Question 1

What is generative AI and how does it work?

Accepted Answer

Generative AI uses deep learning models, particularly transformers, to create new content — text, images, code, and audio — by learning patterns from training data. Unlike traditional AI that classifies or predicts, generative models produce novel outputs. Enterprise applications include automated document drafting, code generation, and personalised customer communications.

Question 2

What are the top enterprise use cases for generative AI?

Accepted Answer

Leading enterprise use cases include intelligent document processing, automated customer service, code generation and review, content creation at scale, and knowledge management. Industries such as financial services, healthcare, and legal see the highest ROI from generative AI through reduced manual effort and faster decision-making.

Question 3

How do you ensure generative AI outputs are accurate and safe?

Accepted Answer

Accuracy and safety require a multi-layered approach: retrieval-augmented generation (RAG) to ground outputs in verified data, human-in-the-loop review for high-stakes decisions, output filtering for harmful content, and continuous monitoring of model performance. Guardrails and evaluation frameworks are essential for production deployments.

Question 4

What is retrieval-augmented generation (RAG) and why does it matter?

Accepted Answer

RAG combines a generative model with a retrieval system that fetches relevant documents before generating a response. This grounds the output in your organisation’s actual data, dramatically reducing hallucinations. RAG is the most common architecture for enterprise generative AI because it delivers accuracy without costly model fine-tuning.

Question 5

How long does it take to deploy generative AI in an enterprise?

Accepted Answer

A focused proof of concept typically takes four to eight weeks. Production deployment, including integration, security review, and user acceptance testing, usually requires three to six months. Timelines depend on data readiness, compliance requirements, and the complexity of existing system integrations.

Question 6

What data do you need to get started with generative AI?

Accepted Answer

You can start with existing unstructured data — documents, emails, support tickets, and knowledge bases. Generative AI does not require the large labelled datasets that traditional machine learning demands. The key requirement is organised, accessible data with clear governance policies for privacy and compliance.

Question 7

What are the risks of generative AI in business?

Accepted Answer

Key risks include hallucination (generating plausible but incorrect information), data privacy exposure if models are trained on sensitive data, intellectual property concerns, and over-reliance on AI outputs without human oversight. Mitigation requires robust governance frameworks, output validation, and clear accountability structures.

Question 8

How much does enterprise generative AI cost?

Accepted Answer

Costs vary significantly based on model choice, infrastructure, and scale. Cloud-hosted API models (GPT-4, Claude) charge per token, making small deployments affordable. Self-hosted open-source models require GPU infrastructure but offer lower per-query costs at scale. Most enterprises start with API-based proofs of concept before optimising cost.

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