Clear GenAI Requirements in an RFP: The Bedrock of Successful AI Implementation

Organisations will turn to external partners for GenAI implementations and the success of your GenAI project depends heavily on the clarity of your requirements.

A well-prepared RFP with comprehensive requirements isn’t just bureaucratic paperwork – it’s your blueprint for AI implementation success. Let’s dive into some insights on creating requirements that will attract the right vendors and set your GenAI project up for success.

What is it?

GenAI requirements differ from traditional technology requirements in fundamental ways. Traditional IT projects typically have clear inputs, outputs, and defined processes. Unlike traditional systems with fixed outputs, GenAI generates probabilistic outputs, making its behaviour less predictable.

When writing GenAI requirements, your RFP should account for key factors that make GenAI different, such as:

Model selection, capabilities and fine-tuning specifications – Rather than simply requesting “an AI model,” you must specify whether you need local or hosted, multi-modal, proprietary or open-source models, what fine-tuning is required, and why your chosen approach is appropriate for your business needs. (Or the vendor needs to be clear on whether fine-tuning is needed in their solution.)
Data protection and privacy considerations – GenAI requirements must address how the system will handle sensitive information, whether it retains data (or uses it for training), and how it complies with privacy regulations – especially critical given the “black box” nature of many AI systems.
Integration mechanisms and API accessibility – Clear requirements must define exactly how the GenAI solution will connect with your existing systems, including authentication methods and the specific APIs needed.
Scalability dimensions – GenAI requirements need to address both computational scalability (handling high volumes of requests) and semantic scalability (adapting to evolving use cases and content types).
Ethical guidelines and bias mitigation – Unlike traditional software, GenAI solutions need explicit requirements around allowing harmful inputs, preventing harmful outputs, addressing algorithmic bias, and ensuring transparency.
New technology: Given that GenAI is a relatively new technology, reasonable vendor experience includes prior AI/ML implementations, expertise in foundation models or model fine-tuning, and relevant industry or solution-specific GenAI experience – particularly with integration into existing enterprise systems.

What does it mean from a business perspective?

Being clear about GenAI requirements in your RFP, like any requirements and RFP combination, delivers real business advantages:

Reduced implementation risks – Precise requirements help you avoid costly mid-project pivots by ensuring vendors fully understand your needs from the start.
Better vendor selection – Detailed requirements allow you to evaluate vendors based on their specific AI capabilities rather than generic promises or marketing claims.
Improved ROI measurement – When requirements are tied to business objectives, you can track whether the implemented solution is delivering the expected value.
Enhanced regulatory compliance – Clear requirements around data handling, privacy, and security help ensure your GenAI solution meets legal and regulatory standards.
Successful integration with existing workflows – Well-defined requirements ensure the AI solution enhances rather than disrupts your current business processes.
Faster time-to-value – By clearly communicating what you need, you reduce back-and-forth during implementation and accelerate deployment.
Future-proofing your investment – Requirements that address scalability and model updates ensure your solution remains valuable as your needs evolve.

What do I do with it?

Here are actionable steps to create clear GenAI requirements in your next RFP:

Explain your use cases – start with business outcomes, not technical specifications – Start with business outcomes, not just technical spec. Define the problem first, instead of saying, ‘We need a chatbot,’ frame it as ‘We need to reduce customer service response time by 40%.’
Specify model characteristics that matter to your use case – Be explicit about whether you need, for e.g., a proprietary or open-source model and why. Detail any specific capabilities required (text generation, image creation, etc.).
Define data parameters clearly – Outline what data will be available for training, what data privacy constraints exist, and who maintains ownership of trained models.
Include concrete performance metrics – Move beyond vague requirements to specific, measurable criteria: “The model must respond to user queries within 2 seconds” or “Generated content must achieve at least 85% acceptance rate from our review team.”
Balance specificity with flexibility – Strike the right balance, be clear on non-negotiables while allowing room for vendor innovation.
Incorporate ethical guardrails – Explicitly state your expectations around bias mitigation, content moderation, and transparency in AI decision-making. Where are ‘humans-in-the-loop’ required?
Include testing and validation methodology – Define how you’ll evaluate the quality of AI outputs, including any human-in-the-loop review processes.
Outline a governance framework – Specify how the solution will be monitored for drift, how updates will be managed, and who maintains control over key decisions.
Post-Deployment, production support: Plan for long-term success: define SLAs; understand how model updates are managed; how vendors handle model deprecation, and ask for a road-map to future-proof your investment.

The difference between successful a GenAI implementation and a costly failure is heavily influenced by the completeness and clarity of your requirements. Take the time to develop comprehensive, thoughtful requirements in your RFP, you’re not just selecting a vendor – you’re establishing the foundation for business transformation.

What challenges have you faced in defining GenAI requirements? Share your thoughts in the comments.

What is it?

What does it mean from a business perspective?

What do I do with it?

Further Reading