BELGIUM Trends and Developments Contributed by: Steven De Schrijver and Carl Dotremont, Allegiance Law
The Rise of AI as a Strategic Driver in Tech M&A Technology has become central to M&A strategy as executive teams return to the deal table with a sharper focus on durable technology moats, including pro- prietary data, secure and scalable infrastructure, and deployable AI capabilities that accelerate growth or margin. Generative AI and broader general purpose AI systems are transforming operating models and creating new integration challenges. Companies now emphasise AI not only in the investment thesis but across the entire deal lifecycle, from target screen- ing and diligence to post closing value creation. This article will reflect on these trends and also provide an overview of the Belgian tech M&A environment and practical Belgian law considerations for AI infused transactions. For clarity, “GenAI systems” refers to models designed to produce novel outputs such as text, images, code, audio or other data by learning patterns from train- ing data, as distinct from systems focused primar- ily on classification or prediction. In practice, buyers increasingly encounter hybrid stacks that combine GenAI with retrieval augmented generation, predic- tive models and traditional analytics. In this landscape, technology due diligence – covering IT, cybersecurity, data governance and digital strategy – has become essential. It tests alignment between technology strategy and growth ambitions, shows the capital and operating expenditure required to sus- tain competitiveness, and reveals hidden security or operational risks. AI specific assessments are rising in importance, probing data set provenance, model governance, compliance with emerging regulation and concrete value creation use cases. Robust technology diligence enables better pricing, tighter risk allocation, and smoother integrations or carve outs. The adoption of GenAI during M&A transactions has risen from a small base over 2024–25. Even where adoption remains below one third of deal teams, early users report speed and coverage gains in document review, red flagging, analytics and bid preparation. Across the transaction lifecycle, GenAI can acceler- ate data analysis and synthesis, streamline diligence by extracting, clustering and summarising large vol- umes of contracts and policies, and enhance valua-
tion work by rapidly generating scenario models and sensitivities. GenAI adds value from the strategy and deal iden- tification stage, where continuous screening models ingest public filings, news, hiring signals and product telemetry to identify targets aligned with a strategic thesis, and where an analyst co-pilot can generate and refresh research memos, market maps and competi- tive landscapes with sources logged for verification by the deal team. During deal preparation and execution, GenAI supports buy-side diligence through contract abstraction on change of control, assignment, data localisation, audit and IP clauses; policy gap analysis on GDPR, NIS2 and AI Act readiness; and code and licence scans for open-source compliance. On the sell-side, it assists with data room quality assurance, consistent Q&A responses, generation of disclosure schedules and clean team extracts that respect anti- trust protocols. For valuation support, it enables rapid scenario modelling and KPI correlation analysis to evi- dence the monetisation path, including attach rates, lifetime value uplift and cost-to-serve reduction. Post deal integration and management also benefit from GenAI through the drafting and harmonisation of security baselines, data retention rules, model gov- ernance frameworks and acceptable use policies, as well as through skills mapping and learning pathways. The use of AI in recruitment should be approached carefully given the likely classification of such tools as high-risk under the EU AI Act. In the transformation and value realisation phase, GenAI can power a “use case factory” by scoring pipelines for impact versus feasibility, enabling pilots with appropriate guardrails and tracking benefits tied to synergy cases, while also supporting risk sensing through continuous monitor- ing for model drift, bias, privacy leakage and shadow AI. Legal, Regulatory and Due Diligence Considerations for AI-Infused Transactions Using GenAI in deals and acquiring AI-rich businesses raises distinct legal and regulatory issues that Bel- gian buyers and sellers should prioritise. From a data protection and confidentiality perspective, parties should avoid inputting confidential deal information or personal data into public models and should prefer
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