NASA<\/a>\u2019s \u201cfaster, better, cheaper\u201d approach to missions. He argued that SAP transformations should similarly reconsider the assumption that improving one dimension requires sacrificing another.<\/p>\n\u201cWhy can\u2019t you choose all three of these things?\u201d he asked.<\/p>\n
That question becomes increasingly relevant as companies confront the remaining volume of SAP S\/4HANA migrations while simultaneously dealing with economic pressure, scarce SAP skills, and the complexity of global transformation programs.<\/p>\n
Ranjeet Panicker, senior vice president and head of Business Transformation at SAP, framed the challenge around the complete life cycle of an SAP project, from initial discovery and analysis through design, build, and ultimately run.<\/p>\n
At every stage, customers are asking the same questions: How can the project be completed faster? How can it cost less? How can my organization extract more value from the investment?<\/p>\n
From offshoring to \u201cdeshoring\u201d<\/p>\n
For decades, mechanisms for reducing delivery costs have been moving work offshore. The economics were relatively straightforward as certain activities were moved to locations where labor costs are lower.<\/p>\n
Browne argued that those savings can obscure another problem. While offshore resources may cost less, distributing work between offshore and onshore teams can increase the overall volume of work through handoffs, communication issues, rework, and coordination.<\/p>\n
Agentic AI introduces another possibility. Instead of asking where human labor should be located, organizations ask whether some of that labor needs to be performed manually at all.<\/p>\n
\u201cWhy can we not reduce the volume of work and reduce the cost of work?\u201d Browne asked.<\/p>\n
That question led to the concept of deshoring\u2014replacing portions of location-based delivery with AI agents capable of performing or accelerating specific SAP transformation activities.<\/p>\n
After analyzing roughly 180 typical activities involved in an SAP ECC to SAP S\/4HANA migration, Browne\u2019s research concluded that AI could potentially produce about a 60% cost reduction by compressing effort and allowing tasks previously requiring highly experienced people to be performed by less experienced workers augmented by AI.<\/p>\n
Some of the most attractive candidates are highly skilled activities that consume significant amounts of time, including writing functional and technical specifications, performing fit-gap analysis, and analyzing custom code.<\/p>\n
\u201cIf your run rate is a million a month,\u201d Browne noted, \u201celiminating months from a program can dramatically change its economics.\u201d<\/p>\n
The 80% solution with humans handling the last mile<\/p>\n
AI may be capable of producing what Browne characterizes as an 80% solution within hours. It means experienced people review, challenge, and refine that work rather than spend time manually producing everything from scratch.<\/p>\n
\u201cWhat AI produces shouldn\u2019t be fully trusted without review,\u201d he said. None of this means eliminating experienced SAP professionals. Instead, the emerging model redistributes where their expertise is applied.<\/p>\n
The objective is not autonomous transformation. It accelerates the majority of the work while concentrating human expertise on the \u201cfinal mile\u201d that includes judgment, validation, design decisions, and other activities where experience adds the most value.<\/p>\n
Custom code could be an early breakthrough<\/p>\n
One area where Browne believes AI is already producing significant change is custom code analysis. Early SAP S\/4HANA migrations often focused on getting existing customizations into the new environment and then determining how to remediate them. Agentic AI creates another option that determines whether the customization needs to exist at all.<\/p>\n
According to Browne, AI can now analyze an entire custom code base, reverse engineer functional specifications, and determine whether the same business requirement can instead be met using standard SAP functionality.<\/p>\n
Rather than migrating large amounts of legacy customization and addressing it later, customers could potentially understand their custom-code landscape and identify opportunities for fit-to-standard before even selecting a systems integrator.<\/p>\n
\u201cYou can actually plan the fit-to-standard of your custom code before your SI\u2019s have even been appointed,\u201d Browne said.<\/p>\n
The implication is significant because customers have already paid for standard SAP capabilities that may eliminate the need for some custom functionality while creating an environment that is simpler to maintain and upgrade.<\/p>\n
SAP is building agents across the transformation life cycle<\/p>\n
Panicker sees similar opportunities emerging across the broader SAP implementation life cycle.<\/p>\n
The terminology of onshore and offshore itself may eventually become less relevant, he argued, because organizations will increasingly think about transformation work in terms of skills rather than locations. AI-led skills can be delivered through assistants and agents and applied across different stages of a cloud transformation.<\/p>\n
SAP is targeting areas including system analysis, data management, custom code, configuration, testing, rollout, and project management.<\/p>\n
Panicker described an assistant as a collection of agents supporting a particular topic area.<\/p>\n
A system-analysis capability can examine the overall transition. Data-management capabilities can address data quality. Custom-code agents can support analysis, recommendations, and, in some scenarios, automated remediation. Configuration assistants can evaluate current and target states, while testing agents can help automate test scripts.<\/p>\n
According to Panicker, these capabilities are connected with SAP Cloud ALM running on SAP Business Technology Platform, along with a data and knowledge foundation designed to provide the customer-specific context agents need. SAP\u2019s overall objective, he said, is to reduce transformation effort by approximately 35%.<\/p>\n
Testing could become the next frontier<\/p>\n
Testing can consume substantial amounts of time, particularly in industries with security, compliance, or validation requirements. AI raises a more complicated question: How much of that testing can organizations eventually delegate to agents?<\/p>\n
\u201cIf I can get the code to get remediated by an agent,\u201d Panicker said, \u201ccan I allow an agent to do the testing and accept the testing?\u201d<\/p>\n
The answer will determine where humans remain directly involved in transformation workflows. Customers must decide not only if an agent can perform a task, but whether they have enough confidence in the agent\u2019s knowledge, context, and guardrails to delegate responsibility for the outcome.<\/p>\n
Context separates useful agents from hype<\/p>\n
SAP programs generate enormous amounts of organization-specific information around architecture decisions, risk registers, test scripts, requirements, and other artifacts that evolve throughout a transformation.<\/p>\n
Browne believes connecting AI to this continuously changing body of knowledge will be essential. \u201cThe ability to converse not with a static LLM, but to converse with that world as well, I think is what will make good agents even better,\u201d he said.<\/p>\n
Organizations may therefore need to rethink not only how they perform SAP work, but how they capture information. Programs traditionally run across Excel, Word, PowerPoint, SharePoint, and other repositories may increasingly need AI-native knowledge environments capable of providing agents with usable business and system context.<\/p>\n
\u201cThat\u2019s where I think this will be won or lost,\u201d Browne said.<\/p>\n
AI could also change who holds the knowledge<\/p>\n
Perhaps one of the biggest changes involves something less technical: who possesses expertise. SAP implementations have traditionally depended heavily on experienced consultants and systems integrators. Customers frequently lack comparable knowledge, creating an imbalance that can make it difficult to challenge recommendations or independently evaluate major design decisions.<\/p>\n
Browne believes someone with only six months of SAP experience could potentially move up the knowledge curve in weeks in ways that previously might have taken years.<\/p>\n
Panicker agreed that access to knowledge is becoming far less constrained.<\/p>\n
His own experience at SAP was initially concentrated in technical roles. Learning the business context surrounding that expertise often required finding someone willing to explain it. AI potentially allows professionals to move outside those traditional \u201cswim lanes.\u201d<\/p>\n
Challenging the accepted SAP timeline<\/p>\n
SAP customers and consultants have grown accustomed to transformations, migrations, and upgrades taking a certain number of months or years. Those timelines have become assumptions embedded into planning.<\/p>\n
Both Browne and Panicker believe those assumptions now deserve to be challenged.<\/p>\n
\u201cIt\u2019s about compressing the time that these tasks make and connecting the decision-makers to be able to make better decisions more quickly,\u201d Browne said.<\/p>\n
Panicker similarly argued that organizations should stop accepting conventional project durations without asking what AI capabilities have been introduced to shorten them. \u201cWhy can\u2019t we do this sooner?\u201d he asked.<\/p>\n
For Browne, the shift is already underway: \u201cI\u2019m not suggesting for one moment that we can press a button and deliver a whole program. Complexities around testing, change management, and design decisions remain.\u201d But he believes customers should stop treating agentic transformation as something waiting over the horizon. \u201cThat world is here now,\u201d he said.<\/p>\n
Panicker\u2019s message was equally straightforward: don\u2019t wait on the sidelines: \u201cLean in, be curious about the technology. Ultimately, the sooner you can get to the outcome that you\u2019re driving from a business standpoint, the more value you can bring.<\/p>\n
Sign up for our newsletter to receive weekly news, stories, and highlights from the SAP News Center<\/p>\n","protected":false},"excerpt":{"rendered":"For more than a decade, SAP customers have been wrestling with a familiar challenge: how to move from…\n","protected":false},"author":2,"featured_media":76585,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21036],"tags":[9693,9695,21874],"class_list":["post-76584","post","type-post","status-publish","format-standard","has-post-thumbnail","category-sap","tag-agentic-ai","tag-sap","tag-sap-s-4hana"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/posts\/76584","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/comments?post=76584"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/posts\/76584\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/media\/76585"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/media?parent=76584"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/categories?post=76584"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/germany\/wp-json\/wp\/v2\/tags?post=76584"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}