How Should Mid-Sized Architecture Firms Start Using AI?

For a 20–50 person architecture firm, artificial intelligence (AI) tools can significantly improve productivity by helping teams summarise project information, draft documentation, and retrieve knowledge stored across collaboration systems. However, most AI tools used within architecture firms rely on the organisation’s existing data environment — including SharePoint, email, project documentation, and internal knowledge bases.

For firms using Microsoft 365, Microsoft Copilot is one of the most practical ways to introduce AI into everyday workflows. Because Copilot pulls information directly from systems such as SharePoint, Teams, Outlook, and OneDrive, the quality of AI results depends heavily on how well project data is structured.

Before enabling AI tools across the firm, mid-sized architecture practices should first ensure their SharePoint environment, legacy server archives, and collaboration processes are properly aligned. Preparing the data environment ensures AI delivers useful insights rather than amplifying existing organisational challenges.

Architecture firms operate within complex digital environments that combine BIM design platforms, project collaboration tools, and long-term archives of drawings and documentation. Many practices maintain project information for years due to contractual requirements, client references, or regulatory obligations.

Because AI tools such as Microsoft Copilot retrieve information directly from systems like SharePoint and Microsoft 365, they will surface whatever information exists within the environment. This can include outdated project documentation, duplicate files, or inconsistently structured folders.

Without proper governance, AI responses may be less accurate or may expose sensitive project information. Preparing the underlying data environment before deploying AI tools helps ensure the technology enhances productivity while maintaining strong information governance.

For many mid-sized architecture firms, SharePoint has become the central platform for storing project documentation, managing collaboration, and maintaining internal knowledge resources. However, these environments often evolve organically as projects and teams grow.

An AI readiness review should begin with an assessment of how project libraries are structured. This includes examining folder hierarchies, naming conventions, version control practices, and how permissions are inherited across project teams.

If project documentation is inconsistent or poorly organised, Copilot may return incomplete or confusing responses. Establishing a clear and logical SharePoint structure helps ensure Copilot  can locate and summarise the most relevant information.

Most mid-sized architecture firms maintain extensive archives of completed projects, sometimes spanning a decade or more. These archives may include drawings, design documentation, tender materials, and client communications.

While this information can be valuable for reference, it should not always be treated the same as active project data. Copilot searches across available data sources, which means archived information may appear alongside current project documentation.

A structured archive strategy helps ensure Copilot prioritises active project information while preserving historical records appropriately. This often involves segmenting archived projects, defining retention policies, and aligning long-term storage and backup strategies.

Within Microsoft 365, Copilot respects existing permission structures. This means that if users have access to specific files or folders, Copilot can surface that information when responding to prompts.

For architecture firms, permission governance is particularly important. Project documentation may contain commercially sensitive design work, client contracts, financial information, or intellectual property associated with design concepts.

Before deploying Copilot broadly, firms should review how permissions are applied across SharePoint libraries and Teams workspaces. Ensuring appropriate access controls protects confidentiality while allowing teams to collaborate effectively.

Rather than introducing AI across every workflow immediately, architecture firms often achieve better outcomes by starting with a few targeted productivity improvements

Common early use cases include:

• Summarising project meeting notes
• Drafting client communications or proposals
• Extracting key actions from long email conversations
• Reviewing internal policy documents
• Generating project summaries from SharePoint documentation

By starting with focused use cases, teams can gradually incorporate AI into their workflows without disrupting established design and BIM processes.

Although AI tools such as Copilot operate within the Microsoft 365 cloud environment, architecture firms still rely heavily on reliable workstations, stable networks, and consistent file synchronisation.

Design workflows involving platforms such as Revit, Rhino, and Bluebeam require strong infrastructure performance to maintain productivity. If file synchronisation is unreliable or network performance is inconsistent, introducing additional AI tools may create complexity rather than efficiency.

Ensuring infrastructure stability and properly aligned storage systems helps ensure AI tools integrate smoothly into existing workflows.