Basics to setting up an AI Transformation Program - High level

Overview: Many large organisations have begun their Artificial Intelligence (AI) journeys, but some have taken unusual directions, while others are adopting a wait-and-see approach.  As a general rule, I believe most organisations should attempt to identify the most important use cases for AI and, using a basic scoring system, prioritise the easy, high-value use cases first. 

A good option is to hire an AI-focused team to implement the AI program within your business.  Hire people from the company, as they possess key domain knowledge, and pair them with IT experts, preferably those with strong AI skills.  

Tip: All Digital Transformation Projects must cover People, Process and Technology in that order of importance.

Thought: The two biggest mistakes I have observed as of July 2025 in clients are a lack of clearly defined benefits of AI projects and poor-quality, insecure data.

1. AI Idea Generation: Collate a detailed list of possible ideas.  I like to use SharePoint, it's a good idea to open ideas up to the business or make gamification of idea generation to get a good set of ideas.  You'll get lots of overlap, distil it into unique ideas, and bring the team/stakeholders together.

Figure 1. AI idea filter funnel

2. Business Impact: How does this help our customers or generate revenue, or does this improve our operations?  Can this be quantified?

3. Technological Feasibility: Can technology meet the requirement, and at what cost?  A high-level technical design is strongly recommended, as illustrated in Figure 2 below.

4. Implement: If the idea holds up to Business impact, and is possible, select the highest value to lowest effort ideas.  It is a good idea to start with the simpler ideas to get the ball rolling.

Figure 2. HLD example

Note:  This post is looking at building AI to improve your business. AI also affects all internal staff, so train them.  Allow them to use AI tools like ChatGPT, and train them not to expose internal proprietary information.  Give your users the correct tools to be the most effective.

AI Digitalisation Transformation Checklist:

1. Data Governance Framework: Do you know what data you have, can you access it, and is it secure?
2. Enterprise Data Model: Define the rules for quality and integration between the EDM areas (these may be functional or geographic), ensuring we share data and make it available.
3. Master Data Management: Ensure the data and applications are working off the same sheet of music.  
4. AI Framework: What are the limitations on what you can do within business governance? How will you identify projects and prioritise them? How do you ensure alignment with the tools?  AI is expensive; don't just start picking our individual solutions.  I'd also call this 'measure twice, cut once.'
5. Don't eat the whole Elephant: Pick small projects to start with, clearly defined goals that show the wins from AI.
6. One Process: When the AI solution/project is built, will you still use the old way? It's expensive to create, document, and support multiple processes over the lifetime of solutions that have duplicate steps to accomplish the same task. Make your process simple and the preferred or only approach.
7. People: Have they checked the process? Is it optimised and not merely converted from the old paper process to digital?  Have you engaged key stakeholders and experts?  We should have asked if this is the best process, but also verified it with non-technical stakeholders.
8. Cost Optimisation and Value Realisation: AI-based transformations like Software development or engineering projects are about whole-life cost and value, ensure you are not just looking at building something.
9. Measurements and KPI's: How do we measure value? Are you monitoring usage and costs? They tend to be the easier measurements.  Security is also essential to measure, as well as what constitutes success.  Many benefits are intangible, so list them out. Can a value be guesstimated for intangibles?

I'm sure there are plenty more items, but this would be a good check to run repeatedly.

An brief introduction with two demos on OpenAI

OpenAI has a couple of service such as ChatGPT, and DALL-E.  The recording below, shows two a demos: 

• ChatGPT to gain insight, and
•  DALL-E to generate some artwork.

https://youtu.be/TdGjp171wAk - 3min 48 seconds

There are other suppliers of large scale AI engines such as Googles PaLM 2.

Updated Dec 2023: 

I created an Azure Open AI Service instance.  Very easy to access using the API's and nice to play with.  There is also the playground, and I generated 8 images of a train emerging from a tunnel in eight different artistic styles.  The hardest part of AI and DALL-E is framing the Prompt/Question.

OpenAI Studio on Azure AI using the DALL-E playground.

Using the OpenAI PowerFx language helper leading into building a Tax chatbot using Power Apps (No model training)

I had some time today, and I've been hearing a lot about OpenAI/GPT-3, so I thought I'd have a look around and wow it's good.  There are so many uses and it's just easy to use.

For instance, Power Apps uses PowerFx which is a simple language that Microsoft had added the "Ideas" are to that helps you build PowerFx code.  Honestly I found it extremely irritating when it was released recently but after reading more on Open AI I to take a closer look and it definitely is a great idea.

Thoughts:  I don't memories code so I feel this is a great tool for citizen and professional developers.  I think it will get a lot of attention and improve over time and become a key part of PowerFX and app development.

More Info:

https://learn.microsoft.com/en-us/power-apps/maker/canvas-apps/power-apps-ideas

OpenAI - Took less than an hour to setup the demo

The Power Platform has a Premium Connector to use the OpenAI API's.  Robin Rosengrun has a demo and built and published a connector. 

I spent some time playing around with the OpenAI GPT-3 API using the "davinci-002" engine in a Power App connector.    

First search data result is correct

This is completely wrong, although HMRC have amended the go live date many times.  I was hoping for "6 April 2024"

The third question result is good - but could be better.