Planning applications can contain large amounts of information. Just one application can include drawings, planning statements, environmental reports, transport assessments and other supporting documents for planning officers to review before reaching a decision.

As planning systems become increasingly digital, artificial intelligence is being explored as a way to help process this information more efficiently.

For land developers, this could potentially mean faster access to planning data, more consistent analysis of documents as well as greater transparency between applications. However, it is important to note that AI is intended to support planning professionals rather than replace their judgement.

Why Planning Applications Take Time

A planning application might need the assessment of different documents against planning policies, any requirements and site-specific considerations.

Planning officers may need to establish:

• What is being proposed?
• Which planning policies apply?
• What technical issues have been identified?
• Any objections or received consultation responses?
• Any necessary requirements?

Much of this involves reviewing and comparing information from different sources which is where AI can potentially assist with the information-heavy parts of this process.

How Can AI Assist Planning Officers

AI systems can process large quantities of text and identify specific information from documents.

In this case, an AI tool could help identify references to any relevant planning policies, highlight key information from supporting documents, or summarise large volumes of material for further review. This can make repetitive processing tasks much less time consuming.

Though it is important to note that AI tools would only be supporting the review process but not automatically deciding whether a development should receive planning permission.

Reviewing Planning Documents

One potential application is document analysis.

Planning applications often have much information spread across multiple documents. AI-powered document intelligence can help spotlight structured information from these files, for officers to more easily locate relevant details.

For example, an AI system could identify:

• Proposed type an scale of development
• Number of residential units
• Site area
• References to relevant policies
• Transport or environmental considerations
• Proposed planning conditions.

The condensed information can then be reviewed by planning professionals.

Learning from Previous Planning Decisions

Historical planning decisions can also contain valuable information.

If planning decisions are stored in a consistent digital format, AI and other analytical systems could help reveal  patterns across past applications.

For example, developers and planning teams could analyse:

• Previous approval or refusal decisions
• Reasons for refusal
• Common planning conditions
• Applications with similar development types
• Planning outcomes of particular authorities

This could make historical planning information much easier to search for and to compare.

The UK Is Already Testing These Approaches

The UK Government’s digital planning programme has been testing tools designed to help local authorities process planning information more efficiently. One example is Extract, which uses AI to convert information from historic planning documents into structured planning data, with human review helping to verify the results.

Other local authorities are also experimenting with AI-assisted planning processes, including Milton Keynes City Council, which has worked with Valon to explore AI-supported planning workflows designed to help officers research policies and planning policies more efficiently.

These projects show how AI is beginning to move from general experimentation towards specific planning workflows.

Benefits for Land Developers

Although planning authorities remain responsible for planning decisions, faster and more structured processing of applications could also benefit developers. Developers could potentially receive clearer information about major issues, any changes needed or planning conditions much earlier in the process.

This could not only improve communication between development teams and planning authorities but also help reduce unnecessary delays.

However, the extent of this benefit will depend on how individual authorities utilise and implement these technologies.

AI Supporting Planning Judgement

Planning is not just a process of matching an application against a database of rules.

Applications can involve competing considerations, local circumstances, consultation responses and any professional interpretation. Planning committees and officers must also consider the legal policies that can be applied to the specific proposal.

Thus, AI-generated analysis should be treated as a tool for supporting decision-making information, rather than a planning decision itself. Naturally, human oversight remains essential for checking accuracy, interpreting context and making the final professional judgement.

The Importance of Reliable Data

If planning documents have errors, incomplete historical information, or any inconsistent datasets, an AI system might produce inaccurate results.

This means the development of standardised, machine-readable planning data is especially important. As in our previous article, structured planning information gives digital systems a more reliable foundation for searching, analysing and connecting planning data.

AI and machine-readable planning therefore complement each other: structured data makes information easier for AI systems to process, while AI can help people work with that information more efficiently.

The Role of Enterprise Technology

For large development organisations, AI-powered planning analysis could eventually form part of a wider digital environment.

Enterprise technology from Oracle Corporation can support the integration of business, financial and operational information, providing organisations with more easily accessible and manageable data across complex development portfolios.

Connecting planning information with project, financial and property data would also allow developers to assess planning activity alongside wider commercial considerations.

The goal is not to automate the entire planning process, but to create a more connected information environment where developers and professionals can make better-informed decisions.

Conclusion

AI-powered planning application review has the potential to reduce administrative burden from processing large amounts of planning data. By identifying relevant information and material with the review of previous decisions, AI can help planning professionals work more efficiently.

For land developers, this evolution could make planning information easier to understand and use, while potentially improving the efficiency of the wider development process.

Future articles will explore how geospatial AI can analyse land beyond traditional mapping, LiDAR technology providing detailed information about development sites, and how computer vision is being used to monitor construction progress.