Navigating ERP and AI – Part 1: Operating Model and Vendor Selection 

Aligning the operating model and selecting the right ERP system remain to be complex and high-stakes decisions. According to recent Gartner research, 75 percent of ERP strategies are not strongly aligned with overall business strategy, and more than 70 percent of recent ERP implementations fail to fully achieve their intended outcomes. At Searchlight, we bring extensive experience guiding organisations across industries, from fast-growing SMEs to global enterprises, through complex ERP transformation journeys. 

As technology continues to evolve, one question is becoming increasingly important: how can AI enhance the value delivered by ERP programmes while reducing risk and maximising benefits? 

In this blog series, we explore how AI can be applied across the ERP lifecycle, highlighting practical considerations and current market capabilities. We also offer a balanced, experience-led perspective across three key stages: 

Part 1: Operating Model and Vendor Selection
Part 2: Design, Build, and Test phases of the ERP programme
Part 3: Service Transition, Benefits Realisation, and the End-state Operating Model 

Part 1 – Operating Model and Vendor Selection 

ERP systems and business process solutions have been around for over 30 years. While the shift to cloud has reduced infrastructure complexity and improved scalability, many of the core challenges remain the same. Organisations still grapple with questions such as whether their data is accurate and validated, whether the business will adopt new processes, and whether they are truly set up for long-term success. 

What has changed significantly is the pace of technological advancement, particularly with the emergence of AI. 

Today, many ERP vendors and supporting tools are embedding AI capabilities into their platforms. Solutions such as LeanIX use AI to accelerate understanding of enterprise architecture, helping organisations map systems and dependencies more efficiently. The Project Management Institute (PMI) has also introduced “PMI Infinity”, an AI driven chatbot to support Project Managers. Beyond technology vendor tools, a broader question emerges: how can AI be applied meaningfully during the early stages of an ERP programme? 

Operating Model and AI 

Defining the right operating model is a critical foundation for any ERP transformation. It ensures that organisational structures, processes, and governance align with strategic goals. AI has the potential to support this phase, but it is important to be clear about where it adds value. 

Generative AI tools such as Microsoft Copilot, ChatGPT, Perplexity, and Gemini can play a useful supporting role in creating operating model related artifacts. They might help summarise workshops, capture meeting notes, and suggest additional considerations or risks when prompted effectively. Tools like Otter.ai and AI features in collaboration platforms such as MS Teams can reduce administrative overhead and potentially improve documentation quality. 

However, there is no single AI solution that can design an operating model for an organisation. Every business has its own strategy, culture, and operational realities. AI can assist, but it cannot replace the depth of understanding required to define an operating model that works in practice. 

Operating model design is inherently business-led and this raises key considerations which are connected to embedding AI into the operating model, for example:  

• Impact Analysis: Identify where AI can create the most business value and which business processes and roles are likely to be affected? 

• Architectural Strategy: Should the organization rely on out-of-the-box AI capabilities within ERP platforms, or maintain / introduce specialized tools in the broader IT estate? 

• Governance and Accountability: Who owns AI-generated outputs, and what additional oversight is required to manage risk? 

Vendor Selection and AI 

Vendor selection is another critical phase where AI can offer tangible benefits. Traditionally, this process involves significant manual effort, including preparing RFI / RFP materials, assessing potential implementation partners, reviewing vendor proposals and comparing offers. 

Generative AI tools can support some of these activities, for example:  

• Enhancing requirement wish lists, 

• Augmenting research into ERP platforms and system integrators,  

• Supporting comparisons through automated analysis of vendor documentation, and 

• Helping to identify gaps or unforeseen risks across proposals.  

This might reduce time and effort required, particularly in the early stages of shortlisting, but vendor selection is a one-off exercise within the ERP programme; the costs of repeating the process are significant. AI might assist and accelerate decisions, but cannot substitute for human input, judgement and decision making. 

As part of vendor selection organisations should also consider how ERP vendors themselves are embedding AI into their products. Leading providers such as SAP, Oracle, Microsoft, IFS, Infor and others are dynamically evolving their AI capabilities. During selection, it is important to ask about both current functionality and the future AI roadmap. 

At this stage, it is equally important to align AI use cases with expected business benefits. For example, AI may enhance supply chain planning, demand forecasting, improve access to large amounts of unstructured data locked in scanned contract documents, and enable more effective preventive maintenance in asset-heavy industries. The focus should remain on delivering tangible business benefit for stakeholders. 

Using AI in early stages of ERP project preparations 

When applied thoughtfully, AI can bring clear benefits to the operating model and vendor selection phases. Generative AI might provide additional perspectives on required functionalities from a modern ERP, highlight overlooked project risks, and support more comprehensive decision-making. It can also accelerate data research and reduce the time required to evaluate options. 

However, there are important considerations when understanding what AI can realistically support: 

• ERP transformations are as much about people as they are about technology. AI can process data, but it does not understand organisational culture, leadership dynamics, or readiness for change. These human factors are often the most critical to success and enabling decisions. 

• There are also practical risks to manage. Data security and intellectual property must be protected when using AI tools.  

• AI outputs highly likely to contain inaccuracies and require human validation. Ultimately, accountability remains with human decision-makers. 

Most importantly, AI does not replace experience. Successful ERP programmes depend on asking the right questions at the right time, something that comes from expertise and context rather than automation alone. AI is already influencing how organisations approach ERP transformations, but in the early stages of an ERP project, its role is best understood as an enabler.  

By combining AI capabilities with experienced guidance, businesses can improve efficiency, reduce risk, and make more informed decisions during the early stages of their programmes. Assembling an effective team of experts to define a clear operating model, align business and IT strategy, and select the right technology and partners will create a strong foundation for long-term success. 

If you are planning or currently undertaking an ERP transformation, our team at Searchlight can support you in navigating these decisions with confidence and clarity. Get in touch to explore how Searchlight can help you achieve your desired outcomes. 

In the next part of this series, we will explore how AI can support the design, build, and test phases of an ERP programme. Follow us on LinkedIn to stay updated.