HLD Architecture Designs for Finding SharePoint File data

 SharePoint data source using Microsoft Foundry to build an Agent

AI for developers and Architects

The cost of prototypes is unbelievably low using AI. 

Rapidly creating a prototype, especially with new or less well-known technology, is where I derive significant benefits from AI.

How to build application prototypes?

1. Write /reverse prompt/Adjust instructions into md file
2. Agentic AI (specialising in Doc Extraction) to extract and refine from md file
3. Run using IDE-based copilot (VS Code with GitHub Copilot) (AmazonQ) (Cursor, Windsurf, Steamlit) 

Thoughts: Developers are adjusting to using Ai to support software solutions.  The developer role will continue the trend of making technical implementation more accessible, allowing knowledgeable IT engineers or domain experts to build faster and better than citizen/amateur developers.  Ai assists in complex decisions!  

What needs to improve?

• Knowledge is key.  AI needs to have narrow expertise at the right time. i.e. only domain knowledge, not influenced by other data.  Quality of input data used to train.  Allows for dynamic reasoning.
• Session/long-term contact agreement/understanding to improve the understanding between your IDE and me.  Remember how I prompt and provide feedback on how I digest information.  Context between the human developer and Ai is Paramount.
• Control of IDE integration with coding copilots, clear return to the user developer to make better decisions.  Context is Paramount.
• Governance & Data (Connectors, API's, code complex processes (MCP maybe), quality of data).

Retrieval Augmentation Generate (RAG)

AI needs to be able to connect to my existing Tool Landscape:
I use Azure, C#, Playwright, and GitLab.  I want my IDE to work with these tools and many more.  MCP servers publish their functionality, and I can connect my Copilot/Agent to use multiple MCP servers.  This is what GHCP does for VS Code, allowing you to add MCP clients dynamically to use existing MCP Servers. 

Model Context Protocol (MCP) MCP is a protocol (created by Anthropic) that allows an MCP client to connect to an MCP server, which in turn provides specialist knowledge. Authentication should use OAuth to secure access. My Applications/Agents use the MCP to ask the MCP Server, 'What can you do?' so they know how to use it.  The MCP server allows you to interact with a system.  It is often referred to as the "Arms and Legs" of AI. The MCP Server, when built, informs the client of its capabilities and then performs actions, such as updates, via an API.   Summary: Use MCP to enable the client to communicate with other resources/tools. NB: An Agent can utilise multiple MCP Servers.

Agents-to-agent (A2A) 

A2A allows agents to work together.  So two agents can leverage each other; the other agent solves the issue and returns the answer for the first agent to use.  Whereas MCP allows any agent to speak to a source.  Agents complete a task and give it back to the calling agent. 

 

Summary: Use A2A to talk to specialised Agents, and the agent returns the calling agent's answers.

Artificial Intelligence as a mega trend

Overview of AI

The world has gone mad for AI.  Constantly, I see overhype and poor messaging leading to a misunderstanding of potential.  

AI is not new; the ability to use it for commercial gain is new at the scale we now have.  AI excels at helping us identify patterns, gather information, and is primarily a decision support system (DSS).

AI is not intelligent, but rather good at making complex decisions, and it has biases that we teach it.

This means AI is useful for specialisation, not a generalisation of "smartness", now that ChatGPT et al. are wide-ranging, people are assuming it is a general-purpose tool.  Actually, ChatGPT is a specialist in breaking down and grouping language based on a data source.  For those in the technology industry, we pretty much know that ChatGPT is a good Google (search engine).  

So, what is AI going to be successful at?  Well, this is my prediction:

AI will have a massive impact on many industries:

1. Healthcare - guess what? More surgeons and people will be needed, not fewer.  Here, I focus on Healthcare examples.  People need to interact with others; avatars are a joke. I can talk to Alexa already.  There is very little to nothing in this space except for snake oil salesmen.  Please prove me wrong! More skilled people are needed.

2. Software Development/IT - This is a significant one.  Programmers' roles will change significantly; those with a good understanding and knowledge will thrive, while those with superficial knowledge and a lack of ability to truly understand and work through challenges will likely disappear.  Technologists will focus on challenging problems and introduce significant improvements to all business processes.  The amount will continue to grow.  There is not a lot of agentic, "smart AI" in the space, and we are 50 years away from this, imo.

3. Manufacturing - it won't make the impact that the media says it will.  We are good at manufacturing.  The sub-functions that will benefit include machine maintenance, sensor usage, and performance/behaviour will change.  This will allow you to improve Machine Maintenance (MM) and scheduling.  Think of railway lines; they need to be shut down, and it costs millions to trim hedges. Imagine now that you know the level crossing "lifty uppy-doowny" box/bar is showing signs of fatigue.  Shift the fix left and save the unscheduled breakdown; the train line and knock-on effects shall result in massive improvement.  We are already proficient in manufacturing and, to some extent, automation. If the AI is not significantly better, it is not worthwhile. More skilled people are needed.  

Machine Maintenance in Manufacturing.  AI is needed to mature MM. 

Techniques such as defect detection are already well-established using Visual AI at the micron level.  Rubbish detection.  Using AI will be beneficial - sure, it will become cheaper and easier to acquire this system capability for less, but AI is merely an enabler, and it has been available for well over a decade.  More skilled people are needed.

4. Service Industry - Robots serving people, please, it's mad, except at MacyD's (McDonald's) and honestly, minimum wage workers are pretty efficient there, and it will be too sterile.  Pushing out patties, well, if you need AI for this, you don't know what AI tries to do.  AI & automation are already in the processing and packaging processes.  The big stuff with AI will be in social media and advertising (and don't get me started there, automated advertising will absolutely fail. We need to invent a missile to destroy non-human posts).  More people will be required in these new and changed services.  

Analogy:
1. Old technology: Hand weaving material was a big profitable business in Britain; along came looms; these workers got upset and broke the looms and ended up in prison or broken; these were the Luddites (who refused to embrace technology).  The Luddites ended up broke, and all this could have been avoided by embracing technology, as they were the most knowledgeable about materials and production. They are the natural experts.   

2. Trend jumpers on: Too many companies wanted to build looms, and a handful of players did brilliantly and still exist today.  Think Microsoft and AWS; they are transitioning from being programming technology companies to AI technology.  They still solve the same problem of process improvement.  The weavers who decided to go into building and repairing looms did exceptionally well, but ultimately ran out of demand, and their prices were driven down as there was an excess of supply.  Still a good change.  Many people also got hurt here. Be careful inventing new technology in processes; you get it right, you are a hero; get it wrong, go find a new job.  Lots of sales silver bullets are being produced.  There are tons of "AI experts", but mostly this is absolute rubbish.  With rare exceptions, you are not an AI expert unless AI was in your job description more than 5 years ago.  Beware the snake oil salesmen, nowadays they come in many forms, sizes and shapes :)

3. Embrace change:  Normal, common-sense (smart) people realised they actually had 4 options:

1. Learn how to use a loom.  Use the technology available and use it to build garments faster;
2. Build looms and support the loom business;
3. Do nothing, continue to offer hand-weaving labour to the market.  So take your pension and hope like hell you win the lottery (I'm still backing this option for myself); or
4. Expert hand craftsmen or women :) Become the best hand weaver in the world, and people pay you for your expertise; these people's descendants/business still exist.  But big surprise: it's hard, it takes a long time, it's unlikely to make you rich.,, So, sure, go do this if you are a genius in your field and love it, but don't die of surprise when you go broke or don't get the return you deserve for all that hard work.

Summary: Embrace technology and AI, it is only a decision support system.  More skilled people are needed, as you have the background, being professional and embracing change means you are more in demand.  Sitting on your backside waiting for the lottery means you are like 90% of people, and you'll get 2 jet skis and a new husband! yipee.

Healthcare

Good Use Case: Diagnostic medicine

Diagnostic medicine has become the centre of healthcare, and the ability to use AI, which is better at detecting abnormalities than the best radiologist using a single trained model, yields results in near real-time.  This means that consultant radiologists and specialists can receive reports in seconds that are of unparalleled quality.  GPs have the best guess within seconds, rather than well... we all know this.

AI also provides probability, so it's easy to prioritise any reporting that's life-threatening to a specialist, allowing them to focus on the most challenging work and receive the in-depth information provided by the AI.  

This is possible because we are dealing with a relatively narrow field of data that we have taught AI to handle. Think of X-rays; the results are far superior to an expensive resource (a Radiologist) that takes at least 12 years to train.  And more to get brilliant.

Should we stop training Radiologists and diagnosticians and allocate our resources to AI?  Absolutely not!!   

Radiologists should be using the AI reports, validating, using the info and extrapolating, when an issue is detected, this must be added back into the learning model resulting in improving the AI.   AI should not act. It must only be used to support.  Acting should be restricted to notifying relying parties such as GPs.  

Good Use case: Online GP Appointments and triage

If you have an issue, you go onto an NHS app that will ask you for your symptoms and ask a few follow-up questions.  It will only give you its best guess (this is already amazing imo.), this in turn, will triage your call into "go to your emergency department, they know you are coming", "let's book you an emergency appointment", or "this is my recommendation, and why".  Dr Google actually becomes useful (weird medical insider joke).  Honestly, we could do so much more, but care is given to the right people, "shift-left" (the sooner you catch it, the cheaper and better the solution, 100% applies to healthcare).

Preventive medicine and nudging technology will have profound improvements in people's lives and lifestyles.  Hooking into ambulance services and driverless automated vehicles,.. people do the hard stuff and make the decisions. AI does the piece efficiently and quickly that we as humans aren't good at. Hopefully, you are understanding the difference between narrow and wide industries.

Bad Examples: Robot Surgery or treatment rooms

Robots replace people in operating theatres. It is insane!! A surgeon could use AI to obtain better diagnostic data more quickly; they could even utilise technology like AI-assisted operations, then send messages if it detects that the actions are not optimal or there is a clear risk that a priority has changed.  It is brilliant for decision support. It's not a good idea to try to automate complex decision-making.

My AI Posts

Copilot Studio 2025 Simple Notes

Artificial Intelligence as a mega trend

Microsoft's Copilots Explained 

Prompt Engineering with Microsoft Copilot 

AI Model Comparison (this post)

Ignite 2023 - Keynotes - big push to AI Summary

This post does not look at Strategy and Purpose

All AI solutions give themselves an exponentially better chance of success, regardless of industry, if they have a strategy, purpose, and FAIR data (Findable, Accessible, Interchangeable/exchangeable, and Reusable).

AI Vendor Management - Formiti

AI is going crazy, and you can build your own but generally you need to look at a supplier, so it's worth understand management of Vendors, you as the controller using their service are at risk of them not making their AI operations transparent.  It's a big business risk to my clients.  

GDPR is closely linked to AI, and if you use a service/vendor, the reputation and fine risk may fall on you as the provider.  Need visibility into each vendor, how they are using AI, in turn they are using vendors so it's a nice complex dependency problem.  You need to be aware of what you are relying on.

Ensure contracts with vendors consider AI, how the process your data and how their sub process vendors do the same.

Track website customer behaviour, we use a vendor to clean up the data.  In turn, I have no idea that they are using AI outside of the UK or EU.  Follow the dependency chains as all this needs to be transparent to the end customer if needed.

Microsoft's AI & Copilots Explained as the naming kills me

Microsoft's Copilots products... starting with M365 Copilot options

Source: Screenshot from a presentation, 6 May 2025 - from MS.

1. Microsoft 365 Copilot versions

1.1. Copilot (Microsoft 365 Copilot Chat) (Is Free)

It allows access to ChatGPT-4 with web content.  AI like ChatGPT, Perplexity, ...

1.2. Microsoft 365 Copilot Pro

(+$20/month full retail if bought as an add-on).  Additional features, but not all that M365 Copilot for Business offers.  All users receive Copilot Builder (maker capability), and consumers require the M365 Pro license to utilise M365 Copilot agents.

1.3. Microsoft 365 Copilot for Business

I believe that many people are grouping "M365 Copilot Pro" and "M365 Copilot for Business" together, and only see the free and paid versions of M365 Copilot. 

M365 Copilot Pro for Business (I know :) ) is grounded in the business data you can access in your enterprise.  Inline copilots, including grounded business data inside the office applications.

Microsoft 365 Copilot is an AI-powered Agent with multiple child Copilots for Microsoft 365 apps like Word, PowerPoint, Outlook, Excel, and Teams. It leverages large language models (GPT-4 and GPT-4Pro) and your enterprise data through the Microsoft Graph. To work with M365 Copilot Pro, you need the M365 Copilot license for each user accessing or creating the new agent, which is pricy, per user per month. 

Tip: Optimise results by preparing content!  Like docx, pptx and pdf files. PDF, DOCX, XLSX (kind of), PPTX. Also works on CSV, text, HTML, MD, and WAV audio files.  Supported file type.

Microsoft-specific app copilots included in M365 Copilot Pro:

• 2.1. Word Copilot: Helps with drafting, rewriting, editing, summarising, and generating new ideas.
• 2.2. Excel Copilot: Assists with data analysis, generating insights, creating complex formulas, and automating tasks.
• 2.3. PowerPoint Copilot: Aids in creating presentations, suggesting layouts, creating slides, and enhancing visual content.
• 2.4. Outlook Copilot: Supports email management, drafting responses, scheduling, and organising tasks.
• 2.5. OneNote Copilot.

Note: As of March 2025, Microsoft Copilot does not currently allow uploading images or pictures, unlike ChatGPT; however, I don't doubt that this feature will be added in the future.  ChatGPT can also redraw (using DALLe).

Ease of use, Options for working with Microsoft AI

Extending the use of M365 Copilot Pro licensing

1. M365 Copilot takes a no-code approach to building agents with our company data. Each user needs an M365 Copilot Pro licence to access the new bot/agent/copilot.
2. Copilot Studio is a more advanced way to build copilots and requires a separate license.

Options: Building your own Agents/Copilots with business-aware data.

Agent Builder - all users need the M365 Copilot Pro licence.

2. Microsoft Dynamics 365 Copilots

Overview of Dynamics 365 Copilots:

Source: Microsoft (I lost the reference)

2.1. Customer Engagement (CRM) Apps

1. Dynamics 365 Sales – Manage leads, opportunities, and customer relationships.
2. Dynamics 365 Customer Service – Case management, knowledge base, and omnichannel support.
3. Dynamics 365 Field Service – Manage field operations, work orders, and technician scheduling.
4. Dynamics 365 Marketing (now part of Customer Insights - Journeys) – Campaign automation and customer journeys.
5. Dynamics 365 Customer Insights:
   • Data – Unify and analyse customer data.
   • Journeys – Design and automate personalised customer experiences.
6. Dynamics 365 Customer Voice – Collect and analyse customer feedback.

2.2. Finance & Operations (ERP) Apps

1. Dynamics 365 Finance – Core financials, budgeting, and global accounting.
2. Dynamics 365 Supply Chain Management – Inventory, manufacturing, and logistics.
3. Dynamics 365 Project Operations – Project planning, resource management, and billing.
4. Dynamics 365 Commerce – Unified retail, e-commerce, and POS.
5. Dynamics 365 Human Resources (being merged into Finance) – HR management and employee self-service.

2.3. SMB-Focused App

• Dynamics 365 Business Central – All-in-one ERP for small and medium-sized businesses (finance, sales, purchasing, inventory, and more).

2.4. Industry-Specific Solutions

• Microsoft Cloud for Industry (e.g., Healthcare, Financial Services, Manufacturing) – Built on Dynamics 365 and Power Platform with tailored capabilities.

Note: Microsoft D365 Copilot/ Microsoft Copilot for Dynamics 365 and Power Platform/ Dynamics 365 AI are in this area.

Dynamics Product Copilot Licenses

3. Copilot Studio

Build copilots and distribute them to the business. This is awesome, but from a naming perspective, it actually makes sense. 

Copilot Studio - requires a Studio Builder licence for each maker, utilising low-code AI for build agents.  Each query is charged using messages tokens as its currency.  There is complex pricing; for instance, a query can easily cost 13 message credits.  Update: Copilot Studio agent usage estimator (preview)

4. Azure AI Foundry

Organisations and developers can use the AI Foundry platform to build AI-driven solutions.  Can use 1,800 models and 200 Azure Services.  There are numerous AI services available on Azure.  I'm not even going to start that conversation.

AI Foundry - The individual paid services on Azure relate to token usage within AI Foundry for billing.  A simple query is 4 tokens, depending on how the question is broken down.  Once again, it is difficult to estimate the costs. 

5. Security & Governance of Microsoft 365 Copilot 

The M365 copilot will respect the Access Controls you have permission to, so Jim in operations won't see Tim's sales figures in a Word document in SharePoint. Jim does not have access to the document from the sales site collection. Therefore, they can't query Copilot for the data they don't have permission to (done using MS Graph).

Purview: DLP improves what your users can do and promotes better governance.  Ensure M365 copilots are used ethically.

Understand how users are using your M365 Copilot.

6. Code Copilots

GitHub Copilot

GitHub Copilot is an AI-powered tool that assists developers in writing code by suggesting snippets and completing lines in real-time. Developed by GitHub and OpenAI, it learns from public code repositories and supports various programming languages and frameworks. Its goal is to boost productivity and reduce repetitive coding tasks.  It's amazing!! And getting better all the time.

Code Copilots: GitHub Copilot extensions in VS and VS Code.  GitHub comes in three flavours: Individual, Business, and Enterprise.

GitHub explains code, optimises your code's performance, improves readability, generates unit tests, improves error handling, adds new code with requested functionality, ensures coding consistency, and improves modularisation to help with the DRY principle.  GitHub is the diggity bomb.

Note: ChatGPR is owned by OpenAI.  Microsoft is a significant investor but does not own the service.

Amazon Q

Amazon Q Developer is an AWS code generator designed for building code and solutions.

Cursor, Windsurf.io, ...

Both are based on VS Code and have great AI tooling built in.

7. DSPM (Purview)

DSPM stands for Data Security Posture Management. It's a cybersecurity approach focused on identifying, monitoring, and securing sensitive data across cloud and hybrid environments.  Copilot integrates seamlessly with DSPM in MS Purview, controlling access through DLP and various other options, such as SIT (Sensitive Information Types). It offers numerous out-of-the-box features but can also be customised to help reduce the exposure of organisational data using AI.  Document Fingerprinting also helps reduce exposure. 

Content Explorer in Purview is handy.  

To access all this valuable information, ensure that all your data sources are OAuth-enabled.

7. Summary

Naming and complexity with Microsoft are tough. The AI parts are massive. The M365 copilots break down into Dynamics or O365 worlds, each with many options.  I wish Microsoft had a hierarchical, sensible naming system, which no doubt the Marketing department would hate.  Sources must be secured correctly, and purview can help you manage access.

My AI Posts

Copilot Studio 2025 Simple Notes

Artificial Intelligence as a mega trend

Microsoft's Copilots Explained 

Prompt Engineering with Microsoft Copilot 

AI Model Comparison

Ignite 2023 - Keynotes - big push to AI Summary

Microsoft AI Landscape Pie.

Amazing Summary..

AI Copilot comparrison

 Lunchtime play ...  Claude.io was the best I used... Deepseek didn't work....

AI Engine Comparison Test:

I recently asked a few AI engines a complex tax query:

Perplexity Pro (paid): While I love the app, the answer was not great, 6/10

ChatGPT (free): Excellent, factually correct, not too clear in one area, 8/10 

DeepSeek: I registered and tried the search, but got the result "The server is busy. Please try again later."  I tried a few times, and it is working for common queries. However, I assume that since this logic wouldn't be cached, it can't even attempt to work with the free version, 2/10.

Bing/M365 Copilot: Got a fairly similar result to ChatGPT, missing an option and not as well laid out, 7/10 

Claud.io: Clean result that is factually correct and offers possible items that would affect the calculation, 10/10

Perplexity Pro is my default AI AI-powered search Engine on my iPhone:

Perplexity and the others are great for teaching me this (I use a homemade flip learning type approach), and here are my "follow-on" questions. However, to truly understand something, I still use books (mixed with perplexity), but nothing beats a human-written or recorded topic to give the best understanding.

My AI Posts

Copilot Studio 2025 Simple Notes

Artificial Intelligence as a mega trend

Microsoft's Copilots Explained 

Prompt Engineering with Microsoft Copilot 

AI Model Comparison (this post)

Ignite 2023 - Keynotes - big push to AI Summary

Prompt Engineering with Microsoft Copilot

Check what M365 Copilot license you have?

I have an Enterprise license, which allows me to create M365 apps and share them. I can also consume M365 apps shared with me for free.

Prompting Microsoft Copilot  

(Prompt engineering is a stupid name for search using AI)

Prompting is asking the copilot to give you information.  I advise using four parts to cover in your prompt> 1. goal 2. context 3. source 4. expectation, I remember it as "GCSE prompting".

1. Goal - what do I want, e.g., tell Copilot what we want

I want tactics for pricing my SaaS products

2. Context - Why do I need it

e.g., this is a new product in the B2C space

3. Sources - optional - ask from specific resources

e.g. provide my company stats and ask to compare to my market (from the LLM)

4. Expectations - Table of info, summary, or even an image(maybe not)

M365 Copilot app. 

There are tons of options to help with prompt engineering, the key is to give it clear instructions as shown using the the "GCSE approach" mention above.

Microsoft Copilot is integrated with Office Applications

Apps that M365 Copilot embeds with, if you have the M365 licence, include: 

1. Word, 2. Excel, 3. PowerPoint, 4. Outlook, and 5. Teams

1. Word

Word Copilot is part of the M365 Copilot branding.  Generates scaffolding/first draft.

2. Excel

Use in Excel with your own data - allows easy query using English/natural language.

3. PowerPoint

Draft a presentation using org themes based on prompts. You can also use Copilot to add images you create using Copilot (DALL-e).

My AI Posts

Microsoft Copilot Studio 2025 Simple Notes

Artificial Intelligence as a mega trend

Microsoft's Copilots Explained as the naming kills me

Prompt Engineering with Microsoft Copilot (this post)

AI model Comparison

Ignite 2023 - Keynotes - big push to AI Summary

My First Mendix Widget - Azure AI component to convert Text to audio

 https://www.youtube.com/watch?v=0bxJeYSDV28 (3min video)

Mendix Marketplace - Azure AI Services - Convert Text to Audio  - Here is is in the Marketplace.  Thanks to Ameer for helping me build the widget.

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 identify the most important AI use cases and, using a basic scoring system, prioritise the easy, high-value ones first. 

A good option is to hire an AI-focused team to implement the AI program within your business.  Hire internal staff with 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.

FAIRS for:

1. Findable, 
2. Accessible, 
3. Interoperable, 
4. Reusable, and
5. Secure.
   
   Tip: If your organisation's data is in a good FAIRS state, AI projects are far more likely to succeed.

1. AI Idea Generation: Collate a detailed list of possible ideas.  I like to use SharePoint, it's a good idea to open up idea generation 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, generate revenue, or 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 shown in Figure 2.

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 right tools to be as effective as possible.

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, or merely converted from the old paper process to digital?  Have you engaged key stakeholders and experts?  We should have asked whether this is the best process and 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 are the easier measurements.  Security is also essential to measure, as well as what constitutes success.  Many benefits are intangible; list them. Can a value be guesstimated for intangibles?

There are many more items, but this is a good check to run repeatedly.

Ignite 2023 - Keynotes - Summary

15 & 16 November 2023

Good Keynote: AI is driving a lot of innovation.

Microsoft Fabric in GA (25k instances already).  New feature is 'Mirroring' - copy cosmos/SQL et al into Fabric.  OneLake.  Can bring lots of data from multiple sources into Fabric in near rela time.

MS Teams (320 million users):  Bring everything to the user in one place, not just communications but a canvas for apps.  Good place to build line of business applications.  New teams app - way faster, easier to use.  Teams Premium - intelligent meeting recap is working well, can integrate recap with copilot. 

Copilots - needed for nearly everything you do.  understand context of where you are.  MS has hundreds of copilots.

Copilot Studio - Custom GPT's, can add plugin's to add your own data, can hook into an enterprises unique data.

Copilot for Service - allows agents to get information to provide support, looks interesting.

Personal thoughts: AI is going to be a mega trend that will influence the world hugely, there will be lots of weird decisions on the journey. Currently, it is mainly proving useful as another tool to help improve existing processes.  AI helps me work faster and spend my time on exploration rather than bring base understanding together.

Part 2 Keynote:

Microsoft Graph gives the copilot context within an organisation.  Use plugins to add enterprise data or Open AI GPT's.

Surface Pro Hub 3 released  - looks good, rest of the hardware looks higher spec.

SharePoint Premium - improved knowledge and content management on SharePoint.

Copilot Studio - useful to build internal copilots.  1. Connect copilot to other systems using plugins or GPTs 2. create workflows 3.  Controlled by IT.

Copilot Studio overview

Mesh - Teams can join immersive experience/events, not sure what this means.  GA expected Jan 2024.

Microsoft 365 copilot release to GA 1 November. 

Why Copilot? MS are describing it as a productivity multiplier.  Allows users to be more productive and more creative.  Improves quality of work, avoids searching - as expected.  Makes mistakes but is getting better.

Microsoft Copilot - Bing chat is just MS copilot

So when logging use Entra Id (Azure AD), get contextual enterprise information.  Inherits security and privacy policies.  ACL controlled.  Includes MS graph and Apps.

Try it out: Copilot.microsoft.com  - Chat data is not saved/stored by MS.  Change from "web" to "work".  Also available in Windows taskbar

Ability to use copilot to pull in information, show more graphs, get data.  Good word example getting data from a pptx.

Great example of querying Excel using copilot, created a pivot table. Contrived but looks good, added rainfall from web to look at sales.  Powerful.

Never thought of copilots for being a participant in a meeting - might us amazing.  In teams meeting, takes real time notes, and pulls in info and summaries points for next meeting.  Add as a collaborative partner, can visualize discussions on a meeting whiteboard.

Loop - flexible collaboration, now with copilot.  Not my area but sounds impressive - i don't get it.  People, working with people, now also working with copilot.  Okay.

Copilot for Sales - looks promising.  Hooks into existing CRMs.

Copilot for Service - working with customers, get data that is correct to solve customers problems.  Concise summary, helps craft emails, updates CRM.  Looks very interesting!

Viva - Microsoft copilot dashboard powered by Viva - not sure on this topic.

Summary: People using copilot don't want to loose it.  AI is bringing big changes to many industries.  Promise is to take the grind out of work - sounds great let's see.  Copilot/AI will be a tool and shape how we work.

Microsoft Azure Artificial Intelligence (AI) - AZ-900 Notes

The foundation of all successful AI is high-quality, findable, secure data!

The FAIR principles are guidelines for ensuring that data is 

1. Findable, 
2. Accessible, 
3. Interoperable, and 
4. Reusable.
   
   I think it should be FAIRS for:

1. Findable, 
2. Accessible, 
3. Interoperable, 
4. Reusable, and
5. Secure.
   
   Tip: If your organisation's data is in a good FAIRS state, AI projects are far more likely to succeed.

1. Artificial Intelligence (AI) 

• AI is attempting to make computers use and behave using human intelligence.  
• Teach PC to do tasks for us - automation.  
• PC predicts using patterns and can act.  And good at looking for anomalies.
• PC uses camera/photos to look for patterns.
• Engage in valuable conversations, use multiple sources of knowledge.

2. Machine Learning (ML)

• Train PC's to see patterns and see patterns, and look for anomalies.
• Example. Predict stock prices by looking at factors that affect stock prices.
• Anomaly Detection - Detects unusual patterns, e.g. CC used in Asia when typically in Europe, but transactions 10 min apart.  Therefore, it is likely to be fraudulent.  Sort rubbish.
• Predictive models by finding relationships.  Give model data and train the model.  
• Example: flowers have features/characteristics, e.g. colour, size, no petals, ...
• Using data to teach a machine
• Supervise ML - need quality data, including labels.  Avg humidity, hrs sunshine, rainfall, temp, month of year (features), ice creams sold(label/class), so we feed in temp is Regression ML.  Patient has features (weight, sex, age, bmi,...) give value btwn 0-1 of the person developing diabetes.  is Classification ML
• Unsupervised ML - data is not labelled.  Just the feature provided will group into clusters.  Pulls data out and figures out its own criteria is Clustering ML.  Useful for fraud detection.
• Training - good data based on a training set and a validation set.  Train model, with most data, then check with the remaining - allows us to see how close to what happened.  Service tries to figure out relationships.  Model is used by test data - see how close/useful the model is.

3. Compute Vision 

• Self-driving cars, sorting. Sort rubbish.
• Facial recognition, object recognition,..
• How do computers see?  The picture is cut up into pixels, data is pulled and used to find possible answers. 
• Some types on Azure:  object detection, i.e. car, bike, bus.  Image classification, i.e., horse, car.  Semantic segmentation, i.e. Teams blur background.   Image analysis context by bringing various tougher.  Face detection.  OCR - reads the image and converts it to text.

4. Natural Language Processing (NLP) 

•     interpret, e.g Grammarly, Spam Check, Alexia, 
•     Knowledge Mining - Extract info from knowledge and gain insights, e.g. social media marketing.

Principals:

1. Fairness - ensure bias is excluded, e.g., based on gender.
2. Reliable & Safety - need high confidence and in specific systems cannot fail, e.g. health systems, autonomous cars.
3. Privacy & Security - Ensure data is protected and not giving away sensitive data.
4. Inclusiveness - should be fair, i.e. VI users
5. Transparency - what is the model based on, what could be an issue?
6. Accountability - who is liable for AI decisions

Azure:

1. Scalability & Reliability
2. AI Resources: sit in an Azure Resource Group

AI Services in Azure:

1. Azure Machine Learning - Developers to train, test and deploy ML models.  Within a subscription, create a Azure ML Workspace (consists of: compute, data, jobs, models) can then publish as a service.  Azure ML Designer, used for creating an ML pipeline, data to train the model.  Automated Machine Learning user only needs to provide the data and select the model to use; the service figures it out. 
2. Cognitive Services - vision, speak, language, decision.  REST API endpoints have already been trained; choose the model.  Can deploy multiple parts individually or together.

    3. Azure Bot Service - developing and managing intelligent bots like chatbots
    4. Azure Cognitive Search - Data extraction & enrichment for indexing.  Makes data searchable.

Anomaly detector resource - wizard to set up - Add Keys and endpoints to allow access.

Create a new Azure Machine Learning Service, which will create a Workspace.  Use multiple Azure services such as Key Vault, AI, and storage accounts.   

• Launch Studio
• Add Compute Cluster
• Add Data (csv, spreadsheets, nearly any form,...)
• AutomateML (figure it out without me) and run the job
• Will show trends
• Deploy the model (i.e. to a web service)
• Shows "Endpoints" - get URL and a test rig.

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.

Dynatrace Product Play

 Dynatrace is pretty similar to Azure Monitor.

• Dynatrace (really good if you use multi cloud) Dynatrace - Saas offering is on AWS.  Can be on-prem.  
• Making workloads observable is using Logs, Traces, Events, and metrics into Dynatrace.  From these ingested events we analyse and can automate behavior. 
• OneAgent is deployed on the Compute i.e. VM, Kubernetes.  Can import logs from other SIEMs or Azure Monitor, so you can eventually get Azure service logs such as App Service or Service Bus.  
• Does Full stack and includes code-level and applications and infrastructure monitoring, also can show User monitoring.  
• Dynatrace offers scalable API's that are sitting on Kubernetes.  
• "Davis" is the AI engine used to help figure out the problems.  
• Alerting is solid.  
• Dynatrace can log against 1) network/Infra 2) SDK 3) DEM (User monitoring,..)  logs, traces, metrics are ingested either using OneAgent or OpenTelemetry.
• Management Zones - user only see's information they have access to and need.
• Define a Site Reliability Guardian (SRG) to each program/project, this allows you to identify thru RAG boards the current and recent state of the various pieces.  There are Guardian templates to use as a starting point.
• W3C Trace Context is used - it allows for end-to-end tracing.  OpenTelemetry or Dynatrace keep the trace and provide in headers (traceparent.
• Create documentation and tutorials for Dynatrace.  Dynatrace has a playground tenant for playing on.

High-level Architecture hosted on AWS.

High-level architecture for capturing logs et al. and then using the data.

Product Screen Shots:

Azure & Dynatrace

• Abnormality detection using AI. shall greatly improve observability and security. 
• End-to-end visibility is what makes it so amazing.
• Enterprises often use Dynatrace as there central SIEM solution, shipping from Azure in Dynamics takes planning but works well, categorise and ensure the right into is pushed into Dynatrace.  
• Dynatrace is the leader in Gartner and Forrester in it's space.
• Grail - lake house, schema-less, allows for easy fast query.  Massive scale.  Bring all data together and query at hyperscale.  Grail is in 15 regions either on AWS, Azure, or GCP for customers to use.  UK looks like AWS only. 
• Grail: Record level protection, masking data, support access controls (elevate privileges).  

Dynatrace architecture for Grail from Barcelona conference 5 Oct 2023.

Collect all events in Grail, automate the process of identify suspicious activity relating to security.  Faster reaction time.

Azure offers Dynatrace as a SaaS service

Updated 16 Feb 20224