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VP / Partner

Support pre-sales and proposal development

Enhances◐ 1–3 years

What You Do Today

Partner with sales to scope and price consulting engagements. Develop proposals, present to clients, and ensure what's sold can actually be delivered profitably.

AI That Applies

AI-assisted proposal generation that pulls from past proposals, methodology libraries, and pricing databases to accelerate proposal development.

Technologies

How It Works

The system tracks learner progress, competency assessments, and engagement patterns across the learning environment. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Proposal development accelerates. AI generates first drafts from past winning proposals and relevant case studies.

What Stays

Scoping a complex engagement requires understanding the client's real problem — not just what they wrote in the RFP. That requires experienced consultants in the room.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for support pre-sales and proposal development, understand your current state.

Map your current process: Document how support pre-sales and proposal development works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Scoping a complex engagement requires understanding the client's real problem — not just what they wrote in the RFP. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support proposal management platforms tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long support pre-sales and proposal development takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your board chair or lead independent director

Who on the team has the most experience with support pre-sales and proposal development — and have they seen AI tools that could help?

They shape expectations for how AI appears in governance

your CTO or CIO

What's the risk if we DON'T adopt AI for support pre-sales and proposal development — are competitors already doing this?

They own the technology infrastructure that enables AI adoption

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.