According to a recent article in Harvard Business Review (online, 23 January 2025) the traditional Diversity, Equity, and Inclusion (DEI) frameworks are facing increased scrutiny, with support among American workers dropping to 52% amid rising anti-DEI sentiments. In response, thought leaders like Lily Zheng advocate for a shift towards the FAIR model (Fairness, Access, Inclusion, and Representation) to address systemic inequities more effectively.
This evolution aligns with the transformative potential of emerging technologies. Artificial Intelligence (AI) and big data analytics offer tools to operationalise the FAIR principles:
- Fairness: AI can identify and mitigate biases in recruitment and performance evaluations, ensuring equitable treatment across demographics.
- Access: Data analytics can uncover disparities in opportunities, guiding interventions to bridge gaps in resources and support.
- Inclusion: Machine learning algorithms can monitor workplace interactions, promoting inclusive communication and collaboration.
- Representation: Predictive analytics can inform succession planning and talent development, fostering diverse leadership pipelines.
However, the deployment of these technologies must be approached with caution. AI systems can perpetuate existing biases if not carefully designed and monitored. Ensuring diverse representation in tech development teams and implementing transparent algorithms are critical steps toward ethical AI use. Consistent and routine integration of impact assessments and risk assessments are vital to identify and uproot inherent biases.
Embracing the FAIR model, supported by responsible technology integration, can lead organisations toward more equitable and inclusive environments. This approach not only addresses the shortcomings of traditional DEI initiatives but also leverages innovation to drive systemic change.
Conclusion
The HBR article provocatively frames the moment as “what comes after DEI,” but it may be more accurate—and more constructive—to see this not as the end, but as an inflection point in a longer, non-linear journey toward equity. DEI has always evolved in response to cultural and political headwinds, and the current backlash may be less a death knell than a familiar dip in the cycle of progress. Rather than abandon DEI, this is an opportunity to regroup, refocus, and retool using emerging technologies. DEI is not merely about terminology and reframing terminology to make it more acceptable or palatable is hardly the end game. AI and big data, if ethically designed and purposefully applied, can help expose hidden bias, track structural inequality with new precision, and build systems that are not just performatively inclusive but measurably fair. For those truly committed to equity, the challenge now is not to retreat, but to reimagine DEI as a digitally-enabled, evidence-led strategy that puts justice and representation back on the frontline—where it belongs.
King Advisory 
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