Fast or thorough? The great ERP implementation debate

By Stuart Scanlon, Managing Director, epic ERP

Everyone wants an ERP project delivered faster. But should they?

The ERP industry likes absolutes. Rapid is better. Custom is better. The reality is more nuanced. Some businesses can go live in weeks and see immediate benefits. Others need a carefully phased transformation to manage operational complexity and protect what makes them distinctive. Neither approach is inherently better.

The best ERP implementation is not the fastest, nor the most elaborate. It is the one that fits the organisation’s goals, risk appetite and operational reality.

The case for rapid implementation

A rapid ERP implementation is built around focus: adopting a proven platform, limiting unnecessary scope and getting the business live quickly.

For many organisations, that is exactly the right approach. The clearest benefit is faster time to value. Teams work with better information, more reliable workflows and stronger controls sooner. Shorter projects also tend to cost less and reduce the time employees spend working around a change programme.

Adoption can be easier too. A clear, contained scope gives people something tangible to learn, rather than a transformation that appears never-ending. Rapid projects also encourage businesses to challenge old habits rather than recreate every legacy process in a new system.

That matters. Not every established process deserves preserving simply because it is old. An ERP project is the right moment to simplify approvals, standardise reporting or remove workarounds that have quietly become “the way we do things”.

Speed comes with trade-offs, though. A rapid implementation leaves less room for accommodating complex processes and may require the business to change its procedures rather than the system. Where operations are highly specialised, a standard approach may not meet every requirement.

The question is not whether a business can implement quickly. It is whether it can do so without giving up something that matters.

The case for a complex transformation

For some organisations, a broader and more tailored ERP programme is not an indulgence. It is a necessity.

A company with multiple business units, complex manufacturing, demanding compliance needs or extensive integrations may need more than a straightforward deployment. The same applies to global organisations and those whose processes deliver real competitive advantage.

In these cases, a phased and more comprehensive implementation can create substantial long-term value. It allows the platform to reflect the business, supports more sophisticated automation and provides the flexibility to scale. It can turn a system change into a strategic transformation, improving visibility and building a stronger foundation for growth.

That does not make complexity automatically better. Longer projects require more investment, sustained leadership attention and stronger change management. Without clear governance, it becomes an attempt to solve every historic frustration at once, at the expense of time, budget and momentum.

Complexity succeeds when it is purposeful, controlled and tied to measurable outcomes. Complexity for its own sake is not a strategy.

The questions businesses should really be asking

Too often the first question is: “How quickly can we implement?”

That is understandable. Businesses want certainty and minimal disruption. But speed is only one part of the decision. The more useful conversation starts here:

  • What business problem are we trying to solve?
  • Are our current processes genuine differentiators, or simply habits?
  • How much customisation do we actually need?
  • What level of organisational change are we ready for?
  • What would success look like 12 months after go-live?
  • How important is speed compared with flexibility and future scalability?

The answers rarely point to a purely rapid or purely complex model. Most successful programmes sit somewhere between the two.

A business might choose a rapid core deployment, then add functionality in phases where the value is clear. Another may invest upfront in critical integrations and compliance while standardising less distinctive areas.

That’s good judgement, not indecision.

Outcomes matter more than methodology

ERP projects should not be judged by how long they take or how many customisations they include, but by whether they deliver measurable business outcomes.

Does the business have better visibility? Are teams spending less time on manual work? Has decision-making improved? Can the organisation manage growth with confidence and operate with less risk? Those are the measures that matter.

Whether your business suits a rapid deployment or a broader transformation, the first step is understanding your objectives. Choosing the right implementation strategy matters as much as choosing the right ERP platform.

Your workforce has adopted AI. But has your governance?

Written by: Dr Daneél van Eck, Strategy Director, epic ERP

Here’s a question worth considering: Do you know which AI tools your people are actually using right now? Not just the ones IT has approved. Not just the ones listed in your software inventory. Which tools are your teams genuinely using – today, to do their jobs – and what information is going into them?

For most organisations, the honest answer is: probably not.

AI has moved fast – faster, in most cases, than governance. ChatGPT, Claude, Microsoft Copilot, Google Gemini, DeepSeek – these tools are brilliant, free or nearly free, and they make people meaningfully more productive. So people use them: quietly, efficiently, and often without telling anyone.

Consider the case of an accountant using ChatGPT to summarise and add insights to the board pack, a developer who pastes error logs into Claude to debug faster, or a developer who sends their whole code base to Claude Code to match standards for a new section. Your HR head could use Gemini to draft sensitive communications. They are each solving problems with the best tools available.

What worries CIOs and compliance officers: most organisations lack a clear, real-time view of where, how often, and what kind of sensitive data enters AI tools. When the data involves finance, personnel, or customer details, risk is immediate.

“The question is no longer whether your people are using AI. It’s whether you have any visibility into how – and at what risk.”

Where the risk actually lives

AI risk comes not from tools, but from the environments they operate in and assumptions about data once it leaves the organisation.

Public AI platforms are external services. Unless your organisation has a formal enterprise agreement with defined data-handling terms, you cannot assume that what your employees type into a prompt remains private.

Inputs may be logged, stored, or used to develop future model versions. Enterprise agreements with most major providers do offer real protections, but only if they’re actually in place. A free or consumer account with the same provider gives you none of that.

Two incidents illustrate this well. In 2023, Samsung engineers pasted proprietary source code into ChatGPT to solve a problem faster, triggering a company-wide ban on generative AI on internal devices. Separately, GitHub Copilot – a widely used AI coding assistant – was found to suggest passwords and API keys it had learned from public code repositories, some of which contained credentials that were never meant to be public. In both cases, the risk wasn’t the tool. It was the lack of any framework around how the tool was being used. For South African organisations, POPIA adds a further dimension. If personal information is flowing into public AI tools without appropriate safeguards, that’s a compliance exposure, not just an IT concern.

AI tools generate probable outputs, not facts; they can hallucinate or produce biased or ambiguous content. Relying on “AI-generated” is not quality control.

And then there’s accountability – the risk most frameworks miss entirely. If the financial analysis is AI-generated/assisted, who takes responsibility? Or if a message to a customer is generated, who owns that outcome? Using AI could end up greying who is ultimately accountable.

What getting it right looks like

Responsible AI governance isn’t a policy document – though a policy is part of it. It’s a layered system in which four factors tend to separate organisations that manage this well from those that don’t.

Data discipline

Clear standards for what can and cannot enter AI environments. Sensitive information should be masked or pseudonymised before it is sent to an external tool. A rule of thumb that travels well: if you wouldn’t send it in an unencrypted external email, don’t paste it into a public AI tool.

Coherent permission structures

AI inherits the environment’s access controls. If your system permissions are poorly structured or have drifted from actual job functions. AI won’t fix that; it will amplify it. Implementations consistently surface pre-existing governance weaknesses. Better to find them before deployment than to discover them after deployment through an incident.

Cultural maturity alongside technical capability

The shift that matters isn’t from “AI is exciting” to “AI is dangerous.” It’s from “AI is exciting” to “AI is powerful, which means it needs structure.” Innovation without structure doesn’t contain risk. It scales it.

The bigger picture

Make AI governance a priority. Actively commit to sustained oversight, assign clear accountability and reconsider your frameworks. Begin building your organisation’s governance capacity today! Don’t wait until a critical incident exposes the gaps.

For organisations where enterprise systems are the backbone of operations – where finance, procurement, inventory and customer data all flow through a central platform – the stakes of getting this wrong are higher than average. The more integrated your systems, the more carefully AI needs to be governed at the boundary.

Start laying the foundations for your AI governance now. Don’t wait to react. Take ownership. Evaluate your current visibility, policies and team awareness, then set the next step for governance improvement today.

About Author

Dr Daneél van Eck is Strategy Director at epic ERP, a Microsoft Dynamics & Epicor implementation partner operating across Africa. His work focuses on the intersection of enterprise systems architecture, AI governance, and digital strategy.