The firm transforming your OMS sets your AI ceiling. I’d think about that before you sign the statement of work.
If you’re choosing a partner to modernize your order management system (OMS), ask what your own team will be able to do six months after go-live. Can they trace why an order was routed to a particular store? Can they test a change to that rule? Can they update the workflow when the business changes?
McKinsey describes three horizons of AI transformation: enablement, automation, and reinvention. I see the same progression in OMS delivery. First, engineers use AI to help with individual tasks. Then teams build it into repeatable workflows. Over time, those workflows change how the work gets done and who can do it.
Horizon 1 — Enablement: AI in the hands of every engineer and practitioner
We rolled out Claude across ~150 engineers and delivery staff. Documentation that took 30–60 minutes now takes 10–15. Bug fix cycles that took 4 hours now take under 30 minutes. Performance analysis that previously consumed days—feeding JFR profiles, thread dumps, and logs manually—runs in under 5 minutes. On one engagement, 1,200–1,500 QA test cases were generated and refined in 10–15 days.
This stage matters. But McKinsey & Company is right: companies that redesigned workflows at this stage were 5.3 times more likely to report enterprise value capture than those that left workflows unchanged. Handing people a tool is not the same as changing how the work gets done.
Horizon 2—Automation: AI running workflows, not just assisting them
This is where Nextuple has pushed hardest over the past year.
On a major jeweler engagement, we built an OMS health monitoring platform that eliminated a 40-minute manual cycle run multiple times daily. It now runs automatically every 4 hours, 24/7—28 engineer-hours recovered per week, overnight coverage going from 0% to 100%, three production-critical bugs caught before deployment. Zero incidents. The engineer who built it used AI as the build partner throughout: architecture design, code review, documentation — all generated collaboratively in a single development session.
On a furniture retailer engagement, technical documentation turnaround dropped from 2–3 hours per document to 15–20 minutes. A full product registry across multiple repos—estimated at 5+ days of manual effort—was produced in 2 hours. A feature scaffold that used to take 2–3 days now generates in 30 seconds.
For an OMS AI Assessment on a large specialty retailer, a multi-agent Claude framework analyzed the full OMS install—configs, code, UI—surfacing architectural insights that would have taken weeks to compile manually. That same tool has now been deployed across multiple client environments.
At this stage, organizations with highly AI-fluent leadership teams are 3.9 times more likely to report value capture. That's why we invested in building AI fluency into delivery leadership, not just individual contributors.
Horizon 3 — Reinvention: Operating models rebuilt around AI
This is the hardest horizon and where the real competitive distance opens up. Only 11% of organizations are here. We're pushing toward it—and so are the clients who are paying attention.
A QA framework built on Claude is now published org-wide—six reusable skills that sequence the entire workflow correctly, from project knowledge bootstrapping through gap analysis to test generation. Any team inherits it on day one. The institutional knowledge doesn't leave when the engineer does.
A governance framework is embedded directly inside a client's code repository — a project constitution, domain-specific skills, slash commands, persistent memory — so every developer who opens that repo works with AI that already knows the patterns, the conventions, the things to avoid. The AI doesn't write code faster. It writes code that follows the team's actual standards because those standards are now encoded infrastructure.
One of our engineers is building an Enterprise AI Gateway—intelligent routing that sends simple tasks to lower-cost models and complex architecture problems to the most capable ones. That's not a productivity tool. That's AI governance infrastructure. An operating model built for a world where AI is the substrate, not the add-on.
And on the client side: a major Latin American retailer was so impressed with our AI-led discovery acceleration on a DB2 migration that they are now formally launching Claude across their entire technology organization—with OMS as the highest priority workstream. They didn't just buy an implementation. They bought a model for how to operate.
Three questions to ask an OMS implementation partner
- Can you show us how AI helped on a difficult OMS workflow? Pick a scenario from your own business. Ask the delivery team to walk through the inputs, the output, and how they validated it.
- What happens when the data disagrees? Ask what the team would do if the OMS shows inventory available but the store or warehouse system says otherwise. Find out where a person steps in and how that decision is recorded.
- What will our team be able to run and change after go-live? Ask to see the test suites, project instructions, and operating procedures you’ll inherit. Have the partner explain how your team would update them when a fulfillment rule changes.
We’re still iterating on how we do this at Nextuple. That’s why I’d ask any partner, including us, to show you the work. Pick an order flow your team actually struggles with and see what the partner leaves behind that your people can use.
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Adapted from an article originally published on LinkedIn on August 11, 2026
