As organisations define their AI strategy, immediate focus often turns to identifying the tools that can deliver the biggest impact. But there is a more important, and simpler, question that must be answered first – which deployment model actually fits with the way your business works today? Get this wrong, and even the most powerful AI will fail to gain user buy-in and achieve the desired outcomes.
Microsoft 365 Copilot provides a foundation for many AI strategies, and for good reason. It builds on familiar tools and offers a clear way to introduce AI into everyday work. At the same time, agentic solutions present exciting avenues to deploy AI with the capability to perform autonomous and semi-autonomous actions, opening attractive new opportunities for businesses to benefit.
There’s also a place for more focused, private AI deployments like Private ChatGPT solutions, which can be custom-built for specific, data-intensive workflows like legal contract reviews, which benefit from a more bespoke and targeted approach.
With a host of options on the table, ensuring that AI delivers genuine value must begin with deciding which type of deployment is the best fit for your identified use cases. This is where the support of an expert partner like Advania becomes an enabler.
With technical expertise and demonstrable real-world experience across the AI journey, there’s no one better positioned to help you assess the best route for your project.
Deploying AI where your work and data already live
It’s not a surprise that business AI delivers more value when it is deployed close to where people already work, and where data is already governed. Ensuring this is factored into your deployment strategy is critical for maximising success, as is making an appropriate deployment choice to fit the needs of your business, as well as any priority workflows.
For many organisations, the centre of gravity is Microsoft 365 and Microsoft Azure. It is where collaboration happens, data resides, and day-to-day workflows converge, especially within tools like Teams and SharePoint. When AI is deployed into your existing Microsoft environment, users do not have to jump between platforms to see value, and IT teams can use tools they’re already familiar with to support and manage the AI.
This approach also makes the hard parts easier to manage. Access controls, information governance, and compliance oversight tend to be stronger when they build on foundations already in place, rather than being bolted onto a disconnected tool. It reduces the risk of stale content, duplicated knowledge stores, and AI experiences that drift away from the actual operations of your business.
Microsoft 365 Copilot is the ideal foundation
Microsoft 365 Copilot provides a ready-made business AI solution that meets people where they already work. It exists inside Microsoft 365 apps and helps users move faster on everyday tasks such as drafting copy, summarising meetings, and interrogating resources – all actions that reduce the admin burden within common workflows to boost productivity.
However, unlocking the full potential of Copilot goes beyond purchasing licences and “giving” the technology to your users. As with any new technology, enablement is essential for users to understand its capabilities and harness Copilot as part of their everyday workflows. Our advice is to start with a pilot that tightly aligns with the goals of the business and targets common friction points for users. Keeping it focused builds business-user confidence and ensures value is seen from day one. This empowers employees, making them part of the AI journey, and creating champions to lock in know-how.
At Advania, we’ve not only defined our own Copilot adoption methodology built around these principles but bring a unique level of first-hand Copilot expertise to the table to help guide your deployment. As a business, we were among the first to deploy Copilot across our teams and have seen an 86% adoption rate by our users – and that’s in a business where some of our teams and engineers simply don’t work in the Microsoft ecosystem. Outside of the positive productivity impact this has made on our business, this decision saw us recognised as one of the world’s first Microsoft partners to earn the new Copilot specialisation.
Support technical use cases with Private ChatGPT
While Microsoft 365 Copilot can be a natural beachhead into AI and often proves its value quickly, some workflows are better suited to a more bespoke platform. This need becomes most apparent where a wide variety of data should be integrated, or processes are highly technical or data intensive. In these scenarios, the AI must be exposed to precise sources of information and well-guided in how it should behave, leveraging distinct and repeatable prompts that guide user interactions.
This is where a Private ChatGPT instance can shine. These solutions are still built on the Microsoft stack, using Microsoft Foundry, and deliver a similar assistant experience, but one which can be tailor-made for specific use cases or complex workflows.
This makes them strong examples of a business AI deployment done with purpose, defined by how it can meet the needs of your teams, the data you possess, the boundaries you set, and the demands of day-to-day work streams. Crucially, since Private ChatGPT leverages Microsoft Foundry, it can still be delivered within your existing Microsoft environment, deployed using Azure and aligned with the controls and security measures you already operate.
Like Copilot, Advania has a proven track record of implementing our Private ChatGPT solution, helping organisations achieve high levels of user adoption with an AI experience focused on specific use cases.
Agentic AI built around key workflows
Where a contextualised deployment is required for specific use cases, a dedicated AI agent can also present an attractive option.
With the advanced capabilities of the latest AI models, agents can now be created with a greater level of understanding, comprehension and autonomy than ever before. Like with a Private ChatGPT deployment, any effective agent needs to be centred around the context of the tasks and workflows being supported and built on the data foundations – such as those within your Microsoft estate – that exist in your business today. Focused on the right use cases, an agent becomes a powerful tool that boosts productivity and gives users more time to focus their skills and expertise on other areas.
A growing number of organisations also want to empower employees with the flexibility to build their own agents. Inspired by the capabilities for low-code app development in tools like Power Platform, Copilot Studio allows users to self-serve, building custom agents via the same low-code approach. This removes friction close to the point of need while reducing the burden on IT and development teams.
But self-serve only works with the right foundations. Without guardrails, organisations end up with shadow tools, inconsistent outputs, and unnecessary risk around data exposure. Safe self-service needs clear governance, approved building blocks and templates, and a route from experimentation to trusted production use – all things that we can help define and deploy as part of a foundational exercise before this is shared across your business.
This is an area where our team excels, collaborating with your key stakeholders to define the most valuable use cases, before applying our AI and automation expertise to your specific challenges. Leveraging our expertise in Microsoft specialisations for Copilot, AI Platform on Microsoft Azure and Low-Code Application Development, we’ve quickly established an impressive track-record of agent creation, building customised agents to support our own internal workflows, as well as a number of ready to deploy agents designed to meet common customer needs like RFP creation and sensitive document redaction.
What to do next
If you are trying to place the right bets for your AI strategy, you need to start from a position of clarity – what are the use cases you want to support, and how are they best approached?
The goal should never be to try and choose one tool and hope it solves everything. Instead, seek to build an end-to-end approach that matches your organisation’s reality and meets the needs of your users across multiple workstreams.
If you want to sense check your approach, Advania’s team can help you map the right path, from strategy and use case discovery to build, integration, and safe scaling.
