被颠覆还是被超级赋能？绘制服务和软件领域的AI风险图谱

Disrupted or supercharged? Mapping AI risks in Services andSoftware In this note, we propose a simple methodology to assess AI disruption risks for software andservices providers, based on our numerous conversations with corporate IT/business teamsand industry experts. Richard Nguyen+33 1 42 13 54 22 While our methodology is far from perfect, we believe that it could nonetheless help investorsevaluate each segment’s and company’s position both independently by AI risk and incomparison with one another. However, this framework is relatively static at present, andneeds to be regularly updated, as it does not factor in each company’s continuous AI efforts(e.g. devoted R&D, number of data/AI experts, number of use cases already available/soon-to-be-released, etc.). Mark L. Moerdler, Ph.D.+1 917 344 8506 Derric Marcon+33 1 58 98 06 30 Specialist Sales Our approach is a hybrid quantitative-qualitative, as our scoring codify structured judgmentrather than coming from a single dataset, but they are grounded in widely observed AI impactpatterns across categories - where AI does core work versus where it only enhances it, andhow moats/switching costs behave in each. Kiran Shah+44 20 3547 1533 Based on our estimates, SAP, Dassault Systèmes, and Nemetschek would have an AI riskprofile more favorable than many software peers, with a significant upside potential if theyexecute their respective AI roadmaps well. Among services providers, our assessment shows that Alten and Reply are the least exposedto AI disruption risks. Capgemini is relatively well-positioned (see Capgemini - An "AItransformer"), in our view. Furthermore, we are convinced that a rapid transition to AI-firstis crucial for services providers as they must rapidly reinvent their business model to fullycapitalize on the seismic shift toward Services-as-Software (IT Services: The irresistible riseof Services-as-Software). BERNSTEIN TICKER TABLE INVESTMENT IMPLICATIONS We reiterate our ratings and price targets. Outperform: 74software (PT €53)Alten (PT €140)Arcadis (PT €52)Aubay (PT €65)Capgemini (PT €208)Dassault Systèmes (PT €29)Indra (PT €57)SAP (PT €273 / $322)Sopra Steria (PT €218) Market-Perform:Nemetschek (PT €132)Sage (PT 1,340p)Reply (PT €133)TeamViewer (PT €11) Underperform:Atos (PT €45)CGI PT C$143) DETAILS ASSESSMENT OF AI DISRUPTION RISK FOR SOFTWARE PROVIDERS We propose here a simple methodology to assess AI disruption risks for software providers based on our numerous conversationswith software industry experts, services consultants, and corporate IT/business teams. While our methodology is far from perfect, we believe that it could nonetheless help position different software segments, andcompanies, relative to each other. Moreover, this assessment is theoretical and static as it does not consider each company’scontinuous AI efforts (e.g. R&D devoted, partnerships with leading AI providers, number of use cases/features already availableand planned, current internal usage, etc.). We consider two key dimensions: •Automatability (i.e. ability to be automated) of core value: “How much of what this product does could reasonably be doneby ageneral‑purposeor slightlyfine‑tunedmodel plus some glue?”. Low automatability means value depends on proprietarydata, complex workflows, and deep integration (difficult for generic AI models to fully replace). High automatability means valueis mostly created by processing public/standardized information and/or repeatable tasks that AI frontier models can easilyreplicate. •Defensibility of product/client switching cost (moat): “How hard is it to displace this product once adopted?”. Lowdefensibility means low switching cost, limited ecosystem, and weak lock-in/network effect. High defensibility means highswitching costs, strong ecosystem, embedded enterprise workflows, and powerful brand/community. Client switching costis a substantial part of this dimension as it reflects migration complexity, retraining users,re‑implementingintegrations, andperceived risk of switching. Adding to the defensibility of any solution is the complexity and value of the data. In AI data is gold and system of recorddata is even more valuable. In addition to the data itself is the semantic knowledge(also called the metadata or knowledgegraph) that defines the context of how the data relates to itself and the functions that the application will be performing.Without having the context and understanding the business processes any application (AI or non-AI) fails. The morecomplex the data, semantic knowledge, and a deep understanding of the business processes the more defensible is theproduct. In addition, system of record data has its own legal, regulatory and governance requirements that protect the solutionfrom being replaced or even augmented with automated agents. For the weighting, one can explicitly score each software segment on a scale of 1 to 10 on automatability (A) and defensibility (D);and then comput