Generative AI for Business

The state of generative AI in business

Generative AI has moved from research to production. Three years after the consumer breakthrough of ChatGPT, the question for most organisations is not “should we use it” but “where, how safely, and how to measure return.”

• Mature use casesCustomer support automation, code generation, marketing content, internal knowledge search, document summarisation, drafting and editing.
• Emerging use casesDrug discovery, materials design, video generation, voice cloning, agent-based workflow automation, scientific reasoning.
• What it does wellReproducing patterns, generating variations, summarising long content, translating language, drafting routine output.
• What it does poorlyReasoning with high accuracy, complex math, novel research, anything requiring verifiable factual correctness without retrieval grounding.

High-value production use cases

• Customer support augmentationAI drafts responses to common queries; agents review and send. Typically 30–50% productivity gain on tier-1 contacts.
• Software developmentCode completion, bug detection, test generation, documentation. Productivity gains of 25–55% on routine engineering tasks in well-tested deployments.
• Knowledge managementRAG-based search lets employees query internal documents in natural language. Cuts “where is that policy” time from minutes to seconds.
• Sales and marketing contentPersonalised emails, ad variants, landing page copy at scale. Quality control matters — the volume can become noise without curation.
• Internal communicationsDrafting announcements, formatting reports, summarising meetings.
• Compliance and legalContract review, policy comparison, regulatory tracking — with human-in-the-loop confirmation.
• Operations and financeAnomaly explanation, variance commentary, narrative reporting that previously required analyst writing time.

Deployment patterns that work

Three patterns separate successful deployments from expensive disappointments:

• Human in the loopThe AI drafts; a human reviews, edits, and sends. This is where most measurable productivity gains come from. Removing the human step is where most accuracy failures come from.
• Retrieval-augmented generation (RAG)Connect the model to your actual data sources so it can ground answers in your specific policies, products, documents — not just its training data. RAG is what turns “impressive demo” into “production value.”
• Narrow scope, measured rolloutPick one workflow, deploy with metrics, measure, expand. Cross-functional “AI strategy” initiatives without specific KPIs almost always fail to ship.

Risks and governance

• HallucinationsModels generate plausible-sounding but wrong content. Mitigations: RAG, structured output, citation requirements, human review for high-stakes use.
• Data leakageSensitive prompts can leak to model providers if processed through public APIs. Enterprise deployments use isolated tenancies, on-prem models, or contractual data-protection commitments.
• Prompt injectionMalicious input can manipulate model behaviour. Especially relevant for customer-facing AI that processes untrusted text.
• IP and copyrightTraining data provenance and output ownership are still evolving legally. Risk varies by jurisdiction and use case.
• Bias amplificationModels reflect biases in training data. High-stakes applications (hiring, lending, healthcare) require specific bias auditing.
• ComplianceEU AI Act, NIST AI RMF, sector-specific regulations are now real constraints. Get compliance involved before deployment, not after.

What to measure

The most common reason gen AI investments don’t pay back is unclear success metrics. Useful framing: pick one specific workflow, establish a baseline (time, cost, quality), deploy, measure the delta. Time-saved-per-employee, cost-per-contact, customer-satisfaction-score, and revenue-per-rep are all valid metrics; “adoption rate” is not.

Pair this with platform-level governance — security, data classification, model access controls, audit logging. The best gen AI deployments are joint efforts between product, engineering, security, and compliance.

Frequently asked questions

• Will generative AI replace knowledge workers?It replaces specific tasks within knowledge work, not the workers themselves. The job mix shifts toward higher-value work. Most organisations end up with the same headcount producing more.
• What is RAG?Retrieval-augmented generation. The model is given relevant documents from your own data as part of the prompt, so it can answer based on your specific information rather than just training data.
• How do we prevent hallucinations?Combine RAG, structured output formats, source citation, confidence scoring, and human review for high-stakes decisions. No single technique eliminates hallucinations entirely.
• Should we build or buy?For most use cases, buy. Foundation models are commodity infrastructure. Differentiation lives in your data, workflow integration, and governance — not in training your own model.
• What about data privacy?Use enterprise tier deployments with data-isolation guarantees, or on-prem/private-cloud models for the most sensitive workloads. The privacy landscape has matured rapidly.
• Where do most gen AI projects fail?Unclear KPIs, missing human-in-the-loop step, weak data foundation, no governance — in that order. Technical issues are rarely the root cause.