4 lessons from how top financial services firms use AI for RFPs and DDQs

The financial services industry has been fertile ground for AI innovation since before generative AI went mainstream. In a field where the sheer volume of data is staggering, machine learning and predictive analytics have had a seemingly endless supply of raw material for decades. However, initial gen AI benefits and positive ROI may surprise some: managing RFPs and DDQs. 

Firms using AI-powered Strategic Response Management platforms to manage RFPs and DDQs pursue better opportunities, respond with greater confidence, and turn institutional knowledge into a competitive asset. According to the 2026 Financial Services State of Strategic Response Management Report, leading firms are more than 1.4x more likely to report growth in revenue from RFPs than less mature organizations.

Rather than spinning their wheels trying to figure out how to use AI for productivity, they’re building trusted knowledge foundations, applying AI across the entire pursuit lifecycle, governing every response, and measuring outcomes that matter to the business. How? We’ll explore that here, with four practical lessons from FinServ leaders that are moving beyond AI experimentation to measurable business impact.

For more on this topic, view the on-demand webinar.

1. Prepare your knowledge before scaling AI

AI can’t compensate for fragmented, outdated, or poorly governed content. RFP and DDQ responses often draw from dozens of sources, including:

• Investment philosophy and process documentation
• Performance and risk disclosures
• Operational controls
• ESG policies
• Regulatory language
• Fund materials
• Previous questionnaires
• Input from Portfolio Managers, Legal, Compliance, Operations, and Client Service

When this information is spread across documents, inboxes, personal drives, and disconnected systems, AI retrieves conflicting answers. It may surface an expired disclosure, reuse language approved for a different strategy, or generate a response that sounds plausible but cannot be defended.

Top firms begin by building a trusted source of knowledge. They identify content owners, standardize metadata, establish review cycles, remove duplicative entries, and control access to sensitive information. 

One global asset manager with more than $1T in AUM overhauled its Responsive Content Library before expanding their AI usage. After ensuring AI was directed toward accurate information, the firm used AI to answer 85% of questions. Conversational AI also resolved 78% of ad hoc and proposal-related queries.

2. Govern knowledge as an ongoing practice 

AI often reveals content problems that were already there. Duplicate answers, expired disclosures, unclear ownership, and inconsistent language become easier to spot once a system starts retrieving and generating responses at scale.

Visibility like this shows teams where governance needs to improve before those issues reach an investor, consultant, or client. Strong governance also gives users more confidence in the answers AI produces.

Leading firms set clear rules for how content enters the library, who owns it, and when it must be reviewed. They also use AI to flag content that may be outdated, duplicative, or rarely used. SMEs then decide what to update, merge, or remove.

With ongoing ownership, a well-built library gains value over time as it reliably reflects the changes in investment strategies, regulations, performance data, and approved language shifts. Governance should cover:

• Named owners for key content
• Review schedules based on risk and change frequency
• Metadata for product, strategy, region, and audience
• Access controls for sensitive information
• Approved language that must remain unchanged
• Processes for removing outdated or duplicate answers

SRM reduces the burden on content managers while helping firms maintain approved language for future RFPs and DDQs. Teams spend less time verifying sources, reviewers can focus on higher-risk answers, and AI has a stronger base for generating relevant responses.

3. Scale AI responsibly

Financial services firms operate under strict standards for accuracy, privacy, and compliance. Those standards should shape the AI program from the beginning. 

Leading firms involve Legal, Risk, Compliance, IT, and Security early. These teams help define which content AI may access, which answers require added review, and how data should be protected. Early involvement also prevents late-stage delays when a pilot is ready to expand. 

A responsible approach includes clear controls for:

• Data ownership and privacy
• Role-based access
• Source citations
• Audit trails
• Approval requirements
• Verbatim use of compliance-approved language
• Human review before external delivery

With this structure, firms can expand AI use without stranding proposal, investor relations, or due diligence teams to manage risk on their own. It also gives time back to SMEs. Portfolio Managers, Compliance Officers, Legal teams, and operations leaders should not have to rewrite the same standard answers for every request. AI can prepare grounded drafts and route questions to the right people while experts focus on judgment, exceptions, and client-specific details.

Human review remains critical. AI should help reviewers work faster by showing sources, flagging unsupported statements, and identifying answers that need closer attention.

4. Measure outcomes from the start

AI impact gains traction when leaders can see proof of success. Usage data can help explain adoption, but it does not show whether AI is improving the response process or the big three metrics leadership really cares about: win rate and revenue influenced; client retention and revenue protection; and sales and client service velocity. 

Leading firms track these outcomes to connect AI to priorities that executives already understand:

• RFP and DDQ turnaround time
• Time spent drafting and reviewing
• Percentage of answers drawn from approved content
• SME hours required per request
• Number of follow-up questions from clients
• Response volume handled without more headcount

Measuring these outcomes also helps teams improve the SRM system. Some examples… if one type of DDQ still requires heavy review, the content may need work. If certain questions produce weak drafts, ownership or metadata may be unclear. If turnaround time improves but follow-up questions increase, response quality may be slipping.

Measurement creates a feedback loop between content, workflows, AI, and business results.

Build the foundation before you scale

The firms getting AI right follow a clear pattern. They prepare trusted content, maintain it through strong governance, involve risk and compliance teams early, and measure results in business terms.

This approach helps proposal, investor relations, and due diligence teams respond faster with fewer manual steps. It also gives them more time for pursuit strategy, client context, and the work that helps win and retain mandates.

Start with one high-value workflow. Define the outcome, identify the knowledge it depends on, and set the right controls. Once that process is working, expand into the next use case.