The intelligent core: AI changes everything for core modernization
For years, core and enterprise resource planning systems have been the single source of truth for enterprises’ systems of records. AI is fundamentally challenging that model.
Kelly Raskovich
Bill Briggs
Mike Bechtel
Ed Burns
Thorsten Bernecker
Abdi Goodarzi
Zillah Austin
Many core systems providers have gone all in on artificial intelligence and are rebuilding their offerings and capabilities around an AI-first model. The integration of AI into core enterprise systems represents a significant shift in how businesses operate and leverage technology for competitive advantage.
It’s hard to overstate AI's transformative impact on core systems. For years, the core and the enterprise resource planning tools that sit on top of it were most businesses’ systems of record—the single source of truth. If someone had a question about any aspect of operations, from suppliers to customers, the core had the answer.
AI is not simply augmenting this model; it’s fundamentally challenging it. AI tools have the ability to reach into core systems and learn about an enterprise’s operations, understand its process, replicate its business logic, and so much more. This means that users don’t necessarily have to go directly to core systems for answers to their operational questions, but rather can use whatever AI-infused tool they’re most familiar with. Thus, this transformation goes beyond automating routine tasks to fundamentally rethinking and redesigning processes to be more intelligent, efficient, and predictive. It has the potential to unleash new ways of doing business by arming workers with the power of AI along with information from across the enterprise.
No doubt, there will be integration and change management challenges along the way. IT teams will need to invest in the right technology and skills, and build robust data governance frameworks to protect sensitive data. The more AI is integrated with core systems, the more complicated architectures become, and this complexity will need to be managed. Furthermore, teams will need to address issues of trust to help ensure AI systems are handling critical core operations effectively and responsibly.
But tackling these challenges could lead to major gains. Eventually, we expect AI to progress beyond being the new system of record to become a series of agents that not only do analyses and make recommendations but also take action. The ultimate endpoint is autonomous decision-making, enabling enterprises to operate quickly compared with their current pace of operations.
Now: Businesses need more from systems of record
Core systems and, in particular, enterprise resource planning (ERP) platforms are increasingly seen as critical assets for the enterprise. There’s a clear recognition of the value that comes from having one system hold all the information that describes how the business operates. For this reason, the global ERP market is projected to grow at a rate of 11% from 2023 through 2030. This growth is driven by a desire for both greater efficiency and more data-driven decision-making.1
The challenge is that relatively few organizations are realizing the benefits they expect from these tools. Despite an acknowledgment that a centralized single source of truth is key to achieving greater operational efficiency, many ERP projects don’t deliver. According to Gartner research, by 2027, more than 70% of recently implemented ERP initiatives will fail to fully meet their original business case goals.2
Part of the reason ERP projects may fail to align with business goals is that the systems tend to be one-size-fits-all. Businesses needed to mirror their operations to fit the ERP system’s model. Applications across the organization were expected to integrate with the ERP. It was the system of record and held all business data and business logic, so the organization acquiesced to these demands, even if they were hard to meet. However, this produced a certain level of disconnect between the business and the ERP system.
AI is breaking this model. Some enterprises are looking to reduce their reliance on monolithic ERP implementations, and AI is likely to be the tool that allows them to by opening up data sets and enabling new ways of working.
New: AI augments the core
With some evolution, ERP systems will likely maintain their current position as systems of record. In most large enterprises, they still hold virtually all the business data, and organizations that have spent the last several years implementing ERP systems will likely be reluctant to move on from them.
Orchestrating the platform approach
In this model, today’s core systems become a platform upon which AI innovations are built. However, this prospect raises multiple questions around AI orchestration that IT and business leaders will have to answer. Do they use the modules provided by vendors, use third-party tools, or, in the case of more tech-capable teams, develop their own models? Relying on vendors means waiting for functionality but may come with greater assurance of easy integration.
Another question is how much data to expose to AI. One of the benefits of generative AI is its ability to read and interpret data across different systems and file types. This is where opportunities for new learnings and automation come from, but it could also present privacy and security challenges. In the case of core systems, we’re talking about highly sensitive HR, finance, supplier, and customer information. Feeding this data into AI models without attention to governance could create new risks.
There’s also the question of who should own initiatives to bring AI to the core. This is a highly technical process that demands the skills of IT—but it also supports critical operational functions that the business should be able to put its fingerprints on.
The answer to these questions will likely look different from use case to use case and even enterprise to enterprise. But teams should think about them and develop clear answers before going all in on AI in the core. These answers form the foundation upon which rests the larger benefits of the technology.
“To get the most out of AI, companies should develop a clear strategy anchored in their business goals,” says Eric van Rossum, chief marketing officer for cloud ERP and industries at SAP. “AI shouldn’t be considered as a stand-alone functionality, but rather as an integral, embedded capability in all business processes to support a company’s digital transformation.”3
AI enables new ways of working
Forward-looking enterprises are already answering these orchestration questions. Graybar, a wholesale distributor of electrical, industrial, and data communications solutions, is in the middle of a multiyear process of modernizing a 20-year-old core system implementation, which started with upgrades to its HR management tools and is now shifting to ERP modernization. It’s leaning on the best modules available from its core systems vendors when it makes sense, while also layering on third-party integrations and homegrown tools when there’s an opportunity to differentiate its products and services.4
The growth of AI presented leaders at the company with an opportunity to not only upgrade its tech stack, but also to think about how to reshape processes to drive new efficiencies and revenue growth. Trust has been a key part of the modernization efforts. The company is rolling out AI in narrowly tailored use cases where tools only have access to specific databases based on what they need to accomplish the assigned task. And in each instance, humans are kept in the loop to help ensure the accuracy of information that comes from AI tools before it reaches customers.
Graybar is piloting AI in sales and customer service and plans to expand inventory forecasting and planning. It’s adding AI to ordering systems to help surface cross-sell and upsell ideas to sales agents. It’s also developing an AI-based tool that will help agents build quotes for customers. The tool will allow workers to use natural language to query product catalogs, pull together options for customers, and compile the information into a communication for the customer.
“These tasks used to take hours or days to complete; now it takes minutes,” says David Meyer, chief financial officer at Graybar. “Empowered with AI-based tools, employees can now focus their time on selling and business development versus spending half a day looking for info and typing up a response to a customer request.”5
This change is about more than just freeing up some time for customer-facing staff. Graybar leadership is eyeing billions of dollars in new revenue growth from expanding its use of AI in core systems. AI in the core is all about driving growth by enabling new ways of working.
Software company ServiceNow is seeing this trend play out with many of its clients, says Michael Park, senior vice president and global head of AI go-to-market at ServiceNow. One especially impactful use case he’s seeing is in new employee onboarding. Every new hire needs access to HR systems as well as tools and data specific to their role. In the past, the worker would have had to engage with a range of helpdesk workers, retrieve passwords, log into different systems, and assemble the credentials they needed to start doing their job. Now, AI enables the HR systems to learn more quickly what new hires need and to automatically provision access by the start date.
This automated learning approach can be applied to all sorts of business processes, Park says. Automating these tasks through gen AI capabilities such as summarization, notes generation, conversational chat, AI search, and task automation may save two minutes or two days, depending on the use case. Once they offload simple workloads to bots, enterprises can redeploy workers to more valuable tasks, like improving service levels, driving margin growth, or developing new product offerings, a trend ServiceNow is seeing with its customers.
“AI in core systems is merely a new capability, a tool to be employed,” Park says. “The bigger strategic imperative is using these new capabilities to redefine the status quo for exponential value creation versus just bringing over existing processes onto a new technology capability.”6
AI in the core, and beyond
As more and more software tools across the enterprise become embedded with AI, workloads that were traditionally owned by core systems could eventually leave the core entirely. With AI, business logic doesn’t need to reside in the core. AI can train on structured and unstructured data from across the enterprise. Organizations’ business data will be instrumental in developing the most accurate and insightful outputs from AI models. Leveraging the core to help harmonize this data and subsequent AI models for insights will provide companies an opportunity to run their operations on truly insight-driven actions.
In this model, the core becomes just another repository of training data that AI can use to learn and improve business process management. This is where the real power of AI in the core comes in.
Every technology provider knows it needs to build AI into its offerings now, says Chris Bedi, chief customer officer at ServiceNow.7 ERP systems will continue to be effective as the enterprise’s system of record, providing transactional control and reliability as a source of truth. But increasingly, work is being done across domains, with AI as the connective tissue. This means a lot of the major efficiency gains will come from business process innovations happening outside the core.
“AI tech built into systems of record is going to be decent at incremental improvements to existing ways of working,” Bedi says. “But for that step function change, it has to come from AI that works across domains, that takes advantage of data that’s not just resident of one system of record, [that] can look at all of it, run the model on all of it, take actions across all of it. That’s the real unlock here."8
Next: More automation creates opportunities—and potential risks
For many enterprises, core modernization has been a years-long, ongoing task. They may be tempted to view AI as just the latest look to something they’re already familiar with. This may not be the right mindset.
This modernization will likely look very different from past rounds. The speed and scale of change will likely be faster and larger than previous efforts. In the past, modernization was primarily about implementing upgrades, a laborious and time-consuming task, but nevertheless one that was well understood. Software vendors typically provide an upgrade path to give their users a playbook to follow.
This time around, there is no prewritten playbook. The architecture will likely be different because a lot of it will involve AI modules in peripheral software interacting with core systems. Rather than the business aligning everything it does with the core, now the core has to be aligned with what the business is doing. This may become particularly challenging when enterprises take advantage of AI to create new business processes backed by core data. The job becomes more complex and demands more expertise and different skills. Similar to what we discuss in This automated learning approach can be applied to all sorts of business processes, Park says. Automating these tasks through Gen AI capabilities such as summarization, notes generation, conversational chat, AI search, and task automation may save two minutes or two days, depending on the use case. Once they offload simple workloads to bots, enterprises can redeploy workers to more valuable tasks, like improving service levels, driving margin growth, or developing new product offerings, a trend ServiceNow is seeing with its customers. "IT, amplified: AI elevates the reach (and remit) of the tech function", understanding business problems will become a crucial skill for IT teams adding AI to their core systems. This will likely be a major change for IT workers who, in the past, advanced their careers based on deep technical expertise.
Once core systems are modernized through AI, maintaining them becomes a very different exercise. As mentioned in "What's next for AI?" AI agents could soon execute many core functions. Imagine a customer service bot that can interact with customers, understand their issues, and diagnose problems. This bot may then be able to interact with another bot that can take actions like process returns or ship new items. Leading companies are already starting to do this. For example, luxury retailer Saks’ customer service bots can interact with ordering and inventory systems to smooth delivery of items bought online, ease returns, and empower customer service representatives.9 In the truly agentic future, we expect to see more of these kinds of bots that work autonomously and across various systems. Then, maintaining core systems becomes about overseeing a fleet of AI agents.
Done wisely, AI may help reduce technical debt for core systems and push for a cleaner core, which could make enterprise systems less complex to maintain and cater to business demand in a more agile manner.
The core is on the cusp of a major AI-driven revolution. Early adopters are riding the first crest of this wave to increased efficiency and new ways of generating revenue, but soon enterprises will likely turn over much larger core functions to autonomous agents. It remains to be seen what organizations will do with the improved efficiency and effectiveness that come with this change. But the opportunity exists to reshape not just how the core operates but, at a more fundamental level, how business gets done.
