Before investing in a telecom AI company or service, establish what the AI can do in a live network, what data and rights it depends on, and whether the target can prove that its service is secure, reliable, compliant, and commercially adopted. Then translate verified weaknesses into the deal’s valuation, remediation budget, contractual protections, and downside cases. The right conclusions depend on the target’s service, deployment, jurisdictions, and transaction structure.
What does the AI service actually do—and who controls it?
Start with an inventory of every AI service, model, and feature included in the business. A customer-support assistant has a different operational risk profile from a system used for network planning, security, or live operations. The central question is whether the service only summarizes information or makes recommendations, or whether it can trigger changes such as altering configurations, routing traffic, or prioritizing faults.
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Ask management for a system diagram and evidence from actual deployments, not just a product roadmap. Map the model provider, hosting and cloud layers, network interfaces, subcontractors, customer, deployer, human decision makers, and party responsible for monitoring. Record where a person must approve an action and where the software can act automatically. The ITU’s telecom-focused guidance treats deployment and assessment as engineering questions; Ericsson’s vendor white paper argues that trustworthy telecom AI requires more than performance measures.
What data does the service use, and does the target have the rights to use it?
Request an end-to-end data-flow and data-rights schedule covering inputs, outputs, and any data used to train or fine-tune models. For each data category, establish its source, sensitivity, permitted use, storage location, retention period, access controls, subprocessors, cross-border transfers, deletion process, and incident-notification obligations. Check whether vendor contracts permit reuse of customer or subscriber information and whether those terms align with the target’s customer commitments and confidentiality obligations.
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Telecom data can also be subject to sector-specific privacy rules. Deutsche Telekom’s 2025 annual report discusses EU privacy and cross-border transfer issues, potential exposure through partners, and privacy and security assessments for new AI solutions; it also notes constraints associated with ePrivacy rules. These disclosures flag issues to investigate, not rules that necessarily apply to every target. Have counsel assess the service’s actual data flows and jurisdictions under current law. Deutsche Telekom describes GDPR administrative fines as reaching between 2% and 4% of an undertaking’s total worldwide annual revenue; treat that as the company’s description of the applicable ceiling, not a forecast of a target’s exposure, and confirm the current law and circumstances with counsel.
Can the service work safely and reliably in the target network?
Require deployment-specific evidence from the environment the target serves. A general model benchmark or demonstration does not show that a system will perform reliably when connected to a particular operator’s data, integrations, and legacy equipment.
- Review test results using representative data, including edge cases and failure conditions, and identify the service-quality measures used.
- Check compatibility with current and legacy systems, integration dependencies, and the operational owner responsible for the service.
- Inspect monitoring for accuracy, robustness, model and data drift, and ongoing compliance, along with the records used to audit decisions and changes.
- For systems that can affect operations, examine change control, access boundaries, approval requirements, fail-safe behavior, rollback, and recovery procedures.
- Ask how the service handles manipulation or unexpected inputs, what human oversight is available, and how incidents are detected, escalated, and resolved.
The ITU report discusses compatibility and continuous monitoring; Ericsson’s white paper frames trustworthy telecom AI around safety, security, transparency, reliability, and explainability. These are useful evaluation dimensions, not evidence that a particular target passes them.
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How exposed is the service to cyberattacks or operational disruption?
Review the threat model across model endpoints, APIs, cloud services, network interfaces, data stores, the model supply chain, and administrator access. Request penetration-test summaries, vulnerability and patch records, relevant incident history, access logs, and business-continuity and disaster-recovery evidence. Establish which security duties belong to the vendor and which to the operator.
Examine contract terms for incident-notification windows, cooperation during investigations, and access to information needed to contain an incident. Deutsche Telekom reports that cyberattacks and IT or hardware and software failures can disrupt internal systems, networks, and customer services, and identifies supplier cyber disruption as a supply risk. This is a sector exposure to assess, not evidence of an incident at the company being considered.
Which rules apply to this service in each jurisdiction?
Build a jurisdiction-by-jurisdiction assessment from the actual deployment, customers, and data flows. Ask qualified counsel to determine which telecom, privacy, cybersecurity, consumer-protection, and AI requirements apply, who has provider or deployer responsibilities, and what documentation or oversight is required. Do not assume that describing a product as “AI,” or making a generic compliance statement, resolves the scope question.
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Assess external claims separately from legal compliance. Compare statements made to investors and customers with shipped features, validation records, documented limitations, and customer outcomes. Mayer Brown’s 2026 transaction guidance identifies misleading AI claims and weak internal governance as potential diligence, disclosure, and deal risks. Doximity’s SEC filing offers a cross-sector example of disclosure concerning evolving AI-law, privacy, IP, and data-rights exposure; it does not establish which requirements govern a particular telecom transaction.
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Map dependencies on model providers, cloud platforms, network-equipment vendors, data sources, and specialist integrators. For each critical dependency, review exclusivity, renewal and termination rights, price changes, service levels, audit access, IP and output rights, data reuse, liability caps, indemnities, security obligations, transition support, and data or model portability.
Test the practical exit path: could the target change providers without losing necessary approvals, customer commitments, service quality, or access to essential data? Consider the time, engineering effort, and cost of a switch, rather than treating a contractual portability clause as proof that switching is easy. Deutsche Telekom identifies limited supplier choice and reduced switching flexibility as risks in some areas; Mayer Brown highlights platform terms, data rights, output ownership, liability, and transaction representations.
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Is there commercial proof beyond pilots and announcements?
Separate production revenue and recurring use from proofs of concept, pilots, and announced partnerships. Ask for customer concentration, renewals and churn, referenceable deployments, implementation duration and cost, support burden, gross margin by service, usage-based compute costs, and service credits. Where the target claims customer benefit, request the underlying measurement and the comparison used to establish it.
Test whether the economics remain attractive after integration, security, compliance, and ongoing model-evaluation costs. Compare the offer with non-AI alternatives and consider the effect of service-level commitments and compute costs on margins. Deutsche Telekom describes intense competition, shorter innovation cycles, and the challenge of integrating new solutions while maintaining network quality. The evidence available here establishes no market-size or return figure for an unnamed target, so valuation should rest on the company’s verifiable operating and financial data.
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How should diligence findings change the transaction?
Request the target’s AI inventory and policy, employee-use controls, data provenance and licensing evidence, validation and monitoring records, incident and complaint history, security assessments, and insurance policies. Reconcile these records against customer contracts, management statements, and public claims. Missing documentation increases reliance on management representations, but a representation does not substitute for understanding the system or quantifying remediation work.
Use verified findings to model costs and downside scenarios, including remediation, supplier replacement, delayed deployment, customer loss, or operational disruption where the evidence makes those scenarios relevant. Consider whether the purchase price, closing conditions, or post-close plan should reflect the findings. Transaction protections may include tailored representations about AI inventory and use, training-data rights, validation, known failures, legal compliance, security, and disclosures; suitable covenants and remediation commitments; and analysis of indemnities, escrow, and insurance. Mayer Brown notes that cyber and AI coverage and deal representations warrant careful review. These protections should be matched to the specific risk and transaction rather than treated as a substitute for diligence.
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