40 Questions Telecommunications Should Ask Before Trusting AI
Your Ericsson or Nokia AI system can optimise network uptime, but it cannot tell you whether customers are leaving because of service quality your metrics do not measure. Your Salesforce Einstein churn model was trained on last year's competitive landscape, not this quarter's new market entrants.
These are suggestions. Use the ones that fit your situation.
Network Operations: When AI Misses What Engineers See
1Your network AI flagged 99.2% uptime as acceptable last month. Which specific customer complaints came in during the 0.8% downtime, and did they mention service quality issues your uptime metric would not catch?
2When Ericsson or Nokia AI recommends shifting traffic between nodes to improve uptime, who confirms the recommendation will not degrade latency or packet loss for specific customer segments?
3Your network management system optimises for measurable metrics. Which three types of degradation are your experienced engineers noticing that do not appear in your AI's input data?
4Can you name the last time your AI recommended no action on a network segment, and what made your team confident that recommendation was correct rather than a gap in the model?
5When your AI detects an anomaly, does it explain whether the anomaly is unusual because it is new to the dataset, or because it genuinely indicates a problem that affects customers?
6Your Nokia or Ericsson system uses historical performance data to predict failures. How many customer-impacting failures has occurred in configurations the AI had never seen before deployment?
7If your network engineers disagree with an AI recommendation, what process exists to capture their reasoning before accepting the AI decision anyway?
8Your AI prioritises uptime. If a maintenance action the AI recommends reduces redundancy temporarily, who decides whether that trade-off is acceptable for your specific customer base?
9When network capacity planning AI recommends infrastructure investment, does it account for changes in customer demand patterns, or only historical growth curves?
10Your network operations team has domain knowledge. What happens to that knowledge after two years of following AI recommendations instead of exercising their own judgement?
Customer Service: When AI-First Strategies Create the Problem They Predict
11Your customer service AI handles 60% of first contacts. Which specific types of customer problems does the AI deflect back to humans, and do those deflections frustrate customers enough to drive churn?
12Salesforce Einstein recommends routing high-value customers to automated service. When this policy was tested, how many of those customers reported the experience as poor compared to customers who spoke to a human?
13Your AI chatbot resolves issues without human review. If a customer's problem was resolved incorrectly but the system logged it as closed, how would you know?
14When your customer service AI detects frustration, does it escalate to a human or offer automated solutions that may increase frustration further?
15Your organisation tracks first-contact resolution rates. Do those rates measure customer satisfaction with the resolution, or only whether the customer stopped asking for help?
16AI-mediated customer service became your default six months ago. Which customer segments have increased their complaint volume to your management team since that change?
17When a customer reaches a human after AI service failed, does your system tell that human what the customer already explained to the AI, or does the customer repeat themselves?
18Your ChatGPT integration answers customer questions about billing. If the AI gives incorrect information, who verifies accuracy before the customer discovers the error?
19Customer service staff now spend more time overriding AI decisions than handling new customer contacts. Is this an efficiency improvement, and if not, why is the AI still the first point of contact?
20You measure customer satisfaction with service interactions. Has satisfaction changed since AI became the primary channel, and have you tracked satisfaction separately for customers who prefer to speak with humans?
Churn Prediction: When Historical Models Cannot See Market Disruption
21Your Salesforce Einstein or IBM Watson churn model was trained on customer data from the last two years. What new competitors entered your market in the last six months, and does your training data include any customers affected by them?
22Your churn model identifies at-risk customers. When you intervened with retention offers, did it prevent churn, or did it only delay the churn until your next intervention?
23Churn prediction AI uses historical behaviour patterns. Which customer cohorts have changed their behaviour recently in ways your AI would not predict, because that behaviour was rare in the training data?
24Your model predicts churn risk by looking at usage patterns. If a large customer changes usage because they are migrating to a competitor's service, how much notice does your AI give you before they leave?
25You built retention campaigns around AI churn predictions. How many customers has your team contacted who were not actually planning to leave, and did those contacts increase frustration with your organisation?
26Your churn model uses account tenure, contract value, and service quality metrics. Which non-measured factors caused customer churn in the last three months, according to exit interviews?
27When competitive pricing changed in your market, did your churn model alert you before the change affected your customer retention rate, or did it only show the damage after it happened?
28Your IBM Watson model recommends discounts for high-churn-risk customers. If you offer discounts to customers the model flags, and they accept discounts but leave anyway, how does the model learn from that failure?
29Churn prediction AI is trained on customers who left in the past. For each customer segment that left, can you list the reasons they cited, and verify those reasons appear in your training data?
30Your AI suggests which customers to prioritise for retention based on lifetime value. Does this strategy account for the fact that your most valuable customers may have the most attractive options with competitors?
Governance: Protecting Judgement When AI Becomes Standard
31Your organisation has adopted AI for network management, customer service, and churn decisions. Which of these three decisions still requires a human to approve before acting?
32When network engineers, customer service managers, and commercial teams disagree with AI recommendations, what is the actual process for documenting and learning from those disagreements?
33You use Ericsson, Nokia, Salesforce, IBM, and ChatGPT AI systems. Which person is responsible for understanding how each system makes its recommendations, and does that person have time to do the work?
34Your customer service team followed AI recommendations that degraded customer experience. Who is accountable for that outcome, and what changed to prevent it happening again?
35Network engineers with fifteen years of experience are now overseeing AI systems instead of designing networks. Does your organisation have a plan to rebuild engineering expertise once your dependence on AI systems becomes too high to safely reverse?
36You measure whether AI recommendations are correct by checking if they improved your target metrics. Who checks whether your target metrics are the right ones to measure?
37Your organisation is slowly automating decisions away from humans who understand customer and network context. At what point will that trend become a problem you cannot solve?
38When an AI recommendation creates a problem that affects thousands of customers, does your governance process allow you to pause the AI system, or must you wait for the vendor to fix it?
39Your teams are being trained to use AI tools, not to question AI outputs. If an AI system makes a recommendation that conflicts with customer needs, does your staff have the authority and confidence to override it?
40Strategic decisions about infrastructure investment, customer service investment, and churn mitigation are increasingly shaped by AI models. Who is responsible for ensuring those models account for industry disruption and market change?
How to use these questions
Assign one senior engineer, one customer service leader, and one commercial manager to review AI recommendations before they become standard practice. Meet monthly to identify patterns where AI misses important context.
For every AI system you deploy, name the human who is accountable for its performance. That person must have time to understand how the system works, not just monitor its outputs.
When your churn model makes predictions, compare those predictions to what your customer success team hears in conversations. If they differ, stop treating the model as ground truth until you understand why.
Your network engineers have pattern recognition skills that AI systems do not yet possess. Before you reduce their headcount, establish that you can detect and prevent the problems they catch without them.
Run quarterly reviews where you examine decisions where AI recommendations conflicted with human judgement. Track which side was right, and use those results to decide how much authority to give the AI system.