Are you frustrated that AI can do everything except talk to your SYSPRO data?

You can ask an AI almost anything now. Draft an email, summarise a contract, plan a supply run. Then you turn to the one system that actually runs your business, and none of that AI capability reaches it. SYSPRO doesn't answer questions. It holds the answer, and waits for someone to go and find it.

That's the frustration Danie Grobbelaar, Lancea's founding director, puts to SYSPRO manufacturers thinking about AI ERP: are you not frustrated that AI can do all these other things for you, but it can't speak to your data?

It's a fair question. And it has an answer. But are you ready for it?

The AI ERP readiness question everyone's asking is the wrong one

Ask most vendors whether your business is ready for AI ERP and you'll get a checklist: data quality thresholds, specific modules, API configuration, licensing tiers. For a lot of AI and ERP combinations, that checklist is the right place to start. Systems vary, data quality varies, and getting the technical foundation right matters.

It's just not the checklist that applies here.

If you're running SYSPRO, and you are asking the question, we believe you're already ready. AGx (our SYSPRO AI integration) is built to sit on top of a SYSPRO environment that's already live, not on top of some separate technical bar you need to clear first. There's no additional data audit, no API rebuild, no new licensing tier to unlock before AGx becomes possible.

"If you have SYSPRO running in your business, adding AGx on top has no other system requirements. We just want to add it on top of the system you already have."— Danie Grobbelaar, Founding Director, Lancea Konsult

That's a different starting point to most AI ERP implementation questions. There are many systems where the checklist genuinely does change business to business. Inside the SYSPRO and AGx relationship specifically, that variability isn't there. The technical bar has already been cleared by the fact that SYSPRO is running.

What AI ERP readiness actually means, if it isn't technical

If the technical checklist is already satisfied, the readiness question moves somewhere else. Not "can we", but "will we".

That gap between adoption and follow-through shows up clearly at the industry level. Parsec Automation's 2026 State of Manufacturing Industry Report, a global survey of 1,200 manufacturing leaders, found that 72% of manufacturers have adopted AI in some form, but only 10% have done so at scale.1 Most of the industry has started. Far fewer have gone far enough to change how the business actually operates.

In our experience, that gap isn't mainly about which tools are on offer. It's about whether adoption became a decision the business acted on, or a pilot that quietly stayed a pilot.

For a SYSPRO manufacturer looking at AGx, the equivalent question isn't a systems audit. It's whether AI ERP becomes something the business actually uses, or something that gets evaluated once and shelved.

For the fuller framework behind where AI fits relative to your ERP, we've mapped it in ERP after AI.

The real advantage isn't AI. It's speed to your own data.

Danie's frustration points at something real. An AI that can write your emails but can't touch your ERP is missing the one dataset that actually describes your business.

Two pieces of 2026 research make the case for why that gap matters more than it might first appear. One study of manufacturing data practices found that 42% of manufacturers now report integrated manufacturing data, but only 29% consistently use that data to make real-time operational decisions, and 27% still cite a lack of real-time visibility as a major challenge.2 The data exists. The path from data to decision is where most manufacturers are still stuck.

Separate research on real-time operational capability found that companies in the top quartile for real-time capability achieved more than 50% higher revenue growth and net margins than their peers.3 That's not a marginal edge. It's the difference between a business that can act on what's happening on the floor today, and one that finds out next week.

Key takeaway: AGx is Lancea's conversational interface to live SYSPRO data. It's LLM-agnostic, built to work with the AI model your governance policies already support, including Claude, so questions that currently take a report, a query, or a phone call to answer can be asked in plain language instead. What gets executed still runs through SYSPRO's own guardrails and audit trail, so the governance you already trust doesn't change.

So what's actually stopping you?

If the system requirement is already met, and the advantage of getting there first is real, the honest question isn't whether AGx works with SYSPRO. It's whether you're acting on that this quarter, or answering the same frustration again next year.

That's not to say there's no work at all. Deciding what you actually want to ask your data, agreeing who gets to ask it, and setting the governance boundaries around what AGx can act on versus what it can only answer: that's real work, and worth doing properly. What it isn't, is a technical rebuild of a system that's already running.

Yes, it really can be that straightforward. Not because the work disappears, but because the part most AI ERP content assumes is the hard part, the system side, is already done. What's left is the part every business controls directly: the decision to start.

Where to start

The next step isn't a system upgrade. It's a conversation about what your SYSPRO data could already tell you, if you could ask it.

AGx is available now through Lancea's Evaluation and Early Adoption Program, working alongside the team behind 100+ SYSPRO implementations to explore what it makes possible in your specific environment.

Learn more about AGx.

Or talk to the Lancea team directly.

References

1. Parsec Automation (2026). 2026 State of Manufacturing Industry Report, global survey of 1,200 manufacturing leaders. Published via Businesswire, 2026-07-16. ↩
2. Arch Systems Inc. (2026). The State of Connected Manufacturing 2026, benchmark data from 100 senior manufacturing leaders. Vendor-commissioned research, small sample — flagged as a methodological caveat. ↩
3. Weill, P. and van den Berg, E. (2026-01-27). "Build Business Advantage With Real-Time Decision-Making", MIT Sloan Management Review, drawing on MIT Center for Information Systems Research (CISR) data: a global study of 259 companies (2022 to 2023) with a follow-up survey of 152 companies in 2025. ↩