Case Study

From Manual Orders to a Scalable AI Order Automation Blueprint

ExIQ mapped fragmented email, phone, Pagemaker Pro, spreadsheet and manual pick-list work into a staged automation roadmap designed to protect margin, expand seasonal capacity and move orders faster from customer request to dispatch.
Anonymous primary producer and wholesaler Primary production and wholesale distribution
Illustrative wholesale flower team reviewing orders on a tablet inside a busy packing facility.
Anonymised wholesale operations engagement

Seasonal growth was being constrained by the order desk.

A primary producer and wholesaler was managing peak demand through phone calls and free-form emails, with staff re-keying details into Pagemaker Pro and spreadsheets before preparing pick lists manually. Customers were not receiving order confirmations. Each new order added another handoff, another re-keying step and another chance for margin to disappear through delay, waste or a selection error.

ExIQ shaped the opportunity into a staged order-automation blueprint: capture customer intent once, validate it, connect it to the operational source of truth and turn it into a faster, clearer fulfilment flow.

Engagement status This case study presents the proposed automation blueprint and commercial model. Every financial figure is a proposal estimate based on stated assumptions, not a measured post-implementation result.
A$555K modelled potential annual impact
10,000 monthly orders in the design scenario
3 connected automation opportunities
01 / Operating pressure

Every order created another manual handoff.

The current-state review described five staff spending much of the day re-entering order details across Pagemaker Pro and spreadsheets, checking customer intent and assembling pick lists manually. Without customer order confirmations, changes or misunderstandings returned to the order desk as another call or email. During seasonal peaks, the warehouse still needed accurate product, quantity and delivery details.

In a perishable supply chain, the commercial cost is larger than administration. Slow confirmation can lose the sale. A mis-pick can become waste. Weak stock visibility can trigger urgent purchasing or leave demand unfilled.

5 staff described as heavily occupied by order administration
2 channels free-form phone and email demand entering one operation
Disconnected Pagemaker Pro, spreadsheets and manually prepared pick lists
No confirmation customers were not receiving order confirmations
02 / Order flow

One order flow, whichever channel the customer chose.

The AI automation layer would turn unstructured email and voice AI conversations into consistent order data. Rather than creating a smarter inbox, the design aimed to move validated information into the operating system and carry it through to confirmation, procurement and picking.

Human review remained part of the design. Low-confidence items, substitutions, unusual delivery instructions and commercial exceptions could be surfaced for staff instead of being silently guessed.

Illustrative flower order workspace with a tablet, barcode scanner, image-rich pick sheet and fresh stems.
Illustrative, anonymised visual. The blueprint connected customer intent to structured order data and a clearer physical picking process.
  1. 01 / Capture Email or voice

    Receive natural-language orders around the customer's preferred channel.

  2. 02 / Structure Products and quantities

    Translate free-form intent into consistent order fields and candidate SKUs.

  3. 03 / Validate Customer and delivery

    Check critical details, availability and exceptions before commitment.

  4. 04 / Connect MRP or ERP record

    Write approved data into the operational source of truth and procurement flow.

  5. 05 / Fulfil Confirm and pick

    Issue a customer summary and create an image-rich, bar-coded pick list.

Human exception lane Ambiguous items, substitutions, pricing decisions and unusual instructions stay visible to staff.
03 / Modelled value opportunity

The model identified A$555K in potential annual impact.

The business case concentrated the opportunity into three commercial levers: redeploy repetitive administration, protect more product from waste and mis-picks, and move more efficiently from demand to packing. The model is directional, but it gives leadership a concrete value hypothesis to validate before implementation.

Modelled potential annual impact

Modelled scenario across labour, waste and fulfilment improvement

A$555K
Labour redeployment A$140K

Two of five administrative roles at A$70K each.

Reduced waste and mis-picks A$250K

A 0.5% saving applied to the model's A$50M revenue assumption.

Pick, pack and forecasting A$165K

A 2% gross-margin improvement scenario requiring baseline validation.

Illustrative Year 1 model. These are estimated opportunities, not realised savings, forecasts or guarantees. Actual value depends on baseline validation, scope, adoption, data quality and implementation.
The most useful outcome was a testable value model.

Discovery could replace broad automation claims with verified order volumes, handling time, error rates, spoilage, margins and implementation cost. That turns A$555K from a persuasive hypothesis into a measurable investment case - or narrows the first release before unnecessary spend.

04 / Planning roadmap

A focused planning sprint made the next investment decision concrete.

The proposed engagement was deliberately short. It combined on-site workflow evidence, technology research, interaction design and architecture into a Product Requirements Document and a costed path to implementation.

  1. Week 1 Discover the real workflow

    Observe staff, map channels and systems, capture pain points and agree success measures.

  2. Week 2 Design the requirements

    Define user journeys, system behaviour, integrations, controls and minimum viable scope.

  3. Week 3 Review with the operation

    Test the draft PRD against practical exceptions, user needs and technical feasibility.

  4. Week 4 Decide the next release

    Complete revisions, prioritise the roadmap and provide implementation costing.

05 / Decision-ready deliverables

Enough clarity to fund the right first release.

  • A current-state process map grounded in real order and fulfilment work.
  • Functional and non-functional requirements for email, voice and pick-list flows.
  • User journeys, exception paths, acceptance criteria and rollout steps.
  • An MRP/ERP review with source-of-truth and integration options.
  • Security, privacy, performance and operational-control requirements.
  • A prioritised implementation roadmap with cost and value assumptions exposed.

For an operator considering workflow automation for wholesale distribution, this sequence reduces the risk of automating a broken handoff or buying a platform before the operating requirements are understood.

06 / Commercial path

Capture first. Connect the operation. Then optimise the network.

The blueprint separated immediate order-desk relief from the broader MRP and forecasting opportunity. That creates a more credible path for wholesale and distribution leaders: prove the workflow, measure the result and scale only when the data holds.

  1. Start Capture demand once

    Structure email and voice orders, speed confirmations and reduce re-keying.

  2. Connect Create operational flow

    Link approved orders to procurement, inventory records and visual pick lists.

  3. Optimise Use the new data

    Improve seasonal planning, purchasing, service performance and margin visibility.

Your opportunity

Could your order desk support more growth without more admin?

ExIQ can map the workflow, model the value and define a staged automation plan before you commit to a build.