AI-Enabled
Pursuit Intelligence
How I replaced a canned-content RFP process with a research-driven pursuit capability that made deep client customization possible at scale.
Industry: National insurance brokerage
Business Context: Multi-line regional sales organization
My role: VP, Client Strategy and Cross Sell
Focus: AI-powered research · Pursuit strategy · Decision intelligence
The Constraint
The challenge was larger than writing proposals faster. It was to make rigorous research and customization practical for every qualified pursuit.
RFP teams were working under a fundamental tradeoff. They could move quickly by relying on reusable content, or spend significantly more time researching the client and building a highly customized response.
Important intelligence was spread across client information, industry sources, relationship knowledge, prior opportunities, and internal expertise. Gathering and synthesizing that information manually for every pursuit required more time than the process could support.
As a result, teams often began with existing content and adapted it to the opportunity rather than beginning with a deep understanding of the client.
At the same time, qualification and execution varied across pursuits, reusable intelligence was difficult to carry forward, and leadership had limited visibility into where resources should be invested
The System
I designed a governed pursuit system that uses AI to automate deep research and synthesis, then combines that intelligence with qualification standards, human judgment, reusable expertise, and structured execution.
Opportunity Qualification
Formal criteria determine which pursuits justify significant investment and where the team's time and expertise should be focused.AI-Powered Deep Research
AI accelerates the research required to understand the client's business, industry environment, priorities, risks, relationships, and relevant competitive context.Pursuit Intelligence
Research is synthesized into the themes, insights, differentiators, and strategic considerations that shape how the team approaches the opportunity.Customized Response Development
Existing expertise and reusable content remain available, but the response is built around the specific client's situation rather than assembled primarily from canned material.Human Judgment & Governance
People validate sources, interpret findings, determine strategy, review recommendations, and approve final content. AI expands the team's analytical capacity. It does not make the pursuit decision.Performance Intelligence
Leadership gains visibility into pursuit quality, resource requirements, outcomes, capacity, and the reasons opportunities are won or lost.
Strategy & Operating Model
Redesigned the pursuit model around qualification, deep client intelligence, customized strategy, and clear decision points.AI Research Architecture
Designed how AI would automate client, industry, competitive, and opportunity research, turning large volumes of information into usable pursuit intelligence.Governance & Decision Quality
Established qualification standards, source verification, human review, and oversight so AI accelerated the work without replacing expert judgment.Knowledge & Workflow Design
Created the structure for turning research, institutional knowledge, and reusable expertise into a repeatable process for developing highly customized pursuits.Adoption & Performance
Led implementation and team adoption, then built the reporting needed to measure capacity, pursuit outcomes, and where the model should continue to improve.
My Role
I designed and led the capability from concept through regional implementation, with ownership spanning strategy, AI application, governance, adoption, and performance.
The Result
A stronger pursuit capability that delivered better outcomes.
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Every qualified pursuit could begin with substantially more client-specific intelligence, allowing teams to build the response around the individual client's business rather than primarily around reusable proposal content.
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AI compressed hours of manual research and synthesis, allowing teams to move through highly customized pursuits far more quickly than the previous process.
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The team could sustain approximately four times the formal pursuit volume without proportional headcount growth.
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Better qualification, deeper intelligence, and more tailored strategies contributed to materially stronger pursuit outcomes.
What This Demonstrates
AI changed what was economically practical.
Deep client research had always produced better proposals, but doing it well required too much human time to apply consistently across every pursuit. By automating much of the research and synthesis, AI removed that constraint while keeping interpretation, strategy, judgment, and final decisions with people. The impact went beyond faster proposal production. It made a much deeper level of client-specific understanding possible across a far greater volume of work.
The Shift
FROM
Canned content, manual research, and customization constrained by time.
TO
A research-driven pursuit capability where AI makes deep client intelligence and highly customized strategy scalable.
NEXT CASE STUDY
Cross-Sell Intelligence
How I turned fragmented client data into a repeatable system for identifying and activating growth opportunities.
