Requirements buried deep in documentation only surface during UAT. SMEs explain the same processes multiple times to different teams. Decisions get revisited because context is lost between phases. Test scripts take months to prepare and weeks to execute manually. Training gets diluted as it passes through layers before it reaches the end user.
We’ve seen what happens when a 12-month plan turns into 18.
Momentum fades. Priorities shift. Teams disengage. And by the time you go live, parts of the business have already outgrown the original design.
It’s not the ERP system that creates the problem.
It’s the way implementations are executed — sequential, manual, and heavily dependent on a few key people.
And that’s exactly what’s changing.
→ Requirements: From fragmented input to full visibility
Traditional requirements gathering is slow and incomplete by design. It depends on workshops, documentation reviews, and human recall — which means important details are often missed until much later.
AI changes this completely. It can ingest and analyze vast amounts of documentation at once: process descriptions, legacy system data, industry templates, and edge cases. Instead of discovering gaps during testing, teams identify them upfront.
The result isn’t just speed — it’s better accuracy from day one, and far less rework downstream.
→ Configuration: From bottlenecks to scalable execution
Configuration is often constrained by a small number of senior consultants who understand both the system and the business context. This creates a natural bottleneck.
With AI support, configuration becomes more accessible. Requirements can be translated into system setups through guided, natural-language interactions. Junior consultants can execute with greater confidence, while senior experts focus on validating decisions, not performing repetitive setup tasks.
This doesn’t reduce quality — it redistributes effort to where it matters most.
→ Testing: From manual effort to continuous validation
Testing is one of the most time-consuming phases of any ERP project. Writing test scripts, executing them manually, documenting results — it’s slow, repetitive, and prone to human error.
AI enables a shift from periodic, manual testing to continuous, automated validation. Test scenarios can be generated based on requirements and system configurations, executed at scale, and re-run instantly after changes or updates.
What used to take weeks can happen overnight.
And more importantly, issues are caught earlier — when they’re easier to fix.
→ Training: From static materials to living knowledge
Traditional training approaches rely on static documentation and scheduled sessions, often created long before go-live. By the time users are trained, the system has already evolved.
AI allows training to become dynamic and contextual. Users receive role-specific guidance based on what they’re doing in the system, not what was documented months earlier. Content stays up to date automatically, and support doesn’t end after go-live.
Adoption improves because learning becomes continuous — not a one-time event.
→ Support: From reset to continuity
One of the biggest inefficiencies in ERP projects happens after go-live. Support teams often start from scratch, without full visibility into the decisions, configurations, and trade-offs made during implementation.
AI changes that by carrying context forward. Every requirement, decision, and configuration becomes part of an accessible knowledge layer. When issues arise, they’re resolved faster because the system “remembers” how and why things were built.
This reduces dependency on specific individuals and creates a more resilient support model.
DOWLOAD FULL GUIDE: FROM MONTHS TO WEEKS:INFOR ERP GUIDE
18 months becomes 9. Not by cutting corners — but by removing the friction that slows everything down.
The steps don’t disappear: requirements are still gathered, systems are still configured, testing still happens, and users still need training. But the way those steps are executed fundamentally changes.
Work that was once manual becomes automated.
Work that once depended on memory becomes data-driven.
Shorter implementations mean less exposure to changing business conditions. They keep stakeholders engaged, reduce the risk of team turnover, and allow organizations to realize value sooner. In many cases, moving faster is actually the safer path.
ERP implementations don’t have to be long, exhausting transformations.
With AI embedded across the lifecycle, they become more predictable, more efficient, and far less dependent on bottlenecks.
The question is no longer whether ERP projects can move faster.
It’s whether there’s still a reason not to.
We’ve put together a practical guide to faster Infor ERP implementation — outlining how each phase can be accelerated without compromising quality, where traditional bottlenecks can be eliminated, and how teams can start executing differently today.
Download the full guide below.
DOWLOAD FULL GUIDE: FROM MONTHS TO WEEKS:INFOR ERP GUIDE
18 months becomes 9. Not by cutting corners — but by removing the friction that slows everything down.