Where AI Fits on Your Team: A Practical Framework for Putting AI Beneath, Beside, and Between Your Workers

Last Tuesday, a friend who runs a seven-person marketing agency told me she'd added three different AI tools in the past month — and her team was more confused than before. Not because the tools were bad, but because nobody had stopped to ask a basic question: what job, exactly, is the AI supposed to do here?

That question matters more than which tool you pick. And it finally has a useful answer.

A framework that's been gaining traction in AI strategy circles describes three distinct ways AI enters an organization's workflow: beneath workers (handling the grunt work), beside them (acting as a collaborative peer), and between organizational layers (bridging communication gaps). Sometimes called the "beneath, beside, between" model, this way of thinking about AI roles has been discussed in the context of how AI reshapes the modern firm — and it's especially relevant now that partnerships like the one between OpenAI and ServiceNow are making AI-agent-to-enterprise-workflow integration a commercial reality.

But here's the thing: most of us don't run enterprises. We run small teams, freelance operations, growing businesses where one person handles marketing and bookkeeping and customer support. The framework is genuinely useful, but it needs translation.

That's what this guide does. We'll break down each of the three positions AI can hold on your team, give you concrete examples you can act on this week, hand you copy-paste prompts, and walk you through a simple worksheet so you can map your own workflows. No jargon. No abstract org-chart theory. Just practical moves.

What the "Beneath, Beside, Between" Framework Actually Means

Before we get tactical, let's make sure the framework itself is clear — because the labels are simple but the implications are not.

AI Beneath: The Workhorse

When AI sits beneath a worker, it's doing the tasks that person used to do manually — the repetitive, time-consuming, low-judgment work. Think of it as a very fast, very tireless assistant who never complains about data entry.

Examples: transcribing meeting notes, reformatting spreadsheets, pulling data from emails into a CRM, scheduling social media posts, generating first-draft replies to routine customer questions.

The key characteristic: the human is still in charge. They review the output, make corrections, and own the final result. AI beneath is about speed and volume, not creativity or strategy.

AI Beside: The Collaborator

When AI sits beside a worker, it's more like a brainstorming partner than an assistant. You're not just handing it tasks — you're thinking with it. The human and the AI are working on the same problem at roughly the same level, each contributing something the other can't.

Examples: drafting a blog post together (you provide the ideas and expertise, AI helps with structure and phrasing), exploring different angles for a pitch deck, analyzing customer feedback to spot patterns you might miss, pressure-testing a business plan by asking AI to poke holes in it.

The key characteristic: the interaction is iterative. You go back and forth. The output improves because of the conversation, not just because you gave a good instruction.

AI Between: The Bridge

This is the least obvious position, and honestly the most underused. When AI sits between layers of your organization — or between you and your clients, or between departments — it's translating, summarizing, and connecting information that would otherwise get lost or distorted.

Examples: summarizing a 45-minute team meeting into action items that get sent to people who weren't there, translating technical project updates into plain-English client reports, consolidating feedback from five different Slack channels into a single weekly brief, turning a developer's bug report into something a non-technical founder can actually understand.

The key characteristic: AI between reduces communication friction. It doesn't do anyone's job — it makes sure everyone has the information they need, in the format they can use.

Why This Framework Matters Right Now

You might be wondering: isn't this just common sense? Put AI where it's useful?

Sort of. But the reason most small teams struggle with AI adoption isn't that they picked the wrong tool. It's that they never decided what role AI should play in a given workflow. So they end up in one of two failure modes:

1. They treat AI as purely beneath — using it only for rote tasks, missing the bigger opportunities for collaboration and communication.
2. They treat AI as purely beside — trying to use it as a creative partner for everything, including tasks where they really just need fast, reliable execution.

The framework gives you a vocabulary to be intentional. When you're assigning a task to AI, you can now ask: Am I putting this beneath, beside, or between? That single question changes how you write the prompt, how you evaluate the output, and how much human oversight you build in.

This also matters because AI agents are becoming more capable across the board. Meeting-focused tools like Otter, for instance, have been expanding beyond simple transcription into broader workflow automation — handling follow-ups, action-item tracking, and task delegation tied to what was discussed in a meeting. As AI moves from "do this one thing" to "handle this whole process," knowing where it fits on your team becomes essential, not optional.

Putting AI Beneath Your Workers: The Practical Playbook

This is where most people should start, and where the ROI is most immediate.

Step 1: Audit Your Repetitive Tasks

Grab a notebook (or open a doc) and spend one day tracking every task that feels like busywork. Not the tasks you enjoy — the ones that make you think, "I really should hire someone for this." Common candidates:

• Copying information from emails into spreadsheets
• Formatting documents to match a template
• Writing routine follow-up emails
• Scheduling and rescheduling meetings
• Pulling together weekly reports from multiple sources
• Resizing images or reformatting content for different platforms

Step 2: Pick the Three Highest-Volume Tasks

Don't try to automate everything at once. Pick three tasks that eat the most time and have the lowest stakes if AI makes a small mistake. Data entry is a better starting point than client proposals.

Step 3: Create a Standard Prompt (or Set Up a Recurring Task)

For each task, write a prompt that you can reuse. Here's a concrete example:

Task: Turning raw meeting notes into a formatted summary.

Copy-paste prompt:

Here are my raw notes from today's team meeting. Please organize them into:
1. Key decisions made
2. Action items (with the person responsible, if mentioned)
3. Open questions that still need answers
4. Any deadlines mentioned

Keep the language simple and direct. Use bullet points. If something is unclear in my notes, flag it with [UNCLEAR] so I can fill it in.

Raw notes:
[paste your notes here]

This kind of task is a perfect candidate for scheduled AI tasks — set it up once, feed it your notes after each meeting, and get a clean summary without thinking about formatting every time.

Step 4: Review, Correct, and Refine

The first few outputs won't be perfect. That's fine. Adjust the prompt based on what the AI gets wrong. After three or four rounds, you'll have a prompt that reliably produces 90%+ usable output. That remaining 10% is your human review — which takes two minutes instead of twenty.

Putting AI Beside Your Workers: Collaboration That Actually Works

This is where AI gets genuinely interesting, but it's also where most people underperform — because they treat "beside" like "beneath" and just ask for a finished product.

Working beside AI means having a conversation. It means pushing back on the AI's first answer. It means saying, "That's close, but here's what's missing," and iterating until you get something better than either of you could produce alone.

When to Use AI as a Collaborator

AI beside works best when:

• You have expertise but need help organizing or expressing it
• You're stuck on a creative problem and need a different angle
• You want to stress-test an idea before presenting it to someone else
• You need to explore multiple options quickly (three different email subject lines, four different ways to structure a proposal, etc.)

It works poorly when:

• You have no expertise in the area and can't evaluate the output
• The task requires specialized, verifiable knowledge (legal advice, medical guidance)
• You need a single "correct" answer rather than creative options

A Real-World Example: Drafting a Client Proposal

Let's say you're a freelance web designer and you need to write a proposal for a new client. Here's how the "beside" approach works:

Round 1 prompt:

I'm a freelance web designer writing a proposal for a local bakery that wants a new website. They have no current site, they want online ordering, and their budget is around $3,000-5,000. Help me outline a proposal that covers: what I'll deliver, the timeline, pricing tiers, and why they should pick me over a template builder. Keep the tone warm and professional — this is a small family business.

After reading the output, Round 2:

This is a solid start, but the pricing section feels too generic. I want to offer two tiers — a "foundation" package at $3,200 and a "growth" package at $4,800 that includes SEO basics and a blog setup. Can you rework the pricing section with those specifics? Also, add a brief section about ongoing support — I offer a monthly maintenance plan at $150/month.

Round 3:

Much better. Now help me write a short, personal opening paragraph. The bakery owner's name is Maria, and we met at a local business mixer last week. I want to reference that conversation and mention that I noticed their competitors all have outdated sites — there's a real opportunity here.

See the pattern? You're not asking AI to write the proposal for you. You're building it together, combining your knowledge of the client with AI's ability to structure and articulate ideas quickly.

This kind of iterative work is where an all-in-one AI tool shines — you can draft the text, generate mockup images for the proposal, and even create a short video walkthrough of your design concept, all in one place instead of bouncing between five different apps.

Putting AI Between Your Team Layers: The Hidden Superpower

If "beneath" saves time and "beside" improves quality, then "between" solves a problem most small teams don't even realize they have: information doesn't flow.

Think about how much time gets wasted in your organization because:

• Someone missed a key decision from a meeting they couldn't attend
• A client asked for a status update and three people had to be consulted to piece it together
• Two team members worked on overlapping tasks because neither knew what the other was doing
• A weekly report took two hours to compile from four different sources

AI between doesn't do anyone's work. It makes sure the right information reaches the right people in the right format.

Practical Setup: The Cross-Team Weekly Brief

Here's a workflow I've seen work well for teams of 5-15 people:

1. Each team member (or department lead) spends 3 minutes at the end of each week typing a quick brain-dump of what they worked on, what's stuck, and what's coming next week.
2. Feed all of those brain-dumps into a single AI prompt.
3. AI produces a consolidated weekly brief organized by theme, not by person — so everyone sees the full picture.

Copy-paste prompt:

Below are weekly updates from [number] team members. Please consolidate them into a single team brief organized by:

1. Completed this week (group related items together)
2. In progress (note any blockers or dependencies between team members)
3. Coming next week (highlight anything that requires coordination)
4. Flags (anything that sounds like it needs leadership attention)

Keep it concise — aim for a brief someone can read in 3 minutes. If two people mention the same project, combine their updates into one entry.

[paste all updates here]

This takes about five minutes total and replaces a 30-minute status meeting. For teams that are partially remote or spread across time zones, this is a game-changer.

If you want to take this further, you can set up custom AI agents to handle the consolidation automatically — team members submit their updates, and the brief generates and distributes itself.

The Worksheet: Map Your Own Workflows in 15 Minutes

Here's a simple exercise you can do right now. Grab a piece of paper and draw three columns: Beneath, Beside, Between.

Now walk through your typical week and place each recurring task in a column:

Beneath — Tasks where you just need fast, reliable execution:

• Does this task follow a repeatable pattern?
• Could someone with zero context do it if you gave them clear instructions?
• Is the main bottleneck time, not thinking?

Beside — Tasks where you need a thinking partner:

• Does this task require creativity, judgment, or expertise?
• Would it benefit from seeing multiple options quickly?
• Is the main bottleneck getting started or seeing a different angle?

Between — Tasks that are really about moving information:

• Does this task exist because two people (or groups) need to stay aligned?
• Are you essentially translating, summarizing, or reformatting for a different audience?
• Is the main bottleneck communication, not creation?

Most people discover that 40-60% of their week falls into "beneath" territory, 20-30% is "beside," and 10-20% is "between." The exact split doesn't matter — what matters is that you stop treating all AI use the same way.

For more frameworks like this, check out our guides and how-to articles — they're built around the same principle of giving you something you can actually use, not just read about.

Common Mistakes (and How to Avoid Them)

Mistake 1: Putting AI Beside When It Should Be Beneath

This is the most common error. Someone spends 20 minutes going back and forth with AI to craft a routine email that should have been a one-shot prompt. If the task is repetitive and the output is predictable, write a solid prompt once and reuse it. Save the conversational approach for work that actually benefits from iteration.

Mistake 2: Skipping the "Between" Category Entirely

Most people think about AI as either a task-doer or a creative partner. They completely overlook the communication layer. But for any team larger than two people, the information-bridging role might deliver the most value per minute invested. Start with meeting summaries — they're the lowest-risk, highest-impact "between" use case.

Mistake 3: Automating Before Understanding

Don't hand a task to AI until you can clearly describe what a good output looks like. If you can't articulate what you want, AI can't produce it. This is especially true for "beside" tasks — the quality of the collaboration depends entirely on the quality of your input.

Mistake 4: Using Five Different Tools for Three Different Roles

One tool for writing, another for images, a third for meeting summaries, a fourth for brainstorming. Each one has its own login, its own interface, its own learning curve. The cognitive overhead adds up fast. This is exactly why AI for small business works best when it's consolidated — fewer tools, less friction, more output.

Mistake 5: Never Reviewing AI's "Beneath" Work

Just because a task is routine doesn't mean you should stop checking the output entirely. Build a lightweight review step — even a 60-second scan — into every "beneath" workflow. AI is reliable, not infallible. Catch the occasional error before your client does.

A Quick-Start Plan: What to Do in the Next 10 Minutes

If you've read this far and want to actually do something with it, here's your move:

1. Pick one "beneath" task from your week — something repetitive that takes at least 15 minutes each time. Write a reusable prompt for it.
2. Pick one "beside" scenario — something you're working on this week that could benefit from a brainstorming partner. Open an AI chat and have a three-round conversation about it.
3. Pick one "between" gap — a place where information isn't flowing well on your team. Try the weekly brief prompt above, even if it's just consolidating your own notes from different projects.

That's it. Three small experiments. You'll learn more from doing this once than from reading ten articles about AI strategy.

If you want to run all three experiments in one place — text, images, video, music, and more — Gab AI is built for exactly that kind of consolidated workflow. No tool-switching, no juggling logins.

Why the "Where" Matters More Than the "What"

The AI tool landscape is noisy. Every week there's a new product, a new feature, a new claim about what AI can do. And most of that noise focuses on what AI can do — generate text! create images! automate workflows!

But the question that actually determines whether AI helps your team or just adds complexity is where it sits in your process. The same AI capability — say, text generation — works completely differently depending on whether you're using it beneath (cranking out routine emails), beside (co-writing a strategy document), or between (summarizing cross-team updates).

The framework isn't complicated. But it is clarifying. And in a space full of hype and confusion, clarity is the most valuable thing you can have.

If you're evaluating different tools and trying to figure out which one fits your workflow, our comparison of AI chat alternatives or our page on ChatGPT alternatives can help you think through the options without the marketing spin.

Wrapping Up

The "beneath, beside, between" framework isn't going to revolutionize your business overnight. No framework does. But it gives you something genuinely useful: a way to think about AI that's more specific than "use it for everything" and more practical than "wait and see."

Start with one task in each category. Refine your prompts. Notice where AI saves you time, where it makes your thinking sharper, and where it helps your team stay connected. Then expand from there.

The teams that get the most out of AI aren't the ones using the fanciest tools. They're the ones who know exactly what job they're asking AI to do — and put it in the right seat.

Start creating text, images, videos, music, and more in one place at https://gab.ai.