🔗https://www.wissen.com/blog/from-agile-to-platforms-why-genai-demands-a-new-cio-playbook
1. Agile for the Cloud Era: A Solid Foundation
For years, agile methodologies have powered the cloud revolution. Enterprises adopted agile to deliver faster, collaborate better, and prioritize customer-centric development. Key practices included:
Frequent feedback loops
Short release cycles
Transparent teamwork
Iterative improvements
This approach reshaped development, especially with the rise of cloud-native architectures. However, as transformative as agile has been, it’s beginning to show its limits in the GenAI era.
2. Enter GenAI: A Disruption Like No Other
Generative AI isn’t just another tech trend—it’s redefining how we build and operate software. By late 2022, GenAI had begun reshaping industries from finance to healthcare. By 2024, it attracted $33.9 billion in private investment, underlining its potential.
Suddenly, AI wasn’t just augmenting workflows—it was becoming the core engine of innovation, pushing agile to evolve yet again.
3. Code That Writes Code
We are now in the age where AI generates AI. Google, for example, reports that 25% of its code is written by AI. This shift means engineering teams are no longer the sole authors of code; they’re now also orchestrators of machine-generated code.
This fundamental shift calls for:
Rethinking quality assurance
Managing AI-driven outputs
Creating development environments that can adapt to this new dynamic
Agile, in its traditional form, isn’t equipped for these challenges. A new structure is needed.
4. Why Platform Thinking Wins in the GenAI Age
To manage the complexity of GenAI systems, a platform-centric architecture is far superior to application-centric models. Why?
It unifies architecture, streamlining data, operations, and resources
It improves security and governance
It allows modular upgrades—perfect for fast-evolving AI models
It reduces friction between components, making it easier to integrate new GenAI capabilities
With GenAI, isolated development is no longer sustainable. Platforms are the new battleground.
5. Solving the LLM Puzzle: The Product Engineering Mindset
GenAI runs on large language models (LLMs)—and managing them is nothing like managing conventional software. LLMs:
Are constantly evolving
Can be unpredictably wrong
Rely heavily on the quality and scope of training data
Because of this, companies need to stop thinking in terms of projects and start thinking in terms of products. This mindset:
Encourages long-term ownership
Aligns teams around outcomes, not outputs
Enables constant iteration and monitoring of LLMs
It’s the only way to build GenAI platforms that are resilient, accurate, and ethical.
6. The Real Benefits of a Product Engineering Mindset
Adopting this mindset leads to:
LLM viability — Focus on real-world usability and user-centric outcomes
Continuous improvement — Constant performance tracking and refinement
Cross-functional collaboration — Engineers, domain experts, and product managers working in sync
Enterprise alignment — Better alignment between tech and business goals
This mindset is critical for CIOs who want to lead—not follow—this next digital transformation.
7. FAQ — Common CIO Questions
What is the platform approach?A way to consolidate digital services into modular, unified systems that scale and evolve easily.
Why is AI development different?It’s more complex, due to LLM modeling, training, governance, and unpredictability.
How does GenAI impact development?It accelerates feature creation, enhances productivity, and reshapes traditional coding roles.
8. Final Thought
Agile helped us transition to the cloud. But now, GenAI demands a platform-centric, product-driven strategy. The traditional CIO playbook needs a rewrite. To succeed, enterprises must embrace platforms, invest in LLM infrastructure, and cultivate a product engineering DNA that supports AI at scale.