AI has Fundamentally Changed Enterprise Support

AI is reshaping enterprise support and changing how solution providers think and operate, says Stefan Steinle, executive VP and head of global customer support at SAP, who adds that support has evolved into a strategic driver of business value.

Support in this context refers to services, management and resource allocation used to troubleshoot, address issues and optimise infrastructure within an organisation.

“The focus is on value generation and customer health,” says Steinle. “Support is central to driving business outcomes with AI-integrated platforms. Support teams are becoming more embedded in business strategy. They provide visibility into system performance, user behaviour and operational trends.”

According to Steinle, AI-assisted toolchains and intelligent automation allow organisations to anticipate issues before they arise, streamline performance and improve long-term agility and resilience.

This is an entirely different approach to traditional support execution and application.

“Traditionally, support operated on a break-fix model – issues were addressed when disruption occurred. AI changes this dynamic entirely. Through machine learning, pattern recognition and real-time data analysis, AI can continuously monitor systems, detect anomalies and predict potential failures before they impact operations. This enables preventive intervention rather than reactive troubleshooting.”

AI is also improving decision-making, adds Steinle. “It surfaces insights from vast datasets, identifies optimisation opportunities and recommends actions that improve performance, reduce costs and enhance user experience. In this way, enterprise support evolves from a technical necessity into a value-generating function that actively contributes to business strategy.”

Shift from break-fix significant

AI expert and founder of AIforBusiness.net Johan Steyn says the shift from break-fix to strategic value driver is one of the most significant – and under-appreciated – transformations AI has enabled in organisations.

“For years, IT support was seen as a cost centre: reactive, ticket-driven and measured by how quickly problems were resolved. AI has fundamentally changed that equation. Predictive diagnostics, intelligent triage and automated resolution are not just making support faster – they are making it anticipatory. The best organisations are no longer waiting for things to break; they are using AI to understand system behaviour patterns and intervene before users are even aware of a problem,” says Steyn.

The current status is one of rapid but uneven adoption, he adds.

Rise of autonomous enterprise

AI-assisted toolchains and intelligent automation are transforming ERP systems from static platforms into adaptive, self-improving environments.

According to Steinle, the shift is being driven by predictive maintenance capabilities, where AI analyses historical and real-time data to forecast failures and enable pre-emptive repairs. AI is also accelerating automated root cause analysis by identifying issues across complex system landscapes more quickly and accurately.

In addition, self-healing capabilities are enabling some system problems to be resolved automatically without human intervention, while AI-driven process optimisation tools continue to identify operational inefficiencies and recommend improvements.

The result is ERP systems that become more intelligent over time, enabling organisations to respond more rapidly to change, scale operations more effectively and strengthen operational resilience.

“The integrated toolchain breaks silos and drives collaboration between business and IT. You can confidently modernise your ERP landscapes while staying agile in today’s competitive climate,” says Steinle.

“AI plays a key role in enhancing capabilities by reducing errors and speeding up transitions. Take AI-powered change point detection as an example, where fundamental shifts in system downtimes can be found reliably and hence resolved faster. At the same time, AI-generated requirements reduce manual effort and accelerate the implementation process.”

People and culture

However, Steinle warns that technology alone is not enough.

“While technology is at the core of transformation, you can still fall short when it comes to outcomes if your people are not on board. When you continue to operate in old ways, the AI copilots or the automations are pointless. You get the same issues, but with more dashboards.”

He adds that culture and leadership are critical to successful AI adoption. “As they say, culture is what people do when no one is watching. Establishing this culture is what leadership is ultimately about. How motivated are your people? Are they as excited as you are about new innovations?”

Diverse teams are also essential in solving problems within complex ERP environments.

Nazia Pillay, MD for SAP southern Africa, adds: “The next phase of AI adoption is about execution. Organisations are looking for trusted data foundations, strong governance and practical business use cases that can deliver measurable value.”

Early stages

According to Steyn, larger, more mature organisations are already seeing measurable gains – reduced mean time to resolution, lower support costs and IT teams freed up for higher-value work.

“But many organisations, particularly in markets like South Africa, are still in early stages, often constrained by legacy infrastructure and skills gaps. The outlook, however, is clear: AI-augmented support will become the baseline expectation, not a competitive differentiator,” he continues.

Organisations that invest in this transition thoughtfully – with the right human capability alongside the technology – will build a genuine and lasting operational advantage, adds Steyn.

This article first appeared in ITWeb.