Tenders and Requests for Proposals (RFPs) are routine mechanisms for acquiring goods and services in nearly every industry. Organizations that rely on manual document review, copy-paste workflows, and siloed knowledge-sharing face significant obstacles: delayed responses, inconsistent messaging, and constant interruptions to subject matter experts (SMEs).
AI in tendering is designed to overcome these exact pain points. By automating content analysis, leveraging historical bid data, and streamlining collaboration, AI solutions allow teams to focus on developing compelling, compliant responses—instead of being bogged down by administrative processes.
For instance, a marketing agency handling dozens of RFPs per quarter might use AI to analyze incoming documents, auto-populate response drafts, and highlight compliance checkpoints. As a result, bid managers allocate more time to strategy and less to paperwork, leading to higher win rates and the ability to handle more bids simultaneously.
The science behind AI-powered tender writing
Modern AI-driven tender platforms use several underlying technologies to deliver value:
- Large Language Models (LLMs): These models, trained on vast amounts of text, excel at understanding RFP questions and generating human-quality answers that align with organizational voice.
- Generative AI: These algorithms produce first drafts by matching current RFP requirements to company-approved responses and data from prior successful bids.
- Analytical Engines: These components assess win probability, monitor deadline compliance, and suggest strategic bid/no-bid decisions based on historical outcomes.
When an RFP document is uploaded, the AI automatically reviews the file's structure, parses requirements, and categorizes them by type (e.g., technical, legal, financial). It can spot cross-references and dependencies that could otherwise be overlooked. By analyzing previous responses and the current content library, AI suggests answers most relevant to each requirement, organized in the proper format and with consistent style.
Suppose a software company receives an RFP with 200 detailed questions covering IT architecture, security practices, and customer success metrics. The AI instantly "shreds" the document—extracting each question, mapping it to relevant sections of the company's content library—and generates complete draft answers, ready for human review and refinement. What previously took days of manual work is accomplished in under an hour.
How to write a tender with AI
1. Preparation and content foundation
An effective AI-powered tendering workflow begins with a well-organized content library. Every piece of relevant company information, past project summaries, compliance statements, and standard technical descriptions should be stored centrally and curated for quality and currency.
For example, before using AI, a business services provider might have thousands of outdated or duplicative Q&A pairs in scattered folders. The tender team must review and organize this material, updating references, condensing duplicates, and verifying accuracy. With AI-assisted tools, the library can be maintained regularly, assigning SMEs to review targeted content areas each month. This "single source of truth" is essential: if the input data is current and reliable, AI-generated responses will match.
2. Automated requirement analysis and "shredding"
Upon receiving a new tender, the AI performs an initial pass called "shredding." It identifies and categorizes every requirement (e.g., "List your ISO certifications" or "Describe your data handling protocol"), cross-linking related questions and flagging requirements that recur across different sections. This step ensures nothing is missed and highlights potential conflicts or dependencies early in the process.
A global SaaS company might receive RFPs requiring the same core information but phrased in different ways. The AI's shredding process helps maintain consistency, so answers about certification status or service-level agreements are always accurate and aligned.
3. Rapid drafting and customization
AI matches each requirement to its best-fit content from the library, considering both substance and tone. Proposal builders embedded in modern platforms handle document formatting, logical structuring, and the insertion of up-to-date company statistics or case study excerpts.
The first draft produced by AI is complete and consistent, preserving the company's branding and past best practices. Human reviewers can then focus on strategy—fine-tuning key differentiators, integrating custom value propositions, and adjusting specificity based on the audience (technical evaluators vs. financial decision-makers).
For example, a mid-sized IT security firm might use AI to assemble an RFP response for a healthcare client. The draft includes previous answers about HIPAA compliance, customizes them for the new client's environment, and aligns messaging with the client's stated priorities, as identified during AI-driven RFP "shredding."
4. Refinement and human touch
Finalizing a tender with AI means balancing automation with critical human judgment. Reviewers scan the AI-generated draft for opportunities to add targeted case studies, real-world outcomes, or specific ROI metrics that resonate with the client's goals.
AI can also suggest supporting material, such as concise customer testimonials or project timelines, helping teams make every answer more compelling. Key factors like clarity, evidence, and tone can be adjusted for each section, ensuring the final product isn't just compliant—but persuasive and differentiating.
Teams then use AI-provided analytics to review win rates, identify improvement areas, and track completion time. By closing feedback loops and iterating on the content library, the process is continuously refined.
Responsive offers an intelligent solution for better bidding
Responsive is a leading platform in Strategic Response Management and AI-powered tender authoring. Its integrated ecosystem delivers unmatched productivity and quality for organizations responding to RFPs, security questionnaires, and other external information requests.
Real-World Results:
- Perceptyx, a software company, reduced RFP draft times from days to 30 minutes. Their RFP team now handles 75 RFPs annually without adding staff, and SMEs spend more time on core responsibilities by relying on Responsive's AI to auto-populate up to 90% of answers.
- Data Axle improved RFP completion speeds by 30-50% and reduced SME effort by 80%, owing to a dynamic, well-governed content library.
- Accruent scaled from 6 to 25 live RFPs at a time, completing half of their security questionnaire responses on the first pass and freeing SMEs for only the most technical questions.
In each scenario, Responsive's AI-powered workflow delivers three things: speed, consistency, and capacity for growth.
Ready to see Responsive in action?
Organizations ranging from software providers to marketing agencies trust Responsive for their end-to-end tender and RFP needs. From initial document analysis and requirement "shredding" to fully formatted, human-reviewed responses, Responsive enables teams of any size to compete at the highest level.
AI adoption in tender writing is no longer just a future trend—it is an established competitive advantage for those who want to submit more winning bids, more quickly, without sacrificing accuracy or compliance.
Find out how Responsive can help transform your team's bidding process. Explore features, request a demo, and join the growing community of organizations exceeding their tender goals with AI.
For more information or to schedule a demonstration, visit the Responsive website.
Frequently asked questions
What is the tender process in AI?
The AI-powered tender process involves several key steps. First, teams build a well-organized content library with company information, past projects, and standard descriptions. When a new tender arrives, AI "shreds" the document by identifying and categorizing each requirement. The AI then matches requirements to the best content from the library and generates draft responses. Human reviewers refine these drafts by adding targeted case studies, ROI metrics, and other persuasive elements. Throughout this process, AI handles administrative tasks like formatting and cross-referencing, allowing teams to focus on strategy and creating compelling responses that differentiate them from competitors.
What is the best AI tool for tenders?
Responsive is highlighted as a leading platform for AI-powered tender authoring and Strategic Response Management. It offers an integrated ecosystem that delivers productivity and quality improvements for organizations responding to RFPs and security questionnaires. Companies using Responsive have reported significant benefits, including reduced draft times from days to minutes, improved completion speeds by 30-50%, and the ability to handle more bids simultaneously. The platform enables teams to centralize prior responses, maintain updated content through SME assignments, and generate high-quality first drafts quickly.
What is tender used for?
Tenders and Requests for Proposals (RFPs) are mechanisms organizations use to acquire goods and services across virtually all industries. They provide a structured way for organizations to solicit competitive bids from potential suppliers and service providers. The tender process allows organizations to compare offerings, ensure compliance with requirements, and select the most suitable vendor based on various criteria including technical capabilities, pricing, and experience. For businesses responding to tenders, they represent opportunities to win contracts and grow their client base by demonstrating their capabilities and competitive advantages.
What is the best AI for procurement?
While the article focuses primarily on the response side of tenders rather than the procurement side, AI systems for procurement typically analyze spending patterns, streamline supplier selection, automate purchase orders, and enhance contract management. For organizations that issue tenders, AI procurement tools can help create more precise RFPs, evaluate responses more objectively, and identify optimal suppliers based on multiple criteria beyond just price. These platforms often include features for spend analysis, contract lifecycle management, supplier relationship management, and risk assessment. The ideal AI procurement system would integrate with existing enterprise resource planning (ERP) systems and provide actionable insights to drive cost savings and improve supplier performance.