Discovery Engagement

Before you build anything,
know what is worth building.

Most organizations have more AI opportunities than they realize — and more constraints than they expect. This engagement surfaces both, so you act on the right things first.

The Process

Eight steps from assessment to action

Peak Trails does the work. You participate in focused review sessions and receive a complete set of deliverables — no weeks of workshops, no internal project management required.

Discovery 01 Discovery and Education Align on goals 02 Asset Inventory Catalog platforms and data sources 03 Support Mapping Document staffing and manual work 04 Use Case Analysis Log frequent requests and time spent Prioritization 05 Value Scoring Rate automation value per use case 06 Effort Estimation Assess technical feasibility per case 07 Priority Matrix Map value vs effort quick wins vs big bets 08 Selection and Scoping Top 2 use cases Deliverables: AI readiness assessment · Use case matrix · Prioritized roadmap · Two fully scoped use cases Peak Trails does the work — you participate in focused review sessions and receive a complete set of deliverables
How It Works

Two phases. Eight steps.
One clear output.

The discovery process runs in two distinct phases — first we understand what you have, then we decide what to do with it.

Phase 1 — Discovery
01

Discovery and Education

We align your stakeholders on what AI can actually do — the practical reality, not the marketing version. Everyone leaves speaking the same language.

02

Asset Inventory

We catalog every internal platform, data source, and system your organization runs. You cannot identify the right opportunities without knowing what ingredients you have.

03

Support Mapping

We document who is currently answering questions manually — IT staff, call center agents, coordinators. These are the roles where AI delivers the fastest return.

04

Use Case Analysis

We log the frequent data requests across your organization — who is asking, how often, how long it takes. These are the opportunities hiding in plain sight.

Phase 2 — Prioritization
05

Value Scoring

We rate each use case against a consistent framework — cost reduction, time savings, staff relief, constituent experience. Every use case gets an objective score.

06

Effort Estimation

We assess technical feasibility and level of effort for each use case — data readiness, integration complexity, build requirements. Realistic estimates, not optimistic ones.

07

Priority Matrix

We map every use case on a value versus effort grid. Quick wins rise to the top. Big bets get flagged for future planning. Your leadership team can align around it immediately.

08

Selection and Scoping

We select the top two use cases and build a detailed scope — timeline, level of effort, resource requirements, and integration dependencies. Ready for immediate execution.

Sample Output

What a real priority matrix looks like

Every use case scored and plotted — value on one axis, level of effort on the other. Your leadership team sees exactly where to invest first.

The matrix below reflects the output of a real discovery engagement. Each bubble represents a cluster of use cases that scored at that value and effort combination — larger bubbles mean more use cases at that score.

  • AI Readiness Assessment — baseline snapshot of your organization's current state
  • Use Case Matrix — every identified opportunity scored by value and effort
  • Prioritized Roadmap — sequenced plan organized by quick wins and big bets
  • Two Fully Scoped Use Cases — detailed timeline, level of effort, and integration requirements
Use Case Priority Matrix Bubble size indicates number of use cases at that value / effort score Quick Wins Big Bets 5 4 3 2 1 Value 1 2 3 4 5 Level of Effort Use cases per score: Few Moderate Many Most

Ready to find out what AI
can do for your organization?

The discovery process starts with a 30-minute conversation. We will tell you honestly whether your organization is ready and what the engagement looks like.

Schedule a Conversation →