How AI Creates Presentations

Now you understand what AI presentations can do — and where they struggle. But how does it actually work? What happens in the computer when you send a presentation prompt?

The Three Steps from Prompt to Slide

When you send a prompt to a presentation generator, the AI goes through three main steps:

Step 1: Text Understanding and Content Extraction

The AI "reads" your prompt and tries to understand what you need. This isn't trivial. The prompt "Business presentation about a new software product for startups. Audience: investors. Tone: confidence and innovation" is immediately clear to humans.

The AI must break down this text into components:

• Topic: "Software product"
• Audience: "Investors" → means: risk awareness, ROI focus
• Tone: "Confidence, innovation" → means: colors like blue/green (trust), layouts with modern shapes

The AI has learned to connect certain keywords with visual patterns. "Investor pitch" activates a different template than "training material." This is language pattern recognition, not true understanding.

Step 2: Layout and Structure Generation

After the AI understands what you need, it must decide: How many slides? Which layout for which slide?

There are various strategies:

• Type classification: "This is a title slide, a problem slide, a solution slide, a CTA slide"
• Template assignment: Each type gets a predefined layout
• Sequencing: The AI orders content in logical order

This isn't design yet. This is structure. The AI says: "Slide 1: Text in center, large font." "Slide 2: Three boxes side by side." "Slide 3: Image left, text right."

All of this is based on millions of presentation examples the AI "saw" during training. It has learned: For investor pitches, this sequence works well. For training, this one. For creative pitches, that one.

Step 3: Visual Design and Asset Selection

Now comes the "magical" part. The AI must decide visually:

• Which colors? (Color palette generator)
• Which fonts? (Typography rules)
• Which icons or images? (Asset database or generation)
• Which effects? (Animations, transitions)

For colors, the AI uses color theory principles it learned:

• "Investor pitch" → trustworthy → blue, green, white
• "Creative pitch" → energetic → orange, pink, contrasts
• "Training" → friendly-clear → light blue, gray, lots of whitespace

For images, the AI draws on its training data or generates images (if it has a generative image model). It selects images that fit the tone — "investor pitch" = professional business photos, "creative pitch" = experimental, colorful images.

Why Structure Is Easy, Storytelling Is Hard

Here's the core problem: AI can do structure very well. It can say: "Point 1 gets a slide, point 2 does too, point 3 does too." That's mechanical.

But AI can't decide: "Point 1 is really the tension, point 2 is the resolution, point 3 is the lesson." That requires emotional understanding.

Example: You say, "Presentation about our startup's problem and our solution." The AI makes:

• Slide 1: Title
• Slide 2: The problem (with red images, warning symbols)
• Slide 3: The solution (with green images, victory symbols)

That's symmetrical, it's structured. But it's not compelling. A great pitch designer would say:

• Slides 1-3: Depict problem very dramatically (linger with the pain)
• Slide 4: First hint of solution ("There is a way")
• Slides 5-6: Build solution slowly (the audience "releases" it)

That's narrative. That's storytelling. The AI can't automatically generate this.

The Three Task Types in Presentations

Now we connect this to your experiences from K01-K06. There are three task types that AI can handle in presentations — and three weaknesses:

Task Type 1: Structuring

AI is a master at structuring content. You give chaotic text: "Startup makes software, helps companies save, is fast, is secure, costs X." The AI would structure it like:

• Slide 1: What is it? (Title)
• Slide 2: What's the problem? (the pain point)
• Slide 3: How does it help? (Savings, speed, security)
• Slide 4: Price
• Slide 5: Call to Action

That's good. That's helpful. The AI orders the chaos.

Task Type 2: Visualization

AI can translate visually. You say: "We save companies 40% costs in 3 months." The AI generates a slide with:

• Large number "40%"
• Bar chart shows cost comparison
• Clock shows "3 months"

This isn't creativity, but it's helpful. The AI takes abstract numbers and makes them visual.

Task Type 3: Cohesion

AI can maintain cohesion across many slides. It ensures all 20 slides fit together — same color palette, same icon styles, same typography. That's valuable. Humans often forget this.

Weakness 1: Understanding Hierarchy

AI doesn't know what's really important. You have 10 points. The AI will try to present them equally. A good designer would say: "Points 1, 2, 3 are central. Points 4-10 are support. These must have visually different weight."

The AI can't decide this alone. It needs explicit direction.

Weakness 2: Emotional-Visual Alignment

The AI can read a tone like "confidence," but it can't feel emotional depth. A real investor pitch designer would know: "After the problem slide, the audience needs hope. So now I show the solution — but not immediately. Let them breathe. Then — boom — the solution."

The AI doesn't do this. It structures, but it doesn't orchestrate.

Weakness 3: Cultural Contextualization

The AI knows "standard patterns," but it doesn't know your specific audience's culture. If you're presenting to a German bank, you need gravitas and precision. If you're pitching to a California tech startup, you need energy and irreverence.

The AI will do something neutral — it works for both, but it shines for neither.

The Difference Between "Works" and "Lands"

An important distinction:

• A presentation that "works" conveys information. The viewer understands your point.
• A presentation that "lands" conveys information + emotion + persuasion. The viewer feels something.

AI can "work." AI can "land" only if you tell it very explicitly how.

Example: You want your investor pitch slide 5 to say to the audience: "Here's the moment everything becomes possible." The AI can't generate this from nothing.

But if you say: "Slide 5: Solution. Tone: A moment of hope. Visually: Large image, light breaking through." — then the AI will make something close to that.

Cross-Links: Comparison with K01-K06

Remember the theory lessons from the other clusters:

• K01 (Text Theory): Text is linear. AI understands sequence easily. But emotion in text is hard for AI.
• K02 (Music Theory): Music has time dimension. AI generates temporal sequence, but emotional-psychological tension arcs are hard.
• K03 (Image Theory): Images are static. AI can do visual design, but not sequential narratives.
• K04 (Video Theory): Video is time + space. AI can interpolate, but narrative arcs are hard.
• K05 (Audio Theory): Audio is tone + time. AI can structure, but "voice" is hard.
• K06 (Code Theory): Code is logic. AI understands structures, but creative solutions are hard.

K07 (Presentations): Presentations are structure + visual + narrative. AI is good at structure and visual. Narrative is hard.

The pattern: The more emotional dimension a medium has, the harder it gets for AI.

A Thought to Take Away

When you understand how AI generates presentations — that it's structure + template assignment + visual rules — you also understand how to use the AI optimally.

You give the AI structure: "Title, problem, solution, proof, CTA." You give it visual direction: "Trust, not creativity." You give it tone: "Energetic, not formal."

And then the AI adds what it does well: consistency, rapid iteration, visual cohesion.

That's optimal collaboration: You think, AI executes.