Analect

On Developing an AI Strategy

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You’ve been tasked with developing an AI strategy for your business …

… and you’ve read about foundation models …

… and you’ve heard there are software solutions out there …

… that might get you started on that journey.

As you research the field, it seems that AI tooling is just a subset of Machine Learning tooling.

Soon, you’re overwhelmed.

So we’ve put together a blog-series to get you started, instead.

A foundation model, also known as large X model (LxM),
is a machine learning or deep learning model that is
trained on vast datasets so it can be applied across a
wide range of use cases.

Generative AI applications like Large Language Models
are often examples of foundation models.

Building foundation models is often highly resource-intensive,
with the most advanced models costing hundreds of millions of
dollars to cover the expenses of acquiring, curating, and
processing massive datasets, as well as the compute power
required for training.

 …

https://en.wikipedia.org/wiki/Foundation_model



AI Strategy Series

This is a collection of five blog-posts intended to introduce the reader to how they may begin to think about introducing AI or machine learning to their business. It is deliberately not diving into the technologies. Rather, it is a distillation of my own thinking as well as readings from elsewhere that I have found valuable in evolving a better understanding of what’s needed to succeed, ancillary to the tooling and technologies.

  1. Framing an AI Strategy: Where Do You Start?
Even if the term AI is flawed for its imprecision, the shift in thinking that it has unleashed, in terms of acknowledging digitalisation as table stakes for having a competitive edge in any business, upon which to layer these emerging technologies, can’t be disputed.
Colum McCoole
Feb 3, 2025

  1. AI is Here, But its Business-Applicability may not be Obvious
Even if a business has a highly-capable IT function, it may only have a handful of specialists experimenting with AI technologies. How do you scale that? Even if you have built valuable AI-driven prototypes (or are even in-production), getting customers or business users to adopt that solution as…
Colum McCoole
Feb 10, 2025

  1. Platform Engineering for Evolving AI / ML Solutions
To do machine-learning well requires rigour. Models trained on data are only as good as the data they are trained on and that data is changing, rendering older models useless. That’s where platforms come in. They contain the foundational blocks on which the process of MLOps gets performed. Clearly…
Colum McCoole
Feb 14, 2025

  1. DataOps: Emerging powerful modular tooling
Although many data scientists are eager to build and tune ML models, the reality is an estimated 70% to 80% of their time is spent toiling in the bottom three parts of the hierarchy — gathering data, cleaning data, processing data.
Colum McCoole
Feb 28, 2025

  1. LLMs: Key Emerging Components of the AI Tech Stack
LLMs have become progressively more capable and exponentially cheaper over the past two years. While they need careful curation when deployed as part of an overall business solution, LLMs have the potential to bring significant value to a business.
Colum McCoole
Mar 7, 2025

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About Analect

I have been working at the intersection of finance and technology most of my career. While all my roles in finance involved leveraging technology, I focused on deepening my technical knowledge with my own start-up, Analect, formed in 2012. My motivation at the time, as a financial analyst, was to bring some of the compelling open-source tooling in the scientific community to the finance domain. More recently, my focus has been on cloud-based platform engineering for machine-learning use-cases (MLOps). Direct hands-on experience with these systems is a necessary part of being able to articulate ideas and strategy around how they can be leveraged within a financial services domain but also other industry domains too. Let’s collaborate together.