Agentic AI & ML Solutions: Microsoft Copilot, AI Agents & Machine Learning

Agentic AI describes AI systems that can plan and take actions towards a goal, rather than only answering questions, for example gathering data from several systems, updating records, drafting and routing documents or triggering workflows. In the Microsoft ecosystem, agents are built with Copilot Studio and Microsoft Foundry, and operate within the permissions and human oversight you define.

Licences alone rarely change how people work. Value comes from targeting specific use cases for each role, making sure your data is well organised and permissions are correct, training people with real examples, building a network of champions and measuring usage and time saved. That is what separates a pilot from lasting adoption.

Copilot can surface anything a user already has access to, so oversharing, missing sensitivity labels and outdated content become visible quickly. A readiness check reviews permissions, SharePoint and Teams sharing, data classification and retention, typically using Microsoft Purview, so Copilot gives useful answers without exposing sensitive information.

Agents built with Copilot Studio extend Microsoft 365 Copilot: they are quicker to build, live where people already work (such as Teams and Microsoft 365) and suit knowledge and process tasks. Microsoft Foundry is the Azure platform for bespoke AI applications and agents, with a choice of models, custom orchestration, evaluation and deployment: the better fit for complex, customer-facing or high-scale scenarios.

Microsoft Foundry (formerly Azure AI Foundry) is Microsoft’s platform on Azure for building, evaluating and running AI applications and agents. It provides access to a broad catalogue of AI models, tools for grounding AI in your own data, and built-in security, monitoring and responsible-AI controls.

It depends on the length and quality of your historical data and how volatile the metric is. We benchmark models against your current forecasting approach, show forecasts with confidence ranges rather than a single number, and retrain models as patterns change, so you know how far to trust each forecast.

If you use AI beyond a small pilot, yes. A framework sets out acceptable use, data access, risk assessment, human oversight and monitoring. The GDPR and regulators’ guidance on AI and data protection apply, and organisations operating in the EU also need to consider the EU AI Act.

Start with two or three use cases that have clear business value and data that’s available and trustworthy. We often begin with a short discovery workshop to prioritise use cases, check data readiness and agree how success will be measured before anything is built.