SAP on Monday announced plans to acquire Dremio, which bills itself as an agentic lakehouse company, for an unspecified price. The move is complicated by similar offerings from existing SAP partners Snowflake and Databricks, but analysts point to key differences with Dremio, especially in its ability to work with data while it sits in the enterprise’s environment, rather than having to live externally.

One of SAP’s justifications for the acquisition is that it will theoretically make it easier for IT executives to combine SAP data with non-SAP data. But its strongest rationale involves Dremio’s ability to make complex data more AI-friendly, so that it can more quickly and cost-effectively be made usable. 

“Most enterprise AI projects fail to deliver value not because of the AI itself, but because the underlying data is fragmented, locked in proprietary formats and stripped of the business context that makes it meaningful,” the SAP announcement said. “The result is a familiar and costly pattern: pilots that cannot scale, slow integration of new data sources, duplicated engineering work and compliance risk when organizations cannot explain how an AI-driven decision was reached. Dremio helps eliminate that data fragmentation and integration friction.”

While SAP is citing the data quality argument, there are many elements of enterprise data quality, including data that is outdated, from unreliable sources, or that exists without meaningful context that aren’t addressed by Dremio.

However, SAP said, “With Dremio, SAP Business Data Cloud will become an Apache Iceberg-native enterprise lakehouse that unifies SAP and non-SAP data to power agentic AI at enterprise scale. Apache Iceberg is the industry-standard open table format, and SAP Business Data Cloud will natively support it as its foundation.” This means that there need be no data movement or format conversion; SAP and non-SAP data “can coexist on the same open foundation, with federated analytical reach across every enterprise data source.”

Complicated comparison

Analysts and consultants said that any comparison of Dremio to existing SAP partners Snowflake and Databricks is complicated. For example, Dremio is younger and less established than either Snowflake or Databricks, which suggests that it is a less ideal match for enterprises. 

SAP strategy specialist Harikishore Sreenivasalu, CEO of Aarini Consulting in the Netherlands, said that both Snowflake and Databricks would have been ideal acquisition targets many years ago, but they would be far too expensive today. 

“Databricks and Snowflake are better [for enterprise IT] for sure because they have a mature platform, they do multi cloud” whereas Dremio “is the new entrant in the market and they have to mature more to be enterprise ready. Their security aspects need to mature,” Sreenivasalu told CIO.

But Sreenivasalu added that the situation could easily change after SAP invests and works with the Dremio team. He advised CIOs to “stick with where you are today but watch how technologies get integrated. Listen to the SAP roadmap.”

In a LinkedIn post, Sreenivasalu said the move still is very positive for SAP: “This is the missing piece. SAP has Joule. SAP has BTP. SAP has the business processes. Now it has the open data fabric to feed AI agents the context they need to act, not just answer. For those of us building on SAP BTP + Databricks + SAP BDC, this is a signal: the lakehouse and the ERP world are converging, fast. The future of enterprise AI just got a whole lot clearer.” 

Addresses LLM limitations

During a news conference Monday morning, SAP executives focused on how this move potentially addresses some of the key large language model (LLM) limitations with enterprise data, especially with predictive analytics.

Philipp Herzig, SAP’s chief technology officer, said that LLMs have various limitations, noting, “LLMs don’t deal really well with numbers” and that they struggle with structured data “where we have a lot of differentiation.” 

The practical difference is when systems try to predict the future as opposed to analyzing the past, such as when asking how well a retailer’s product will sell over the next 10 months, or predicting likely payment delays and their impacts on projected cashflow. “This is where LLMs struggle a lot,” Herzig said. He also stressed that Dremio’s ability to work with enterprise data while it still resides in that organization’s on-prem systems is critical for highly-regulated enterprises. 

Local data difference

Flavio Villanustre, CISO for the LexisNexis Risk Solutions Group, also sees the ability to handle data locally as the big draw.

Databricks and Snowflake both offer strong functionality, he pointed out, but users must move the data to their platform and reformat it. After this is complete, the result is a central data lake to address data access needs. “Dremio, on the other hand, provides easy decentralized data access, allowing users to access their data in place,” he said. “Of course, this could be at the expense of data processing performance, but the ease of use and flexibility could outweigh the performance loss.” Implementation speed in days versus weeks or months is another plus, he added. “There is a significant benefit to that.”

Sanchit Vir Gogia, chief analyst at Greyhound Research, agreed with Villanustre, but only to a limited extent. 

“The distinction is not as clean as ‘Dremio lets data stay in place, while Snowflake and Databricks require everything to move,’” he noted. “Snowflake and Databricks have both invested significantly in external data access, sharing, open formats, governance layers, and interoperability. So it would be unfair to describe either as old-style ‘move everything first’ platforms.’” But, he added, the broader argument is correct. “[Dremio] starts from the assumption that enterprise data is already distributed and that the first problem is often access, context, federation, and governance, not wholesale relocation. For SAP customers, that matters a great deal,” he said.

That’s because of the nature of many of SAP enterprise customers’ datasets. 

“Most large SAP estates are not clean, centralized data environments,” he pointed out. “They are brownfield landscapes: SAP data, non-SAP data, legacy warehouses, departmental lakes, regional repositories, acquired systems, partner data, and industry-specific platforms.” While telling these customers that AI-readiness begins with moving everything into one central platform may be good for the vendor, it’s a lot of work for the buyer.

Dremio gives SAP “a more pragmatic story,” Gogia said. “It allows SAP to say: keep more of your data where it is, access it faster, apply more consistent catalogue and semantic controls, and bring it into Business Data Cloud and AI workflows without forcing a major migration program upfront.”

Aman Mahapatra, chief strategy officer for Tribeca Softtech, a New York City-based technology consulting firm, noted that an acquisition of either Snowflake or Databricks would obliterate SAP’s marketing message/sales pitch.

“SAP did not buy a data warehouse. They bought a position in the open table format wars, and the timing tells you exactly why Snowflake and Databricks were never realistic targets,” he said. “Acquiring either would have collapsed SAP Business Data Cloud’s neutrality story overnight and alienated half the customer base in either direction. SAP’s strategic position depends on sitting above the warehouse layer rather than inside it, and Dremio is the federated layer that talks to both Snowflake and Databricks without requiring SAP to pick a side.”

Assume things will change

Mahapatra urges enterprise CIOs to be extra cautious. 

“For IT executives with active Snowflake and Databricks contracts this morning, nothing changes in the next two quarters, but by the first half of 2027, expect SAP to steer net-new AI workloads toward Business Data Cloud regardless of what the partnership press releases say today. The CIOs who plan for that trajectory now will negotiate from strength,” Mahapatra said.

Compute and storage that data warehouse vendors provide is rapidly becoming a commodity, he said, and the “defensible value” in enterprise AI is migrating up the stack to the semantic layer, the catalog, the lineage graph, and the business context that lets an agent know what ‘active customer’ means within an organization.

“SAP just bought the toolkit to own that layer for any company running SAP at the core,” he said. “If you are an SAP-heavy shop running analytics on Snowflake or Databricks, your warehouse vendors are about to feel less strategic and more like high-performance compute backends.”

Corrects a strategic error

Jason Andersen, principal analyst for Moor Insights & Strategy, noted that for quite some time, SAP has been relentlessly encouraging enterprises to host all of their data within SAP systems. SAP can’t reverse that position even if it wanted to. 

What the Dremio deal does, Andersen opines, is to instead address the pockets of data that many enterprise CIOs, especially in manufacturing and highly-regulated verticals, have refused to turn over to SAP. The Dremio deal gives SAP a face-saving way to get an even higher percentage of its customers’ data, he said. 

“Manufacturing is loath to put things in the cloud and [manufacturing CIOs] put up a violent protest [against] going into the cloud,” Andersen said. “This [acquisition] lets SAP access a lot of data that hasn’t yet moved to SAP.”

Shashi Bellamkonda, principal research director at Info-Tech Research Group, said he sees the SAP Dremio move as fixing a strategic error that SAP made years ago, when it did not develop its own Apache Iceberg capabilities. 

“Apache Iceberg is an open-source table format designed for large-scale analytical datasets stored in data lakes, a kind of bridge between raw data files and analytical tools,” Bellamkonda said. “[SAP] should have done this earlier rather than waiting till 2026.”

With the rise of AI, APIs have once again become increasingly vital tools for fueling transformation. Enterprise software APIs, in particular, provide a critical link for CIOs’ AI strategies, enabling them to extract data from core business systems and feed it into their AI models of choice, for analysis, decision-making, and action.

In response to the rapidly increasing use of APIs by non-SAP systems, enterprise software giant SAP has introduced a new API policy limiting access to the data housed in its systems. According to an official statement, the policy stipulates that only those interfaces listed in the SAP Business Accelerator Hub or in the respective product documentation are considered published APIs.

“Customer and third-party applications must not access, invoke, or interact in any manner with APIs that are not Published APIs,” the policy states.

‘This is unacceptable.’

While SAP justifies its new API policy as “designed to safeguard solution health” and as a necessary guarantee of technical stability, the policy could jeopardize the security of customers’ strategic plans as well as their innovation capabilities, the German-speaking SAP User Group (DSAG) warns.

“For SAP-to-non-SAP scenarios, this means: They will only be reliably supported where SAP has explicitly published and documented the underlying interfaces,” DSAG Chairman Jens Hungershausen explained in a statement.

Furthermore, the DSAG believes that the SAP Business Accelerator Hub and the vaguely defined product documentation have not yet been clearly established as contractual components. From the customer’s perspective, this necessitates the creation of clear and reliable framework conditions to enable early assessment of the impact of changes, Hungershausen stated.

“The DSAG has long been demanding absolutely reliable contract documents. However, SAP has taked a contrary position, for example with the SAP Business Data Cloud and now with its API Policy,” says Michael Bloch, DSAG board member for licenses, contracts, and support. Customers currently have questions regarding the interpretation of the documentation, and from DSAG’s perspective, there is a need for clarification regarding their contractual classification. “This is unacceptable,” Bloch states.

Cutting off AI system access?

The DSAG points out that potential new pricing models or usage regulations surrounding APIs must be communicated transparently — and early — to ensure planning fidelity for customers and partners. SAP, for example, has already developed a pricing model with its Digital Access model for creating certain document types in indirect usage.

“According to SAP information, there will be a fair-use model. However, the specific details are currently unclear and should be transparently documented in the API policy,” Bloch says.

Another critical point is that SAP links API usage to technical and organizational requirements. Moreover, use of APIs is restricted for certain scenarios, including:

• Undocumented purposes
• Systematic or large-scale data extractions
• In conjunction with use of (semi-)autonomous or generative AI systems

Here, API usage is permitted only if it explicitly takes place within architectures or services provided by SAP.

“Except through and within the limits of SAP-endorsed architectures, data services, or service-specific pathways expressly identified and intended for such purposes, SAP prohibits API use for: (a) interaction or integration with (semi-)autonomous or generative AI systems that plan, select, or execute sequences of API calls, and (b) scraping, harvesting, or systematic and/or large-scale data extraction or replication,” the policy states.

“According to the information available to us, existing customer integrations and authorized partner solutions are not affected,” says DSAG CTO Stefan Nogly. However, he believes this important protection for existing integrations should be explicitly stated in SAP’s API policy.

Nogly points out that many user companies are already working on proofs of concept (PoC) and pilot projects based on the current interpretation of API usage. “From a customer perspective, we see a significant need for clarification and adaptation — especially to avoid disrupting existing business-critical end-to-end processes or making them legally vulnerable,” he says.

More transparency and transition periods needed

The SAP user group is particularly critical of SAP’s lack of transparency. Its members point out that the new API policy does not clearly document which specific APIs are affected, nor is the extent of the impact clearly defined. “The question is which interfaces are used in the partner solutions,” says DSAG Chairman Hungershausen.

According to DSAG’s understanding, those using official APIs don’t need to take any action, although the lack of contractual safeguards doesn’t guarantee absolute security. For some partner companies, however, the effort involved could be significant, and business models could collapse.

“Therefore, it is essential that SAP grants customers more time for the transition,” Hungershausen says. Customers and partners also need concrete technical and organizational support for switching to SAP-supported interfaces.

From DSAG’s perspective, it is crucial that customers are not forced to resort to other solution providers due to a lack of viable alternatives when existing scenarios are limited.

Management for Germany’s record-holding football championship team aims to optimize processes and provide new digital services using AI. Here, CIO Michael Fichtner discusses what the club’s IT department has implemented, and what advantages they’ll bring to the company internally, and to fans around the world.

Why did FC Bayern migrate to SAP Cloud ERP Private?

Migrating to the cloud gives us access to innovation and other developments. Some SAP services are only available in the cloud environment, so these are now accessible to us. An important aspect was the simplified integration of other technologies or services predominantly or exclusively provided as cloud services.

Another important aspect was the realignment within IT. The migration allows us to focus more on process, application, and business innovation, and therefore on topics that’ll further develop and future-proof our company.

The use of highly available cloud infrastructures also provides us with additional security since in critical situations, we’ll benefit from professional backup and disaster recovery strategies. With all the dedication our employees have shown so far, this will be a further step toward professionalizing operations and further reducing risks.

In addition to security, scalability and flexibility are always important to us. Computing power, storage, and network resources can be scaled more quickly with a cloud provider. This is particularly significant in the frequent peak situations of our business model. For our projects, new systems like sandbox, test, and POC systems can be deployed faster and in a more standardized way, without requiring any investment or new equipment. Plus, security and compliance are becoming increasingly important for us. So migration allows us to leverage our partner’s established security features, and centrally managed access and authorization concepts simplify our operations. Certified data centers also directly support us to meet regulatory, association, and official requirements.

SAP’s strategy is consistently moving toward the cloud, and migration has allowed us to eliminate the risk of eventually having to rely on an outdated on-premise technology so we were able to eliminate legacy tech through migration as well as upgrade to modern, high-performance hardware.

How many applications or systems have been migrated to the cloud?

We migrated our multi-tiered SAP S/4HANA system. But before the migration, we worked together to consolidate our system landscape, merging 52 systems carrying fan data into S/4. There, the central fan database was established, the Golden Fan Record was built, and the data was combined into a redundancy-free, 360-degree view. So this approach was a significant milestone to implement our sovereign cloud strategy.

So we’ve only migrated one system physically, but in abstract terms, our phased approach allowed us to migrate data from all 52 systems to the cloud through consolidation, thus taking a big step toward controlled and consistent data sovereignty.

Which digital innovations does FC Bayern want to implement with the cloud?

Our business model is heavily influenced by peak situations like knockout phases in sporting competitions, live broadcasts, and special sales activities. In these situations, we need to not only scale technically, but provide innovative process solutions that reliably support peak loads.

Consider the short timeframes of ticket requests that must be processed during knockout stages. Or the launch of jerseys, where fans, even during peak periods, have the right to expect that goods will be delivered as quickly as possible. So in departments experiencing significant annual peaks in volume, it’s crucial employees receive highly automated support. Handling these seasonal peaks would otherwise be impossible.

We rely heavily on solutions supported by AI and digital agents, so developing them is always a joint initiative with our specialist departments.

What digital services and personalization strategies is FC Bayern planning to use to reach fans worldwide with the help of the new cloud platform?

Our aim is to address our fans in an individual, personalized way. The way forward is to move away from mass communication and large target groups or segments, and toward a personal approach, specifically tailored to the needs of each fan.

For this, we need the relevant data and ability to process large amounts of data in compliance with data protection regulations. This isn’t feasible without the appropriate infrastructure and scalability. We see personalized communication as a crucial element to remain relevant to our fans in the future. Mass mailings to fans via email, push notifications, or standardized content without specific relevance to the individual fan won’t help us remain attractive to them.

By providing targeted, relevant content, we want to further increase the attractiveness of FC Bayern Munich, and ensure the relationship with fans for the future.

What advantages do you expect from SAP Cloud ERP Private and AI?

A crucial factor in our decision to migrate was the conviction that we could significantly optimize our internal processes by using AI approaches. Specifically, we’re working on corresponding implementations in HR using SAP’s SuccessFactors and Concur. Initial approaches have also been developed and are being put in logistics and financial accounting. We expect this will allow us to increasingly automate more activities, freeing up colleagues in specialist departments to focus on specific tasks that require a particular approach or interaction. Ultimately, this will enable us to provide better service to fans as we gain time to address other issues.

What role did digital sovereignty or data sovereignty play in the decision to migrate to the SAP cloud?

Digital sovereignty, and control over our data and the data of our fans, have been of paramount importance for many years, and have guided our actions for just as long. Driven by this principle, we’ve developed and operated our key applications ourselves.

With the capabilities our partners have made available to us, we could implement these requirements in a sovereign cloud environment without compromising standards. So we’re confident we’ve not created any dependencies and will remain operational in the years to come. We’re convinced that the de facto and legal control of our critical data is sustainably ensured in our chosen setup.

SAP S/4HANA로의 전환은 여전히 대기업 IT 환경에서 핵심 의제로 자리 잡고 있다. 그렇다면 기업들은 실제로 어느 단계에 와 있으며, 어떤 전략이 효과를 거두고 있을까.

SAP 전환의 현재 진행 상황과 S/4HANA 도입 과정에서 CIO가 직면한 주요 과제, 그리고 최신 ERP 트렌드를 살펴보기 위해 기업용 컨설팅 업체 CBS(Corporate Business Solutions)의 대표 홀거 쉘과 인터뷰를 진행했다.

Q: S/4HANA 전환이 현재 프로젝트 사업에 미치는 영향은 어느 정도인가?
A(홀거 쉘): 핵심 동력이다. 현재 우리가 구축하는 솔루션의 약 98%가 SAP 기반이며, 상당수 프로젝트가 ERP 시스템 현대화와 S/4HANA 전환 필요성에서 비롯됐다.

Q: 현재 기업들은 전환 과정에서 어느 단계에 와 있는가?
A: 주요 산업군인 대기업과 중견 제조기업을 기준으로 보면, 긍정적으로 평가해 약 절반 정도가 전환을 시작한 상태다. 다만 완전히 완료한 기업은 매우 적다.

Q: 많은 기업이 2027년 SAP 마감 기한에 직면해 있다. 현실적인가?
A: 많은 기업에는 해당되지 않는다. 특히 ERP 시스템이 다수인 대기업은 2027년까지 확장 유지보수 단계에 도달하기 어려울 가능성이 크다. 2030년이 보다 현실적인 목표 시점이다.

Q: 현재 주로 사용되는 전환 전략은 브라운필드인가, 그린필드인가?
A: 어느 하나의 순수한 방식이 지배적이지 않다. 완전히 새로 구축하는 전통적인 그린필드 방식은 실제로 성공 사례가 매우 제한적이다. 그렇다고 단순 기술 전환인 브라운필드가 제조기업에서 표준 방식으로 자리 잡은 것도 아니다.

Q: 그렇다면 실제로 선호되는 접근 방식은 무엇인가?
A: 대부분 기업은 혁신과 전환을 선택적으로 결합한 하이브리드 전략을 채택하고 있다. 흔히 ‘스마트 브라운필드’ 또는 ‘믹스 앤 매치’라고 부르는 방식이다.

그 이유는 명확하다. 순수 브라운필드 접근은 비용과 시간이 많이 들지만 추가적인 비즈니스 가치를 만들어내지 못한다. 기업은 단순히 지원 기간을 맞추는 데 그치지 않고, 실제 성과를 만들어내기를 원한다. 즉, 프로세스를 개선하고 혁신을 도입하며, 전환이 왜 필요한지 경영진에게 설명할 수 있는 명확한 가치를 확보하려는 것이다.

Q: SAP가 ‘클린 코어(Clean Core)’ 접근 방식을 강하게 강조하고 있다. 실제로 얼마나 현실적인가?
A: 단순하게 볼 수 있는 개념은 아니다. 클린 코어는 표준 소프트웨어만 사용하고 모든 커스터마이징이 사라진다는 의미가 아니다. 오히려 새로운 설계 패러다임에 가깝다. 핵심은 ERP 코어, 즉 S/4를 가능한 한 표준에 가깝게 유지하는 것이다. 그 외 기업별 맞춤 기능은 SAP BTP 같은 플랫폼이나 새로운 개발 방식으로 외부에서 구현하는 구조다.

Q: 과도한 표준화가 경쟁력을 약화시킬 수 있다는 지적도 있다. 어떻게 보는가?
A: 차별화는 여전히 매우 중요하다. 기업은 고유한 역량을 계속 보여줘야 한다. 클린 코어는 이를 구현하는 기술적 방식이 바뀌는 것일 뿐, 차별화 필요성 자체가 사라지는 것은 아니다.

그래서 우리는 ‘더 깨끗한 코어(cleaner core)’라는 표현을 사용한다. ERP 코어가 단순하고 정돈될수록 업그레이드와 신규 기능 도입이 쉬워지고, 기업의 민첩성도 높아진다. 다만 이는 급격한 변화가 아니라 점진적인 진화 과정이다.

Q: 기업들이 S/4HANA 프로젝트에서 복잡성을 가장 과소평가하는 부분은 무엇인가?
A: 가장 중요한 요소는 출발점이다. 특히 제조업에서는 20년 이상에 걸쳐 축적된 ERP 시스템이 많다. 이로 인해 데이터 구조는 복잡하고, 프로세스 역시 역사적으로 누적된 형태를 띤다. 이를 해체하고 표준화·통합된 목표 구조로 재정비하는 작업은 매우 큰 과제이며, 동시에 혁신 도입과 병행해야 하는 경우가 많다.

Q: 이러한 요소가 실제 프로젝트에는 어떤 의미를 가지는가?
A: 레거시 시스템이 많고, 프로세스·시스템·데이터 환경의 준비도가 낮을수록 전환 기간은 길어진다. 결국 미래 지향적인 ERP 플랫폼으로 가는 과정이 그만큼 복잡해진다.

Q: 현재 많은 기업이 SAP가 아닌 외부 AI 솔루션을 활용하고 있다. SAP가 압박을 받고 있다고 보는가?
A: 일정 부분 그렇다. 소프트웨어 기업이라면 반드시 성과를 보여줘야 한다. 다만 SAP는 역사적으로 신기술에서 선도 기업 역할을 해온 경우는 많지 않았다. 대신 비즈니스 맥락을 시스템에 통합하는 데 강점을 가져왔다.

현재 시장을 보면 AI는 고객에게 매우 중요한 요소다. 기업은 AI를 통해 경쟁력을 확보하고 효율성을 높이기를 기대한다. 그 결과, 과거에는 많은 AI 솔루션이 SAP 외부에서 구현됐다.

앞으로 중요한 지점은 SAP가 강조하는 ‘비즈니스 AI’다. 기업의 비즈니스 프로세스에서 생성되는 방대한 데이터를 어떻게 효과적으로 활용할 것인가의 문제다. 이러한 데이터는 ERP 시스템, 즉 기업의 핵심에 존재한다. SAP는 비즈니스 스위트와 ERP 중심 아키텍처를 기반으로 이 영역에서 매우 유리한 위치에 있다.

Q: 최근 AI와 전통적인 ERP 시스템의 미래를 둘러싼 논의가 활발하다. 비즈니스 모델이 위협받고 있다고 보는가?
A: 오해에 가깝다. 많은 사람들이 이른바 ‘챗GPT 모먼트’를 과대평가하면서 ERP 기반이 더 이상 필요 없다고 생각한다. 하지만 AI 시스템은 신뢰할 수 있고 의미적으로 정제된 데이터 기반을 필요로 한다. 이 지점에서 SAP는 구축된 시스템 환경과 고객 기반, 그리고 제조업 고객과의 경험을 바탕으로 분명한 강점을 가지고 있다.

Q: 그렇다면 ERP 공급업체의 비즈니스 모델이 구조적으로 위협받고 있다고 보지는 않는가?
A: 현재의 과열된 분위기는 점차 진정될 것이며, 견고한 ERP 백본 아키텍처의 중요성이 다시 인식될 것으로 본다. ERP는 ‘에이전트 기반 기업’과 같은 새로운 개념을 구현하는 기반이 된다. 이는 ERP와 같은 기존 트랜잭션 중심 시스템 위에 행동 시스템(system of action)을 구축하는 구조다.

Q: 이는 서비스나우(ServiceNow)와 같은 플랫폼 모델과 유사한 개념인가?
A: 그렇다. 그런 방향으로 발전할 가능성이 크다. 기존 시스템 위에 에이전트 레이어가 올라가는 구조다. SAP 역시 이러한 흐름에 맞춰 관련 기능과 솔루션을 아키텍처에 통합하고 있다.

Q: 전체 구조를 어떻게 이해해야 하는가?
A: 크게 두 가지 계층이 있다. 하나는 데이터 기록 시스템(system of record), 다른 하나는 실행 시스템(system of action)이다. 그리고 그 사이에 데이터 레이어가 존재한다. SAP는 ‘비즈니스 데이터 클라우드(Business Data Cloud)’를 통해 데이터에 맥락을 부여하고, 분석과 트랜잭션 영역을 통합하는 방식을 제시하고 있다. 이는 시스템과 벤더를 넘어서 적용된다.

Q: 이러한 구조가 의미하는 바는 무엇인가?
A: 핵심 가치는 여전히 기업이 보유한 데이터에 있다. 단순한 데이터가 아니라, 명확히 이해되고 맥락이 부여된 비즈니스 데이터다. 이러한 데이터가 있어야 ‘에이전틱 기업’이 안정적으로 작동할 수 있고, 궁극적으로 신뢰를 확보할 수 있다.

Q: AI 공급업체들이 오류율을 크게 낮췄다. 그럼에도 기업이 요구하는 수준의 신뢰성에는 아직 못 미친다는 평가가 있다. 어떻게 보는가?
A: 현재 수준은 아직 충분하지 않다. 기업의 핵심 의사결정에 필요한 100% 신뢰성에는 도달하지 못한 상태다.

지금의 AI는 기술적으로 잘 구현됐다고 하더라도 상당 부분 추정에 기반해 작동한다. 물론 이를 계속 개선할 수는 있지만, 본질적으로는 추정의 성격을 완전히 벗어나기 어렵다.

결국 중요한 것은 의미적으로 정제된 데이터 기반의 신뢰성이다. 이를 위해서는 감사 기준에도 부합할 수 있는 안정적이고 신뢰할 수 있는 데이터 코어가 필요하다. 그리고 바로 그 기반을 ERP 시스템이 제공한다.

Q: 그렇다면 ERP 시스템은 여전히 핵심 인프라인가?
A: 그렇다. ERP는 여전히 기업의 핵심 백본이다. 특히 제조기업은 ERP에 막대한 투자를 해왔다. 이를 단순히 에이전트 기반 시스템으로 대체하는 것은 현실적으로 불가능하다고 본다.

Q: 현재 SAP 환경에서 AI 프로젝트는 어느 정도 진행되고 있는가?
A: 이미 1년 전부터 명확한 기준을 세웠다. SAP 환경에서 고객의 디지털 전환 목표를 설계할 때 AI를 초기 단계부터 필수 요소로 포함하고 있다. 가능한 모든 영역에 AI를 적용해 최대한의 가치를 창출하려는 방향이다.

AI는 컨설팅 사업 자체를 변화시키고 있으며, SAP 기반 미래 솔루션의 핵심 요소로 자리 잡고 있다. 현재도 이 분야에 지속적이고 적극적으로 투자하고 있다.

Q: SAP 내 AI는 어떤 방식으로 구분되는가?
A: SAP는 AI를 임베디드 AI와 커스텀 AI로 구분한다. 임베디드 AI는 표준 소프트웨어에 포함된 기능이며, 커스텀 AI는 SAP 기술 기반, 예를 들어 비즈니스 테크놀로지 플랫폼을 활용해 특정 업무에 맞춰 개발하는 솔루션이다.

그동안은 코어 영역에서 제공되는 기능이 제한적이었기 때문에 커스텀 AI 중심으로 프로젝트를 진행해왔다. 하지만 최근 들어 상황이 크게 달라지고 있다.

Q: 현재 프로젝트에서 AI의 역할은 무엇인가?
A: 기대 수준이 크게 높아졌다. 이제는 현업 부서와의 워크숍에서 AI 활용 가능성이 기본 논의 항목으로 자리 잡았다.

프로세스를 분석하면서 반복 작업이 어디에 있는지, 자동화가 가능한 영역은 어디인지, 분석이나 제어 과정에서 복잡성을 AI로 줄일 수 있는 부분은 어디인지 등을 함께 검토한다. 실제 적용 가능한 영역이 점점 늘어나고 있으며, 고객의 관심도도 빠르게 증가하고 있다.

Q: AI가 향후 새로운 성장 영역이 될 수 있는가? 현재는 S/4HANA 전환이 주요 사업 동력인데, 이후에는 어떻게 되는가?
A: 결국 기업들은 S/4 전환을 완료하게 된다. 기술 중심 마이그레이션에만 집중한 사업자에게는 위험 요소가 될 수 있다. 하지만 시장은 그렇게 단순하지 않다.

S/4 수요가 갑자기 사라지지는 않는다. 단기적으로는 마이그레이션 중심 프로젝트가 줄어들 수 있지만, 그 자리는 후속 프로젝트가 대체하게 될 것이다.

Q: 후속 프로젝트란 무엇을 의미하는가?
A: 이미 ‘포스트 S/4 전환’이라는 개념이 논의되고 있다. 많은 기업이 브라운필드 방식으로 S/4HANA로 전환했지만, 프로세스를 근본적으로 바꾸지는 않았다. 즉, 실제 핵심 변화라고 할 수 있는 혁신과 비즈니스 프로세스 고도화는 이제부터 시작되는 단계다.

대외적으로는 기업의 전환 여정이 실제보다 단순하게 인식되는 경우가 많다. 하지만 이는 단순히 새로운 시스템을 도입하는 문제가 아니라, 데이터 기반 조직으로 점진적으로 진화하는 과정이며, 궁극적으로는 ‘에이전틱 엔터프라이즈’로 나아가는 흐름이다.

Q: 그렇다면 전환은 장기적인 과정이라고 볼 수 있는가?
A: 그렇다. 기업은 시간을 두고 역량을 체계적으로 확장해 나간다. S/4HANA는 목적지가 아니라 출발점에 가깝다. 현재 기업들은 비즈니스 전환, 기술 전환, 프로세스 혁신, 디지털 전환 등 보다 폭넓은 과제를 동시에 추진하고 있다.

Q: SAP가 자체 기능을 확대하면 컨설팅 파트너의 역할은 줄어들지 않는가?
A: 핵심은 컨설턴트가 기업을 어떻게 변화로 이끄는가에 있다. 비즈니스 프로세스를 어떻게 설계하고, 미래로 가는 경로를 어떻게 제시하느냐가 중요하다. 이러한 역할은 앞으로도 컨설팅의 중심이 될 것이다.

실제로 컨설팅 영역은 축소가 아니라 확대되고 있다. 시스템과 프로세스의 복잡성이 크게 증가하고 있으며, ‘에이전틱 엔터프라이즈’와 같은 개념은 새로운 의미적 계층을 추가하고 있다. 그 결과 IT와 프로세스 환경은 더욱 정교하고 어려워지고 있다.

기업은 이러한 복잡성을 이해하고 구조화하며, 이를 기반으로 실질적인 비즈니스 해법을 도출하는 데 점점 더 많은 지원을 필요로 한다. 이것이 바로 컨설팅의 역할이다.

반면, 전통적인 개발·테스트·설정과 같은 단순 구현 중심 서비스는 압박을 받고 있다. 이러한 흐름은 이전부터 존재했지만, AI와 SAP의 전략 변화로 인해 앞으로 더 가속화될 가능성이 크다. 해당 영역은 대체 가능성이 높아지며 수요도 점차 줄어들 것으로 보인다.
dl-ciokorea@foundryco.com

Migrating to SAP S/4HANA remains a dominant topic in the IT landscape of large corporations. But where do these companies really stand — and which strategies are proving successful?

Computerwoche spoke with Holger Scheel, managing director of cbs (Corporate Business Solutions), to get the SAP consultant’s insights into the current state of SAP migrations, the challenges CIOs face in shifting to S/4HANA, and the latest ERP-related trends.

Here is that interview, edited for clarity and length.

Computerwoche: How strongly is the topic of S/4HANA transformation currently shaping your project business?

Holger Scheel: It’s a key driver — around 98% of the solutions we implement are based on SAP, with a large number of projects triggered by the need to modernize ERP systems and implement the S/4HANA transformation.

Where do companies currently stand in this transformation?

If we look at our core sectors — large and midsize industrial companies — then I would say: To put it positively, about half of the companies have started the transformation. But very few have completely finished it.

Many companies are facing the SAP deadline of 2027. Is that realistic?

Not for many. Larger companies with numerous ERP systems, in particular, are unlikely to make it to extended maintenance by 2027. The 2030 timeline is a more realistic target.

Which migration strategy currently dominates — brownfield or greenfield?

Neither in its purest form. The classic greenfield approach — developing everything from scratch — has proved successful and feasible for very few companies. But even the purely technical conversion, i.e., brownfield, is not the dominant standard among manufacturing customers.

What is the preferred approach instead?

The majority of companies are pursuing a hybrid approach — a selective combination of innovation and transformation. This is often called “smart brownfield” or “mix & match.”

The reason: A purely brownfield approach costs money and time but delivers no added value. Companies want more than just to stay within the release window — they want to create real added value. They want to improve processes, introduce innovations, and be able to explain to their management why the transformation is worthwhile.

SAP strongly promotes the “clean core” approach. How realistic is that in practice?

You have to look at it in a nuanced way. Clean Core doesn’t mean I only use standard software and all custom developments disappear. It’s more about a new design paradigm. The idea is that the ERP core — i.e., S/4 — remains as close to the standard as possible. Everything a company needs beyond that in terms of customization for its business is then organized externally — for example, via platforms like SAP BTP — or through new development approaches.

Critics argue that excessive standardization jeopardizes competitive advantages. What is your view?

Differentiation remains absolutely crucial. Companies must continue to showcase their specific capabilities. Clean core only changes how this is implemented technically — not the need for differentiation.

We therefore speak more of a “cleaner core”: The cleaner the ERP core, the easier upgrades and the use of new functionality become, and the more agile the company becomes. But it remains an evolutionary process — not a radical break.

Where do companies most underestimate the complexity of S/4HANA projects?

A key point is the initial state. Especially in industry, many companies have ERP systems that have grown organically over 20 years — with correspondingly complex data structures and historically evolved processes. Breaking all of this down and achieving a more standardized, harmonized, and consolidated target state is an enormous task that often has to be mastered in conjunction with the establishment of innovations.

What does this mean specifically for the projects?

The more legacy systems a company has and the less prepared its process, system, and data landscape is, the longer the transformation will take. The path to a future-proof ERP platform then becomes correspondingly more complex.

Many users are currently relying on external AI solutions rather than SAP offerings. Do you see the company under pressure in this regard?

Of course, SAP is under pressure — a software manufacturer has to deliver. But historically, SAP has rarely been a first mover with new technologies. Its strength has always lay in the business context that SAP integrates into its systems.

Looking around, AI is clearly of enormous importance to customers. Companies expect it to give them a competitive edge and increase efficiency. Consequently, many solutions have been implemented outside of SAP in the past.

Looking ahead, the crucial point is that SAP is talking about “Business AI”: How can I effectively utilize the wealth of data from my business processes? And this data resides in the ERP system — the “heart and soul” of the company. SAP is, of course, exceptionally well-positioned here thanks to its Business Suite and ERP-driven architecture.

There is currently a lot of discussion about AI and the future of traditional ERP systems. Is the business model threatened?

That’s a misconception. Many people are overestimating this “ChatGPT moment” and think an ERP foundation is no longer needed. AI systems require a reliable, semantically clean data foundation. And that’s where SAP has a significant advantage with its environments, installed base, and experience with industrial customers.

So you do not believe the business model of ERP providers is under sustained threat.

We believe that the current hysteria will subside and that the crucial importance of a solid ERP backbone architecture will be recognized. It forms the basis for implementing new concepts such as an agent-driven company — that is, a system of action built on a classic transactional system of record, such as ERP.

Is this similar to platform approaches — such as ServiceNow’s — that function as a higher-level platform on which agents work and retrieve the necessary data from various systems?

Exactly. That’s how it will be. This is essentially the agent layer that’s placed on top of the existing systems. SAP is also adding this and integrating corresponding solutions and functions into its architecture.

Ultimately, we’re talking about different levels: the system of record on the one hand and the system of action on the other. The data layer lies in between. SAP addresses this, for example, with its Business Data Cloud, to contextualize data and merge analytical and transactional levels — even across systems and vendors.

What does that mean?

My conviction is that the real value still lies in the data treasure trove of companies — that is, in clearly understood and contextualized business data. This remains the crucial foundation for an “agentic company” to function in the future and ultimately be trustworthy.

AI providers have significantly reduced error rates. Nevertheless, we are probably still far from the 100% reliability that companies need for business-critical decisions.

What AI systems can do today, in many cases, is guesswork, provided it’s technically well implemented — and while you can perfect that further, it ultimately remains guesswork.

The reliability of the semantic foundation is crucial. This requires a stable, trustworthy data core — a foundation that even auditors will accept. And that is precisely the foundation that ERP systems provide.

The ERP system therefore remains the backbone.

I am convinced this foundation remains indispensable. Manufacturing companies in particular have invested heavily in their ERP systems. They will not simply abandon them and replace them with purely agent-based systems. I consider that impossible.

Are you already managing many AI projects in the SAP environment for your customers?

We issued a clear guideline over a year ago: When we develop target scenarios for our clients’ digital transformation in the SAP environment, AI is an integral part of the process from the very beginning. We strive to consistently incorporate it and use it to create as much added value as possible. AI shapes our consulting business; AI shapes the SAP-based solutions of the future. AI is a driving force for us — we are investing heavily and sustainably in this area.

It’s important to understand that the possibilities within SAP itself have only developed gradually. SAP distinguishes between embedded AI and custom AI. Embedded AI is what the standard software already provides. Custom AI, on the other hand, refers to specific solutions developed for individual use cases based on SAP technologies — such as the Business Technology Platform.

In recent years, we’ve primarily worked in the custom AI environment because there simply weren’t that many ready-made features available in the core area. However, that has changed significantly since then.

What role does AI play in your projects today?

The expectations have risen significantly. In our workshops with the specialist departments, the question of AI potential is now a standard part of the discussion. We examine processes and consider: Where are there repetitive tasks, where can automation help, where can complexity — for example in analysis or control — be reduced through AI?

And we are finding there are more and more meaningful areas of application and a growing interest on the part of customers to actively address such topics.

Will AI become a new growth area for you? Currently, your business is primarily driven by S/4HANA migrations. What will happen when this wave subsides?

Eventually, companies will have completed their S/4 migration. For providers who have focused exclusively on technical migrations, this may be a risk. But that’s not how we see the market.

Demand for S/4 will not disappear abruptly. While purely migration-driven projects will decrease in the long term, they will be replaced by follow-up projects.

What do you mean by follow-up projects?

We’re already talking about “post-S/4 transformations.” Many companies, for example, migrated to S/4HANA using a brownfield approach, without fundamentally changing their processes. This means that the real substantive transformation — innovation and further development of business processes — is still to come.

The public perception often underestimates the true extent of a company’s transformation journey. It’s not just about a new system, but about a gradual evolution towards a data-driven organization — ultimately, an “agentic enterprise.”

So it’s a longer process?

Companies systematically expand their capabilities over time — and S/4HANA is more of a starting point than the destination. Today, companies are increasingly pursuing a broader agenda: business transformation, technology transformation, process innovation, and digital transformation.

Will there be enough left for consulting partners if SAP increasingly provides its own functionality?

The essential thing is the question: How do I, as a consultant, bring a company along? How do I design business processes? And how do I convey the path to the future? That will continue to be the core task of consulting — and that’s how we are positioned.

We see very clearly that the field of consulting is not shrinking, but expanding. Complexity is increasing massively. Concepts like the “agentic enterprise” add a new semantic layer. This makes IT and process landscapes even more demanding.

Companies increasingly need support to understand and structure this complexity and derive meaningful business solutions from it. That is precisely where our role lies. Services that are very close to pure implementation — classic development, testing, or configuration tasks — are under greater pressure. Although this has always been the case, AI and SAP’s strategic direction will likely intensify this pressure. Such tasks will become easier to replace and will tend to be in less demand.

SAP has taken a beating of late in the stock market due to perceptions that company’s enterprise software offerings and foothold are vulnerable to the rise of AI. Now, SAP customers are voicing their concerns — less about the replaceability of SAP platforms at the hands of AI than in terms of the AI outcomes and clarity they are getting from SAP’s platforms and vision.

At last month’s German-speaking SAP User Group (DSAG) conference, the overall sentiment was clear: There is still a long way to go between SAP’s ambitious AI plans and the reality its customers face. Stefan Nogly, DSAG’s technology expert, warned in an interview with Computerwoche against further divergence — but also says he sees some progress.

“We need to be careful that the gap between SAP and its users doesn’t widen further,” he says — a concern recognized by SAP itself, as SAP CTO Philipp Herzog admitted in his keynote address at the event that a significant gap exists between AI innovation and actual outcomes.

“SAP intends to actively improve in this area. I am generally satisfied with the answers and the announced measures,” Nogly adds.

The top tier: AI for IT

Nogly understands why many companies remain hesitant regarding AI and SAP, as the DSAG Investment Report 2026 recently revealed. Integration of AI agents into business processes is essentially the “final stage” — and in many cases, trust, experience, and, above all, a suitable data foundation are still lacking. “We are in a phase where we have a lot to learn and try out,” said the DSAG spokesperson.

From Nogly’s perspective, it makes sense to promote AI experimentation first within IT. SAP has already announced its intention to provide greater support in this area — for example, through migration tools and additional AI functions within IT and transformation processes. “Often, the initial focus is on coding support, such as through ‘Joule for Developers.’ However, there are actually many more areas of application,” Nogly explains.

Especially in the context of cloud transformations, AI can significantly contribute to efficiency, for example, in adapting interfaces. Many companies have not just a few, but hundreds or even thousands of interfaces — from business-to-business to application-to-application — that need to be adapted and optimized. Here, AI can significantly increase speed and productivity. The same applies to user interface development, says Nogly. If developers can create multiple UI variants more quickly and coordinate them with the relevant departments, the benefits are immediately apparent.

Overall, there is a wide range of potential applications for AI, where the added value often becomes apparent more quickly than with direct integration into business processes.

In search of added AI value

Many companies are not yet ready for such integration, however, the DSAG representative adds. The industry is currently in a learning phase, he says, with the focus primarily on gaining experience and understanding where AI actually delivers added value.

To that end, Nogly recommends testing AI in a controlled manner, with clearly defined areas of application, developed step by step. More complex use cases, such as those SAP is currently strongly promoting, is not yet within reach of many companies, for example, when it comes to public cloud scenarios or the use of data products in the Business Data Cloud, Nogly adds. This level of maturity takes time to build — nevertheless, customer companies expect SAP to demonstrate a clear and practical path to get there.

For that, companies primarily need planning certainty, he says — and time to uplevel operations. “This takes a bit of time, and we need to allow ourselves that time. We should consciously say: We’re trying things out and learning,” a process that also includes fundamental strategic realignment within customer companies themselves.

Some pioneers closely aligned with SAP’s strategy, such as Frosta and Hörmann, have demonstrated that SAP’s approach works in principle — however, such flagship projects, highlighted in the event’s keynote, have been rather isolated. Many midsize companies, in particular, are still acting cautiously, observing costs, benefits, and risks, and waiting to see how things develop.

A new dimension of security

A key issue in this context is security. Nogly emphasizes that, for example, critical infrastructure companies in the energy, transport, and healthcare sectors already have to comply with very strict requirements under the IT Security Act (IT-SiG 2.0) — regardless of AI — while for the wider economy, the requirements of the NIS2 Directive and the BSI IT Baseline Protection serve as the benchmark. “This must become the standard practice for any company,” he says. However, AI adds an additional dimension.

“Many people find it more enjoyable to talk about productivity and simplification,” says Nogly. “But we also need to know precisely what data AI accesses, whether it modifies data, and how decisions are made.”

Trust in AI systems can only be built through security. Therefore, a new discipline is emerging within IT security. Nogly warns against focusing investments solely on efficiency gains: Companies must also invest in understanding the technology and its risks. “Those who only look at productivity and neglect security are missing the mark,” he says.

The situation is becoming increasingly complex, especially with regard to AI agents taking on increasingly autonomous tasks and linking processes together. This development marks a new level of complexity — and significantly increases the demands on governance, control, and security mechanisms.

AI needs data — and patience

A key obstacle for companies remains the data foundation. For many SAP customers, analytics landscapes are fragmented, and a unified data layer is lacking. “This is the reality for the majority of companies,” says Nogly.

With its Business Data Cloud (BDC), SAP has chosen a sound strategic approach, with concepts like data products and a semantic layer fundamentally suitable for bringing order and transparency to data landscapes. But the solution is coming late: Numerous organizations have already invested in platforms such as Snowflake or Databricks to address precisely these problems. Accordingly, the question now arises as to how existing solutions can be meaningfully combined with the BDC — without adding complexity or high costs. “This needs to be explained,” says Nogly. Introducing yet another tool is neither trivial nor inexpensive.

Furthermore, he sees room for improvement in SAP’s implementation: The product needs to mature further, become more understandable, and be more accessible. Besides technological hurdles, the commercial model also plays a role. “The idea is good — but it still needs to be proven,” he summarizes.

Public cloud ERP systems haven’t yet reached sufficient maturity to be a viable alternative for the majority of customers, Nogly points out, though he no longer considers implementation of SAP BTP (Business Process Transfer) to be a major obstacle. The fact that many companies still rely on on-premises or private cloud models is primarily due to the realities of the transformation process: companies have to prioritize. Often, the ERP system migration comes first, followed by facets like analytics or data platforms. “It doesn’t all happen at once,” Nogly emphasizes. Limited resources, budgets, and organizational capacity mean that the transformation can stretch over years.

This context also clarifies why many AI initiatives are still in an early stage. Only when a solid data foundation exists can AI applications be used effectively and scaled.

“We talk a lot about AI these days, but at the same time we’re still in an experimental phase,” says Nogly. The pace of new models and applications is rapid — but their actual implementation in companies is lagging behind.

Pressure on IT is increasing

At the same time, the pressure on IT departments is increasing. Nogly reports that many CIOs are currently being confronted by their management with AI initiatives. “Everywhere, solutions are supposed to be tested quickly,” he says. This approach often contradicts necessary foundations such as data quality, security, and governance, creating a tension between the pressure to innovate and technological reality.

Regarding the question of standardization versus individualization, the DSAG representative also advocates for a clear course. The goal must be to create stable and maintainable systems. “We want to move away from a situation where a single patch terrifies an entire company,” he says.

SAP has laid the right foundations with its Business Technology Platform. However, the platform and its associated extension concepts now need to be understood and consistently used in practice, says Nogly. “First, you have to fully explore its potential and learn how to use it.” This includes technologies such as Fiori and CDS Views, which will play a central role in the future.

SAP has clearly confirmed this direction: “Philipp Herzog said on stage: Absolute investment protection in Fiori, in CDS Views, in this entire underlying framework. Yes, that is the future,” Nogly notes. However, this also means a profound transformation for companies, he adds. Developers must move away from classic ABAP approaches and understand and apply the new platform landscape.

Once this step is completed, further expansions will still be possible — but within clearly defined guidelines. The goal is a platform approach that ensures stability and security and eliminates the fear of updates, according to Nogly.

At the same time, Nogly points out that this change also takes time. SAP began its transformation 10 to 12 years ago, whereas many companies started much later — some seven or eight years ago, others only now. Consequently, their levels of maturity vary considerably. In many organizations, a fundamental rethinking of operations and development is only just beginning.

The necessary technologies and expansion options are fundamentally available. But it remains to be seen whether they will be sufficient in every case. Furthermore, the possibility of integrating other solutions into modular IT landscapes still exists.

DSAG demands more clarity, maturity and support

During its Technology Days event, DSAG also compiled a list of demands, primarily calling on SAP for more clarity, maturity, and support in implementing key future topics.

• For AI to truly become enterprise-ready for SAP customers, orchestrated agents, transparent decision logic, secure data, and open integration for third-party agents are needed. At the same time, DSAG expects a clearer strategic vision, simpler implementation, and investment protection for existing technologies such as Fiori.
• In terms of data, the focus is on expanding Business Data Cloud. It should serve as a unified, trustworthy data layer. This requires clearly defined data products, improved cataloging, and practical migration paths to modern data architectures.
• In ​​security, DSAG seeks binding best practices, clear governance models, and, above all, transparency and traceability of decisions — both technical and regulatory.
• For transformations, the user group would like more concrete support: for example through funding programs, more migration tools, more practical reference architectures, and closer coordination with SAP on roadmaps.

SAP 파트너 어워드는 한 해 동안 고객의 비즈니스 혁신과 성과 창출에 기여한 글로벌 파트너를 선정하는 프로그램이다. 성과 지표와 데이터 기반 평가를 통해 수상 기업이 결정된다.

오픈텍스트에 따르면, 이번 수상은 ‘파트너 솔루션 성공(Partner Solution Success)’과 ‘인적 자본 관리(Human Capital Management, HCM) 솔루션 우수성(Human Capital Management Solution Excellence)’ 부문에서 이루어졌으며, SAP(SAP) 솔루션 기반 혁신과 고객 가치 창출 성과를 인정받은 결과다. 오픈텍스트는 SAP 솔루션 확장(SAP Solution Extensions) 분야에서의 기술 리더십과 협업 성과를 인정받았다. SAP 환경 내에서 정보관리(Information Management), 콘텐츠(Content), 데이터(Data), AI(Artificial Intelligence) 기능을 통합해 기업의 운영 효율성과 규제 대응 역량을 동시에 강화한 점이 주요 평가 요소로 작용했다.

양사는 협력을 통해 기업이 SAP 기반 비즈니스 프로세스 전반에서 AI 기반 콘텐츠 활용, 자동화(Automation), 클라우드 전환(Cloud Transformation)을 보다 빠르게 추진할 수 있도록 지원하고 있다. 이를 통해 기업은 SAP S/4HANA(SAP S/4HANA) 클라우드 전환 과정에서 복잡성을 줄이고 생산성을 높일 수 있다.


또한 오픈텍스트는 SAP 석세스팩터스(SAP SuccessFactors)와의 통합을 통해 인사(Human Resources, HR) 영역에서도 디지털 문서 관리(Document Management)와 업무 자동화를 지원하고 있으며, 전사적 업무 효율 개선을 돕고 있다.

오픈텍스트 SAP 파트너십 담당 부사장 마크 베일리는 “이번 수상은 SAP와의 긴밀한 협력을 통해 고객의 디지털 전환(Digital Transformation)과 AI 기반 혁신을 실질적으로 지원해 온 성과를 보여준다”라며 “앞으로도 기업이 보다 빠르고 안전하게 AI 시대에 대응할 수 있도록 지원을 확대할 계획”이라고 전했다.

오픈텍스트는 향후에도 SAP와의 협력을 기반으로 클라우드 전환과 AI 활용을 가속화하고, 글로벌 고객의 정보관리와 비즈니스 혁신을 지속 지원할 방침이다.
dl-ciokorea@foundryco.com