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Simply Put |
Enable Solution Architects to generate high-quality, first-draft content for the early stages of the solution plan using AI — reducing manual effort, improving consistency, and accelerating pursuit timelines. |
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Problem / Pain Point |
Teams spend excessive time and effort piecing together the Vision → Capabilities → Architecture → Dev-object chain for each new solution. Inputs arrive in many formats (RFPs, spreadsheets, emails), which makes it hard to keep the plan complete, consistent, and easy to iterate. |
Enable Solution Architects to generate high-quality, first-draft content for the early stages of the solution plan using AI — reducing manual effort, improving consistency, and accelerating pursuit timelines.
Each layer should be generated by a dedicated module with clear logic.
Outputs should be editable and exportable (e.g., into slides, Jira, Concourse).
Defined prompt structures per layer (e.g., “Given this RFP, generate a vision statement with industry relevance”).
Reusable and customizable for each offering and practice. Ability to create at a generic level for most practices.
Content should be returned in structured formats (e.g., bullets, tables, slide-ready format), including excel as currently works for Oracle
Allow for sizing of the opportunity
Support for tailoring based on client maturity, region, or solution scope.
Input Type |
Description |
|---|---|
RFP or Proposal Request |
Source document used to extract themes, scope, and goals. |
Client Info |
Industry, geography, public filings (e.g., 10-K), size, maturity. |
Pursuit Metadata |
Opportunity type, offering, team roles, size, client tier. |
Prebuilt Templates |
Vision statement formats, architecture layouts, benefits frameworks. |
Past Pursuits / Proposals |
Structured examples tagged by offering, industry, and plan layer. |
Layer |
AI-Generated Outputs |
|---|---|
1. Vision |
Executive summary-style narrative tying client goals to PwC solution vision; aligned with sector trends and strategy insights. |
2. Capabilities → Benefits |
A table or bullets mapping business capabilities to quantified benefits and KPIs; tailored to industry and offering. |
3. Architecture |
Logical or technical diagrams (or scaffold text) outlining future state architecture, integration points, and data flows. |
4. Dev Objects / Components |
A list of solution components (e.g., RICEFW objects, user stories, config elements) based on project type and scope. |
Metrics:
General use of the tool = X use per tool
Copy or download of suggestion provided
Goal (What do we want to learn?) |
Signal (What shows it’s happening?) |
Metric (How will we measure it?) |
|---|---|---|
Are teams assembling proposal layers faster? |
Elapsed time from first layer created → final layer saved (event timestamps in Concourse) |
Average “proposal-layer build time” (mins)Target: −30 % vs. pre-launch baseline |
Are users pulling content from the repository instead of writing from scratch? |
“Insert from repository” or AI-assist events vs. manual text entry per layer |
% of layers populated via repository/AITarget: ≥ 70 % |
Is template reuse driving consistency? |
Distinct template IDs reused across proposals |
Template-reuse rate (unique template uses ÷ total layers)Target: ≥ 70 % |
Is overall proposal throughput increasing? |
Completed strategic solution plans per week |
Plans completed per weekTarget: +25 % over baseline |
Are users satisfied with AI-assisted layer creation? |
In-app thumbs-up / thumbs-down after each assist |
Positive feedback rateTarget: ≥ 85 % |