You know you need AI.
You don’t know where to start.
You sit on years of POS, inventory, and finance data with no path to using it. We ran 400+ retail locations before we built Ward. Now we help operators turn that data into decisions.
Schedule a strategy session →AI vendors sell tools. Nobody helps you build the plan.
You’ve seen the demos and the pitch decks. But your data isn’t ready, your systems don’t talk, and your team has no bandwidth to figure out where to start. That’s where we come in.
POS in one place, inventory in another, finance in a third. No single source of truth, a 10-year-old ERP, and a POS that can’t export clean data. Modernization feels impossible.
Every AI company says they’ll solve everything, and none of them understand your operations. You don’t have data scientists on staff. You need guidance, not another headcount.
We started on the floor, not in consulting — operators who became data engineers who became AI architects.
Operators who became data engineers who became AI architects.
We ran 400+ locations and built custom ERPs, POS, inventory, and data pipelines from scratch. We know what breaks at scale because we lived it.
Before Ward, we built the data lakes, ran the AI orchestration, and modernized the legacy stacks ourselves. That’s the difference between advisory from operators and advisory from a deck: every recommendation has already survived a real retail floor.
Strategy broken down by what you actually need.
Every retailer is at a different stage. Some need a data foundation. Others need AI orchestration. We meet you where you are.
Where do you actually stand? We audit data, systems, team, and workflows — data maturity scoring, integration gap analysis (ERP, POS, WMS, BI), capability mapping, and a prioritized roadmap with quick wins.
Start assessment →Unify POS, inventory, ERP, marketing, labor, and finance into one AI-ready layer. Unified data model, ETL/ELT pipeline design, quality and governance framework, and cloud architecture (Snowflake, BigQuery, Databricks).
Design your data lake →Decide what to build, what to buy, and how the pieces work together: LLM selection and routing, retail-specific agent design, prompt engineering and retrieval architecture, and build vs. buy analysis per capability.
Plan your AI stack →When legacy ERP, POS, or WMS is the bottleneck, we plan an AI-first migration: legacy audit, vendor evaluation with an AI lens, API-first architecture, and a phased rollout that minimizes disruption.
Modernize your stack →Move from pilot to production: KPI definition and success measurement, AI monitoring, alerting, and governance, closed-loop feedback design, and change management that keeps systems sharp.
Deploy with confidence →First, we figure out what kind of stack you actually run.
Revenue is a lazy proxy. What matters is how many systems need to talk, how many brands you carry, and how much custom logic sits on top. Four profiles cover almost every retailer who calls us.
One POS, maybe accounting, inventory in spreadsheets. No ERP, WMS, or BI layer. 1–15 locations, one channel. Off-the-shelf schema. Typical: regional specialty chain, single-brand DTC, family-owned grocery.
POS, ERP, WMS, e-commerce, finance — mostly off-the-shelf. 15–75 locations, retail plus e-com, custom KPIs on a standard base. Typical: mid-market retailer on NetSuite or Dynamics with a modern POS.
Retail, DTC, wholesale, marketplaces, sometimes franchise. 75–500 locations, custom domain logic (fresh weights, seasonality, pharmacy compliance, royalties), PCI scope, real data engineering in place.
Multiple legal entities, often multiple ERPs and POS inherited through M&A. 500+ locations, cross-entity benchmarking, regional data residency, SOC 1 and SOC 2 in scope. You have a data team; capacity is the bottleneck.
One package per stack type. Fixed scope, fixed price.
Every engagement is billed at $200/hour and scoped to deliver a finished plan, not a slide deck. You own everything we produce. Hours are estimates of senior operator time, not analysts learning on your dime.
- Stack and data audit, three opportunity areas ranked by payback
- Vendor shortlist for the first AI use case
- 30-60-90 day execution plan, one executive readout
- Full data maturity score; target data lake, AI orchestration, and agent design
- Build vs. buy per capability; vendor evaluation with reference checks
- 12-month roadmap with budget envelopes, board-ready deck
- Everything in Architect on a multi-channel domain model and custom KPIs
- Orchestration design, data governance, and access-policy framework
- RFP authoring, vendor negotiation, and reference architectures
- Fractional Head of AI / Data attached to your leadership
- Federation strategy across entities; build vs. buy on every decision
- Board prep, RFP/MSA/DPA review, team coaching, SOC and compliance support
Five phases. No surprises. Everything written down.
Every package runs the same five phases. Diagnostic compresses them into two weeks; Platform stretches them across ten; Embedded runs them on a quarterly loop. Where are you, where do you need to be, what do we build, how do we ship it, how do we keep it sharp.
Interviews across ops, IT, finance, and merchandising. System inventory, read-only access, and data sample pulls. Deliverable: stakeholder map, system inventory, top-10 friction list, signed scoping memo.
Score data maturity, integration coverage, governance, and team capability against the workloads you want to run. Deliverable: maturity scorecard, gap register with severity, prioritized opportunity list with payback.
Architecture diagrams, build vs. buy, vendor shortlists with reference checks, sequencing and budgets. Deliverable: target architecture, 12-month roadmap, budget, and risk register your board can defend.
Plans rot in PDFs, so we help you ship: RFP drafts, vendor negotiation, MSA and DPA review, kickoff playbooks, team enablement. Deliverable: signed vendors, kickoff packets, success metrics in writing.
For Embedded clients we stay attached: quarterly architecture reviews, vendor scoring, a steering-committee seat, team coaching. The goal is to make your team good enough to stop needing us.
Compared to hiring a Head of AI internally.
The real comparison isn’t Ward vs. another consultancy. It’s Ward vs. the headcount you were about to post. Most mid-market retailers don’t need a permanent AI lead yet — they need a finished plan, a few months of senior judgment, and a path to building the team later.
| Hire a Head of AI / Data | Big-4 / strategy consultancy | Ward Architect package | |
|---|---|---|---|
| Base salary | $240K–$300K | n/a | n/a |
| Bonus + equity | $50K–$100K | n/a | n/a |
| Benefits + payroll tax (~30%) | $80K–$100K | n/a | n/a |
| Recruiter fee (one-time) | $60K–$90K | n/a | $0 |
| Tooling, training, conferences | $15K–$30K | n/a | Included |
| Engagement fee | n/a | $300K–$1.2M | $40K |
| Time to first deliverable | 4–7 months (recruit + ramp) | 10–16 weeks | 5 weeks |
| Operator experience | Variable. Depends on the hire. | Junior-led, partner-reviewed. | Operators who ran 400+ stores. |
| Retail-native judgment | Maybe | Rare | Yes |
| Attrition / continuity risk | High in Year 1–2 | Medium (team rotation) | None. Same team start to finish. |
| Year 1 fully loaded | $445K–$620K | $300K–$1.2M | $40K |
A senior AI lead joining an unarchitected stack spends their first six months doing the work this engagement delivers in five weeks.
We’ll write the JD, sit on the interview panel, and onboard them. Most Embedded clients graduate to internal leadership — that’s the goal.
We work alongside, not over. The fastest unlock for a new lead is a finished architecture and a sequenced roadmap they didn’t have to build alone.
Built for operators who are ready to move.
Three kinds of retail operator get the most out of Ward’s advisory — each sitting on data they can’t yet turn into decisions.
50–500 stores. You have the data but not the team to use it. You need a partner who gets your constraints.
500+ locations. You’ve invested in systems but AI keeps stalling. You need a strategy that cuts through internal complexity.
Portfolio companies under pressure to modernize fast. You need a roadmap that maps to EBITDA improvement, not science projects.
Tell us where you are. We’ll tell you where to start.
No pitch, no pressure. A 30-minute conversation with someone who’s been in your shoes.
You know you need AI. You just need the right starting point.
Operational experience meets implementation strategy. Let’s map it out together.
Find out what your data has been hiding.
Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.