Agefi Luxembourg - avril 2026

Avril 2026 47 AGEFI Luxembourg IA & Tech W hile generativeAI (GenAI) enhances indivi- dual productivity, agen- tic systems go further, chaining tasks, invoking tools, and opera- ting autonomously. This distinc- tion ismost critical across the investment lifecycle, fromdeal screening to portfolio oversight. Nearly two-thirds of private equity (PE) GPsarepilotingAIinportfoliocompanies, andover 40%are embedding it into busi- ness processes. (1) Yet 95% of enterpriseAI initiativesfailtodelivermeasurableROI (2) . The issue is not technology maturity, but the absence of two essentials: strong AI and data engineering capabilities, and deepprivatemarkets expertise. Without embedded investment logic, AI remains a productivity tool rather than a strategic capability. This article explores where domain expertise andAI deploy- ment intersect, why it matters, and how firms canmove frompilots toproduction andmeasurable value creation. Industry challenges:Where the investment lifecycle breaks down As firms move up the value chain, com- plexityconcentratesintheinvestmentlife- cycle, where opportunities are screened, riskispriced,andportfoliosaremonitored across private equity, infrastructure, real estate, andprivate credit. Deal origination illustrates the challenge. A mid-market PE manager may review over 500 teasers annuallybut has the ana- lytical bandwidth to deeply assess fewer than20%ofthem. (3) Analystsspend8to12 hoursperweekonmanualconfidentialin- formation memorandum (CIM) parsing and data entry alone. Without systematic filtering aligned to fundmandates, sector theses,andEBITDAthresholds,firmsrisk missing high-quality opportunities while expending diligence resources on irrele- vant deals. Duediligenceintensifiesthestrain.Finan- cial, legal, ESG, and commercial work- streamsruninparallelbutrarelyconverge inaunifiedanalyticalframework.Insights are manually consolidated into invest- mentcommitteematerials,oftenlateinthe process. In infrastructure and real estate, complexity increases as asset-level engi- neering data, concession terms, and lease structures must be translated into valua- tion anddownside scenarios. Portfolio monitoring introduces its own friction. Quarterly valuation packs across 25to30assetscanrequireonetotwodays of analyst time each to reconcile data, run sensitivities,andprepareinvestmentcom- mitteematerials (4) .Portfoliocompaniesre- port KPIs and financials in non-standard formats, oftenweeks aftermonth-end. APEfirmmanaging20+assetsacrosssec- tors must navigate incompatible data schemas, fromdifferent revenue recogni- tion policies and covenant definitions to fragmented system, ranging from enter- prise ERP systems tomanual Excel track- ers. As a result, early warning signals emerge too late, narrowing the window for intervention. Finally, LP reporting expectations are be- comingmore formalized through frame- works such as those advanced by the Institutional Limited Partners Associa- tion (5) Yet many managers still manually reconcileadministratordataandportfolio metrics each quarter. These are not iso- lated inefficiencies; they are structural gaps in how investment judgment and data converge, compoundingwithevery fund cycle. (see first infographic above) WhereAI is accelerating value creation Each of these friction points above has a correspondingagentic response. The four use cases that follow stand out not as ex- periments, but as deployed solutions de- livering measurable results. Deal screening directly addresses the origina- tion bottleneck. Instead of hours spent manually parsing CIMs, a purpose-built agent ingests document in seconds, ex- tracts key financial and operational met- rics,andscoresopportunitiesagainstman- date criteria. It filters top-of-funnel noise while identifying true alignment with a fund’ssectorthesisorbuild-and-buystrat- egy. Critically,itextendsbeyondanalysis. Through MCP server integration, the agentwritesdirectlyintothedealpipelines and customer relationship systems, up- dates records, routes shortlisted opportu- nities, and flags exclusions without requiring a separate interface. What once tookhalf a day is completed inminutes. Due diligence addresses the convergence problem through parallel agent architec- tures.Insteadofsequentiallydataroomre- view, agents run simultaneously: parsing quality of earnings reports for normaliza- tion issues, reviewing legal contracts for covenant risk, and scanning ESG disclo- sures against fund policies. A synthesis agent then reconciles these streams, flags inconsistencies,andproducesastructured investmentmemo or risk summary. Inbuy-and-buildstrategies,recurringpat- terns,suchasworkingcapitaladjustments or earn-out structures, surface in hours rather than weeks. Deloitte Luxembourg deploymentsreportuptoa90%reduction in riskmemoproduction time, compress- inganeight-dayprocesstounderoneday. (see second infographic below) Valuation benchmarking addresses the quarterly workload across 20+ assets. In- stead of buildingmodels fromscratch, an agentpullssectorcomparablesfrommar- ket data sources, filters outliers based on EV/EBITDA thresholds, runs DCF sensi- tivities, and proposes a valuation range within minutes. Crucially, it surfaces key assumptions for challenge, avoiding black-boxoutputs.WhiletheInternational Private Equity andVenture Capital Valu- ation (IPEV) Guidelines set the standard, their inherent discretion, particularly around comparables and discount rates, must be encoded as guardrails, not dele- gated to themodel. (7) Portfolio monitoring closes the loop. Ratherthanstaticdashboards,agentscon- tinuouslytrackcovenantcompliance,flag breach risks, and detect variance against budgetsandvaluecreationplansaheadof quarterly reporting. In portfolios with fragmenteddata,automatedbreachdetec- tion is operationally critical. For Article 8–classified PE funds—grow- ing from25%of newonboardings in2022 to 40%by 2024—the same layer also nor- malizesESGKPIsalongsidefinancialmet- rics. (9) The productivity gains across these four areas are real, and the potential of agenticworkflows is transformative, par- ticularly in domains defined by unstruc- tured data, documents-heavy processes, andlimitedvendortooling.Realizingthat value at scale, however, is far more chal- lenging. It requires more than applying off-the-shelf AI models to unstructured data and expectingmeaningful insights. Technology versus industry knowledge as a requirement The statistics on AI adoption are unam- biguous. A recent MIT study found that 95% of enterprise GenAI pilots fail to de- liver measurable P&L impact, primarily due to integration, data, and governance gaps rather thanmodel capability. (10) Sim- ilarly, International Data Corporation (IDC) researchshows that only4out of 33 AIreachedproductions,a12%conversion rate for AI POCs. (11) This gap between proof of concept andproduction ispartic- ularly acute in private markets, where firms often lack experience in large-scale technologytransformationandoperateon fragile data foundations. Thepathfromproofofconcepttoproduc- tion is complex. Successful firms embed deep industry knowledge into agent de- sign fromthe beginning, combining tech- nical and domain capability with strong executivesponsorshiptoaligninvestment, technology, compliance, and operations under a shared governance framework. Without this, validation ownership re- mains unclear, escalation triggers unde- fined, and adoption stalls. Effective deploymentteamsbringtogetherdataen- gineers, machine learning engineers, and investment professionals to validate out- puts, challenge model assumptions, and encode edge cases early. Firms that fine- tune models on proprietary data, such as QoEs, IC memos, or covenant packages, build systems that improve with use rather thanplateau after the pilot. Trustisbuiltonaccuracyinhardcases,not easy ones.Avaluation agent that handles straightforward comps but fails on dis- tressed assets or minority stakes quickly loses analyst confidence. Human-in-the- loop designmust be explicit, defining es- calation points, IC triggers, and how outputs are surfaced to reinforce, not by- pass, professional judgment. Firms pulling ahead embed investment expertisedirectlyintoagentreasoninglay- ers.Thosethatsimplyapplymodelstoun- structured data remain stuck in pilot purgatory. (see third infographic below) Whatmanagersmust do now The question is not whether to act, but whether the cost ofwaiting is acceptable. Inaction compounds, while even imper- fect deployments create lasting value: a strongdata foundation, andmoreAI-lit- erate, adaptable teams. Firms that start will outpace thosewaiting for the “right” moment. EmbeddingAI into the invest- ment lifecycle is an operatingmodel de- cision. Private market leaders should avoid unfocused experimentation and instead follow a disciplined approach: aligning data, governance, and invest- ment logic before scaling. - Phase 1: Foundations Start with a data audit. Identify critical sources across the lifecycle—CIMs, QoEs, portfolioKPIs, ESGdisclosures, and fund financials—then assess quality and stan- dardize. Define KPI taxonomies by asset class, aligned with ILPA reporting stan- dards and IPEV valuation guidelines. Every agentic system is only as reliable as the data layer beneath it. - Phase 2: Targeted agentic use cases Focusononeortwoboundedpilotsrather than fragmented parallel efforts. A deal screening agent for a single sector fundor automated covenant monitoring for a credit strategy provides the right starting scope.Definesuccessmetricsupfront:an- alyst time, deal coverage, error reduction, and early risk detection. Without clear metrics, pilots drift. - Phase 3: Governance and scaling Define which outputs can flow directly intoworkflowsandwhichrequirehuman sign-off. Covenant breach alerts may be automatable,ICmemosshouldnot.Estab- lish clear escalation triggers, validation ownership, and audit trails, and embed them investment and valuation commit- tee processes. Once governance is proven at the pilot level, scale across funds and strategies. - Phase 4: Strategic integration At maturity, AI becomes embedded in howthefirmcompetes.Integratecapabil- ities intovalue creationplaybooks andLP reporting. Leading GPs are already posi- tioning proprietaryAI infrastructure as a fundraising differentiator, as operational alpha becomes something LPs can evalu- ate, not just claim. Firms that follow this sequence will embed AI into how capital is allocated andmonitored. Those that treat it as a se- ries of disconnected tools risk spending multiple fund cycles in pilot purgatory as the gapwidens. Thibault CHOLLET, Partner,Alternatives, Deloitte Luxembourg Piotr ZATORSKI, Senior Manager,Alternatives, Deloitte Luxembourg 1) Pictet – AI adoption in private equity, https://urls.fr/VAjRAm 2)Fortune -https://urls.fr/ssLg5s 3) Source Scrub – Deal Sourcing Survey 2023 - https://urls.fr/BiZ5RE 4)Industrypractitionerbenchmarks,Chronograph GPplatformdocumentation 5)ILPA -https://urls.fr/T55hxK 6) This infographic was generated using AI technology (Gemini)basedonpromptsandconceptualdirectionpro- videdbyDeloitte. 7)IPEVguidelines2025 -https://urls.fr/o3ZnLR 8) This infographic was generated using AI technology (Gemini)basedonpromptsandconceptualdirectionpro- videdbyDeloitte. 9)LanghamHall-Beyondtherhetoric:Whereisin- vestor appetite for ESG in Europe really landing? https://urls.fr/-iF0gk 10)Fortune -https://miniurl.be/r-6r1m 11)IDC/Lenovo-CIOPlaybook2025- https://miniurl.be/r-6r1n 12) This infographic was generated usingAI tech- nology(Gemini)basedonpromptsandconceptual directionprovidedbyDeloitte. 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