Generative AI

Generative AI produces content in response to instructions and other inputs. Depending on the system, that output can include text, images, audio, video or code.

For a business, it can assist with drafting, exploring creative directions, summarising material and supporting other tasks involving language or media. The useful question is how the output fits into a complete process, including the checks needed before it is used.

How does generative AI work?

Generative systems use models trained to learn patterns from data. At use time, the model produces an output based on those learned patterns and the context supplied.

Different systems use different architectures and training methods. A business does not need to understand every technical detail to evaluate a use case, but it does need a realistic view of what the system can and cannot establish.

Generated content is an output to assess. Fluency, visual polish or confident wording does not by itself demonstrate that the result is accurate or suitable.

Generative AI versus other AI

AI is a broad category. Some systems classify information, estimate an outcome or support a decision without generating a substantial new piece of content.

Generative AI is distinguished by producing an output such as a passage, image or code sequence. A product can combine generation with search, classification and other capabilities.

This distinction helps clarify a project brief. Predicting demand, finding documents and drafting a report may require different methods even when all three are described casually as AI.

What is a prompt?

A prompt is an instruction or input supplied to the system. It can describe the task, audience, constraints and required output.

Useful context often matters as much as wording. A request to draft a service page becomes more grounded when it includes verified service details, intended readers and clear boundaries on what may be claimed.

A well-written prompt cannot replace missing evidence. If the task requires a verified specification, the workflow needs a trustworthy source for that specification.

Where can businesses use generative AI?

Potential applications include preparing draft content, exploring concepts, summarising approved information, assisting with code and generating variations for review.

Choose a task with a recognisable outcome. “Prepare an initial summary from these approved reports” is easier to evaluate than “improve marketing”.

The value should be assessed across the whole process. Include preparation, checking, editing and any rework. A fast first draft is useful only if the finished result is reliable and appropriate.

Why can generative AI get facts wrong?

A generated answer can contain unsupported or incorrect statements. The system may also fill gaps with plausible material when the available context does not establish the answer.

This is particularly important for product details, attributed quotations and claims about business results. The output should be checked against evidence appropriate to the task.

Providing source material can help ground the response, but it does not eliminate the need for evaluation. Retrieval-augmented generation explores one approach to supplying relevant reference information.

Generative AI in creative work

Generation can help teams explore directions and produce material for consideration. The role of judgement remains central: selecting, refining and connecting the work with the brief.

A large set of variations is not the same as a resolved idea. Evaluate whether the material expresses the intended message and works in the actual format where it will appear.

Brand identity and editorial guidance provide useful constraints. They help distinguish work that fits the organisation from work that is merely polished in a general way.

What is the difference between a model and an agent?

A model generates or interprets outputs. An AI agent is a wider system that can use a model to choose and carry out steps towards a goal.

An agent may retrieve information, use tools and respond to the results. Its capabilities depend on the surrounding software and permissions, not simply the model's ability to write.

This matters when scoping a project. Drafting a response and sending it through a connected service are different responsibilities and need appropriate operating boundaries.

Information handling and review

Establish which information the workflow needs and how the selected service handles it. Avoid assuming that all products provide the same arrangements or settings.

Define review according to the consequence of an error. A private concept draft and a customer-facing technical instruction require different checks.

Make ownership clear. Someone should be responsible for the sources, output quality and decision to use the material. Adding AI to the process should not make that responsibility ambiguous.

How do you evaluate a generative AI workflow?

Use realistic examples and explicit criteria. Assess accuracy, completeness, style, usefulness and the amount of correction required.

Include difficult cases, such as incomplete source material or requests outside the task's scope. A system that recognises uncertainty may be more useful than one that always produces a confident answer.

Compare the complete workflow with the current approach. Track time and quality together so the evaluation reflects the work the business actually needs to deliver.

Where should a business begin?

Choose a limited task with reliable input information and a clear review process. Test it before expanding into more consequential or connected activity.

Keep the goal concrete and revisit the arrangement as the tools and business needs change. Avoid adopting a broad capability without deciding how success will be recognised.

For technical background, see IBM's generative AI introduction. Explore Linford AI for the relationship between strategy, creative work and business workflows.