Twenty-five years ago, a small group of software practitioners gathered in the mountains of Utah and produced the Manifesto for Agile Software Development (n.d). Disappointed with the way software projects were developed then, their ideas challenged the dominant project management paradigm of the time: detailed upfront planning, rigid processes, and extensive documentation. Instead, they advocated for adaptability, collaboration, customer feedback, and continuous learning. Little did they really understand the concepts of just-in-time, limiting work in progress, eliminating waste, and progressive elaboration with rolling wave planning that were already foundational to project management where practice created a non-existent theory (Rajagopalan, 2014).
Although the adaptive Agile ways eventually spread across industries and continents, its adoption was far from uniform. Some organizations embraced it enthusiastically, while others struggled or rejected it altogether. The reasons were often deeper than process or technology; they were cultural. While later Agile practitioners called any Agile Transformation to be a practice rooted in change management to modify the team and organizational culture, the adoption of culture was an after-thought! It shows the myopic foresight and the lack of diversity of the practitioners which is evident from the second manifesto principle "working software over comprehensive documentation" limiting the agile approaches to software development.
Based on numerous years of research, the Dutch social psychologist Geert Hofstede (1980) provided a comprehensive lens to view the geographical culture across five dimensions first and later adding the sixth dimension. These dimensions include: Power Distance, Individualism, Motivation towards achievement and success (which was originally called Masculinity), Uncertainty Orientation, Long-Term Orientation, and Indulgence (sixth dimension added later). More details of these cultural elements can be found in Hofstede's Culture Factor Group (n.d) or in other scholar-practitioner materials (Hofstede & Bond, 1988; Hofstede, 2011).
In summary, countries with lower power distance often found it easier to embrace self-organizing teams and decentralized decision-making. Cultures with lower uncertainty avoidance were generally more comfortable with experimentation, iterative delivery, and evolving requirements. Highly individualistic societies often adapted quickly to Agile's emphasis on empowered individuals, while collectivist cultures sometimes preferred greater structure and consensus-building. None of these approaches were inherently better or worse, but they influenced how Agile was interpreted, implemented, and sustained. When Agile was promoted because people attended a Scrum Master workshop or gained an online training, little to no attention was given to the cultural considerations of the team members, their hybrid cultures due to long travel, immigration, expatriate arrangements, etc. So, agile didn't fail but we failed agile.
Today, organizations face another transformative wave: Artificial Intelligence. Much like Agile, AI is frequently presented as a universal solution capable of improving productivity, accelerating innovation, and transforming business models. The landscape of existing roles are being refined or even redefined where people have began question why do we need developers as AI tools can write them, why do we testers because tools can help with automation, and why do we need project managers, product owners, or scrum masters because AI can replace them! Organizations and leaders have not yet learned the lessons that Agile adoption taught. Extending these observations, AI adoption will not be determined solely by technological capability. As a popular saying goes, "A fool with a tool is still a fool," successful AI adoption needs people to realign AI ways of working with the individuals and team culture, organizational culture, leadership philosophies, risk tolerance, and societal values. The same cultural dimensions that shaped Agile adoption are likely to shape AI adoption as well.
Consider uncertainty avoidance, which is the orientation to risk. Organizations and societies with high uncertainty avoidance often demand predictability, governance, and clear accountability. Such environments may be slower to adopt generative AI systems whose outputs can be probabilistic and occasionally unpredictable. Conversely, cultures more comfortable with ambiguity may experiment aggressively with AI, accepting occasional failures as part of the learning process. Similarly, power distance influences whether AI is viewed as a democratizing force that empowers employees or as a centralized tool controlled by senior leadership. Long-term oriented cultures may prioritize investments in AI capabilities that take years to mature, while short-term oriented cultures may focus on immediate productivity gains and rapid return on investment.
However, AI introduces a dimension that Agile largely avoided: ethics at scale. While Agile transformed how work was organized, AI has the potential to transform how decisions are made. This shift requires leaders to move beyond questions of efficiency and effectiveness toward questions of responsibility and societal impact. Ethical frameworks such as Utilitarianism ask whether AI creates the greatest good for the greatest number. Deontological perspectives ask whether certain actions remain wrong regardless of outcomes. The Indian concept of Lokasamgraha (Singh & Awasthy, 2023) reminds us that decisions should contribute to the welfare and stability of society as a whole. Likewise, the bioethical principles of beneficence, non-maleficence, justice, and autonomy provide valuable guidance for evaluating AI systems that increasingly influence human lives.
One of the most important lessons from Agile is that transformation cannot simply be imposed through frameworks, certifications, or technology investments. Many organizations adopted ceremonies without embracing values, resulting in what practitioners often call "Agile in name only." AI faces a similar risk. Organizations may deploy chatbots, copilots, and predictive algorithms without addressing trust, transparency, governance, workforce readiness, or ethical safeguards. Just as Agile required cultural adaptation rather than procedural compliance, AI adoption requires organizations to align technology with values, leadership behaviors, and stakeholder expectations.
The future may belong not to the organizations that adopt AI the fastest, but to those that adopt it the wisest. Agile taught us that successful transformation occurs when methods align with culture and when people are empowered to learn and adapt. AI extends that challenge from teams and organizations to society itself. As leaders navigate this next era, the question is not simply whether AI can improve performance. The more enduring question is whether our cultural values, ethical principles, and leadership choices can ensure that AI improves humanity as well.
References
The Culture Factor Group (n.d.) Retrieved from https://www.theculturefactor.com/
Hofstede, G. (1980). Culture's Consequences: International Differences in Work-Related Values. Beverly Hills, CA: Sage.
Hofstede, G. & Bond, M. H. (1988). The Confucius connection: from cultural roots to economic growth. Organizational Dynamics, 16, 4-21.
Hofstede, G. (2011). Dimensionalizing Cultures: The Hofstede Model in Context. Online Readings in Psychology and Culture, 2(1).
Manifesto for Agile Software Development (n.d.). Retrieved from https://agilemanifesto.org/
Rajagopalan, S. (2014). Review of the myths on original software development model. International Journal of Software Engineering & Applications, 5(16), 103-111.
Singh, D. & Awasthy, R. (2023). Lokasamgraha: An indigenous construct for social entrepreneurship. IIMB Management Review, 35(4), 344-358.
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