USA – GEORGIA Trends and Developments Contributed by: James Paine and Sarah Parker, Holland & Knight LLP
The Shifting Strategy of Modernising Enterprise Resource Planning (ERP) Systems The proliferation of ERP modernisation projects con - tinues to be a dominant theme across the technology landscape. However, a seismic shift is occurring in the underlying strategy driving this evolution, as enter - prise leaders re-evaluate the foundational systems central to their operations. This strategic transforma - tion 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 man - agement, 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 finan - cial 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 man - agement. 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 unprece -
dented 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 mod - ern 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 solu - tions 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 gener - ate 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 provid - ing 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 ana - lyse 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 down - time. 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 minimis - ing 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.
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