Happy New Year! Welcome to 2025.
The first edition of 2025 is here, and while writing this one, everyone is going through the ritual of making New Year’s resolutions and then forgetting it. On the other hand, you have seen almost all the leadership gurus sending you emails about how to make New Year resolutions and how to stick to them. You must have a lot of knowledge by now.
I want to leave my learning in this area, and I will leave some of the actions I am following. You can also take some tips that may help you.
Over the last 2 years, I have been following a new technique, which is helping me out.
But before I share this technique, let me also share with you that before January 2023, these were my stats on the success of New Year resolutions, published over here:
“And it turned out that only 30% of the New Year’s resolutions I had made were fulfilled, and some of them I had pushed to the next year over the previous four years.”
This new technique, which I took from ‘The rule of building small habits, or “atomic habits,” as defined by James Clear, is one of the processes I have followed over the last two years. “Rather than making big promises and writing resolutions, you should begin with a small action.”
It is helping me a lot, and my success rate has increased to over 70%, so I want to leave to you the lessons I learned from the latest edition of Sahil Bloom’s Curiosity Chronicle to have attention focus on those things that are in your control and forget the rest with the small actions to achieve and move forward.
The Number 1 question everyone asks for Generative AI?
What question comes to mind if you talk to anyone in the business realm about AI specifically for Generative AI?
The most sought-after question is: What is the use case of Generative AI?
Everyone person asks you this question, whoever you talk to, and then the discussion moves to the use case itself and its viability or not;
suddenly, all the powerful capabilities of these Large Language Models (LLMs) are behind the scenes, and you will spend countless times convincing/talking about the use case.
This question is undoubtedly essential; without it, Generative AI will not exist in businesses, and it won’t be easy to get the organization’s budget to adopt it.
However, we must remember that Generative is a transformative technology, and you must be willing to explore and research its efficiency in your business context.
There are two ways of looking into this.
The first one is to only go behind the hunting of the Generative AI use case and then open up something to do with it.
The second way is to look up how to bring the technology capabilities into the organization and then map it to your organizational ecosystem and where you can utilize it.
I thought of exploring it in more depth; below is a comparison of two common strategies for introducing Generative AI into an organization
- chasing specific use cases first and
- building foundational capabilities and then finding suitable applications.
Both approaches have merits, and a pragmatic call to action often combines them in some way.
1 – Use Case–Driven Approach
This approach identifies discrete, high-impact, generative AI use cases (e.g., automated customer service chatbots, content generation, or document summarization). You drive adoption in the organization by selecting a potential use case and then building or procuring the capabilities to address that need.
Merits:
- Tangible ROI: You can quickly measure value in a specific business scenario.
- Fast Prototyping: Narrow scope enables quick pilots and reduced time to market.
- Clear Business Alignment: Stakeholders see immediate relevance, boosting buy-in.
- Lower Initial Investment: Fewer large-scale infrastructure or Gen AI services costs if you only stand up what’s needed for that specific use case.
Potential Drawback:
- Siloed Solutions: Focusing only on isolated use cases can lead to fragmented technologies, duplicating efforts later.
- Lack of Long-Term Vision: The overall strategy may stagnate if each new AI use case re-invents the wheel.
2 – Capability–Driven Approach
In this strategy, you must first invest in the availability of LLM capabilities, talent, and processes before identifying specific AI use cases. Then, these capabilities will be mapped to various departments or processes, and use cases will be arrived at.
Merits
- Future-Proof Foundation: A robust data and Generative AI infrastructure is more straightforward to scale for any new use case.
- Reusable Assets: Shared platforms (e.g., LLMs infrastructure, pipelines, prompt engineering frameworks) reduce duplication.
- Strategic Alignment: Integrates generative AI into long-term business and technology roadmaps.
- Ecosystem Coherence: Encourages consistent standards and best practices across the organization.
Potential Drawback:
- Delayed ROI: Building core capabilities is costlier and slower to show direct returns.
- Risk of Over-Engineering: If not guided by near-term business needs, organizations risk creating solutions that aren’t used.
Call for Action:
You can adapt generative AI effectively in an Organization by balancing immediate use-case wins with a broader, capability-driven vision.
The first path (use-case first) delivers quick success and proves value;
The second path (capability first) ensures sustainable growth and consistency.
An integrated approach, starting with a clear, high-impact pilot and building a robust foundation, often yields the best of both worlds.
