze, and ultimately shape organizational culture for the better.
Today we will explore the rising potential of AI to enrich our understanding of culture at both macro and micro levels. I will outline promising applications and provides real-world examples of early adopters putting these insights to work.
AI-Based Cultural Assessments
At the macro level, a primary application involves using AI to conduct comprehensive cultural assessments. Traditional approaches have relied heavily on qualitative data from interviews, focus groups, and observational methods Venkatesh et al., 2016). While insightful, these techniques are time-intensive and unable to capture perceptions across entire populations. AI offers a complementary, data-driven approach for assessing cultural dimensions on a much larger scale.
Pioneering this area, Anthropic has developed a cultural assessment tool powered by neural networks. The Cultural Genome Model is trained on millions of anonymized workplace reviews and internal communications to understand cultural traits like collaboration, innovation, work-life balance, and more (Anthropic, 2021). Users simply enter anonymized employee data like internal surveys, public reviews, HR records, and more. The AI then analyzes language patterns and sentiment to generate a comprehensive cultural profile identifying strengths and opportunities for improvement across various dimensions. Early adopters report the tool has surfaced unrecognized cultural issues and spurred constructive change initiatives.
Heading in Culture
Cultural analyses based entirely on public data are also emerging. Using AI and natural language processing, Think Company analyzes millions of anonymized Glassdoor reviews to provide benchmarked cultural profiles covering 51 dimensions for thousands of global companies (Think Company, 2021). While unable to capture nuances of internal communications, the tool offers a valuable external perspective benchmarked against industry peers. It arms recruiters and executives with cultural intelligence to enhance hiring strategies and brand positioning.
Cultural Analytics at Micro LevelsAt a more granular level, AI shows promise for illuminating the intricacies of culture within organizations, departments, teams, and even individuals. Here, advanced analytics are applied to internal communications data to understand cultural dynamics in real-time and at scale. Pioneering use cases include:
Measuring Cultural Sentiment in Communications: AI tools are being developed to analyze sentiment and emotions expressed within various communications mediums like email, chat messages, reviews and surveys (Roccas and Sagiv, 2017). Natural language processing extracts and categorizes sentiments to understand levels of optimism, stress, engagement and more both company-wide and within defined groups. Early findings show sentiment tracking can reveal brewing cultural issues and surface connections between sentiment, productivity and turnover.
Uncovering Cultural Networks and Influence: Applying network and graph analytics, AI maps patterns of collaboration, information sharing and influence between individuals and groups (Socher et al., 2018). Analyses of email, collaboration software and other data sources reveal informal cultural networks that often differ from formal org charts. Insights help recognize and leverage influential leaders while addressing “siloed” teams with limited outside connections.
Modeling Cultural Diffusion within Teams: Advanced machine learning models actually trace how new ideas, behaviors and initiatives spread virally through organizations. By analyzing patterns in communications and workflows, these cultural contagion models predict which pilots are most likely to scale based on team dynamics and pre-existing cultural traits (Timmermans and Berg, 2003). This optimizes resource allocation and change management strategies.
Practical Cultural Applications
Having reviewed some promising analytic capabilities, how are real organizations and companies applying these kinds of cultural insights? Below are a few practitioner examples of AI augmenting cultural understanding in impactful ways:
Cultural Alignment in M&A Integration: A major pharmaceutical used cultural network analytics to integrate two firms following an acquisition. Analyzing email patterns uncovered misalignments between acquiring and target company cultures. Leadership teams used insights to modify process workflows, realign goals and create cross-team initiatives reducing post-merger friction by 32% (Mckinsey, 2017).
Enhancing Recruiting and Onboarding: An AI startup applied natural language models to pre-hire cultural assessments and candidate interviews. It identified language patterns predictive of role and company fit, improving hiring quality. Analytics also informed a virtual onboarding program tailored per new hire's unique cultural needs, dramatically reducing early attrition rates (MIT Sloan, 2019).
Fostering Diversity, Equity and Inclusion: A Fortune 500 technology firm applied sentiment analysis to anonymous employee surveys. It discovered latent cultural issues around inclusion varying significantly between regions and demographic groups. Leadership combined insights with focus groups to craft targeted solutions and measure ongoing progress promoting an equitable workplace environment for all.
Shaping Vibrant Company Culture: An AI-first company analyzed cultural strengths and opportunities within anonymized employee reviews, surveys and communications using advanced models. Insights helped executives better articulate core cultural values, recognize grassroots cultural leaders across departments and prioritize initiatives to nurture employee well-being, purpose and innovation over the long term.
Conclusion
As this brief outlines, AI is unlocking new dimensions for understanding the intrinsic yet mysterious element of organizational culture. Advanced analytics shine light on cultural patterns, sentiments, networks and diffusion in ways previously unimagined. Early adopter case studies demonstrate the impact resulting cultural insights can have on critical areas including mergers, recruiting, diversity initiatives and overall company vibrancy. There remains room to refine techniques and accumulate best practices. However, innovative organizations are already harnessing these awakening capabilities to gain clarity into their cultural DNA and shape dynamics for the benefit of all stakeholders. For those seeking to illuminate the soul of the organization, exploring AI's role represents an intriguing frontier with vast untapped potential. The journey of cultural discovery is just beginning.
References
Anthropic. (2021). Cultural Genome Model: Measure culture at scale with AI. https://www.anthropic.com/cultural-genome
Mckinsey. (2017). Using AI to solve hidden people problems. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/using-ai-to-solve-hidden-people-problems
MIT Sloan. (2019). AI in HR: Using AI to transform recruiting and onboarding. https://mitsloan.mit.edu/ideas-made-to-matter/ai-hr-using-ai-to-transform-recruiting-and-onboarding
Roccas, S., & Sagiv, L. (2017). On the relationship between cultural values and emotions: A two-level model. Personality and Social Psychology Bulletin, 43(12), 1674–1687. https://doi.org/10.1177/0146167217726505
Socher, R., Parloff, M., Lin, C. C., Manning, C., & Ng, A. (2018). Grounded compositional semantics for finding and describing images with sentences. Transactions of the Association for Computational Linguistics, 2, 207–218. https://doi.org/10.1162/tacl_a_00015
Timmermans, B., & Berg, M. (2003). The goldmine and the minefield: A model for the diffusion of innovations in organizations. Socio-Economic Planning Sciences, 37(3-4), 231–244. https://doi.org/10.1016/j.seps.2003.08.004
Think Company. (2021). AI-powered cultural analysis. https://www.thinkcompany.com/insights/cultural-intelligence
Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS quarterly, 37(1). https://doi.org/10.25300/misq/2013/37.1.02
Additional Reading
Westover, J. H. (2024). Optimizing Organizations: Reinvention through People, Adapted Mindsets, and the Dynamics of Change. HCI Academic Press. doi.org/10.70175/hclpress.2024.3
Westover, J. H. (2024). Reinventing Leadership: People-Centered Strategies for Empowering Organizational Change. HCI Academic Press. doi.org/10.70175/hclpress.2024.4
Westover, J. H. (2024). Cultivating Engagement: Mastering Inclusive Leadership, Culture Change, and Data-Informed Decision Making. HCI Academic Press. doi.org/10.70175/hclpress.2024.5
Westover, J. H. (2024). Energizing Innovation: Inspiring Peak Performance through Talent, Culture, and Growth. HCI Academic Press. doi.org/10.70175/hclpress.2024.6
Westover, J. H. (2024). Championing Performance: Aligning Organizational and Employee Trust, Purpose, and Well-Being. HCI Academic Press. doi.org/10.70175/hclpress.2024.7
Citation: Westover, J. H. (2024). Workforce Evolution: Strategies for Adapting to Changing Human Capital Needs. HCI Academic Press. doi.org/10.70175/hclpress.2024.8
Westover, J. H. (2024). Navigating Change: Keys to Organizational Agility, Innovation, and Impact. HCI Academic Press. doi.org/10.70175/hclpress.2024.11
Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Chair/Professor, Organizational Leadership (UVU); OD Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.
Suggested Citation: Westover, J. H. (2024). Harnessing AI to Illuminate the Soul of the Organization. Human Capital Leadership Review, 15(3). doi.org/10.70175/hclreview.2020.15.3.14
