Agile Meets AI: Chunking, Adaptability, Focus

Agile Meets AI: Chunking, Adaptability, Focus

Today, we'll talk about Agile, AI, and strategy, and what they mean in the context of business analysis and architecture. What's most interesting is that some of the principles that humans use to be agile also apply to AI. Agile methodologies have revolutionized the way we approach project management and software development, emphasizing adaptability, collaboration, and delivering value to customers. Interestingly, many of these Agile principles also apply to artificial intelligence (AI), underscoring the synergy between human adaptability and machine learning. In this post, we'll explore how Agile and AI intersect and complement each other, offering a comprehensive understanding of their impact on modern business practices. Let's get started.Let's get started.

What is Agile?

At its core, it is about delivering work in small, consumable chunks that allow for regular feedback and course corrections. It is a comprehensive approach in which cross-functional teams collaborate in a transparent and communicative environment to achieve a common goal. This methodology not only speeds up delivery, it also improves the relevance and quality of the final product or service.

"We are uncovering better ways of developing software
by doing it and helping others to do it."
- The Agile Manifesto

The Three Horizons

Understanding the three horizons of Agile business analysis is similar to moving through different layers of a complex ecosystem, each with its own focus and contribution to the overall goal of delivering value quickly and effectively.

Strategic Horizon

The Strategy Horizon focuses on the long game. Here, we focus on the future, creating a vision that guides every decision we make. It is the realm in which we define our goals and the paths that will lead us there. This horizon is about understanding the market, identifying opportunities, and establishing goals that are consistent with our company's mission and values.

In this horizon, we are considering not only the immediate next steps, but also the potential impact of our decisions years from now. It's a balancing act between innovation and practicality, ensuring that we're not just following trends but also creating long-term value for our customers and stakeholders.

"Strategy is about making choices;
it is about deliberately choosing to be different."
- Michael E. Porter

Initiative Horizon

Moving on to the Initiative Horizon, we shift our focus from what we want to achieve to how we plan to get there. This is the planning stage, in which we prioritise projects and initiatives that will help us achieve our strategic goals. It is about turning high-level goals into practical plans.

In this middle ground, we identify unmet customer needs and develop solutions to address them. We're also constantly evaluating our initiatives, ready to pivot or refocus our efforts in response to changing market conditions or feedback. This agility is critical to remaining relevant and competitive.

Delivery Horizon

Finally, at the Delivery Horizon, we're right in the thick of it. This is where strategies and initiatives become measurable results. We break down large projects into smaller, more manageable tasks and approach them with precision and focus. This horizon is distinguished by fast iterations, continuous feedback loops, and incremental improvements.

It's all about execution here: ensuring that every task, sprint, and release contributes to something that not only meets, but exceeds, customer expectations. This mindset shines brightest during this hands-on, detail-oriented phase, when teams work closely together to deliver high-quality results quickly and efficiently.

AI Chunking

In agile we talking about breaking things down into smaller parts to process. Actually we do this in AI too, it is called "chunking". This chunking allows AI to stay focused on a small set of data and avoid hallucinating. 

Chunking in the context of artificial intelligence (AI) and knowledge management refers to the process of breaking down large volumes of complex information into smaller, more manageable units. This technique is essential for improving information processing, comprehension, and retrieval, both for human users and AI systems.

Key Aspects of Chunking in AI

  1. Structured Data: Chunking enhances efficiency and organization by segmenting data into logical units.
  2. Unstructured Data: This includes texts, images, or videos, which require sophisticated approaches like natural language processing (NLP) for text or image recognition algorithms for visual data. Identifying natural or logical breakpoints, such as chapters, paragraphs, or sentences in a text, is crucial for effective chunking.

Practical Implementation

To implement chunking effectively within an organization, especially for enterprise knowledge management and AI applications, it is important to:

  • Identify natural breakpoints in the data.
  • Use appropriate algorithms and techniques for different types of data (e.g., NLP for text, image recognition for visual data).
  • Ensure that chunks are semantically relevant and manageable for AI systems to process and analyze.

In summary, chunking is a pivotal strategy in AI and knowledge management, enhancing the usability and accessibility of data, improving AI performance, and facilitating efficient information processing and retrieval.

"Intelligence is the ability to adapt to change."
- Stephen Hawking

What does it mean to be agile?

Daniel Lambert's book, "Practical Guide to Agile Strategy Execution," emphasises the importance of equipping oneself with knowledge and skills to navigate the complexities of business transformation. Lambert emphasises that there is no one-size-fits-all solution, but having the discipline and corporate logic to carry out strategies is critical. Training helps you develop these skills, allowing you to design, architect, prioritise, and deliver successful projects.

Training in these methodologies can help bridge the gap between traditional business roles and the changing digital landscape. As Lambert suggests, as digital technologies and artificial intelligence (AI) become more integrated into business operations, it is critical to align strategies, people, capabilities, data, initiatives, processes, and technology. A well-structured training programme introduces you to these concepts in an understandable manner, allowing for a more seamless transition to customer-driven, agile enterprises.

Key Steps to Improve your Agile Approach

  1. Consider receiving formal training: Taking courses like the Agile Master Class from Agora Insights will help you understand the principles and how to apply them effectively in your organisation.
  2. Understand the various methodologies used in an Agile environment; remember that being agile and running an agile process are not the same thing. 
  3. Understand how you can be agile in your organisation. Daniel Lambert's book is an excellent starting point for understanding the value of agility.
  4. Engage with the business community.
  5. Become certified. Many bodies of knowledge provide certification, including the IIBA.


"Rule: Continuously learning is a minimum requirement
for success in any field."
- Brian Tracey on X

In conclusion, the relationship between Agile and AI is both insightful and practical. Both Agile and AI emphasize breaking down complex tasks into smaller, manageable parts—Agile through iterative development and AI through chunking. This similarity highlights the importance of adaptability and continuous improvement in both fields. By leveraging Agile principles, businesses can effectively integrate AI into their operations, ensuring that AI systems are not only efficient but also aligned with strategic goals. Embracing Agile methodologies and AI technologies together can lead to more innovative, responsive, and customer-focused business practices.

"Happy learning, everyone."
- Deirdre Caren

Don't forget to visit Agora Insights AAC Master Class today







Post sponsored by Agora Insights Ltd 

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