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Talking about AI in professional English

Published on 28 November 2025

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Being able to discuss artificial intelligence in English is essential in a professional context, whether with a client or a technical team. Here are some bilingual mini-dialogues, vocabulary sheets and tips to help you practise for all situations.

Illustration of practical guide Talking about AI in English

💼 1. Consultant ↔ Non-technical client (highlighting the business impact)

🇬🇧 Customer : How can this AI solution help our company?
🇫🇷 Customer: How can this AI solution help our company?

🇬🇧 Consultant : This AI-powered tool will reduce manual work by automating repetitive tasks. It will also improve decision-making by providing real-time insights.
🇫🇷 Consultant: This AI-based tool will reduce manual labour by automating repetitive tasks. It will also improve decision-making through real-time analysis.

🇬🇧 Customer : That sounds interesting. Can we measure the benefits?
🇫🇷 Client: Interesting. Can we measure the benefits?

🇬🇧 Consultant : Yes, the proof of concept demonstrated a 201% reduction in operational costs within three months.
🇫🇷 Consultant: Yes, the feasibility demo showed a 20% reduction in operating costs in three months.

👥 2. Manager ↔ Team (technical discussion)

🇬🇧 Manager : Where are we with the machine learning model?
🇫🇷 Manager: Where are we with the machine learning model?

🇬🇧 Engineer : We trained the model with historical data, but we noticed a bias in the predictions.
🇫🇷 Engineer: We trained the model with historical data, but we noticed a bias in the predictions.

🇬🇧 Manager : What is the next step to address this?
🇫🇷 Manager: What is the next step to rectify this?

🇬🇧 Engineer : We plan to balance the dataset and retrain the model to improve accuracy.
🇫🇷 Engineer: We plan to balance the dataset and retrain the model to improve accuracy.

🎤 3. Conference/Workshop (strategic vision)

🇬🇧 Speaker : Artificial intelligence is no longer merely a future trend — it is already transforming industries today.
🇫🇷 Speaker: AI is no longer just a future trend — it is already transforming industries today.

🇬🇧 Participant : What is the biggest challenge in adopting AI?
🇫🇷 Participant: What is the biggest challenge in adopting AI?

🇬🇧 Speaker : The main challenge is not the technology itself, but integrating it with existing processes and ensuring ethical use.
🇫🇷 Speaker: The main challenge is not the technology itself, but integrating it into existing processes and ensuring its ethical use.

🧑‍💻 4. Internal AI project meeting

🇬🇧 Colleague A : What is the timeline for deploying the AI solution?
🇫🇷 Colleague A: What is the timeline for deploying the AI solution?

🇬🇧 Colleague B : We plan to launch a pilot project next month and scale up after evaluation.
🇫🇷 Colleague B: We plan to launch a pilot project next month and scale up after evaluation.

🇬🇧 Colleague A : And what risks should we monitor?
🇫🇷 Colleague A: What risks should we be monitoring?

🇬🇧 Colleague B : Data privacy and model bias are the key issues we must address.
🇫🇷 Colleague B: Data confidentiality and model bias are the main issues to be addressed.

🔤 Essential vocabulary sheet

Artificial intelligence → Artificial intelligence (AI)
👉 The artificial intelligence is transforming the way companies operate.
Generative AI → Generative AI
👉 Generative AI can create text, images, and even code.
Machine learning → Machine learning (ML)
👉 We use machine learning to predict customer behaviour.
Machine learning model → Machine learning model
👉 The new model improved the prediction accuracy.
Training data → Training data
👉 High-quality training data is crucial for good results.
Test data → Test data
👉 We evaluate the model on separate test data.
Model bias → Model bias
👉 We must regularly check the model for bias.
Hallucination (of a model) → Hallucination
👉 Sometimes the chatbot produces hallucinations that sound confident but are wrong.
Invitation/instructions (for a template) → Prompt
👉 The output depends heavily on how you write the prompt.
Fine adjustment → Fine-tuning
👉 We fine-tuned the model on our internal documents.
Use cases → Use case
👉 Let's start with one simple use case before scaling up.
Proof of concept → Proof of concept (POC)
👉 The proof of concept demonstrated that the solution is technically feasible.
Scaling up → Scaling / Scale up
👉 Once the pilot is successful, we can scale the solution globally.
Integration via API → API integration
👉 The AI service is available through a REST API.
Data/AI team → Data/AI team
👉 Our AI team works closely with business stakeholders.
Model explainability → Model explainability
👉 Regulators are asking for better model explainability.
AI governance → AI governance
👉 AI governance frameworks help manage risks and compliance.
Data protection → Data protection
👉 Data protection is a key concern in any AI project.
Regulatory compliance → Compliance with regulations
👉 We must ensure regulatory compliance prior to deployment.
Productivity gains → Productivity gain
👉 The chatbot generated significant productivity gains.
Return on investment → Return on investment (ROI)
👉 Management wants to see a clear return on investment for AI projects.

💡 Tips and tricks for remembering AI vocabulary

Use words in your real contexts : emails, reports, presentations in English. For example: “We are conducting a proof of concept with a generative AI model.”

✅ Playing with prompts Practise rephrasing the same request in 2–3 different prompts to see the impact on the response.

✅ Classify vocabulary by theme :

🔎 Data & models : training data, model, bias, hallucination, fine-tuning...
🧩 Business & strategy : use case, ROI, productivity gain, governance, etc.
🛠️ Technology & deployment API integration, scaling, infrastructure, etc.

✅ Reuse orally : Simulate a call in English with a client or colleague, even on your own, by reading the mini-dialogues aloud.

⚠️ Frequent false friends

Current → currentand not actual
👉 Our current AI strategy focuses on customer service.
Experiment (test) → experiment, not experience
👉 We conducted an experiment to compare two models.
Sensitive data → sensitive dataand not sensitive data
👉 We cannot use sensitive data in this experiment.
Check (verify) → often to check or monitor, not always to control
👉 We need to monitor the model in production.