The Shifting Strategy of Modernising Enterprise Resource Planning (ERP) Systems

The proliferation of ERP modernisation projects continues to be a dominant theme across the technology landscape. However, a seismic shift is occurring in the underlying strategy driving this evolution, as enterprise leaders re-evaluate the foundational systems central to their operations. This strategic transformation can be best understood as a transition between two distinct waves of modernisation. The first wave was driven primarily by a straightforward financial calculus: moving on-premise ERP solutions to the cloud to realise direct cost savings by eliminating the costs of resources and personnel required to host an on-premises solution. The now-emerging second wave represents a more sophisticated strategic pivot, focusing on generating broader, more indirect value through the integration of advanced functionalities – most notably, AI and industry-specific solutions.

The first wave: cloud migration for direct cost efficiencies

Until recently, the primary motivation for companies to migrate their ERP solutions from dedicated on-premise environments to the cloud was to exploit direct cost savings. The business case was clear and compelling. This approach allowed for the elimination or reduction of internal costs associated with the management, operation and maintenance of data centre facilities, infrastructure and network components, applications and other resources required to host and support an on-premise solution. This model enabled companies to eliminate or reduce a significant financial burden by shifting the hosting, maintenance and support of the ERP system to the ERP licensor and leveraging the economies of scale associated with shared infrastructure, personnel and other resources. This model also allowed companies to convert capital expenditures into more flexible operating expenses for cloud services.

Furthermore, the consumption-based model of the cloud offered a powerful new lever for financial management. The ability of companies to ramp up and ramp down their use of a cloud solution as needed was a significant benefit, transforming previously fixed infrastructure costs into variable costs directly tied to actual consumption. This model provided unprecedented agility, but the strategic conversation largely remained centred on total cost of ownership and infrastructure optimisation rather than transformative business capability.

The second wave: AI-driven value and broader business impact

More recently, the business strategy has evolved from a focus on direct cost efficiencies to an enhanced functionality-based approach designed to harness the substantial technological improvements of modern ERP systems. This second wave is defined by two key trends: (i) the integration of generative and agentic AI, and (ii) the adoption of industry-specific ERP solutions and mobile capabilities to maintain a competitive edge and support a distributed workforce.

This strategic pivot from a pay-for-compute model to a pay-for-outcome business strategy has company leaders fundamentally rethinking how they approach technology procurement and integration. The value proposition of AI within an ERP system extends far beyond IT budget line items; it promises to generate profound, though often indirect, cost savings and efficiencies across the entire enterprise.

For instance, while early AI functionalities in cloud-based ERP focused on automating routine tasks, enhancing user service with chatbots, and providing predictive analytics, the newest generation of AI tools are marketed as outcome-based capabilities that promise tangible business results. Consider a manufacturing company: an AI-infused ERP can analyse production data, sensor readings, and supply chain logistics to predict equipment failure before it happens, automatically schedule maintenance, and re-route production, thereby avoiding costly downtime. In retail, AI can optimise merchandise selection, product pricing and inventory levels by analysing sales data and trends, weather patterns, and social media trends, ensuring effective pricing and minimising both out-of-stock and excess inventory carrying costs. These benefits are not reflected in the monthly cloud infrastructure bill but are realised in optimised pricing, reduced operational waste, improved asset utilisation, and higher revenue capture.

Vendor strategy and the new landscape of risk

ERP vendors have been quick to recognise the power of this new paradigm. By bundling sophisticated AI capabilities directly into their core cloud ERP offerings, they are not only creating a powerful differentiator but also forging a much deeper, more integrated relationship with their customers. This strategy, however, introduces a new dynamic that corporate buyers and their legal teams must carefully navigate: the creation of significant operational dependency, vendor lock-in and heightened barriers to exit.

When a company’s core business processes – from financial forecasting to supply chain management – become reliant on a vendor’s proprietary AI models trained on that company’s unique data, the ERP system evolves from a system of record into the operational brain of the enterprise. While migrating data to a new system has always presented challenges, the increased functionality offered by modern ERP systems also requires a customer to migrate the accumulated intelligence, the trained models, and the AI-driven workflows, which is a far more complex undertaking. This “stickiness” makes switching providers potentially prohibitively expensive and disruptive, granting the incumbent vendor immense negotiating leverage upon renewal.

This enhanced dependency requires a proportional evolution in how companies assess and mitigate risk. As IT leaders consider these outcome-based AI functionalities, they are grappling with a new set of challenges.

• Data integrity and liability: The old adage of “garbage in, garbage out” has never been more critical. The quality of a company’s data is of paramount concern, as inaccurate or incomplete data will severely undermine the desired results of any AI model and, in some cases, potentially even expose the customer to liability resulting from decisions based on bad analytics. While the costs and timelines associated with cleaning and migrating data have always presented challenges, a key legal question must now also be asked: if an AI provides a faulty recommendation based on poor customer-provided data or a faulty algorithm, who bears the liability for the negative business outcome? Contracts must clearly delineate these responsibilities and provide remedies where fault rests with the ERP provider.
• Data security, data monetisation and privacy: With powerful new AI capabilities come new data security and privacy considerations. Companies must ask potential vendors critical questions. How is our corporate data used to train the AI models? Is it co-mingled with data from other customers? How do you prevent the model from inadvertently exposing sensitive data in its outputs? How do you ensure that the data sets shared with the model will not benefit competitors or otherwise be monetised by the vendor? These new vectors create potential blind spots for data security, data monetisation and privacy compliance that must be addressed contractually.
• Outcome-based commitments: As vendors market their AI capabilities on the basis of business outcomes, buyers are rightly beginning to demand contractual commitments that reflect this value proposition. This marks a departure from traditional service level agreements (SLAs) that were often focused on system uptime, system response time and problem response and resolution times. Customers must now also consider SLAs that measure the quality of the new functionality afforded and the level of quality needed for that functionality to support the business needs and requirements. In addition, in this new frontier, customers are looking for fees and service level credits that scale based on quantifiable business results, such as a percentage improvement in forecast accuracy or a reduction in manufacturing defects. Whether service providers are willing to agree to such firm commitments often depends on the negotiating power between the parties, but it is a conversation legal teams must be prepared to have.
• Business dependency and impact: With the increased integration of ERP systems into the business, operations and strategy of companies, it is critical that companies evaluate, plan for and, where possible, contract in a way that provides them with the right level of protections to guard against the financial, operational and legal risks that flow from the cloud-based model and enhanced capabilities of modern ERP systems. Interestingly, from a contractual standpoint, the risks presented and corresponding protections required have begun to reflect the risk and protections typically found in strategic information technology and business process outsourcing engagements. Customers should include appropriate contractual protections to address the financial, operation and legal risks attendant to each phase of the transaction including the implementation phase, steady state phase and ultimate exit phase.