INBOX INSIGHTS, November 5, 2025: 7 Ways to Get Started with AI, Deterministic vs. Probabilistic - Trust Insights Marketing Analytics Consulting

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INBOX INSIGHTS: 7 Ways to Get Started with AI, Deterministic vs. Probabilistic (2025-11-05)

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7 Ways to Get Started With AI

Last month I was at an in-person event and I overheard someone saying, “we need to leverage AI across our entire customer journey,” while their partner was frantically scribbling notes, clearly trying to figure out what that actually meant in practice. They looked like they were about to cry.

After the session, she asked me, “My boss wants us to ‘do AI,’ but I don’t even know where to start. Do I need to use it for everything? Just some things? How do I know?”

Here’s what I told her (and what I’m telling you): You don’t need to boil the ocean. You just need to understand that generative AI does seven basic things. That’s it. Seven use cases that cover pretty much everything you’ve been hearing about. Once you know what those are, you can stop drowning in vague directives and start making actual decisions about where AI fits into your workflow.

Let me break it down.

The 7 Use Cases for Generative AI

1. Extraction

This is AI pulling specific information out of larger documents or datasets. Think of it as having an intern who can read through 500 pages of contracts and pull out every mention of pricing terms in about 30 seconds.

Where to start: Have your team identify their most tedious “find the needle in the haystack” tasks. Reading through customer feedback to find mentions of specific features? Pulling data points from reports? That’s extraction.

2. Classification

AI sorts things into categories. It’s like having someone organize your messy email inbox, but they can do it for customer inquiries, support tickets, leads, content types, or whatever you throw at them.

Where to start: Look at anything your team currently sorts manually. Support tickets that need routing to different departments? Customer feedback that needs tagging? Product reviews that need sentiment labels? Start there.

3. Summarization

Taking long-form content and condensing it down to the key points. (If you’ve ever asked ChatGPT to “sum this up,” you’ve used this.)

Where to start: Identify the meetings, reports, or documents where people consistently say, “I don’t have time to read all that.” Meeting notes that need to become action items? Industry reports that need executive summaries? Those are your candidates.

4. Rewriting

Taking existing content and transforming it for a different audience, tone, or format. It’s not creating from scratch; it’s adapting what you already have.

Where to start: Look at content you’re creating multiple versions of. Do you write the same update for executives and then again for your team in different language? Do you adapt blog posts for social media? That’s rewriting work AI can handle.

5. Synthesis

This is where AI combines information from multiple sources to create something new. It’s pulling together insights from different documents, datasets, or inputs to give you a unified view.

Where to start: Find the projects where someone is saying “I need to pull together information from marketing, sales, and customer service to see the full picture.” Quarterly business reviews? Competitive analysis? Those synthesis tasks eat up massive amounts of time.

6. Question Answering

AI acts like a really well-informed colleague who’s read all your documentation and can answer specific questions about it. This is your internal knowledge base on steroids.

Where to start: Think about the questions that get asked repeatedly. New employee onboarding questions? Policy clarifications? “Where did we document that decision?” If you’re constantly Slacking the same people for the same information, that’s a question-answering use case.

7. Generation

Creating new content from scratch based on prompts. This is the use case everyone jumps to first, but honestly, it’s often not where you should start.

Where to start: Look at high-volume, formulaic content creation. First drafts of standard email responses? Social media post variations? Product descriptions that follow a template? Those are generation opportunities.

Identifying Where to Start

If knowing the 7 use cases doesn’t help you with where to start, I’d suggest using the Trust Insights TRIPS Framework. TRIPS helps you figure out which tasks are screaming to be automated with AI. Here’s how it works:

The TRIPS Framework: Your AI Priority Filter

Time – How much time does this task consume? The more time something eats up, the better a candidate it is for AI. If your team is spending hours doing something every week, that’s a flashing neon sign.

Repetition – How repetitive is the task? AI loves repetitive work. Humans? Not so much. The more frequently you’re doing the exact same type of task over and over, the more sense it makes to hand it off.

Importance – How important is the task, and what’s the risk if it goes wrong? Here’s the counterintuitive part: high-importance, high-risk tasks need MORE human oversight. You want to start with tasks that are relatively low-stakes. If AI messes up a social media post draft, you can fix it. If it messes up a regulatory filing? Yeah, that’s not your starting point.

Pain – How much do you hate doing this task? Real talk: if your team groans every time they have to do something, that’s a prime AI candidate. And here’s why this matters beyond just morale: when you can show your team they’ll never have to do [insert soul-crushing task here] again, you get instant buy-in for your AI initiatives.

Sufficient Data – Do you have examples of how this task should be done? The more examples you have (templates, past work, documented processes), the better AI will perform. If you’re already using a template for something, AI should probably be doing it tomorrow.

Putting TRIPS Into Practice

List out everything your team is doing. Then score each task against TRIPS:

Task: Sorting incoming customer inquiries into categories

Time: 2 hours per day ✓
Repetition: Every. Single. Day. ✓
Importance: Low risk (we can review before responding) ✓
Pain: Everyone hates it ✓
Sufficient Data: 6 months of categorized emails ✓

Result: This became test #1.

Task: Writing the quarterly executive summary

Time: 8 hours once a quarter
Repetition: Only quarterly ✗
Importance: Very high, CEO reads this ✗
Pain: Actually kind of satisfying to write
Sufficient Data: Only 12 past examples ✗

Result: Not a good starting point for AI.

See how that works? Go through your whole list. The tasks that score high across multiple TRIPS criteria? Those become your testing priorities.

The Bottom Line

Your executives aren’t wrong for having big ideas about AI. But big ideas without a framework for execution just create chaos.

Use those seven use cases to understand what AI can do. Use TRIPS to figure out what AI should do for your team first. Then run small tests, measure what works, and scale from there.

You don’t need a grand strategy. You need to identify which repetitive, time-consuming, painful tasks are sitting in your team’s workflow right now and pick one to test this week.

Start there.

How are you choosing where to start with generative AI? Reply to this email or join our free Slack group, Analytics for Marketers.

– Katie Robbert, CEO

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In this week’s Data Diaries, let’s talk about deterministic versus probabilistic outcomes.

That’s a complex topic indeed. Many people use generative AI incorrectly, failing to leverage what language models do best: generating probabilities and exploring possibility spaces. When you give a vague prompt like “write me a blog post about B2B marketing,” you get slop—high-probability, boring, undifferentiated content. This happens because language models default to accepting the first probable answer.

If you want differentiation—creativity, innovative thinking, things that customers clearly value—recognize that innovation means seeking new things, not recycling what’s old. The word “innovate” comes from Latin in novare, meaning “to make new.” By definition, if you ask for something new while relying on high-probability content, you won’t get innovation. That’s basic logic.

This also means if you’re accepting the first high-probability answer a tool gives you, you’re missing its real capability: showing you a broad field of possibilities.

How do we escape this trap?

First, don’t ask for the answer. Never say “write me a blog post.” Instead, start by requesting options: “Give me three to seven blog post ideas for this topic.” It will generate multiple different ideas.

Provide an ideal customer profile or buyer persona to calibrate your request against what your target audience actually cares about. This helps significantly.

Next, request increasingly rare variations. Say: “Of these ideas, which are commonplace? Give me three to seven new ideas that are less common.” Then add a constraint that disrupts probability: “At least one idea must include a banana.” This arbitrary constraint throws off distributions and forces different results. It doesn’t have to be a banana—any unusual element works, as long as it’s atypical for that domain.

Keep iterating: “Without repeating previous ideas, give me three to seven new ideas—this time, include a tricycle.” The specific constraint matters less than forcing the model to exclude what it’s already generated. This technique produces genuinely different ideas. Once you’ve found a promising one, ask the model to develop it further.

This technique is critical for content creation and especially important for strategy. You don’t want generic strategy for unique problems, nor do you want generic strategy for common problems (since everyone else will use the same approach). If your challenge is “we need more leads,” you know countless others face the same challenge. Using the same strategy everyone else does simply adds noise to an already crowded marketplace.

This is where your subject matter expertise matters. You should know your customers intimately. What do you know about them that others don’t? What won’t others think to ask generative AI? As more people default to lazy cognitive offload, they’ll settle for common solutions. You can differentiate by leveraging what you actually know.

Use generative AI to augment, enhance, and challenge your thinking. Leverage its macroscopic view of the landscape and access to historical data. Ask it: “Show me what’s been tried before and why those tactics no longer work in overcrowded markets.” Its training on centuries of documented knowledge—every published book, historical manuscript—gives it unique perspective you can access.

Ask what marketing tactics could be revived from the past. For example: “Today’s overcrowded, undifferentiated landscape is like the 1930s—limited marketing channels, constrained budgets. How did companies differentiate then? How did they survive the Great Depression with limited channels and scarce buyer money? What did they do?” The world continued; companies found ways to thrive.

History doesn’t repeat, but it rhymes. We’re experiencing economic patterns reminiscent of the 1930s—high tariffs, economic contraction. If your business didn’t exist then, what similar business did, and how did it survive and thrive? Look at the pockets of prosperity that persisted. Examine even earlier periods like the 1867 Swedish famine. Abundant human experience is available if you know to ask.

Don’t ask generative AI for just one answer. Explore. Have conversations. Challenge it. Draw lateral connections across industries. Ask historical questions to revive ideas that have faded or become rare again.

Consider print magazines—now fashionable in certain segments. Not because of love for the environment, but because magazines are analog. They offer a respite from screens, no ad tracking, no privacy invasion. That’s value.

The key takeaway here is this: Give this approach serious thought and analysis as you use generative AI. Don’t accept the first answer it generates without question. Challenge the model, push it to change its probabilities, and let it challenge your own assumptions. That’s how you generate breakthrough ideas instead of recycled mediocrity.

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up – the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

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