How Can the Fall of Agile Guide Us in Navigating Gen AI?
Agile’s decline is a natural evolution, not a failure. Organizations are maturing, shifting from rigid Agile frameworks to a fluid understanding of agility. Agile is transforming, embedding agility into the DNA of successful enterprises. As we moved past bulk-hiring Lean and CMM coaches, we’re now evolving beyond Agile coaches. Leaders must apply these lessons to Gen AI: avoid hype traps, focus on genuine value, and ensure continuous improvement. This will help harness Gen AI’s potential without repeating Agile’s pitfalls.
Agile methodologies have dominated the project management landscape for the past few years, promising flexibility, faster delivery, and closer alignment with customer needs. Once seen as the revolutionary approach that would transform organizational workflows, Agile is subject to much criticism these days.
Like many other trends that come and go, Agile’s luster is fading.
Everything has its time. Methodologies like the Theory of Constraints made famous by Eliyahu Goldrat’s The Goal (first published in 1984), Lean made famous by the whole Toyota revolution, PMBOK, and CMM approaches, all had their hay days of widespread popularity, and seemed — to be the only way forward. Agile is going through a similar cycle.
These topics remain relevant and valuable but have become common foundational knowledge. Agile enjoyed a good run for 10–15 years, and now the industry is naturally evolving.
As we delve into the reasons behind Agile’s decline in popularity, we uncover valuable lessons for leaders, particularly in the emerging trend: Generative AI (Gen AI).
The parallels between Agile’s waning appeal and the potential pitfalls of Gen AI implementation are striking, making these insights crucial for leaders to navigate this new and emerging landscape.
Avoid Over-prescription and Misapplication
Agile’s decline in appeal is primarily due to its misapplication across industries, a cautionary tale for Gen AI implementations. Coming out of software development with the release of the Agile Manifesto in 2001, Agile principles have been stretched into various domains where they don’t quite fit. This misalignment has led to frustration and disillusionment as teams grapple with applying frameworks like Scrum or Kanban in contexts that demand different approaches. I have seen Learning and development teams start calling their monthly training plans sprint-based with no change in their approach. The change was done primarily to meet management demands of everyone to be “agile.” These changes are so unnecessary and wasteful. This is not to say Agile cannot be made to work in those domains, but the right application, contextualization, and adaptation are necessary.
Lesson for Gen AI:
The key is to avoid a one-size-fits-all approach. Instead, identify the areas where Gen AI can genuinely enhance your business and where traditional methods may still be more effective. This discernment is crucial for successful Gen AI implementation.
For example, Teams that do not have basic automation of their Excel-based processes using standard tools like Power BI or others are suddenly hoping that the O365 copilot will be the magical pill for all their worries.
It won’t. You may get better results by starting with Power BI.
Avoid Ritualization and Templatization
Agile aims to provide flexibility and adaptability.
Ironically, it has become rigid and ritualized in many organizations. Daily stand-ups, sprints, and retrospectives often feel more like obligatory chores than meaningful practices.
When the focus shifts from outcomes to strict adherence to process, the spirit of Agile is lost, and teams go through the motions without reaping the intended benefits.
Lesson for Gen AI:
Focus on outcomes rather than rigidly adhering to popular AI tools and options. Evaluate your use cases well and ensure that the integration of Gen AI remains flexible and adaptable to actual business needs.
The key is answering the fundamental question of why we are doing this in the first place. Only join the bandwagon if you can answer the why.
Build Solutions that Scale and Keep them Simple
Scaling Agile practices across large organizations has proven to be a significant challenge. Frameworks like SAFe (Scaled Agile Framework) and LeSS (Large Scale Scrum) add complexity and bureaucracy, contradicting Agile’s core principles. This is not to say that these frameworks are inadequate.
Still, blind adaptation without the proper contextualization and adaptation creates a significant challenge for adoption. As a result, many large enterprises never really scale agile successfully. The resulting end state could be better than where it started because of the overcomplications.
Lesson for Gen AI:
Be cautious of over-complicating Gen AI implementations. Strive for scalable solutions that maintain simplicity and effectiveness. Stick to the fundamentals. Make progress step by step, iteratively, and incrementally.
Prioritize Knowledge and Capability Building Over Certifications
The proliferation of Agile training and certification programs has led to a commoditization of Agile skills. Certificates often emphasize theoretical concepts over practical application, resulting in a workforce that may be certified but not necessarily competent. This industrialization has diluted the essence of Agile, making it more about credentials than real-world effectiveness.
Lesson for Gen AI:
Be wary of over-reliance on certifications and training programs that promise quick expertise. Focus on building deep, practical knowledge and skills within your team.
This pattern is already observed in GenAI, and many training and certifications are mushrooming. This anti-pattern is mainly observed in mid-management and senior leadership training, which is relatively superficial and has few practical insights.
Training is essential and has its utility, but to create value, it needs to go deeper so that people understand enough to apply the concepts in their day-to-day lives and not just pick up terminology to show off their so-called knowledge.
Business Outcomes not, Terminology Transformations
While there is fatigue over agile practices, it is not like businesses have lost their pursuit of agility. Enterprises are moving towards a broader understanding of agility that transcends specific methodologies. True business agility is about being adaptive and responsive to change, not just following a prescribed set of practices. This shift in mindset prioritizes outcomes over processes and emphasizes the ability to pivot quickly in response to market demands.
Lesson for Gen AI:
There is a big lesson for GenAI adoption here. Do not base your GenAI adoptions only based on the most hyped tool in the market or what your competitors or peers are doing.
Spend time and effort to understand your context and use what makes sense in your context and will add value.
Keep your focus on outcomes.
Know that the old and the new live side by side
In response to Agile’s limitations, many organizations are adopting hybrid models that combine the best aspects of Agile with other methodologies.
For instance, Water-Agile-Fall (a blend of Waterfall and Agile) allows for a more structured approach where needed while still embracing iterative development and continuous feedback. These hybrid models offer a more tailored approach better suited to the specific needs of different projects and teams.
With Agile, this was most observed with the contracting models, where despite fully agreeing to the exploratory nature of agile approaches, businesses still hesitated to move their contracting strategies entirely away from the tried and tested fixed cost and schedule because of other business compulsions.
Lesson for Gen AI:
The world will not suddenly move to start leveraging all the benefits of GenAI overnight. Many other technologies and approaches will live side by side to address several regulatory and social considerations.
Be aware of that and take cognizance before throwing all your eggs in the GenAI basket and making huge investments.
Foster a culture of Continuous Learning and Improvement
Agile isn’t dead; it’s evolving.
The principles of continuous learning and improvement, which are at the heart of Agile, remain crucial. However, they are now integrated into broader frameworks emphasizing organizational agility. This includes fostering a culture of innovation, encouraging experimentation, and continuously iterating based on feedback. This emphasis on continuous learning and improvement is also key to staying ahead in the ever-evolving world of Gen AI.
Lesson for Gen AI:
The journey towards successful Gen AI implementation is continuous and long. There is no overnight success here. Foster a culture of learning and improvement, encouraging your team to experiment with Gen AI and iterate based on real-world feedback.
This commitment to continuous improvement is the key to harnessing Gen AI’s full potential.
Conclusion
Agile may no longer be the cool kid on the block. Still, its core values — collaboration, flexibility, and customer focus — are more critical than ever. As organizations navigate the complexities of the modern business landscape, they realize that true agility comes from a mindset, not a methodology. By embracing this broader perspective, businesses can stay adaptable and resilient, ready to meet the challenges of an ever-changing world.
Agile’s decline in popularity is not a failure but a natural evolution. The shift from rigid adherence to Agile frameworks to a more fluid understanding of agility reflects the growing maturity of organizations in their pursuit of excellence. Agile isn’t dead; it’s transforming, paving the way for a future where agility is embedded in the DNA of every successful enterprise. Just as the industry evolved past the bulk-hiring of Lean or CMM coaches, it is now evolving past Agile coaches, adapting to meet the current demands with continuously redefined and refined roles.
For leaders, the key takeaway is to apply these lessons to the Gen AI trend.
Avoid the hype cycle traps, focus on genuine value creation, and ensure your organization remains flexible, adaptive, and focused on continuous improvement. This approach will help harness the true potential of Gen AI without falling into the same pitfalls that led to Agile’s decline.
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