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1. Map the service and its authority
Start with the product as deployed, not the product described on a roadmap slide. Inventory each AI service, model, and feature, and trace how it fits into a customer’s telecom environment.
- Classify the use case: distinguish customer support and marketing from network planning, operations, security, or automated control.
- Identify decision authority: determine whether the system summarizes information, recommends an action, or can trigger a live change—such as altering configurations, routing traffic, or prioritizing faults.
- Map the parties and components: record the model provider, hosting and cloud layers, network integrations, subcontractors, customer, deployer, human decision-makers, and party responsible for monitoring.
- Request deployment evidence: obtain a system diagram and evidence from actual customer deployments, and compare them with the product and sales descriptions.
The ITU’s telecom-focused report treats deployment and assessment as engineering questions. Ericsson’s white paper argues that trustworthiness in telecom AI must extend beyond performance measures. Neither source establishes that a particular target’s service meets those standards.
2. Trace data rights and handling
Request an end-to-end data-flow and data-rights schedule covering every input and output. For each data source, establish:
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- Where the data originates and whether it is personal, confidential, customer-owned, or otherwise restricted.
- What permissions support its collection, processing, model training, and fine-tuning—and whether vendor terms permit reuse.
- Where data is stored and processed, whether it crosses borders, which subprocessors can access it, how long it is retained, and how deletion is verified.
- What access controls, confidentiality commitments, and incident-notification obligations apply.
Compare these terms with the target’s customer commitments and the rules applicable to the service and jurisdictions. Deutsche Telekom’s 2025 annual report discusses EU privacy and cross-border transfer issues, exposure through partners, and its Privacy and Security Assessment for new AI solutions; it also notes that sector-specific ePrivacy rules can constrain telecom data processing. Those disclosures describe Deutsche Telekom’s context, not a legal conclusion for another company.
3. Verify performance and operational controls
Ask for test results from the target environment, using representative data and the integrations customers actually run. Review whether the evidence covers:
- Accuracy and service-quality measures, edge cases, failures, and robustness to manipulation.
- Compatibility with legacy systems and the target’s network interfaces.
- Model and data drift monitoring, explainability, and logs sufficient to investigate decisions.
- Human oversight, change approval, rollback, and recovery procedures.
For a system that can affect network operations, scrutinize its blast radius, fail-safe behavior, access boundaries, and the operational owner’s approval process. The ITU report discusses compatibility with existing and legacy systems and continuous monitoring for compliance, robustness, reliability, and drift. Ericsson frames trustworthy telecom AI around safety, security, transparency, reliability, and explainability. These are useful assessment lenses, not test results for the target.
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4. Examine cybersecurity and service resilience
Review the threat model and architecture 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, incident history, and relevant access logs. Establish whether the vendor or operator owns each security task, and inspect business-continuity and disaster-recovery evidence.
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5. Determine regulatory scope and test AI claims
Build a jurisdiction-by-jurisdiction view from the actual deployment locations, data flows, and service functions. Ask counsel to confirm which telecom, privacy, cybersecurity, consumer-protection, and AI requirements apply, who acts as provider or deployer, and what documentation and oversight duties follow. Do not assume that a single AI label or a broad compliance statement resolves the analysis.
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Separately compare public and customer-facing AI claims with shipped features, validation records, measured customer outcomes, and disclosed limitations. Mayer Brown’s 2026 deal guidance identifies misleading AI claims and weak internal governance as sources of diligence, disclosure, and transaction risk. Doximity’s SEC filing is a cross-sector illustration of an issuer disclosing evolving AI-law, privacy, intellectual-property, and data-rights exposure; it does not determine the rules for a telecom transaction.
Deutsche Telekom’s 2025 annual report describes GDPR administrative fines as “up to between 2 and 4 % of the total worldwide annual revenue of an undertaking.” Treat that as the company’s description of a potential GDPR ceiling, not a forecast of a target’s exposure; counsel should verify current law and the facts that affect applicability and penalties.
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Map reliance on model providers, hyperscalers, network equipment vendors, data sources, and specialist integrators. Review exclusivity, renewal and termination rights, price-change provisions, service levels, audit access, intellectual-property and output rights, data reuse, liability limits, indemnities, security duties, and transition support.
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Test whether the target could switch a critical supplier without losing performance, customer commitments, approvals, or access to necessary data. Ask for a practical migration plan, including expected effort and dependencies. 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 as deal issues.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Establish commercial proof and valuation inputs
Separate revenue from production deployments from pilots, proofs of concept, and announced partnerships. Request customer concentration, renewal and churn history, referenceable customers, deployment duration and cost, support burden, gross margin by service, usage-based compute costs, service credits, and measured outcomes.
Compare the economics with non-AI alternatives, including the costs of integration, security, compliance, and ongoing model evaluation. Test whether reported benefits are independently evidenced and whether the customer will pay to sustain them. Deutsche Telekom describes intense competition, shorter innovation cycles, and the challenge of integrating new solutions while maintaining network quality. Mayer Brown’s deal guidance notes that compliance and remediation costs can affect investment returns. The available sources do not establish market size or returns for an unnamed target.
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8. Convert findings into transaction terms and downside cases
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 those materials with management representations, customer contracts, and the claims used to market the business.
For material gaps, estimate remediation cost and timing, then model downside cases such as delayed deployment, a supplier interruption, a customer loss, or required changes to data use. Consider whether the purchase price and operating plan reflect those costs. Depending on the findings and deal structure, counsel can assess tailored representations about AI inventory and use, training-data rights, validation, known failures, compliance, security, and disclosures, alongside covenants, indemnity or escrow protections, remediation plans, and insurance coverage. Mayer Brown notes that thin documentation increases reliance on management representations and that cyber and AI coverage requires careful review.
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