Are you still thinking about AI as a help mate rather than a partner? It’s time to evolve your relationship so you can extract more value from AI and level up your workflows, output, and strategy.
Promoting AI platforms from tools to collaborators allows you to:
- Increase AI efficiency. Stop fighting AI and get better results by identifying the endpoints and mapping out how to get there.
- Maximize your time and reduce repetitive tasks. Build systems to simplify prompting and drive better results.
- Uncover insights with more relevant and informed analyses and unlock the potential of AI’s processing power in a way that supports your goals. Improve your marketing strategy with targeted feedback that aligns with your audience, needs, and objectives.
Tool vs. Collaborator: Roles in Action
We talk about “AI tools” so it can be confusing to discuss the role AI plays by using the same term. But that term is apropos for a reason — it perfectly describes the way many people use generative AI.
Tools are something you use to complete a particular function. You cut things with scissors. That’s their particular task — they aren’t that good at hammering in nails or painting walls.
AI as a tool simply completes tasks as assigned. It doesn’t provide additional value because it’s just being asked to do one thing.
- Give me five topics to write about.
- Turn this into a social post.
- Write an outline for an article on this topic.
- Create an image of scissors being used to hammer in a nail.
When tasked like this, almost half of what the AI provides won’t be usable. It’ll be too similar to existing content, it won’t be accurate, it will miss nuance, and it won’t be in your brand voice.
This is where a collaborator can shine.
A collaborator is provided insight and examples — and even tools — to complete repeatable tasks that are contextualized within a particular role. Providing the how and why and not just the what dramatically improves gen AI output.
And if “collaborator” feels like too strong of a word, that’s ok. An AI intern is a step in the right direction.
What Working With AI Looks Like
GPTs and agents are primed for collaboration, but they take time to set up and aren’t for everyone. They can be shared with others and are great for completing repetitive tasks. Agents are still relatively new and aren’t as reliable as GPTs, but their future is bright, and “agentic AI” is definitely going to be the buzzy topic for 2025.
Descriptive prompting can also elevate AI from tool to collaborator. Improving prompts and creating a library of your most effective to share with your team can save you time and systematize workflows. Include instructions for iterating prompts to help reduce friction and accelerate acclimation.
Whichever route you choose, here’s what you need to get started with your AI coworker.
Give It Context
To get quality output, you need quality input. That means your AI partner needs context.
Provide insight into:
- Who the AI is. “You are a product marketing expert that excels at creating exciting go-to-market plans…” “You’re a social media strategist attuned to B2B marketing trends…”
- Who you are. This isn’t always necessary, but it can be helpful. Of course, “you” is subjective here. If you want, “you” can be the customer. “I’m a C-suite executive that needs to be convinced of…”
- Who your audience is. “You want to reach a Gen Z audience…” “You want to connect with your coworkers…”
- What you need. “Create a/n [email, outline, social post, etc]…” “Analyze this messaging for…”
- Why you need it. Think of this as the objective. “To encourage engagement…” To persuade the target audience to click through to learn more…”
- How you want it done. “Provide three different examples using different tones…”
There is other contextual information you’ll want to provide on a case-by-case (or prompt-by-prompt) basis. For example, rather than letting a gen AI tool determine how an article should be interpreted, you can share your take and have it help you shape your argument. This allows it to amplify your ideas without having it “think” for you.
You can also provide it with useful documentation and examples. A style guide is key if you want a writing collaborator, while your marketing strategy could be useful if you need support aligning your plans with your strategy or extrapolating from your strategy.
In this post about creating a GPT, we share more about what this actually looks like in practice.
Investment
Working with AI requires you to invest money and time.
Today’s free tools are great, but you’ll probably want to upgrade if you want access to improved LLMs.
You may find one-off prompts aren’t the best way for you to collaborate with AI. Taking the time to create GPTs or agents can save you time in the long run and help you dial in the consistency. And if you want to build your own GPTs and agents to extract maximum value from your LLM partner, a paid account is necessary.
Whatever application you opt for, make sure to set aside time — time to prompt, time to train, time to review, and, of course, time to iterate.
Iteration
If you’ve worked with gen AI output, you know iteration is key for a quality finished piece.
Walking the LLM through improving its answers will not only help you improve its output but also help you create better prompts and GPTs in the future.
Be clear about the modifications you want and why. The LLM will use the latter when working with you in the same chat or GPT for other prompts. For example, you may want certain information consistently formatted in a certain way, or particular examples or tips to illustrate the points you’re making. Providing this feedback in the moment and then editing your prompt or GPT to include it will save you time later.
Does Collaboration = Trust?
After all the effort that goes into collaborating with an AI, you can simply trust it to give you quality output, right?
Not exactly.
AI still needs safeguards — regardless of how much we invest in improving it — and we must always verify its output. LLMs can make errors, and they still hallucinate despite updates. Human QA is critical for safely working with AI.
Think of it this way — you wouldn’t automatically publish something your intern creates. Most of us would proofread it, at a minimum. Anything you receive from an LLM, whether it’s an article outline, code, or data analysis, needs to be reviewed for errors before use.
And what about your IP? Is it safe to share your IP to improve collaboration?
If you’re using a free LLM, the answer is easy: absolutely not.
If you’re paying for an account, maybe. Be sure to check the terms of service before providing a model with any confidential information.
Keep in mind that platforms like ChatGPT and Claude will update their TOSs over time, so don’t ignore that email when it comes in!
Establishing Your Working Relationship
Clearly defining AI’s role can make it easier to integrate into your workflows. Make sure that you build in flexibility, though. What AI can do continues to expand, and its “job description” should take that into account, changing as necessary.
Guidelines
Identify what is appropriate to outsource to AI and what isn’t. Keep in mind your chosen platform’s capabilities, your marketing objectives and workflows, and your organization’s rules around AI.
If your organization doesn’t have an AI strategy, speak with stakeholders before embarking on your own AI journey to ensure you do so appropriately. Generally speaking, you don’t want to share any proprietary information with an AI tool that uses inputs as part of its training data.
“Assigning” Tasks
When it comes time to start working with AI, consider daily/weekly/monthly tasks and what it takes to complete them to find a good match for your platform.
AI can complete repetitive and analytical tasks quickly. It can help you generate multiple drafts of something using different styles so you can explore content possibilities and angles before committing to one.
Common use cases include creating compelling headlines or CTAs, generating ideas for A/B testing of different landing page elements, and repurposing blog content for different channels or audiences.
Multiple Coworkers
If you have the bandwidth, you may find it useful to create multiple GPT coworkers. This keeps their “knowledge” siloed to their particular tasks and allows you to prompt each GPT based on their area of expertise.
For example, you could have distinct GPTs for each of your target markets or by marketing niche. This way, as you iterate to improve output, your feedback is focused on the GPT successfully meeting its one goal, rather than trying to work with a generalist GPT.
Some Examples:
1. 📣 Social Catalyst GPT
Social Catalyst GPT cuts down on brainstorming time and content production. By quickly generating multiple post variations, marketers can focus on refining strategy and testing engagement rather than spending hours on initial drafts.
You can prompt a GPT to:
- Create monthly social media calendars.
- Draft posts in various tones and styles.
- Repurpose existing content for different social channels.
2. 📝 Content Strategist GPT
Content Strategist GPT helps you map out content. You can rapidly iterate on outlines before committing to a full draft, giving you a head start on in-depth pieces and ensuring that each article aligns with your brand and marketing objectives.
Your GPT can be prompted to:
- Generate article briefs and outlines using your tone and style guidelines.
- Suggest potential angles to differentiate your content in crowded markets.
- Provide quick summaries of main points to keep you on track.
3. 🔍 Data Detective GPT
Data Detective GPT delivers fast, digestible insights so you can make data-informed decisions without spending hours crunching numbers. It helps you spot hidden opportunities and fine-tune tactics in real time, saving both time and resources.
Create a “data expert” GPT that:
- Analyzes large data sets to identify trends, patterns, and anomalies.
- Provides quick insights that can inform marketing campaigns or A/B testing strategies.
- Suggests areas for optimization based on historical performance metrics.
(If you opt to provide an AI tool with your data, make sure it isn’t used for training purposes!)
4. 📈 Performance Comparison GPT
Performance Comparison GPT makes it easier to replicate success across your site. By isolating the factors that make certain pages excel — like formatting, keyword usage, or compelling calls to action — you can systematically upgrade pages that lag behind.
Create a GPT that:
- Compares top-performing web pages with underperforming ones.
- Pinpoints key differences in structure, tone, or content approach.
- Suggests quick wins for improving your underperforming pages.
5. 🎯 Audience Aligner GPT
Audience Aligner GPT ensures you always create content with the end user in mind. It surfaces audience insights as you develop or revise your marketing materials, keeping you focused on what your customers truly want and need.
Create a GPT that:
- Reviews your target audience details and campaign goals.
- Suggests content angles or messaging tweaks to resonate better with specific demographics.
- Offers feedback on whether your content aligns with your audience’s preferences and pain points.
This is just an introductory list. There are countless possibilities when you start considering which of your tasks are easily replicable and which require your strategic insight. Whether you build multiple specialized GPTs or train a single generalist agent, consider how each one can be “hired” to handle a specific set of tasks and goals. Let these AI coworkers handle the heavy lifting of repetitive tasks so you can spend your time on high-level strategy and creativity.
Partner With An SEO and AI Innovator
Victorious is an award-winning SEO agency committed to proactivity, flexibility, and innovation. We work with marketers in industries as diverse as SaaS, healthcare, ecommerce, and professional services to provide outcome-focused SEO services aligned to key business objectives. If you’re ready to elevate your marketing with a search-first approach enhanced by our proprietary tech and AI integration, schedule a consultation.
