Why companies are hesitant about AI in ERP

Artificial intelligence in the ERP context raises high expectations, as significant productivity gains, far-reaching automation and more informed decisions are on the cards. Nevertheless, many companies are currently hesitant when it comes to practical implementation. This is mainly due to the fact that implementation risks, unclear potential benefits, high integration costs and cultural hurdles often act as a massive brake.

However, instead of rashly investing in complex large-scale projects, companies need a controlled start. This is precisely where the proof of concept (PoC) comes in: It acts as a focussed test under real conditions, making potentials visible at an early stage and effectively mitigating risks. In this way the necessary trust in the technology, the specific use case and your own organisation is built up step by step.

Key Takeaways

AI in ERP offers great potential, but many companies are hesitant due to integration costs and uncertainties.
A proof of concept (PoC) helps to minimise risks and build trust in new technologies without tying up large resources.
A PoC is a time-limited test to validate AI use cases, but not an extensive implementation project.
Typical objectives of a PoC are validation, identification of hurdles and assessment of the effort required to facilitate decisions.
Focussed PoCs with real data and a narrow scope increase the chances of success and should be completed within four to eight weeks.

Proof of concept: What a PoC is - and what it is not

A proof of concept is expressly not a pilot project with an immediate rollout target, nor is it an extensive implementation project. Rather, it is a test run that is limited in time and clearly measurable. The primary objective is to test the technical and organisational feasibility, without entering into long-term commitments or tying up large resources.

It is also important to differentiate it from a prototype: A PoC is not an MVP (Minimum Viable Product) that is already intended to serve as a productive nucleus. Instead, it is a functional test with a clear commitment to results. The result is therefore not „nice to have“, but provides the binary realisation: „works - or does not work under the given framework conditions“.

Typical objectives of a PoC

A well-organised PoC therefore pursues several strategic goals at the same time:

Validation of an AI use case based on real data sets.
Identification technical and operational hurdles at an early stage.
Precise assessment of future expenses and the expected time-to-value.
Preparation a well-founded and reliable implementation decision.

Thus provide companies with a reliable basis for decision-making before deciding on far-reaching rollouts or scaling. In this way, a PoC significantly reduces the existing uncertainty without nipping the spirit of innovation in the bud.

Ideal sequence of a PoC in the ERP environment

A structured PoC follows a clearly defined phase model. This means that the scope always remains manageable, while expectations remain realistic and the results tangible.

1. define use case
It starts with a clearly described problem with a measurable value proposition. Thisi the use case is narrowed down to a specific process centre or a defined database. At the same time, it is crucial to involve stakeholders with a high willingness to change at an early stage. One example of this is the question: „Can an LLM automatically create requirements documents from email and meeting minutes?“ Already in this phase ensures that the PoC provides a precise answer instead of remaining generalised.
2. needs analysis and system understanding In the next step, the team identifies the necessary interfaces and relevant data sources. In addition, the existing process logics and IT architecture are analysed in detail. At the same time, the project team defines binding success criteria and KPIs. As a result, there is transparency right from the start as to how the success of the project will be measured, which increases acceptance in IT and management.
3. technical structure and implementation Subsequently the team prepares the data and customises the selected model. A temporary test environment is also created - in the form of a sandbox, for example. Ensuring connectivity with the ERP system or other RPA components is key here, so that the use case can be tested under conditions that are already very close to the later reality.
4. test phase with real data Now The real test comes when the PoC is operated with real transactions or authentic user input. During this process the team consistently documents performance and any error behaviour. Finally, the results obtained are checked against the previously defined KPIs. Thereby it is possible to recognise beyond doubt whether the use case is suitable for practical use or where there is still a need for adaptation.
5. evaluation and recommendation for action At the end, there is a structured evaluation in the form of a results report that categorises the cost-benefit ratio. In addition both technical and organisational barriers are explicitly named. On this basis the final decision is made: either scaling the project, adapting the approach or, if necessary, deliberately discarding the use case. Thus the PoC provides a sound basis for further action instead of merely ending up as an isolated experiment.

Best practices for successful PoCs

A PoC only develops its full added value if it is implemented in a focussed manner. Here the following principles have proven their worth:

Narrow scope: Concentration on a clearly defined use case.
Realism: Use of real data instead of a purely synthetic laboratory environment.
Interdisciplinarity: Specialist departments and IT must work together, instead of to operate in silos.
Clear time frame: Ideally, an efficient PoC should be completed within four to eight weeks.

How effective this approach is, shows the example of a Mechanical engineerIn a PoC lasting just six weeks, the company was able to use machine learning to predict the maintenance requirements of its presses with an accuracy of over 85 %. This served as incontrovertible proof of the future return on investment.

Typical stumbling blocks and how to avoid them

Despite all the planning, a PoC is not a sure-fire success. Frequently stumbling blocks arise, but these can be addressed proactively:

Unspecific use cases: Focusing on a measurable question helps here.
Poor data quality: The data situation must be compelling check in advance.
Lack of stakeholder engagement: Therefore decision-makers should be involved at an early stage.

This clarity the PoC does not become a one-off exercise, but an integral component of an overarching AI strategy.

Why PoCs also test the culture

A PoC not only works on a technical level, but It also challenges the organisation's ability to change. In the process questions about error culture and openness to new tools come to the fore. A successful PoC creates however not just visibility, but also provides the necessary tailwind for the entire digital transformation.

Ultimately: Six weeks of focussed testing is better than six months of costly uncertainty. If you have the courage to test in a controlled manner, you will become productive more quickly and make the right decisions. thus the far more well-founded investment decisions.