How Generative AI Projects Fit Into SR&ED Claims

Generative artificial intelligence is a significant field for businesses that create software, automation tools, content systems, analytics platforms and specialized applications. Using an existing AI model for standard business operations does not make a project eligible for a Scientific Research and Experimental Development (SR&ED) claim. Some development work involving generative AI is eligible if it involves technological uncertainties and systematic investigation. Businesses creating new AI capabilities must examine technical activities to determine if specific work and expenditures qualify. Determining how the team conducted the project, what technological obstacles existed and how developers attempted to resolve those obstacles is necessary to prepare an accurate claim.

Identify The Technological Challenge#

A formal SR&ED assessment starts when developers identify the technological problem they are attempting to solve. Generative AI projects often involve challenges regarding model reliability, processing speed, scalability, data handling, integration or the ability to produce consistent outputs. A business is often working toward a capability that is impossible to achieve using existing methods, which requires the team to investigate different technical paths. A business objective is not sufficient on its own. The primary requirement is that the work addresses a technological challenge through systematic search or investigation.

Technical teams should describe the challenge using technical descriptions rather than commercial goals. As an example, the desire to create an AI application that generates useful responses is a general business objective. A technical description is more specific, like the difficulty of achieving output consistency while maintaining low processing times and low resource consumption. Documenting the technical starting point, known limitations and performance targets helps establish if development activities meet SRED requirements.

Define The Technological Uncertainty#

Generative AI development involves uncertainty when developers do not know if a specific technical approach will produce the desired result — this uncertainty is present when developers attempt to improve model accuracy, lower incorrect outputs, handle specialized terminology, integrate multiple AI components or achieve predictable performance. The uncertainty is related to technological knowledge rather than if customers will buy the product.

Distinguishing technical uncertainty from commercial risk is a vital part of evaluating an SR&ED project. Developers should document what information was missing at the start of the investigation and why standard practices were insufficient — this documentation is relevant because rapid changes in model architectures create complex conditions. A project is likely to involve testing different architectures, prompting methods, retrieval techniques, training strategies or system configurations to overcome a specific technological limitation.

Document The Investigation#

Systematic investigation is a requirement when evaluating generative AI development. Teams are encouraged to retain evidence showing how they approached the problem, which alternatives they considered, which experiments they performed and what results they obtained. Development records are composed of experiment notes, technical specifications, test results, model evaluations, code changes and performance measurements — these materials demonstrate that the team followed an investigative process instead of using a known solution.

Unsuccessful experiments are valuable because they show how the team gained knowledge from failure. A generative AI project often requires repeated testing before developers discover a method that produces acceptable results. As an example, a team might modify a retrieval system and evaluate different model configurations before selecting an approach. Records of these iterations provide evidence of the technical investigation and the knowledge gained during the process. The same record-keeping discipline that supports AI governance across a wider technology stack applies here.

Evaluate Model Development Work#

Some activities involving generative AI are routine implementation rather than experimental development. Businesses must distinguish technical investigation from standard configuration or deployment. Using a commercial model through an application programming interface for ordinary content generation is software development but it is not necessarily SR&ED. Integrating a standard AI service into a business process is not automatically qualified work. Substantial development work is present when the team attempts to overcome technological limitations.

Developing or modifying model architectures, experimenting with training methods or creating new techniques for managing specialized data are examples of potentially qualifying work. The nature of the technological work is the determining factor, rather than the use of AI terminology.

Consider Data And Training Activities#

Data preparation and training are often part of generative AI development projects. Teams encounter technical challenges when they create datasets, remove problematic data or determine how training information affects model behavior. Experiments involve different data structures, preprocessing techniques or evaluation methods.

When these activities resolve a technological uncertainty, the work is often part of the broader SR&ED project. Businesses should distinguish experimental data work from routine data preparation. Cleaning data for business operations is not SR&ED even if an AI system uses the information. Documentation should explain the technical purpose of the work and how the work contributed to investigating the uncertainty. Records of dataset versions, testing methods and performance comparisons make this distinction clear.

Track Development Costs#

Businesses should maintain records of expenditures once they identify qualifying activities. Generative AI projects involve employee salaries, contractor costs, cloud computing resources and software — these expenses are connected to the work through project codes, timesheets, invoices and accounting records. Reliable financial records make it easier to determine which costs relate to eligible activities.

Cloud computing costs are important because AI development uses significant processing resources. Businesses use cloud infrastructure for model training, experimentation and storage. Instead of treating all cloud spending as eligible, businesses should examine how resources were used — this practice separates experimental development costs from ordinary production or commercial operating expenses.

Maintain Employee Activity Records#

Employees perform many types of work during a generative AI project and some activities are not related to experimental development. Developers spend time investigating technical problems, writing experimental code, fixing software defects and supporting users. Accurate time records help businesses establish how employee effort was allocated to specific tasks.

Project documentation is a supplement to timesheets — recorded hours are more useful when they connect to technical work documented in experiment records, code repositories or project notes. Technical teams should maintain records as development occurs rather than at the end of the year — this approach improves the accuracy and credibility of the claim. Teams that already keep a record of which assets were generated will recognise the habit.

Review Claims With Special Consulting#

Organizations that manage complex generative AI projects are often more successful when G6 Consulting evaluates their Scientific Research and Experimental Development (SR&ED) documentation — this evaluation assists managers in identifying technical unknowns, experimental tasks and project costs that are eligible for an SR&ED claim. Assessment is valuable when AI creation requires various technologies, third party contractors, remote computing servers plus staff members who contribute to different project phases.

Consultants at G6 Consulting ensure that technical records and financial data correspond accurately. Technical specialists but also accountants examine development logs, test outcomes, labor hours, billing statements and project expenditures together to define the completed work. When leadership maintains these links throughout a project, the claim process is orderly and allows teams to resolve missing documentation before they submit an application.

Separate Experimental And Routine Work#

Generative AI projects often shift between experimental development and routine implementation. A team may spend time investigating if a new technique improves model performance and then use that solution to build a production application — these phases are not identical for reporting purposes.

Businesses should examine activities performed during each stage to determine which work relates to the technological investigation. Separating the activities improves internal project management. Developers can record when an experiment begins, what hypothesis they are investigating and when they determine that an approach is successful. Once the technological uncertainty is resolved, the remaining work is ordinary development or commercialization.