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AI Readiness Assessment & Transformation Roadmap for a Mid-Market 3PL

An AI readiness assessment that turned a logistics operator's scattered AI ideas into a costed roadmap, a validated pilot and a production agent in under 12 weeks.

Build a Similar Solution
3

Use cases funded for build

Value × feasibility × data-readiness scoring

Prioritization
ROI model
Roadmap
10–12 weeks

From first workshop to production pilot

Fixed-scope paid discovery, then pilot sprint

Fast track
Pilot
Production
$1.5–2.5M

Annual savings identified

Bottom-up cost model per use case

Business case
CFO-ready
Payback
40+

AI use cases assessed

Discovery workshops across 6 departments

Discovery
Workshops
Use-case scoring

Ready-made Solution

AI Consulting, Not AI Experiments

Our AI consulting services start with the business, not the model. In one structured engagement we map where AI pays back, check whether the data can support it, and ship the first use case to production.

  • Executive and department AI discovery workshop series

  • Data and systems readiness audit (TMS, WMS, ERP, telematics, email)

  • Use-case portfolio scored on value, feasibility and risk

  • 12-month AI roadmap with budget, team and governance model

AI readiness
AI roadmap
Business case
Pilot
AI Consulting, Not AI Experiments
01

Problem

The client, a US third-party logistics provider with ~$300M revenue, had board pressure to "use AI" and a dozen vendor pitches, but no shared view of where AI would actually move margin.

Results:


  • Disconnected pilots owned by different departments

  • No baseline metrics to judge AI ROI

  • Data spread across TMS, WMS, spreadsheets and inboxes

  • Leadership unable to choose between build, buy and wait

Problem
02

Challenge

  • Get operations, finance, IT and sales to agree on one prioritized list

  • Assess data quality and integration effort before committing budget

  • Produce a business case the CFO would sign, not a slide of ideas

  • Prove value fast with a pilot inside existing systems, without a platform rewrite

Challenge

AI-driven development

Solution

Plavno ran a fixed-scope AI readiness assessment followed by a pilot sprint. The output was a ranked portfolio, a funded roadmap and one AI agent live in production, part of our digital transformation consulting practice for logistics and supply chain.

Product Highlights

  • Discovery workshops with C-level and six department leads

  • Process mining light: shadowing of dispatch, billing, customer service and carrier sales

  • Data readiness audit: quality, access, latency and ownership per source system

  • Use-case scoring model: value, feasibility, data readiness, risk, time-to-value

  • AI roadmap: three waves with budget, team, KPIs and decision gates

  • Governance blueprint: model policy, human-in-the-loop rules, vendor guidelines

User Flows

  • Assess: leadership interviews → department workshops → systems and data audit

  • Decide: scored portfolio → business case per use case → executive prioritization session

  • Deliver: pilot backlog → 4-week build sprint → production launch with KPI tracking

Experience & Scale

  • Fixed price and fixed timeline for the assessment phase

  • Senior engineers in every workshop, so feasibility is answered on the spot

  • Roadmap reusable across business units and acquisitions

Architecture Overview

Deep Dive: Project Architecture

  • Discovery layer: structured interview kits, process maps, pain-point log

  • Data audit layer: connectors to TMS, WMS, ERP and email for sampling and profiling

  • Scoring engine: weighted model with sensitivity analysis on value and effort

  • Business case layer: cost baseline, savings model, payback period per use case

  • Pilot stack: AI agent on the client's existing TMS APIs, LLM gateway, RAG over SOPs

  • Governance layer: model registry, access policies, evaluation and audit logging

  • KPI layer: baseline vs pilot dashboards for leadership

Deep Dive: Project Architecture

Challenges

Hard Problems We Solved

Too many ideas, no owner

Too many ideas, no owner

One scoring model and one executive session replaced six competing pilot lists.

Data that looked ready but wasn't

Data that looked ready but wasn't

Profiling exposed missing carrier IDs and free-text fields before they broke a pilot.

Proving ROI before the build

Proving ROI before the build

A bottom-up cost model per process gave finance numbers it could audit.

Fitting AI into legacy systems

Fitting AI into legacy systems

The pilot ran on existing TMS APIs, with no system replacement.

Data Protection

Security & Privacy

Data handling during assessment

Data handling during assessment

Read-only access, sampled data, NDA and data processing agreement from day one.

Read-only
NDA
DPA
MVP
AI governance model

AI governance model

Approved-model list, human approval for customer-facing actions, prompt and output logging.

AI governance
Human-in-the-loop
Audit
MVP
Vendor and model risk

Vendor and model risk

Build-vs-buy matrix with data-residency and lock-in criteria for every use case.

Vendor risk
Data residency
Exit plan
MVP

Benchmarks

Scale & Reliability

Roadmap built in waves

Roadmap built in waves

Each wave ships independently, with go/no-go gates on measured KPIs.

Waves
Decision gates
KPIs
Reusable AI platform

Reusable AI platform

Shared LLM gateway, RAG and evaluation layer for all later use cases.

LLM gateway
RAG
Evals
Multi-site rollout

Multi-site rollout

Templates for warehouses and regions, so wave two reuses wave one.

Templates
Multi-site
Playbooks
Cost control

Cost control

Per-use-case token and infrastructure budgets with monthly reporting.

FinOps
Token budgets
Reporting

Innovative Experience

Industries & Use Cases

Logistics & 3PL

Logistics & 3PL

Dispatch, carrier sales, billing audit, customer service and exception handling.

Distribution & Wholesale

Distribution & Wholesale

Order intake, inventory planning and supplier communication.

Mid-market & Enterprise ($100M–$1B)

Mid-market & Enterprise ($100M–$1B)

Companies with board-level AI goals and no in-house AI team.

Delivery Crew

Project Team

High-performing developers for growing companies

Eugene Katovich

Eugene Katovich

Sales Manager

Not sure where AI pays back in your business?

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Key Performance Flow

From AI ideas to a production pilot: discover, score, decide, deliver

01

Discover

Leadership interviews and department workshops capture pains and ideas

02

Audit

Data and systems readiness checked against each idea

03

Prioritize

Scoring model ranks use cases; executives fund the top three

04

Deliver

First use case built and launched within the existing stack

Delivery & Automation

Delivery & Automation

    • Pilot agent on existing TMS/WMS APIs

    • KPI baseline vs pilot dashboards

    • Handover of roadmap and backlog

Throughput & Scale

Throughput & Scale

    • Shared AI platform for later waves

    • Multi-department and multi-site playbooks

    • Monthly AI cost reporting

Consulting Stack

Consulting Stack

    • AI discovery workshop kits

    • Use-case scoring and business case models

    • AI roadmap and governance templates

Fulfillment Process

Implementation Roadmap

Our strategic development phases from MVP to advanced real-time capabilities

step 1
step 2
step 3
step 4

Weeks 1 – 2

Leadership interviews, discovery workshops, process shadowing

Weeks 3 – 4

Data and systems audit, use-case scoring, business cases

Weeks 5 – 6

Executive prioritization, AI roadmap, governance model, pilot backlog

Weeks 7 – 12

Pilot build sprint, production launch, KPI tracking, wave-two plan

Results

Real outcomes from production — speed, accuracy, and scale

40+ use cases scored

40+ use cases scored

three funded with signed business cases

3 identified annual savings

3 identified annual savings

across billing, dispatch and customer service

Pilot live in production

Pilot live in production

in 10–12 weeks on the existing TMS

One AI governance model

One AI governance model

adopted across departments

Wave two started

Wave two started

reusing the same AI platform

Tools We Used

Technology stack

Discovery & modeling

Discovery & modeling

Miro
Process maps
Scoring model
ROI calculator
LLMs & agents

LLMs & agents

OpenAI / Claude via LLM gateway
LangGraph
Integrations

Integrations

TMS / WMS / ERP APIs
Email
Webhooks
Data audit

Data audit

Python
dbt
PostgreSQL
Data profiling
Infra & governance

Infra & governance

AWS
Observability
Model registry
Audit logs
RAG

RAG

Vector store
Re-ranking
SOP and contract corpus

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Frequently Asked Questions

Quick Answers

Find answers to your common concerns

How long does it take?

About six weeks to a funded roadmap, and up to twelve weeks including a production pilot.

Do we need clean data first?

No. The audit tells you which use cases your data supports today and what to fix for the rest.

What does it cost?

The paid discovery step starts at $5–10K; it begins after a free 45-minute session with a Plavno engineer.

What is an AI readiness assessment?

A fixed-scope review of your processes, data and systems that ranks AI use cases by value and feasibility and ends with a costed roadmap.

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