Category: OpenAI

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AI Explained: 9 Key Concepts You Need to Know in 2025

Artificial intelligence, whilst a phrase used in most of our daily lives, can feel huge, strange, unknown, scary, exciting and sometimes even intimidating. In this post I decided I would strip back the noise and waffle and share nine crisp, usable concepts. I’ve aimed to provide clarity over jargon and give some practical examples over theory.

Before I start, many and to put into familiar brands, here are a few AI tools and brands you will of already know or at least of have heard of:

1. Common AI Tools to know about

  • ChatGPT – What really started the world of “publicly accessible” Generative AI Chat Bots. ChatGPT (version 5 is the current) is a conversational AI that generates text, pictures, and even video. It can answer questions and help with creative writing. It’s a clear example of generative AI in action, showing how large language models can produce human‑like responses. Free and Paid versions.
  • Copilot (Microsoft) – leverages many different AI models including ChatGPT, Microsoft’s own and others, can do very what ChatGPT can do, but is also integrated across line of business apps and data like Word, Excel, PowerPoint, and Windows. Copilot acts as an AI agent that helps you create, draft, analyse, and even automate tasks. It’s a practical demonstration of how AI agents and retrieval techniques can boost productivity. Free tier (ChatGPT Pro equivalent) and Premium for Consumer/Family. Microsoft 365 Copilot for Business use.
  • Google Gemini – Google’s AI assistant that blends search with generative capabilities, pulling in live information to give context‑aware answers. Free and Paid tiers.
  • GitHub Copilot – A developer‑focused AI that suggests code snippets and functions in real time. It shows how reasoning models and pattern recognition can accelerate software development.
  • MidJourney / DALL·E – Image generation tools that turn text prompts into visuals. These highlight the creative side of AI, where models learn patterns from vast datasets and apply them to new artistic outputs.
  • Perplexity – Great for research including financial data and educational content. Has free and paid versions.
  • Siri / Alexa – typically home style voice assistants that act as simpler AI agents, interpreting commands and connecting to external systems like calendars, music apps, or smart home devices. Great for simple tasks like “what is weather like today” and for linking to smart home devices – “Alexa, turn on the porch light“.

If you are just starting (or are a beginner), the easiest way to decide which AI tool to use is to match the tool to the problem you’re trying to solve. If you need help writing or brainstorming, generative text tools like ChatGPT or Copilot in Word are ideal. If you’re working with numbers or data, Copilot in Excel can analyse and visualise patterns for you. For deeply creative projects, image generators like MidJourney or DALL·E turn ideas into visuals, while GitHub Copilot accelerates coding tasks. The key is not to chase every shiny new AI release, but to ask: what am I trying to achieve, and which tool is designed for that job? If you are starting out, start small, experiment with one or two tools in their daily workflow, and build confidence before expanding into more advanced applications.

Which AI in 5: Pick the AI tool that fits your task- writing, data, images, or code—and grow from there.

2. What is Artificial Intelligence (AI)

Artificial Intelligence (AI) is not really a product though word bingo might have people say ChatGPT or Copilot (at work), but it is far more than that! AI is a broad field of computer science focused on creating systems that can perform tasks which normally require human intelligence. These tasks include many things such as recognising speech, interpreting and understanding images and videos, making decisions, and even generating creative content such as music, videos and images. As of 2025, AI is already embedded in many aspects of our everyday lives – in work and in personal life – from recommendation engines on Netflix to fraud detection in banking, to summarising meetings at work.

At its core, AI combines data, algorithms, and computing power to simulate aspects of human cognition, but it does so at a scale and speed that humans could never achieve.

AI in 5: AI is machines learning, reasoning, and acting like humans.

3. AI Agents

Right, so an AI Agent is a system designed to act autonomously in pursuit of a goal. Unlike traditional software that follows rigid instructions, agents can perceive their environment, make decisions, and take actions with or without constant human input.

For example, a customer service chatbot is an agent that listens to queries, interprets intent, and responds appropriately. More advanced agents can coordinate multiple tasks, such as scheduling meetings, analysing reports, or even controlling robots in manufacturing.

The key is autonomy: agents don’t just follow orders—they adapt to changing conditions.

AI Agents in 5: AI agents are digital helpers that think and act for you.

4. Retrieval-Augmented Generation (RAG)

RAG is a technique that makes AI more reliable by combining generative models (or sub models) with external knowledge sources such as the Web or date from corporate SharePoint sites, email etc.

Instead of relying solely on what the AI model was trained on (which may be outdated or incomplete), RAG can retrieves relevant documents or data in (near) real time and integrates them into its response.

This is especially powerful in business contexts, where accuracy and timeliness are critical – for example, pulling the latest compliance rules or product specifications from an application or data repository, before answering a query. RAG bridges the gap between static training data and dynamic, real-world knowledge.

RAG in 5: RAG = AI that looks things up from multiple sources before answering.

5. Explainable AI (XAI)

One of the biggest challenges with AI is the “black box” problem. What I mean by that is that often do not know how AI arrived at its decisions or answer when instructed.

Explainable AI addresses this by making the reasoning process transparent and understandable to humans. For instance, if an AI is being used by a bank to determine if a customer should/can get a loan or not and that AI  model rejects the loan application, XAI will highlight / explain the factors such as credit history or income that influenced the decision.

In essence this is about seeing it’s workings out. If you have used Microsofts Researcher or Analyst agent at work, you will see some of this as it does its work.

This transparency is vital in ensuring we can trust AI and is required in regulated industries like healthcare, finance, and law, where accountability and fairness are non-negotiable.

By opening this black box, XAI builds trust and ensures AI is used responsibly.

XAI in 5: XAI shows you why the AI answers the way it did, what information it used and how it made its choice.

6. Artificial Super Intelligence (ASI)

While today’s AI is powerful, it is still considered “narrow AI” – specialised in specific tasks despite the advances we see every week.

Artificial Superintelligence (ASI) is a (some say) theoretical future state where machines surpass human intelligence across every domain, from scientific discovery to emotional understanding.

Many might be thinking “The Terminator” here but in reality it is more than conceivable given the current pace of evolution that ASI could in design new technologies, solve global challenges, or even “create” beyond human imagination.

This naturally raises profound ethical and safety concerns: how do we ensure such intelligence aligns with human values and what happens if ASI becomes smarter than the humans that created it?

ASI remains speculative and there are many opinions and research on the matter, but today it is a concept that drives much of the debate around the long-term future of AI.

ASI in 5: ASI is the idea of AI being smarter than all humans in every way.

7. Reasoning Models

Traditional AI models excel at recognising patterns, but they often struggle with multi-step logic.

Reasoning models are designed to overcome this by simulating structured, logical thought processes. They can break down complex problems into smaller steps, evaluate different pathways, and arrive at conclusions in a way that mirrors human reasoning.

This makes them especially useful in domains like legal analysis, financial analysis, scientific research, or strategic planning, where answers are notjust about recognising patterns and finding information but about weighing evidence and making defensible decisions in a way similar to how we as humans might undertake such work.

Reasoning Models in 5: Reasoning models let AI think step by step like us.

8. Vector Databases

AI systems need efficient ways to store and retrieve information, and that’s where vector databases come in.

Unlike traditional databases that store data in rows and columns, vector databases store information as mathematical vectors – dense numerical representations that capture meaning and relationships.

This allows AI to perform semantic searches, finding results based on similarity of meaning rather than exact keywords. For example, if you search for “holiday by the sea,” a vector database could also return results for “beach vacation” because it understands the conceptual link.

Vector Databases in 5: Vector databases help AI find meaning, not just words.

9. Model Context Protocol (MCP)

Finally, MCP is a framework that helps AI agents connect seamlessly with external systems, APIs, and data sources. Instead of being limited to their own training data, agents using MCP can pull in live information, interact with business tools, and execute workflows across platforms. For example, an MCP-enabled agent could retrieve customer records from a CRM, analyse them, and then trigger a follow-up email campaign—all without human intervention.

MCP makes AI more versatile and practical in enterprise environments.

MCP in 5 : MCP is the bridge that connects AI to other tools.

What next and Getting Started

AI is not a single technology but a constellation of concepts – agents, RAG, XAI, ASI, reasoning models, vector databases, and MCP – that together define its capabilities and potential. Understanding these terms helps demystify AI and highlights both its current applications and future possibilities.

As AI continues to evolve, these building blocks will shape how businesses, governments, and individuals harness its power responsibly.

AI is a toolkit of ideas working together to change the world. When we look at what tool to use when, in reality there is not one better than the other it’s more about context of use, the platform you use it on, what your work provides, what you get included in your other software (for example Copilot in Windows, Office apps etc) and what task you are performing. Some AI’s are better at images, some at research and some at writing and analysis.

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GPT-5 Chat & Reasoning vs Copilot Researcher Agent

With all the new AI models in Microsoft Copilot along with the first party agents that serve spefici functions, it can be confusing to know which tool to use for the right task. Microsoft have two specific agents within the Microsoft 365 Copilot domain Researcher and Agent designed to carry outr specific functions. We also now have GPT-5 (now also baked into Copilot) which brings the new “smart” mode, allowing it to switvch between languiage models based on the task at hand.

In this blog, I aim to break down the three tiers of Copilot capability— GPT-5 Chat, GPT-5 Reasoning (also known as Smart mode), and Researcher and to look at the differences, similarities and what is best for what!

GPT-5 Chat: Fast, Friendly, and to the Point

Best for:

  • General Chat and Quick Q&A
  • Content Summarisation
  • Creative brainstorming

How it thinks:
Chat mode is is your rapid-fire assistant. It delivers supoer quick responses to youur questions without deep analysis. This is ideal for when you need an answer or exmplanation, clarity on something and anything not requiring deep thinking, reasoning or complexity.

Example prompt:

“Write a short social post abou the latest updates to [product].”

When to use it:
When you want brevity, speed, and a touch of flair. Think elevator pitches, tweet-length summaries, or light creative riffs.

GPT-5 Reasoning: Structured Thinking for Strategic Tasks

Best for:

  • Logical analysis
  • Problem-solving
  • Multi-step planning such as building a travel itinery, project plan or talk track

How it thinks:
This mode (which GPT-5 will switch too automatically if needed), rolls up its sleeves and gets analytical. It’s designed for tasks that require synthesis, deep exploratory analysis, structured responses and a more strategic (deep thinking) response.

Example prompt:

“Let’s walk through this problem together: I need to launch a new product with a limited budget and a small team to appeal to an already crowded market. What’s the best strategy?”

Use it when:
You’re building timelines, weighing trade-offs, or need a coherent plan that holds up under scrutiny.

Copilot Researcher Agent: Deep Dives and Source-Backed Intelligence

Best for:

  • Comprehensive research and reports
  • Source comparison and opinioning
  • Insight generation

How it thinks:
Researcher mode is a custom build agent from Microsoft specifically designed to work in a recursive, self evaluation and exhaustive approach. It pulls from multiple sources which it compares and contrats against one-another, compares methodologies, and identifies gaps. This is design for for thought leadership, academic-style or research style analysis, or strategic decision-making with different view points.

Example prompt:

“Find and summarise studies on the effects of remote work on productivity. Compare methodologies and results, looking at pro’s cons, impact on society, well-being, productivity and trends across different EU regions.”

Use it when:
You need more than just an answer. You want to combine different view, present the otput in a particular way and when you need comprehenisive evidence, nuance, and a narrative that stands up to boardroom or peer review.

Which One When?

There is no definitiave answer to this, but based on my usage and infomration I have read in the field, guidance from OpenAI, Microsoft and others, this table provides a guide to which mode would work best in the scenaios below

Task TypeUse GPT-5 ChatUse GPT-5 ReasoningUse Researcher
Write a quick summary✅
Build a strategic roadmap✅
Compare academic studies✅
Brainstorm creative ideas✅
Solve a complex problem✅
Generate source-backed insights✅

AI isn’t a one-size-fits-all and not all use cases are the same. As AI models contine to become more context aware and with the ability to switch modes, there are still many times where knowing where to take your problem to is key – just like you know to talk to a specialist consultant vs a generalise at times.

Currently, to get the best from these AI models, the best result often lies in knowing when to switch gears—from conversational to analytical to research-grade depth. Whether you’re a CTO shaping strategy or a content creator chasing clarity, the right mode turns AI from assistant to partner to mentor.

Would you like this adapted into a SharePoint-friendly format or paired with a branded visual? I can also tailor it for internal enablement or customer-facing use.

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What is Copilot Smart Mode?

Copilot Modes July 2025

It snuck in quietly, like all meaningful innovations do. I didn’t see a press release, or announcement – and just saw it “pop” up for me today with one small pop-up. Just one single word, Smart. And yet, beneath the understated label lies perhaps the most pivotal shift in the way generative AI models like Copilot and ChatGPT work since manual “model selectors” first became a thing.

This is, yes, you’ve guessed it GPT5!

Smarter Than Smart – the quiet revolution of reasoning

Copilot (i’m talking the consumer version currently at copilot.microsoft.com) or via the Windows, IOS and Android app, currently has three modes of chat which you choose based on the discussion with Copilot you want to have. This is similar to how ChatGPT works also today.

  • Quick Response [for every day conversations]
  • Think Deeper [for more complex topics]
  • Deep Research [Detailed reports with references]

That is changing – Microsoft Copilot’s new ‘Smart’ mode doesn’t ask you to choose a conversation type anymore. Instead it now adapts for you – automatically and intuitively.

This means, that depending on your query, for example whether you’re scoping customer insights, untangling a tricky dependency in a network diagram, or storytelling your way through a general chat, summarisation of marketing ideas, ‘Smart’ mode calibrates itself to the conversation and task at hand.

In short – in this mode, Copilot will now decide what model it thinks it needs to help you. Copilot has auto reasoning — true adaptive, context-aware reasoning based on the ask.

What Makes This Mode Smart?

‘Smart’ mode is likely powered by OpenAI’s upcoming GPT-5, a model anticipated to merge the OpenAI o-series and GPT-series models into one unified framework.

What we’re seeing as these models evolve is:

  • An intuitive reasoning engine, not just predictive text- task “and” context aware
  • Self-calibrating depth, reducing cognitive load by using the right tool for the right job
  • Model abstraction, freeing users from having to pick the right tool for the job themselves

Microsoft hasn’t just added another dial. It’s looking to hide the dial entirely — and taught the model how to turn it for you (however as it’s in preview you do need to turn the auto mode on – for now at least).

Copilot’s Human Centric UX

As you can see above, instead of users needing to flip between “Think Deeper,” “Quick Response,” and “Deep Research,” (or not evening understanding what these mean and therefore ignoring it), Copilot’s Smart mode does what most tools never do: it assumes responsibility. That’s more than a UX shift — it’s a culture shift. This means no longer asking (non technical) users to understand what the different models the model hierarchy or decoding acronyms like o4-mini. Instead Copilot is getting cognitive delegation.

This means we will be able to “trust”Copilot to know when to dig deeper and when to skim the surface.

Examples: Tech Architects, Storytellers, and Strategists

The table below gives some examples of where Copilot Smart mode can make a big different in use:

RoleBefore Smart ModeWith Smart Mode
Solution ArchitectManually toggling depth based on task complexityInstant adjustment to scope and context
Content Creator/MarketingSelecting modes based on tone and detail requiredNatural flow from quip to deep dive
EnginnerTesting prompts for clarity vs depthGetting both — with nuance — the first time

This isn’t just about being faster. It’s about being right-sized. Strategically aligned, creatively agile, and cognitively respectful.

What about control and “mode anxiety”?

We’ve all wrestled with prompt engineering, hoping we’re not asking too little or too much. Smart mode is Copilot whispering, “I’ve got you.” That’s a leap from assistant to partner — the kind we’ve spent decades trying to design into our workflows, team cultures, and tech stacks.

Copilot Smart Mode Preview?

This is in preview clearly – or was it rolled out silently. Anyway, if you have it, give it a try (I have it on desktop and web plus mobile). Let me know your thoughts.

It will be interesting to see if eventually the modes disappear and we just have an “auto” mode.

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Deep research AI models coming to Microsoft 365 Copilot

As March 2025 comes to an end, Microsoft have unveiled several exciting updates across Microsoft 365 Copilot, Copilot Chat, and Copilot Studio.

Copilot announcements this week

1. Updates to Copilot Studio Message Rates

Effective April 2nd, 2025, updated (cheaper) message rates for Copilot Studio will go live. These adjustments cover tenant Microsoft Graph grounding and agent actions (previously known as autonomous actions). The prices of tenant Microsoft Graph grounding and autonomous actions are being reduced from 30 messages and 25 messages to 10 messages and 5 messages respectively, from April 2nd, 2025.

The following table illustrates the differences in the subscription models for the cost of Copilot Studio events.

Copilot Studio featureBilling rate [non M365 Copilot Licensed users]Billing rate [M365 Copilot licensed users]Autonomous triggers1
Classic answer1 messageNo chargeN/A
Generative answer2 messagesNo charge2 messages
Agent action5 messagesNo charge5 messages
Tenant graph grounding for messages10 messagesNo charge10 messages
Agent flow actions per 100 actions13 messages13 messages13 messages
1 - Autonomous triggers refer to events or conditions that automatically initiate an agent to take action, without requiring a user to manually invoke it.

Also coming is Agent flows which allow agent creators to bring Power Automate automation features directly into Copilot Studio to quickly and consistently automate business processes. There will also be new deep reasoning in agents combines reasoning models including Open AI o1 with the ability to access enterprise data to complete complex tasks.

Microsoft are also updating pricing with a new  zero-rating for Microsoft 365 Copilot licensed users in Microsoft 365 apps and services, ensuring inclusive, seamless integration and cost-effective use of these tools. This means licensed Microsoft 365 Copilot users will not be charged for using agents in their organisation

2. Rule-Based Workflows in Copilot Studio

From April 2025, Copilot Studio will introduce structured, rule-based workflows for agents. This aims to simplify process automation, enabling users to create efficient, consistent workflows with minimal manual effort. Usage of this functionality will contribute to the Copilot Studio meter, encouraging innovation while maintaining transparency in resource utilisation.

3. Deep Reasoning in Copilot Studio

So this is a big one – Microsoft have made deep reasoning capabilities available in Copilot Studio’s public preview from today. This will empowers users to address complex, decision-intensive tasks by leveraging advanced reasoning algorithms.

Whether it’s managing intricate processes or solving challenging problems, this tool offers remarkable precision and depth in its execution.

4. Two new Deep Reasoning Agents

Microsoft announced two new deep reasoning agents—Researcher and Analyst—as part of an early preview which will also be coming “soon” with previews coming in April before wider rollout.

  • Researcher Agent: has been designed for content creation and information synthesis, this agent combines OpenAI’s advanced deep research model with Microsoft Copilot’s orchestration. By integrating Copilot Chat’s web and work grounding capabilities, Researcher enables users to brainstorm ideas, generate high-quality content, and analyze data more effectively.
Researcher Agent in Copilot.
  • Analyst Agent: This is powered by a new reasoning model. the Analyst agent will function as a virtual data scientist and will have the ability to process complex datasets and provide real-time code validation (using Python) and will be able to deliver actionable insights and visually compelling representations of data in minutes.

Microsoft say that these agents will be gradually rolled out to Microsoft 365 Copilot licensed users through the Frontier program, an early access programme for customers to test out early and new Copilot innovations.

Read more

To dive deeper into these updates, visit Microsoft’s official blog.

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Microsoft makes OpenAI o1 model free for Copilot users.

OpenAI’s most advanced AI model “o1” which is known for its problem solving and deeper thinking has been available behind a $20 per month ChatGPT premium subscription. ChatGPT premium gives limited acess for $20 a month and unlimited access for $200 a month.

Copilot let’s you use it for free.

Microsoft has a tight partnership with OpenAI and is also on a mission to put their AI (Copilot) across every Microsoft Service it offers with huge capability and features even on theor “free” tiers.

Copilot Consumer Pro users have had access to Think Deeper (which uses the o1 model) for the past 12 months, but Microsoft have now made this feature free to everyone including those using the free version of Copilot.

To access it, you need to simply head ovee to Copilot on the web, (or via the mobile app) and ensure you are signed in with a Microsoft account (MSA). You then get completed free access to the Think Deeper search (which uses the o1 model).

How to get Microsoft Copilot

To get Copilot, head to the web (you actually find Copilot in the Edge browser) and go to https://copilot.microsoft.com or head over to you phones app store and search for Copilot and install it.

You need to be signed in with your Microsoft account to use these features.

Using o1 features aka Think Deeper

Once in Copilot, use the AI chat as you would before (or like you did in ChatGPT) and you will see a “think deeper” button inside the text input box.

Using Copilot’s Think Deeper (ChatGPT model o1)

Selecting it activates the o1 reasoning model. As it processed your prompt, you also get a spinning symbol since searches and responses using o1 are more thorough that with GPT 4 and typpically take around 30 secs.

Using Copilot’s Think Deeper.

This is Microsoft’s way of letting you know that you’re in for around a 20-30 seconds wait. If you don’t need deep search so for normal use), toggle this back off to go back to the super fast GPT-4o version…

So what can o1 do then?

The deep thinker feature of Microsoft Copilot is much better for more complex tasks and research due to the o1 model ability for in depth reasoning. 

As such it is simply better for solving complex issues like math, logic or science, for analysing or creating long or richer documents and reports or for code creation and debug. The best way to test this is to run two Copilot Windows side by side and test out the same prompt with and without Think Deeper enabled.

Content created with o1 is also more “accurate” with far less AI hallucinations (aka, making things up).

Why do many GPTs Hallucinate? In general, GPT models learn by mimicking patterns in their training data (huge amounts of data). The o1 model uses a different technique called reinforcement learning, whereby it's language model works things out (though it's training) by rewarding the right answers and penalising wrong ones. This takes longer through the iterative and testing process. Once done the model  moves through queries in a step-by-step fashion much like human problem  solving.

o1 limitations?

It is worth noting that o1 isn’t quite on the same level as ChatGPT in some areas. It is less effective with factual knowledge and is currently less able to search the Internet and cannot process files and images.

What about DeepSeek?

The big story this week has of course been DeepSeek, a controversial Chinese AI firm that has announced and launched their own GPT-4 and o1 rivals that have been supposedly built at a fraction of the cost of OpenAI, Google and other US models, shaking share prices, disrupting the market and rasing many questions.

What is more is more is that DeepSeek models are claimed to be more advanced and faster than GPT-4o and smarter that o1.

The advent of DeepSeek has sent shockwaves through the tech industry. Global stock markets have reeled, sparking a cascade of investigations and looming threats of bans.

Yet, the bot hasn’t been without its champions. Interestly, Microsoft – OpenAI’s top financial invester and partner  – has already embraced the DeepSeek R1 reasoning model, and has integrating it into Azure AI Foundry and also GitHub.

These platforms, beloved by developers for fostering advanced AI projects, now stand as the new playground for DeepSeek’s innovative potential.

DeepSeek logo

Open AI Strikes Back

In the wake of its free mobile app’s viral triumph, OpenAI’s CEO Sam Altman swiftly revealed plans to accelerate the rollout of new releases to keep ahead of its new Chinese competitor.

OpenAI are not standing still either though. Et the end of December 2024, month, they began  trialing twin AI models, o3 and o3 mini. Remarkably, the former has surpassed o1 in coding, mathematics, and scientific capabilities, marking a significant advancement in their AI prowess.

There is no doubt this is an area that doesn’t stand still. By the time I click publish this post will likely already be out of date!

DeepSeek has certainly ignited an even greater sense of urgency within the already dynamic AI sector which moves and evolves on an almost daily basis.

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OpenAI launches ChatGPT Gov for U.S. Government Agencies

This week, in a significant development amidst the backdrop of intensifying AI competitiveness, OpenAI has unveiled ChatGPT Gov

This customised version of the AI-powered chatbot platform is tailored specifically for U.S government agencies, providing them with an advanced tool to access and utilise Open AI technology.

Microsoft announced back in 2023 that its Azure OpenAI Service was available for Azure Government customers. The new service will allow government agencies to use generative AI capabilities in a way that meets security and privacy requirements. OpenAI can now also be deployed in Microsoft Gov Data Centres.

What is ChatGPT Gov?

So ChatGPT Gov mirrors many capabilities of OpenAI’s enterprise-focused tier, ChatGPT Enterprise. By leveraging this platform, Open AI say that government agencies can deploy specific OpenAI models on both Microsoft Azure commercial and government clouds as well as using Microsoft’s own Azure AI models for example. This integration brings enhanced management of security, privacy, and compliance concerns, which is crucial for handling non-public sensitive and classified data.

ChatGPT Gov also aims to streamline internal authorisation processes, making it easier for agencies to implement OpenAI’s tools effectively as easily with the relevant guard rails in place.

What about Azure Open AI for Gov

Microsoft Open AI, enables federal, state, and local government agencies to use GPT-3, GPT-4 and 4o along with embeddings via the Azure OpenAI Service REST APIs. This capability helps to improve natural language-to-code translation, semantic search, content generation, and summarisation and for Gov to build and use Microsoft Open AI services across Gov cloud.

Gen AI in Government

Since its introduction, ChatGPT has already seen extensive adoption across the U.S. government as well as here in the UK. I’m. Personally working with a dozen or so local governments and councils here in the UK on AI adoption.

Open AI says that more than 90,000 users from more than 3,500 federal, state, and local agencies have collectively sent over 18 million messages to support their daily operations. This widespread usage demonstrates Open AI and Microsoft’s potential to transform government workflows and decision-making processes.

What about Copilot

While ChatGPT Gov offers a robust AI solution for government agencies, it’s worth exploring how Microsoft 365 Copilot also serves these needs. Microsoft

For many organisations using or exploring Gen AI tools like ChatGPT, many are using a combination of tools and services from different vendors. Open AI and Microsoft are tightly partnered.

Microsoft 365 Copilot is built on Open AI (which in turns runs in Microsoft Azure) and integrates seamlessly with existing Microsoft 365 tools, providing personalised assistance across a range of applications such as Word, Excel, and Outlook and also supports the building (both professional and low code) of autonomous AI agents, and scheduled prompts (coming soon).

This integration ensures that users can enhance productivity and streamline tasks within the familiar Microsoft ecosystem.  So how does Microsoft 365 Copilot differ to ChatGPT?

ChatGPT

  • Targeted for AI-powered chat and conversation but also supports connectors and extebsibikity to other services via extensions and APIs.
  • Recently launched the ChatGPT Gov version for U.S. government agencies and are expected to do similar in other global regions.
  • Deployable on Microsoft Azure commercial and government clouds
  • Doesn’t provide native integration into line of business office apps and services like Office 365, Power Platform and Fabric.

Microsoft 365 Copilot

  • Customised version of ChatGPT that runs in Microsoft 365 Tennant boundaries.
  • Provides chat based conversations and access to company agents and connectors on PAYG basis or via Microsoft 365 Copilot subscription.
  • Embedded within Microsoft 365 applications like Word, Excel, Teams, and Outlook as well as Dynamics 365 and Power Platform.
  • Designed to enhances productivity and efficiency within the existing Microsoft ecosystem and seen as add on to Microsoft 365 on a per user per month billing method.
  • Can provides contextual assistance and automation for daily tasks and workflows through agents and autonomous agents (public preview).

In conclusion, while both ChatGPT and Microsoft 365 Copilot are powerful AI tools, they cater to slightly different use cases

US Government agencies may find ChatGPT Gov particularly beneficial for secure, AI-driven interactions (in place of the general version of ChatGPT) , whereas Microsoft 365 Copilot excels in enhancing productivity and providing natively and seemlessly integration into their wider app services and data. Gov agencies using Microsoft 365 Copilot and Azure AI or Open AI deployed in Azure also benefit from enhanced controls and security protection.

It’s great to see Open AI providing dedicated models and instances for central and federal governments.

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