GAMiller Consulting helps executive teams modernize ERP, connect NetSuite to the rest of the business, and put machine learning to work on financial data — without the failed first attempt.
I've spent nearly forty years inside financial systems — as a Unix administrator in the early 90s, the founder of an IT consulting firm that grew to 200 clients, a Vice President of Information Systems, and, for the last several years, the executive owner of global NetSuite environments for a $1B auction house operating across London, New York, Geneva, and Hong Kong.
I founded GAMiller Consulting in 2015 to bring that experience directly to executive teams: ERP selection, implementation strategy, enterprise integration, financial governance, and the BI systems that turn raw data into decisions leadership can act on.
Now I'm adding a new layer to that. I'm completing a postgraduate program in AI and machine learning, and applying it directly to client work — building ML-powered data lakes and predictive models that change what a finance system can actually tell you.
Six areas, all pulled from the same forty years of financial systems work — now with AI built in rather than bolted on.
Full-lifecycle implementations and OneWorld multi-entity rollouts, including rapid, same-day operational cutovers.
ML-powered data lakes and predictive models — including valuation and demand forecasting built for real business decisions.
API integrations connecting legacy systems, CRM, e-commerce, and payments platforms to NetSuite via iPaaS tools like Workato, Boomi, and Celigo.
IT governance, security, and financial controls that identify revenue leakage and stand up to audit.
BI strategy and reporting — from Crystal Reports to real-time dashboards built for the C-suite and the board.
Custom client, user event, RESTlet, and scheduled scripts that power automation and service-to-service messaging.
For senior leaders, that's the strategic question of the moment — and it calls for a clear point of view on AI investment, implementation, and the capabilities required to drive real outcomes across the enterprise, not just inside another pilot.
Most organizations have already run the isolated AI experiment. The harder problem — the one that actually determines whether AI changes anything — is turning that into structured, enterprise-wide execution.
I work across the full AI lifecycle: concept, data pipeline, and modeling through to implementation — helping teams ship production-ready solutions faster, with better accuracy and efficiency, at lower total cost and risk.
Most leadership teams can already see the transformative power of AI. What's usually missing isn't the ambition — it's the in-house expertise, resources, or clear strategy to implement it effectively.
AI doesn't make ERP less relevant — it makes the foundation ERP provides more important. ERP becomes the governed operational and execution engine, while AI adds a layer of intelligence and orchestration around it. With both working together, leaders have a real opportunity to rethink how work happens across the enterprise.
Anthropic's own research on analytics agents found the same pattern I see in client engagements: writing the query was never the hard part. Three things reliably make agents get the answer wrong — ambiguity, staleness, and retrieval. The fix isn't a smarter model. It's a smarter system around it.
A simplified example of one workflow shape: a trigger, a data pull, an AI decision step, and a write-back into NetSuite — no black box, just nodes you can follow.
This is a simplified illustration of the workflow shape — the real implementation includes retries, audit logging, and reconciliation against the GL.
Most teams already have Excel files, approval flows, shared folders, NetSuite, and QuickBooks. They don't want to relearn another piece of clunky B2B software — they want the work they already do, done faster and with fewer dropped balls.
Automate the repetitive stuff. Upskill the humans. Let them do the parts of the job that actually need judgment, taste, and a pulse.
The tech was never the hard part. Believing your people are worth investing in — that's what most companies still get wrong.
When its AI chatbot started resolving most "where's my order" tickets on its own, Ingka didn't cut headcount. It noticed the tickets still landing with a human were mostly people asking for design advice — and built a paid consulting service around that instead.
That service brought in roughly €1.3B (about $1.4B) in revenue by the end of fiscal year 2022 — a case where automation paid off more by redeploying people than by replacing them.
Four decades, six roles, one thread — financial systems that hold up under pressure.
"Grant and his team are exceptional! We hired them to replace a provider who was highly regarded in our region but who couldn't solve the simplest of problems. Grant single handedly built our entire IT infrastructure, our website, converted us to updated state of the art technology, wrote numerous best in class programs we use to run our back office, and did it all at a fraction of the cost of any brand supplier. I would put his team and their services up against anyone anywhere — he will not only over deliver, he will out perform them on all fronts."
"Grant was always friendly and would go out of his way to help anyone who needed it. He and his staff were responsible for building a reliable IT system and website for our company. Congratulations Grant on your many and varied achievements!"
Whether it's a NetSuite implementation that's stalled, an integration that needs an owner, or a first conversation about what AI could actually do with your financial data — reach out directly.