Match generative AI to business work

Study guide for Generative AI Leader, Unit 1 · Topic 4. This is the topic's lecture in reading form — every slide's teaching, figures and worked examples, in order — followed by the official Google Cloud pages its claims rest on.

What the exam guide asks, quoted. Identifying business use cases where gen AI can create, summarize, discover, and automate (e.g., text generation, image generation, code generation, video generation, data analysis, and personalized user experience). Describing how various data types are used in gen AI and the business implications.

This hive's learning objectives for the topic:

1. Match create, summarize, discover and automate use cases to business outcomes and review needs.
2. Choose input and output data types for text, image, code, video, analysis and personalized experiences.

Matching generative AI to business work

Start from the outcome, name the job, then specify what goes in and out

The job — create, summarize, discover or automate, each tied to a measurable outcome and a review step. The data — which types go in and which come out: text, image, code, video, analysis, personalization.

This topic turns the vocabulary of the unit into business decisions. Google's guidance starts in one place: to create successful generative or traditional artificial intelligence, or AI, solutions, begin by clearly identifying the specific measurable business goals or needs that you want to address. The first objective builds on that. The guide groups the work generative AI does into four jobs — create, summarize, discover and automate — and each job has to connect to an outcome the business can measure and to the review a person still needs to do. The second objective is the data: for every task, which types of information go in and which come out, because that is what decides whether a candidate model can do the job at all. Two questions, asked in that order, and the deck closes by combining them.

Four jobs, each tied to an outcome

Name the job, the business outcome, and who checks the output

• Create: generate and recommend content
• Summarize: condense documents, feedback and reports for decisions
• Discover: conversational search and insights across documents and data
• Automate: scale workflows for repetitive tasks
• Decide whether humans stay in the loop at critical steps

Worked example (synthetic). A membership office drafts renewal letters (create), condenses board minutes (summarize), lets staff ask questions of its policy library (discover) and pre-fills routine forms (automate) — and measures staff hours saved for each.

The guide groups generative artificial intelligence, or AI, work into four jobs, and Google's own list of what these solutions excel at maps onto them. Create: Google's list opens with creating and recommending content, and adds generating code. Summarize: summarizing text content, including documents, articles, customer feedback and reports, to help with more informed data-driven decisions. Discover: powering conversational search and chatbots, and using associative reasoning to find insights and relationships within documents and data. Automate: scaling and automating workflow for repetitive tasks. A job alone is not a use case, though. Google asks you to decide whether the business expectation requires generative AI, another type of AI, or no AI at all, and to clarify how the business plans to measure success. And automation carries a review question: if you automate workflows with generative AI, Google says, assess whether humans should be included in the loop for critical decision stages.

Job, example, outcome, review

Every row needs a measure and a check, not just a capability

Worked example (synthetic). The outcome and review columns are house examples; the job and Google's example columns come from Google's task list.

The table puts each job beside an example from Google's list, an outcome a business could measure, and a review step. Create covers creating and recommending content; a sensible measure is time to a usable first draft, and an editor still approves what goes out. Summarize covers documents, articles, customer feedback and reports, which Google ties to more informed data-driven decisions; the review is a spot-check against the source. Discover covers enabling conversational search through large knowledge bases with natural language queries; the measure might be questions answered without escalation. Automate covers repetitive workflow; Google's own example measures include the percentage of tickets resolved without human intervention — and Google pairs automation with deciding where human review is still needed. The last two columns are house examples, not Google's numbers; the point they make is Google's — every use case needs a way to measure success.

Work backward from the outcome

Goal and measures first; the type of AI is a decision, not a default

Figure: A left-to-right flowchart: start from a measurable business goal and success criteria; decide whether it needs generative AI, other AI or no AI; identify end users and how they will interact; identify process and workflow changes; then measure against the criteria.

Worked example (synthetic). A support team's goal is to cut time-to-resolution by a fifth. Only after naming that measure does it decide a generative chatbot fits, then plans the ticket workflow around it.

Google's use-case guidance reads as a sequence, and the flowchart follows it. Begin by clearly identifying the specific measurable business goals or needs, and clarify how the business plans to measure success — Google names return on investment, or ROI, as one of the key measures of AI project success. Then decide whether the business expectation requires generative artificial intelligence, other types of AI, or whether it doesn't require AI at all; that question sits second, after the goal, so the technology never chooses itself. Next, identify the end users and how they might interact with the application. Then identify the changes the business has to make to existing processes or workflows. Only then is there something to measure against. In an exam scenario, the weak answer starts from a model's capabilities; the strong one starts from the outcome.

What a measurable outcome looks like

Google's support-chatbot example measures outcomes, not output volume

Worked example (synthetic). A team proposes 'number of replies generated per day' as its success metric. It counts output, not outcome; resolution rate and satisfaction say whether the replies helped.

Google's worked example is a customer support chatbot, and its success criteria show what a measurable outcome looks like. For efficiency, one measure is the percentage of tickets resolved without human intervention. For the customer experience, an increase in customer satisfaction, or CSAT, scores in surveys tied to chatbot usage. And above both, return on investment, or ROI, which Google calls one of the key measures of artificial intelligence, or AI, project success. Notice what is missing from the list: a count of generated replies, words or images. Volume of output is easy to measure and says nothing about whether the business goal moved. An exam option that proposes measuring how much a model produced, rather than what changed for customers or staff, is usually the distractor.

Specify what goes in and what comes out

A task is a direction: input types to output types

• Text to image, text to video: creative multimedia from prompts
• Video to text: automatic captions and subtitles
• Text, images, video and audio to text: multi-source summaries
• Natural language to code: functions, commands and scripts
• Behavior and preferences to recommendations: personalization

Worked example (synthetic). A museum wants photos of new exhibits turned into short written captions. The requirement is image in, text out — a text-to-image model is the wrong direction, however good its pictures are.

The second objective is data types, and Google ties them directly to model choice: typically, your use case and the model's modality are closely associated — if your use case involves text-to-image generation, you need a model trained on text and image data. So write each task as a direction. Google's examples cover every type the guide lists. Creating new images from text prompt descriptions, and generating short videos or animations from text prompts or scripts. Automatic captioning or subtitling of videos — video in, text out. Creating summaries of information from multiple sources that can include text, images, and video or audio components. Creating code, functions, command-line commands and scripts from natural language prompts. And analysis feeding personalization: analyzing user behavior, preferences, reviews and past interactions to provide personalized content recommendations. Google adds that generative artificial intelligence, or AI, covers use cases that require processing and inputting information from multiple modalities like text, images, videos or audio.

Task, input, output

Write the direction before you shortlist a model

Worked example (synthetic). Two requests that sound alike — 'make a product video' and 'describe this product video' — are opposite directions: text to video, and video to text.

The table writes Google's examples as directions, because the direction is what screens a model. Creating new images from text prompt descriptions is text in, image out. Generating short videos or animations from text prompts or scripts is text in, video out. Automatic captioning or subtitling of videos runs the other way, video in, text out. Creating code, functions and scripts from natural language prompts is text in, code out. And creating summaries of information from multiple sources that can include text, images, and video or audio components has several types going in and text coming out. The worked example shows why this matters: two requests can share every noun and still need opposite capabilities. Once each task has its direction, the screening question from the model-choice topic — does this candidate accept these inputs and produce these outputs — has a precise answer.

Analysis and personalization, chained

A prediction becomes part of a prompt

Figure: Two left-to-right chains. In the first, historical structured customer data feeds a traditional AI model that predicts churn probability; that prediction is included in a prompt to a generative chatbot that lets staff explore it in plain language. In the second, behavior and preferences feed personalized recommendations.

Worked example (synthetic). A sales team asks a chatbot 'which of my accounts are most at risk this month, and why?' — the risk scores come from a predictive model, the plain-language answer from a generative one.

Analysis and personalization are the two data jobs in the guide that are not simply generation, and Google shows how they combine with it. Traditional predictive artificial intelligence is particularly effective with structured data, and Google's example has it analyze historical data to forecast customer churn probability. Then the chain: you can use output from a traditional AI model as part of the prompt for a generative AI model, and Google describes integrating that churn analysis with a generative AI-powered chatbot, which lets a sales team explore the predictions in natural language conversations. Personalization works similarly: analyzing user behavior, preferences, reviews and past interactions to provide personalized content recommendations, which can be combined with real-time factors like location. The business implication is that the data types differ at each step — structured records in, a number out, then that number as text in a prompt.

Structured prediction or multimodal generation

The data and the output decide between them — or combine them

Figure. Two cards. Traditional predictive AI is particularly effective with structured data and outputs a category, forecast or probability. Generative AI takes inputs from multiple modalities — text, images, video, audio — and outputs new text, images, video or code. A band beneath says the two can be combined: a traditional model's output can become part of a generative model's prompt.

Worked example (synthetic). A bank wants next month's cash demand per branch (structured data, a number — traditional AI) and a plain-language briefing for each branch manager (text out — generative AI).

Put the two halves of the topic together and the data types point to the kind of artificial intelligence, or AI, a task needs. On the left, traditional predictive AI, which Google says is particularly effective with structured data, and whose outputs are categories, forecasts and probabilities. On the right, generative AI, which covers use cases that require processing and inputting information from multiple modalities like text, images, videos or audio, and produces new content. The band underneath is the part exam scenarios like to test: they are not rivals, because you can use output from a traditional AI model as part of the prompt for a generative AI model. The worked example needs both — a forecast from structured data, and a briefing in plain language built from it.

What this topic actually tests

Outcome first, job second, data direction third

Outcome: a measurable goal, decided before the technology. Job: create, summarize, discover or automate, with a review step. Data: the input and output types of each task, and whether a traditional prediction should feed a generative step.

Close on the order of questions. First, the outcome: Google's guidance begins with specific measurable business goals, and decides whether the need requires generative artificial intelligence, or AI, other AI, or no AI only after that. Second, the job: create, summarize, discover or automate, each tied to a measure of success and to a decision about whether humans stay in the loop at critical steps. Third, the data: write every task as input types to output types — text to image, video to text, mixed sources to a summary, natural language to code, behavior and preferences to recommendations — because that direction is what a candidate model must support. And remember the combination: a traditional model's prediction can become part of a generative model's prompt. The next topic stays with data and asks what makes it fit for the task.

Official sources for this topic

• Evaluate and define your generative AI business use case — Google Cloud
• Develop a generative AI application — Google Cloud
• When to use generative AI or traditional AI — Google Cloud