Monday, December 8, 2025

Prompt - build your own AI social media manager

Use ChatGPT to build your own AI social media manager

Smart marketers don't waste hours juggling posts, calendars, and captions. They use AI to build their own social media manager.

Most businesses either wing social media (posting randomly when they remember) or pay agencies thousands for basic posting schedules.

There's a smarter way.

With the right prompts, you can turn ChatGPT into your strategic social media manager — handling everything from content themes to monthly calendars.

Try this workflow:

You are my strategic social media manager. Help me build a complete content system.

First, analyze my business and create 5 core content themes:
- Business: [describe your product/service]
- Target audience: [who you're targeting]
- Goals: [brand awareness, leads, sales, etc.]
- Brand voice: [professional, casual, witty, etc.]

For each theme, suggest:
1. Content angle and purpose
2. 3 post ideas I could create monthly
3. Best format for each platform (LinkedIn, Twitter, Instagram)

Then create a 4-week posting calendar using these themes. Include:
- Posting schedule (which days/times)
- Content mix across themes
- Suggested formats (carousel, video, text post)
- Draft captions for Week 1

Finally, give me 3 engagement tactics to turn posts into conversations.

Sunday, December 7, 2025

The 5 AI investments marketing teams should make in 2026

Marketing leaders aren’t just facing a technology shift – they’re facing a behavior shift. Buyers are changing how they do product research, who they trust, and what they expect from a digital experience.

So a marketer's role shifts from broadcasting messages and optimizing isolated channels to architecting self-serve buying experiences. They’ll spend less time manually tweaking bids, writing generic content, or pushing people from the top of the funnel into a sales cadence – and more time designing the information, data, and journeys that let buyers make up their own minds.

HubSpot’s SVP of Marketing, Kieran Flanagan, says there are 5 AI-powered investments CMOs should make in 2026 to capitalize on this shift.

1. Redesign your paid engine around data quality and creative volume

Paid advertising is moving from manual optimization to model-driven systems. Tools like Google P-Max automate targeting and asset generation, but only perform well with strong data inputs.

“It's kind of doing the job of a paid marketer,” Kieran says.

Three things to keep in mind:

The cleaner your first-party data, the better. These platforms use your data to identify micro-audiences, so poor data leads to broad, expensive targeting.
Do more creative testing with the same resources. AI tools will allow you to generate 40-50 variations of an image or video ad for the cost of producing one.
Your role will shift from operator to orchestrator. Instead of manually optimizing campaigns, teams will focus on data pipelines, creative workflows, and testing. Media buying becomes a strategy problem, not an execution problem.

Kieran’s tip: Focus your paid marketing on these kinds of efficient scalable approaches, learn what works, and optimize quickly. Your paid marketer's job becomes way less about individual campaigns and manual tuning and more about designing end-to-end experiences that AI can operate.

2. Pivot to AI-driven inbound prospecting

Cold outbound email is oversaturated because AI has made it easier for everyone to send more outreach, which vastly reduces its effectiveness. The performance of these campaigns is plateauing.

But inbound prospecting – aka, reaching out when someone has already shown interest in some way (visiting your site, browsing product pages, or engaging with content) – still delivers strong results.

This approach works because the message matches the moment: the user already cares.

“It just comes down to: do you have good data sources and can you prompt well?” says Kieran.

Kieran’s tip: Don’t waste effort on an already noisy strategy. If you have meaningful intent data and an AI-powered personalization layer, this is a high ROI investment.

3. Focus on content that LLMs find valuable

Last week we wrote about why GEO might be hype – but Kieran disagrees. With ChatGPT or Google’s AI Overviews (the only two AI experiences worth optimizing for, in Kieran‘s opinion), buyers don’t use keywords to find something like they have in the past. They articulate context, constraints, and preferences.

“People conversate around your product and service differently than they search about it via keywords,” Kieran says.

There are thousands of ways a person could ask questions about your product or service, which means a few things for marketers:

1. Search visibility now depends on answering intent, not ranking for keywords

Instead of writing five product pages to cover the core keywords you want to rank for, you need 100-200 targeted pieces of content to cover the nuanced, scenario-specific ways buyers describe their needs to AI, for example:

“What’s the best CRM for a three-person solar sales team?”
“I need a scheduling tool for hourly workers rotating weekend shifts”

If your site doesn’t answer a question, AI engines assume you’re irrelevant. So content just became king.

2. AI engines rely on citations, not links

LLMs infer authority by scanning how often you’re mentioned across the web, not just where you’re linked. So mentions on high-trust sites influence which products show up in model-generated recommendations.

3. Your pages must be structured for machine interpretation

AI models parse pages differently than crawlers. Clear headings, consistent schema, and explicit descriptions help models find and summarize your value to users.

Kieran’s tip: AI search is becoming the first step in B2B research and people don’t want a list of links anymore: they expect tailored answers. Teams that invest early will own the top of the AI funnel and starve competitors of visibility.

4. Build a creator-led content engine

AI-generated content is starting to outnumber human-created content from brands. As a result, buyers are paying less attention (and giving less trust) to branded content and relying more on people they can connect with.

“Creators are going to become a bigger and bigger part of your marketing strategy,” Kieran says.

Three things he’s seeing:

Creators produce the human signal buyers crave. In an AI-saturated content landscape, personality, perspective, and lived experience stand out. So creator-led channels – like YouTube, TikTok, and LinkedIn – become massive brand assets.
Founder-as-creator strategies outperform brand accounts. Many high-growth companies now grow the founder’s audience faster than the company’s because buyers want to learn from a person.
Creator partnerships strengthen the paid mix. With Meta and Google CAC rising, creator ads often deliver stronger engagement and trust at lower cost.

Kieran’s tip: This isn’t a new approach for the AI era – influencer marketing has been on the rise in response to waning interest in traditional advertising. But it’s going to become more important for building brand trust in the AI era than before. Your job as a marketer is to deliberately create and distribute voices consumers can relate to and trust – not just campaigns.

Bleeding-edge bonus: Pilot multimodal AI agents

Multimodal AI (aka voice, video, and visual reasoning) is advancing quickly and Kieran believes it will fundamentally change how buyers interact with companies online in 2026.

A multimodal agent can:

Speak naturally with a site visitor
Display product visuals and walkthroughs
Answer technical or workflow-specific questions
Qualify the buyer
Hand off to sales when appropriate

This effectively conducts an early-stage sales call without human involvement.

“AI will bring about the entire sales process earlier in the funnel,” Kieran says.

Kieran’s tip: There is a large population of buyers who do not want to talk to sales but want accurate information about your product. Multimodal agents unlock that demand. Your job as a marketer becomes more about feeding AI and the buyer everything they need to arrive at decisions on their own.

How to lead your marketing team into the AI era

Search, content, paid media, and prospecting is getting an AI facelift and the winning brands will be the ones that seize the opportunity. Here’s Kieran’s parting wisdom for marketing leaders:

Build team-level curiosity. AI is rewriting all marketing workflows, so teams need to explore, test, and learn continuously. You can’t afford to have anyone using the old playbook.
Choose 1-2 bets to make in Q1. Concentrate resources on a few key areas where you feel strongly that AI can meaningfully shift results for your team. Don’t try to pilot it on all Jan 1.
Lead from the front. If you want an AI-enabled team, become the most informed, experimental practitioner in the room. You as the leader need to be the most proficient and prolific AI user.

TL;DR – the modern AI-powered marketer will move from “talking at” the market to landscaping an environment in which informed decisions happen organically for buyers.

6 Overlooked AI Skills, from Prompt Structure to Combining Tools, That Save Hours Weekly

1:40 pm December 5, 2025 By Julian Horsey

Comparison chart of AI tool categories, including LLMs, research assistants, and creative apps, mapped to common tasks.

What if the skills that could future-proof your career and supercharge your creativity were hiding in plain sight? As artificial intelligence (AI) continues to transform industries, most people remain stuck at surface-level interactions, asking ChatGPT for quick answers or dabbling with AI art tools. But here’s the catch: true mastery of AI isn’t about using tools passively; it’s about understanding how to wield them strategically. The difference between someone who merely uses AI and someone who masters it could mean the difference between staying relevant or being left behind. Imagine automating hours of repetitive work, crafting AI prompts that deliver exactly what you need, or seamlessly blending human creativity with machine efficiency. These are the kinds of fantastic skills that 99% of people overlook, but you don’t have to.

Below Enovair uncover six essential AI skills that aren’t just useful, they’re innovative. From selecting the right tools for specific tasks to refining AI outputs with a personal touch, these skills will empower you to work smarter, not harder. You’ll learn how to define problems clearly so AI delivers precise solutions, how to verify its outputs for accuracy, and even how to build automated workflows that save you time and energy. Whether you’re a professional looking to stay competitive or a curious learner eager to explore AI’s potential, these insights will help you unlock capabilities you didn’t know you had. After all, the future isn’t about competing with AI, it’s about collaborating with it in ways that elevate your impact.

Essential AI Skills Overview

TL;DR Key Takeaways :

Mastering AI Tools: Learn to select and use the right AI tools across categories like large language models, research tools, and creative tools to maximize efficiency and tackle diverse tasks effectively.
Defining Clear Objectives: Clearly define goals, audience, and success criteria before using AI tools to ensure relevant and actionable outputs with minimal revisions.
Crafting Effective Prompts: Develop well-structured prompts with detailed instructions, context, and examples to improve the quality and accuracy of AI-generated outputs.
Verifying AI Outputs: Always fact-check and validate AI-generated content using trusted sources or tools to maintain accuracy and credibility, especially for professional or academic work.
Balancing Automation and Refinement: Use AI to automate repetitive tasks while applying human refinement to enhance creativity, emotional connection, and audience relevance in the final output.

1. Choosing the Right AI Tools

Selecting the appropriate AI tools is the foundation of effective AI utilization. Relying on a single tool for all tasks can limit your potential, as different tools are designed for specific purposes. To maximize your capabilities, familiarize yourself with tools across three primary categories:

Large Language Models: Tools like ChatGPT, Claude, and Gemini excel at generating text, answering questions, and summarizing information.
AI Research Tools: Platforms such as Perplexity and Notebook LM are ideal for conducting in-depth research, verifying facts, and analyzing data.
AI Creative Tools: Tools like Midjourney and Ideogram specialize in creating visual content and other creative outputs.

By mastering tools in each category, you can tackle a wide range of tasks with precision. For instance, you might use ChatGPT to draft a proposal, Perplexity to validate your data, and Midjourney to design visuals that enhance your presentation. This strategic approach ensures you’re equipped to handle diverse challenges efficiently.

2. Defining Problems Clearly

A clear understanding of your objectives is crucial when working with AI. Before engaging with any tool, take the time to define your goals, audience, and success criteria. A well-defined problem not only ensures that the AI’s output aligns with your expectations but also minimizes the need for extensive revisions. To clarify your objectives, consider the following questions:

What specific outcome am I aiming to achieve?
Who is the intended audience for this output?
What constraints or requirements must the solution meet?

For example, if you’re using AI to draft a marketing email, specify the tone, target audience, and key message. This level of clarity allows the AI to generate results that are both relevant and actionable, saving you time and effort in the long run.

6 AI Skills 99% of People Don’t Know, But Should

Watch this video on YouTube.

Stay informed about the latest in AI Tools by exploring our other resources and articles.

3. Crafting Effective Prompts

The quality of AI outputs is directly influenced by the prompts you provide. Crafting well-structured prompts that include clear instructions, context, and examples is essential for obtaining high-quality results. Here are some techniques to improve your prompts:

Provide detailed instructions, such as, “Write a 500-word article summarizing recent advancements in AI technology.”
Include examples to guide the AI, like, “Here’s an example of the tone and style I want: [insert example].”
Ask the AI to critique or refine its own outputs to improve quality and relevance.

For more complex tasks, you can even use AI to refine your prompts iteratively. By engaging in a back-and-forth dialogue, you can ensure the system fully understands your requirements, leading to more accurate and tailored results.

4. Verifying AI Outputs

While AI tools are powerful, they are not immune to errors. Verifying the accuracy of AI-generated outputs is a critical step, particularly for professional or academic work where precision is essential. Fact-checking tools like Perplexity or cross-referencing information with multiple sources can help identify inaccuracies. Additionally, you can ask the AI to indicate its confidence level in its responses or explicitly state when it is uncertain.

For instance, if an AI-generated report includes statistical data, verify the numbers using trusted sources. This step ensures that your work maintains credibility and reliability, especially when presenting findings to stakeholders or publishing content.

5. Automating Workflows

AI has the potential to significantly enhance productivity by automating repetitive tasks. By combining multiple tools, a practice known as tool stacking, you can create seamless workflows that save time and effort. For example:

Use ChatGPT to draft content, then integrate it with Midjourney to generate complementary visuals.
Automate routine tasks, such as scheduling emails or managing data entry, using AI-powered scripts or agents.

While automation is highly effective for predictable processes, it’s important to recognize when manual intervention is necessary. Creative or unique tasks often require a human touch to ensure quality and originality. Striking the right balance between automation and manual oversight allows you to achieve both efficiency and excellence.

6. Refining AI Outputs

AI-generated content often serves as a strong foundation, but it typically requires human refinement to make it truly impactful. Refinement involves tailoring the content to better suit your audience and objectives. Key steps include:

Adding your personal voice to the content to make it more relatable.
Simplifying overly technical language or removing unnecessary jargon.
Incorporating emotional elements to create a stronger connection with your audience.

For example, if AI drafts a blog post, you can enhance it by adjusting the tone, adding anecdotes, or tailoring the message to align with your audience’s preferences. This human touch ensures that the final product resonates on a deeper level, combining the efficiency of AI with the authenticity of human creativity.

Mastering AI for the Future

The ability to effectively use AI tools is becoming an indispensable skill in today’s fast-evolving landscape. By focusing on these six essential skills, tool selection, problem definition, prompt engineering, verification, workflow automation, and human refinement, you can unlock the full potential of AI. These skills not only enhance your productivity but also enable you to integrate AI seamlessly into your workflows, making sure clarity, accuracy, and emotional connection in every project. As AI continues to advance, your ability to harness its capabilities will set you apart, making you a valuable asset in any field.

The Fundamentals of AI: A Practical Guide for Work Today

Most teams are experimenting with AI tools, but very few understand the core principles that make these tools useful. Without those fundamentals, outputs feel inconsistent, results vary, and the value never compounds.

This guide highlights the essential skills that apply across every AI platform. Once you understand these basics, you can work effectively with any model, in any environment.

Miro’s new “Fundamentals of AI” course teaches these skills in about 30 minutes. Register for free.

1. Understand What AI Can and Cannot Do

Before putting AI into your workflow, you need a clear sense of its strengths and limitations.

AI is effective at tasks that involve summarizing, transforming, reorganizing, and generating information. It can help you explore options, accelerate early drafts, interpret unstructured inputs, and restructure ideas.

It is less reliable when information is missing, when the task requires precise factual knowledge, or when instructions are unclear.

Treating AI as a capable assistant, rather than an all-knowing system, creates far better outcomes.

The course provides simple mental models for understanding AI behavior.

2. Identify Work You Can Delegate Confidently

Most knowledge work contains steps that follow predictable patterns. These are ideal to hand off.

Examples include:

Creating first drafts of plans or documents
Organizing notes from meetings or research
Turning scattered ideas into clear structure
Producing variations or alternative approaches
Compressing long materials into digestible summaries
Preparing templates for repeatable workflows

A helpful question is:
Which part of this task is structured or repetitive enough that another person could follow it without much context?

That portion is usually a strong candidate for delegation.

The course includes practical examples of how to spot these opportunities.

3. Provide Clear Instructions and Expectations

Strong outputs come from well-defined inputs.

Good instructions explain the context, the goal, the audience, the format you want, and any constraints the result must follow. The more specific you are, the more consistent the output becomes.

Instead of asking for something broad, break the task into the key elements and state the result you expect. Clarity always improves the outcome, regardless of which tool you are using.

Miro’s course introduces simple frameworks you can apply in any workflow.

4. Review and Refine to Improve Quality

Working with AI is an iterative process. The first version is rarely the best version.

Effective users take a moment to check for clarity, quality, accuracy, structure, and alignment with the goal. When something feels off, they ask for adjustments or alternatives.

A small amount of review and refinement leads to significant gains in precision and usefulness.

The course teaches a short feedback loop to help you improve results quickly.

Bringing It All Together

The fundamentals above are now essential skills for modern work. Once you understand them, you can use any AI tool more effectively and integrate it smoothly into your day-to-day tasks.

These principles apply across platforms, and they help you move from experimentation to reliable, repeatable value.

Miro’s “Fundamentals of AI” course covers everything in about 30 minutes. It is free, practical, and designed for anyone who wants to build confidence with AI. Start here.

Prompt: Nano Banana

Restore the image to ultra-high resolution by removing defects, noise, and discoloration. Correct fading, rebalance exposure, and enhance facial details while preserving natural texture. Ensure a photorealistic, archival-quality result.

Analyze and recreate the scene with exact likeness, including subjects, features, clothing, and environment. Generate a 3x3 Cinematic Contact Sheet with 9 different shots from various angles.

Create an 8k, hyper-realistic image of American celebrities from different eras on a rooftop at sunset. Focus on lifelike details, natural interactions, and cinematic lighting with a 35mm lens, set against a city skyline.

"Zoom out to reveal the behind-the-scenes setup, keeping the main subject centered with no changes. Show the photographer, period equipment, and the full environment, creating a documentary-style shot that captures the essence of the iconic photo."