Issue Tree

How do we significantly grow revenue from RFP wins over the next 12 months?

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• 1. Which RFPs should we pursue?

  • 1.1 How do we discover opportunities?
    • 1.1.1 How do we identify and monitor relevant RFP sources? [aggregators, portals, databases, alerts, networks]
    • 1.1.2 How do we handle and triage inbound invites?
  • 1.2 How do we evaluate fit?
    • 1.2.1 How do we assess our technical capability to deliver? [skills, team, prior experience on similar projects]
    • 1.2.2 How do we assess commercial fit? [deal size, margin potential against pricing floor]
    • 1.2.3 How do we assess strategic fit? [geography, client type, follow-on potential, reputation risk, downside risk]
    • 1.2.4 How do we assess our chances of winning? [competitive landscape, prior client relationship, price sensitivity]
  • 1.3 How do we make and execute the bid/no-bid decision?
    • 1.3.1 What criteria should drive the yes/no decision?
    • 1.3.2 Who decides, how quickly, and how is it recorded?

• 2. How do we prepare and submit high-quality bids?

  • 2.1 How do we ensure mandatory compliance?
    • 2.1.1 How do we extract and track all mandatory criteria from RFP documents?
    • 2.1.2 How do we verify full compliance before submission? [pre-submission checklist, review step]
  • 2.2 How do we write compelling proposals?
    • 2.2.1 How do we understand and reflect the client's specific needs?
    • 2.2.2 How do we demonstrate our capability and track record effectively?
    • 2.2.3 How do we build and maintain reusable proposal content? [bid library of strong sections, case studies, team profiles]
  • 2.3 How do we price competitively?
    • 2.3.1 How do we apply a consistent pricing framework? [2–3x actual cost as floor; overhead and margin logic]
    • 2.3.2 How do we calibrate commercial aggressiveness per bid? [price-to-win vs. margin trade-off]
    • 2.3.3 How do we assess and cap downside risk before finalising price? [scope creep potential, client difficulty, strategic value, follow-on opportunity]

• 3. How do we run the process efficiently?

  • 3.1 How do we reduce effort per bid?
    • 3.1.1 How do we standardise and templatise repeatable elements?
    • 3.1.2 How do we use AI to accelerate drafting, research, and compliance checking?
    • 3.1.3 What tooling would best support the end-to-end bid workflow?
  • 3.2 How do we manage the active bid pipeline?
    • 3.2.1 How do we track deadlines and status across all active bids?
    • 3.2.2 How do we coordinate team handoffs per bid? [who does what, when, and how is it handed over]
  • 3.3 How do we resource the team appropriately?
    • 3.3.1 How do we plan capacity when multiple bids overlap?
    • 3.3.2 What roles are needed in the bid team, and who owns each? [discovery, writing, pricing, compliance, submission]

• 4. How do we learn and improve over time?

  • 4.1 How do we track outcomes?
    • 4.1.1 How do we ensure every submitted bid has a recorded outcome?
    • 4.1.2 How do we build a complete historical picture of bids, outcomes, and deal values?
  • 4.2 How do we gather feedback?
    • 4.2.1 How do we get feedback from clients after a win or loss?
    • 4.2.2 How do we run internal retrospectives after each bid outcome?
  • 4.3 How do we apply learnings?
    • 4.3.1 How do we feed learnings back into the process?
    • 4.3.2 How do we improve proposal content based on what wins?

• 5. What should we prioritise and do first?

  • 5.1 What are the quick wins we can implement immediately? [high-impact, low-effort changes]
  • 5.2 What are the high-leverage longer-term investments? [e.g. bid content library, AI tooling, pricing framework]
  • 5.3 How do we sequence the work across 12 months? [months 1–3 vs. 4–12]
  • 5.4 How do we measure whether the process is improving? [metrics: win rate, revenue won, effort per bid, learning velocity]
  • 5.5 How do we create a coherent end-to-end bid pipeline? (cross-cutting) — this question cuts across branches 1–4: how do we capture bids at entry, track them through every stage, and record outcomes consistently? [unified pipeline view spanning discovery → bid/no-bid → writing → submission → outcome]

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Where to Start — Candidate Starting Points

This tree is large and could represent weeks of work. Three concrete starting points were identified, each addressable as a focused sub-project:

Option 1: Build the win/loss record (branches 4.1 + 1.3) Query all rfp + submitted issues in the GitHub tracker, fill in missing outcomes, add deal values where known, produce a simple catalogue/spreadsheet. Output: first real data on win rate, deal size, and which types of bids we win. Feeds everything else in the tree.

• Estimate (AI-assisted): ~half a day (2–3 hours), assuming Drive access available for cross-referencing
• Status: Recommended — do first

Option 2: Retrospective on recent bids (branches 4.2 + 2.1–2.3) Pick 3–5 recent bids (at least one win e.g. Malmö, one loss e.g. UAE, one no-bid) and do a structured review against the RFP documents: did we meet mandatory criteria, how did we price, what was the internal quality like? Output: first real diagnosis of why we win and lose — not gut instinct.

• Estimate (AI-assisted): ~1–1.5 days (2–4 hours per bid × 3–5 bids). Dependent on Drive documents being accessible — add time if Drive is disorganised.
• Status: Do after Option 1

Option 3: Bid/no-bid criteria (branch 1.3) Draft explicit criteria for a yes/no bid decision and test retrospectively against the last 10 bids.

• Status: Deprioritised — Daniela is handling this and current process is working adequately. Revisit later.

Recommended Approach

Start with Option 1 (complete it), then move to Option 2. Box each by time rather than scope — timebox Option 1 to half a day, Option 2 to one day. Combined: ~1.5–2 days of focused AI-assisted work.