Gartner 2025 – From Agentic AI to Organizational Readiness

The Gartner 2025 conference combined Agentic AI insights with data and analytics being at the core of modern enterprises. The conferences this year carried a consistent theme of  AI is advancing fast, but organizations are not yet structurally ready to capture its value.  The human cultural factor remains the biggest blocker. 

Agentic AI – Promise and Pitfalls  

Agentic AI topped Gartner’s **Top 10 Strategic Technology Trends for 2025**, reflecting the excitement around autonomous agents that can act beyond simple query-response models. Yet Gartner cautioned that **only 6% of deployments have delivered value so far**, and even those faced operational challenges. Without strong governance, asset visibility, and oversight, autonomous agents risk creating chaos rather than efficiency.  

Organizational Barriers  

Across sessions from the IT Infrastructure, Operations & Cloud Strategies Conference in London to the IT Symposium/Xpo in Kochi, analysts stressed that the biggest hurdles are organizational, not technological.  

• Silos within IT and business functions block insight democratization.  
• CIOs must strengthen oversight of both internal and external infrastructure.  
• AI readiness requires mapping every dependency and service lifecycle before agents act autonomously.  

Data & Analytics Summit Takeaways  

At the Gartner Data & Analytics Summit, the buzz around AI agents was matched by emphasis on governance, data quality, and ROI. Analysts noted that conversational AI and agentic systems will only succeed if organizations embed trust, transparency, and measurable outcomes into their data strategies.  

The Value Gap  

Gartner predicts that by 2032, only 15% of AI projects will deliver value unless foundational infrastructure and governance are in place. At least 50% of GenAI projects will exceed budget due to poor architectural decisions.  This sobering forecast underscores the need for incremental maturity, starting small with tasks like automated server deployment, while building toward more complex agentic ecosystems.  It is necessary to have use cases that drive the most urgency.

Leadership Lessons  

The Symposium also highlighted leadership imperatives: balancing AI readiness with human readiness, managing geopolitical risks in workloads, and preparing for new regulatory landscapes. The message was clear: technology alone won’t deliver transformation—leadership alignment and organizational maturity will.

The biggest risks with GenAI

• GenAI Alone Isn’t the Right Technique
• Tech Obsolescence
• Responsible AI is an Afterthought
• Inadequate Investment in Data and AI Literacy

GenAI is expensive and 30% of projects will be abandoned so planning is important.

Governance is no longer optional, it is foundational.

In summary the Gartner’s 2025 message is that Agentic AI is rising, but its success depends on breaking silos, democratizing insights, and embedding governance. Organizations that treat AI as a collaborator within a well-understood ecosystem, not just a tool, will be the ones to unlock real value.  

Sources   

Agentic AI Tops Gartner's 2025 Tech Trends -- Virtualization Review

Gartner® Top Technology Trends for 2025: Agentic AI

Gartner IT Infrastructure, Operations & Cloud Strategies Conference 2025 London: Day 2 Highlights

Gartner IT Symposium/Xpo 2025 Kochi: Day 2 Highlights

Gartner IT Symposium/Xpo 2025 Kochi: Day 2 Highlights

Top 7 Insights from Gartner D&A Summit 2025

Gartner Summit 2025: 6 Big Insights for AI & Analytics | Tellius

Top Leadership Takeaways from the 2025 Gartner IT Symposium | LinkedIn

Microsoft AI Skills Fest

The Microsoft AI Skills Fest is starting from 8 April 2025 to 28 May 2025 to enhance your AI skills and unlock the future. From experiential content to hackathons, there is a lot of opportunity to learn at all levels. 

Register today: https://msft.it/6042qSzcE

Customizing Generative AI for Unique Value

A new global report from MIT Technology Review Insights report looks at how business is leveraging AI customisation. It examines challenges faced by technology, motivations and methods to create business value. The report talks about improving efficiency, user satisfaction, and innovation but also looks at challenges such as data integrity and privacy. Simplifying AI customization is key and Azure AI Foundary , formerly AI Studio, helps design, customise and manage AI apps and agents at scale to help with business opportunities.

The AI Hub in Microsoft Purview

The Microsoft Purview AI hub is a preview feature that helps you govern and gain insight to generative AI apps and helps protect through mitigating and managing risks. With the explosion of generative AI this has become a crucial addition in the governance world.

The AI Hub helps with

• strengthening information protection for Copilot
• supporting compliance management for Copilot
• fortifying data security for AI
• insights and analytics that enable that view on AI activity
• policies to help protect and prevent data loss
• compliance for optimal data handling

The use of Purview AI hub works well with other capabilities to improve data security.

• Sensitivity labels and content encrypted by Microsoft Purview Information Protection
• Data classification
• Customer Key
• Communication compliance
• Auditing
• Content search
• eDiscovery
• Retention and deletion
• Customer Lockbox

The insights, policies and control secures data from Microsoft Copilot for Microsoft 365 and AI apps from third-party large language modules (LLMs).

Fundamentals of Generative AI

 Generative AI is a branch of artificial intelligence that focuses on creating new content or data from scratch, such as images, text, music, or code. Generative AI models learn from existing data and use it to generate novel and realistic outputs that are not part of the original data. Some of the applications of generative AI include:

Image synthesis: Generative AI can create realistic images of faces, landscapes, animals, or objects that do not exist in the real world. 

Text generation: Generative AI can produce natural language texts on various topics, such as stories, poems, essays, or code. 

Music composition: Generative AI can compose original music in different genres, styles, and moods. 

Data augmentation: Generative AI can enhance or expand existing data sets by creating new samples that are similar but not identical to the original ones. This can help improve the performance and robustness of machine learning models. For example, generative AI can create new images of handwritten digits or new sentences of natural language.

The main challenge of generative AI is to ensure that the generated outputs are both diverse and realistic, meaning that they cover a wide range of possibilities and resemble the real data. To achieve this, generative AI models often use two types of techniques:

Probabilistic models: These are models that learn the probability distribution of the data and sample from it to generate new outputs. For example, variational autoencoders (VAEs) are probabilistic models that encode the data into a latent space and decode it back into the original space, adding some noise in the process to create variations.

Adversarial models: These are models that consist of two components: a generator and a discriminator. The generator tries to create outputs that fool the discriminator, while the discriminator tries to distinguish between real and fake outputs. The two components compete with each other and improve over time. For example, generative adversarial networks (GANs) are adversarial models that use neural networks as the generator and the discriminator.

Generative AI is a fascinating and rapidly evolving field of artificial intelligence that has many potential benefits and applications for society. However, it also poses some ethical and social risks, such as misuse, deception, or bias. Therefore, it is important to develop and use generative AI models responsibly and transparently, with respect for human values and rights. 

To learn more about the Fundamental of Generative AI , Microsoft Learn has a great course

It also covers what is the Azure OpenAI service. This being a Microsoft's cloud solution for deploying, customizing, and hosting large language models. There is a brief overview of  CoPilot.

Generative AI framework for Government

The UK government has released their generative AI framework created by the Central Digital and Data Office V1.0.  This is public sector guidance with a focus on Large Language Models (LLMs). 

The framework outlines ten principles:

Principle 1: You know what generative AI is and what its limitations are

Principle 2: You use generative AI lawfully, ethically and responsibly

Principle 3: You know how to keep generative AI tools secure

Principle 4: You have meaningful human control at the right stage

Principle 5: You understand how to manage the full generative AI lifecycle

Principle 6: You use the right tool for the job

Principle 7: You are open and collaborative

Principle 8: You work with commercial colleagues from the start

Principle 9: You have the skills and expertise that you need to build and use generative AI

Principle 10: You use these principles alongside your organisation’s policies and have the right assurance in place

It defines Generative AI as a form of AI '– a broad field which aims to use computers to emulate the products of human intelligence or to build capabilities which go beyond human intelligence'

Then within Generative AI  how those public LLM's fit within the field

The framework has lots of information to draw on from  advocating for lawful, ethical, and responsible usage to addressing the the challenges of accuracy, bias and environmental impact.  Transparency and human control are paramount going forward. You can read more here.

A Generative AI course for beginners

Microsoft Cloud Advocates have created a new set of training materials to enable the learning of the fundamentals of building Generative AI applications with a 12-lesson comprehensive course. Each lesson covers a key aspect of Generative AI principles and application development. Throughout this course, you will be building your own Generative AI start up so you can get an understanding of what it takes to launch your ideas. The agenda is below.

	Lesson Link Course Introduction - How to Take This Course	Concepts Taught Tech setup and course structure	Learning Goal Setting you up for success while learning in this course
	Introduction to Generative AI and LLMs	Concept: Generative AI and the current technology landscape	Understanding what Generative AI is and how Large Language Models (LLMs) work.
	Exploring and comparing different LLMs	Concept: Testing, iterating, and comparing different Large Language Models	Select the right model for your use case
	Using Generative AI Responsibly	Concept: Understanding the limitations of foundation models and the risks behind AI	Learn how to build Generative AI Applications responsibly
	Understanding Prompt Engineering Fundamentals	Code/Concept: Hands-on application of Prompt Engineering Best Practices	Understand prompt structure & usage
	Creating Advanced Prompts	Code/Concept: Extend your knowledge of prompt engineering by applying different techniques to your prompts	Apply prompt engineering techniques that improve the outcome of your prompts.
	Building Text Generation Applications	Code: Build a text generation app using Azure OpenAI	Understand how to efficiently use tokens and temperature to vary the model's output
	Building Chat Applications	Code: Techniques for efficiently building and integrating chat applications.	Identify key metrics and considerations to effectively monitor and maintain the quality of AI-powered chat applications
	Building Search Apps Vector Databases	Code: Semantic vs Keyword search. Learn about text embeddings and how they apply to search	Create an application that uses Embeddings to search for data.
	Building Image Generation Applications	Code: Image generation and why it's useful in building applications	Build an image generation application
	Building Low Code AI Applications	Low Code: Introduction to Generative AI in Power Platform	Build a Student Assignment Tracker App for our education startup with Low Code
	Integrating External Applications with Function Calling	Code: What is function calling and its use cases for applications	Setup a function call to retrieve data from an external API
	Designing UX for AI Applications	Concept: Designing AI Applications for Trust and Transparency	Apply UX design principles when developing Generative AI Applications
	Continue Your Learning	Links to continue your learning from each lesson!	Mastering your Generative AI skills

Microsoft Cloud Innovation and Security Summit

I had the privilege to present at the The Form Rooms on Tuesday 14 November 2023 at the Microsoft Cloud Innovation and Security Summit run by Cloud Direct.  

The Forms Rooms is on a quiet street in London’s chic Covent Garden that was once a historic Victorian school that has been lovingly restored. 

My session was on 'The disruptive power of data and AI'.  About how Generative AI, Microsoft Fabric and Data Governance are the talk of the town, but without the building blocks of good quality, secure, accessible data, innovation in business is stunted. 

The talk covered how Data & AI are providing a catalyst for innovation addressing  

• Where is Data & AI tech going
• Why having an agile data strategy and data governance adoption path is important. 
• How data can be a catalyst for innovation 

In today’s world technology changes and enhancements are accelerating at speed. Developments from the 1960’s took decades to move into the modern workplace. But now we see these coming to the fore with the democratisation of technology.  We moved from the 

1970s where Codd invented the relational database

To the 1990s when ‘Big Data’ became a term to address the ever-increasing large volume of data, with discussions of volume, velocity , variety and veracity. Here we saw the mention of that ever-important reference to the quality of data. 

With that grew the ‘Internet of Things’ processing more data from sensors, devices and communication networks. 

Behind the scenes world leading database researchers meet every few years to discuss the impact on practice, challenges and emerging applications. The latest output was documented in the Seattle report in 2019. One of the areas discussed was the fact that it was impractical to continue to move volumes of data between systems. We see this fundamental change in Microsoft Fabric.

And today, it is all about ‘Generative AI’ and what it can create...

There were many really interesting sessions during the conference with Innovation, AI and Security intertwined. 

Learnings from Microsoft Build AI Day 2023

I had an amazing day at #MSBuildAIDay and the opportunity speak with many people about AI.  A keynote opened the event and demonstrated a number of different uses of AI. 

At the event I created some artwork using Azure OpenAI DALL-E lab #InventwithAI . There were a few questions to answer via voice input that resulted in your own artwork being created. The questions were: what is you favourite colour; favourite landscape; where do you live; artwork style; favourite technological innovation. The image created is below. 

I had the opportunity to be a connection hub expert and proctor in the 2 Prompt Engineering workshops run by Amy Kate Boyd and Henk Boelman on Learn how to use OpenAI models (e.g. ChatGPT) using Azure OpenAI.

It is strange to be talking about traditional AI, of a few years ago, as historic as we move to Generative AI. Responsible AI was described as, about reducing the amount of time people spend doing certain tasks, and not replacing them as people are always required to validate the output.

A few points learned in the workshop 

• Generative AI models can generate humanlike text, images, and code
• Generative AI models are stateless: they do not learn, and are constrained by their training data which is frozen at a fixed point in time
• Azure OpenAI Service is a managed service that provides access to state-of-the-art natural language generative AI models, including ChatGPT and GPT-4 from OpenAI with the security and enterprise promise of Azure.
• Azure OpenAI Service provides a simple REST API for accessing these models
• Prompt engineering is a technique for "grounding" generative AI models, and can be used to influence the style of their output, provide factual information, and constrain their behaviour.

With Generative AI systems beginning to disrupt the world, it comes with challenges. Guidelines are put in place in prompts to govern the context of asking questions and receiving relevant results. The term 'Jailbreaking' has sprung up to break the guidelines or restrictions set down in the AI program.  Thus there is the need to consider how to make these AI models safe and secure for use.

In the workshops AzureOpenAIService-Workshop Understanding-LLMs the sizes of the models were shown to see the rapid changes over such a short period of time. 

The prompt engineering lab was great to be able to get started with immediately.

It is important to think about the business use cases that need to be solved and have a high level understanding of what Generative AI can do to help.  A business should look at  the benefits of using AI, the ethics of using AI and how that impacts employees and customers.

Terms explained

Generative AI  is a type of artificial intelligence technology that can produce various types of content, including text, imagery, audio and synthetic data.

A large language model (LLM) is a deep learning algorithm that can perform a variety of natural language processing (NLP) tasks

Prompt engineering is the practice of designing inputs for generative AI tools that will produce optimal outputs.

Microsoft Tools

A couple of tools that are worth looking at:

Responsible AI Toolbox

Responsible AI is an approach to assessing, developing, and deploying AI systems in a safe, trustworthy and ethical manner. Learn how Microsoft thinks about Responsible AI here.

Hands-on tools for building effective human-AI experiences

The HAX Toolkit is for teams building user-facing AI products. It helps you conceptualize what the AI system will do and how it will behave. 

Following the event Microsoft Published

AI Innovations Shine at Microsoft Build: AI Day London

Microsoft Build - AI Day in London

 

There  is a one day free event in London on 19 October 2023 to help developers discover new opportunities with AI and advance their knowledge and skills to deliver more value faster with AI and Microsoft Azure. I am excited to be attending the event and helping out.

The event is at ExCeL London and the event agenda is full of interesting sessions

Opening Keynote: Learn what developers need to know about AI today, and explore new paths forward in your organization and career.

Breakout Sessions: Dive into topics like:

• Building and scaling cloud-native apps
• Generative AI using Azure OpenAI Service
• The power of cloud and AI into your workflow
• Integration of security throughout your code and cloud workflow.
• Integrating Azure AI and Azure Kubernetes Service
• Building and maintaining Copilot with Azure ML and GPT-4
• Building intelligent Apps with GitHub Codespaces
• Developing in the cloud with Microsoft Dev Box

Immersive Workshops: Bring your device and join a 75-minute immersive workshop on topics like building flexible, intelligent apps with OSS on Microsoft Azure and GitHub Copilot, learn how to use OpenAI models using Azure OpenAI, and how to automate your cloud application testing.

Connection Hub: Connect and learn with one-to-one meetings with experts, and spaces to collaborate with other developers and industry leaders to help solve your technical questions.

Evening Mixer: Come together with speakers, industry leaders, and the developer community to continue your conversations, mix and mingle, and have a little fun.

Register Now 

More details

Career Essentials in Generative AI

I came across this LinkedIn set of training for Career Essentials in Generative AI by Microsoft and LinkedIn . Discover the skills needed to apply generative AI in your career. Learn the core concepts of artificial intelligence and generative AI functionality. The objectives are:

• Develop an understanding of generative AI models.
• Learn the ethical considerations of using generative AI.
• Explore the impact of generative AI tools.

There are 5 courses and the learning path takes 3 hours 49 mins

• What Is Generative AI?
• Generative AI: The Evolution of Thoughtful Online Search
• Streamlining Your Work with Microsoft Bing Chat
• Ethics in the Age of Generative AI
• Introduction to Artificial Intelligence

This is a great course for learning those foundational generative AI skills.

Microsoft Inspire 2023 keynote

We are in the middle of a massive platform shift with AI . Two areas Natural Language the developer interface and the Reasoning Engine give more power together with Information Management as we digitise the world. Generative AI together with Microsoft Fabric will be transformative.

Five of the key announcements Satya highlighted 

• Bing Chat Enterprise will probably be the biggest generative AI launch this year. I like the ability to  do SWAT analysis by just asking. The private AI powered chat enables confidential data, user data, conversations and chat activity to stay private an not used to train AI models
• Sales Copilot
• Power Automate Process Mining  is Generally Available
• Microsoft loves OpenAI. The Azure OpenAI Service has regional expansion
• Meta's Llama 2 on Azure and Windows
• Microsoft AI Cloud Partner Program new incentives

There were discussion on how the model effectiveness is the currency of content safety and how prompt engineering with its phrases and symbols was important. All generative AI services are going to change everything. 

The Inspire keynote 18 July used an analogy of rooms in the house to describe the various layers of the Microsoft stack services.  The Copilot stack was discussed with a way to enable AI transformation. 

With this amazing era of AI we need to understanding how to provide that duty of care and ethical advisory. It is more paramount than ever think about data provenance, bias amplification, regulatory compliance and explainability. 

 

DARA Data & AI Readiness App

I came across this really useful app looking at whether your organisation is AI ready, built on generative AI techniques.  DARA is a unique, free-to-consume consulting solution designed to help enterprises accurately baseline their data and AI readiness, visually compare their position against an industry index, and lay the path to further nurture their digital transformation journey.

The key features are

• In-Depth Self-Assessment
• Baseline Your Organisation's Readiness based on Culture, Ideation, Delivery, Impact and Trust using a Likert scale measure
• Cross Analysis of Teams
• Peer Analysis Within Your Industry

The  5 point readiness scale measures are foundational, active, operational, embedded and transformational.