AI can create an analysis report quickly, but making one that's actually useful for presenting usually takes some extra steps.

From trying out various AI analysis tools, the gap between an "acceptable" report and one that's genuinely helpful mostly depends on how you guide the AI, not just what the AI can do.

The Context Problem: Why Generic Prompts Get Generic Results

At first, I would give simple prompts like: "Analyze this data and create a presentation."

I usually got basic charts and surface-level insights—nothing really tailored or insightful.

It turns out that giving the AI clear context and goals is key to getting better, more useful results.

Side-by-side comparison showing how context changes AI output quality

What "Good Context" Actually Means

Here's the difference in practice:

Bad prompt:

Good prompt:

See the difference? The second prompt gives the AI:

• Your goal (explaining Q3 decline)

• Your audience (executives with specific concerns)

• Decision context (Q4 strategy adjustments)

• Key stakeholders (CFO vs CMO perspectives)

With better context, the analysis becomes more focused and actionable.

The Context Checklist

Before starting any analysis, I now answer these questions:

Purpose:

• What decision is this analysis supporting?

• What question am I really trying to answer?

• What would make this analysis actionable vs. merely interesting?

Audience:

• Who's receiving this? (Technical team? Executives? Clients?)

• What's their background knowledge?

• What do they care about most?

• What's their tolerance for technical detail?

Constraints:

• How much time will they have to review this?

• What format works best for them? (Slides? Report? Dashboard?)

• Are there specific metrics or frameworks they expect to see?

Domain Knowledge:

• What industry context matters?

• Are there seasonal patterns or business cycles to consider?

• What terminology is standard in this space?

Getting this right upfront saves a lot of back-and-forth later.

Also, with help from tools, you can also set some of the common context in Workspace for all analysis under it. Saves lots of typing and can be shared/reused by collegues.

Various Setting of Context across all anlysis in this workspace

The Wording Issue

Here's something I noticed: AI-generated text often sounds a bit... corporate.

The Pattern I Keep Seeing

AI tends to use phrases like:

• "Leveraging insights to optimize performance"

• "Key findings reveal actionable opportunities"

• "Data-driven recommendations for strategic initiatives"

Not wrong, just bland.

How to Guide the Tone

I now include wording guidance in my prompts:

For Executive Presentations:

For Technical Teams:

For Client Deliverables:

A Simple Title Technique

Here's one specific thing that helps: show the AI examples of slide titles you like.

Standard AI titles:

• "Sales Analysis Results"

• "Regional Performance Overview"

• "Key Findings and Recommendations"

More useful titles:

• "Northeast Region Outperformed by 23%—Here's Why"

• "Three Product Categories Drive 80% of Growth"

• "Q4 Strategy: Double Down on Digital Channels"

When you include examples in your prompt, the AI follows that pattern.

The Visual Details Problem: Where You Need to Step In

Let me be direct: AI-generated presentations are great, but they need a little more work for actual presentations.

What AI Handles Well

AI is good at:

• Choosing appropriate chart types (bar vs. line vs. scatter)

• Calculating and displaying data correctly

• Creating comprehensive coverage (all the important metrics)

• Maintaining consistency (similar formatting across charts)

• Generating Insights

Where Manual Editing Helps

AI might have problem with:

• Company Brand that is special to your company or any other touch you'd like to be special in this case

• Text placement sometimes there might be discrepencies at begining or end of a sentence

• Layout and spacing that guides the eye

• Visual emphasis on the most important points

Sample Layout problem of AI generated presentation

My Editing Process

Here's what I actually do:

Step 1: Generate the full analysis Let the AI create everything first.

Step 2: Review in presentation mode Look at it as if you're presenting. What needs work?

Step 3: Focus your effort Don't edit everything yet. Ask agent to modify in general for big edits such as:

1. missing content

2. overall wording styles

3. adding/removing pages/charts

4. clarity of chart/speaker notes (labels, what to say, key point for each visual)

My typical editing process: AI generates, then AI tweaks

Step 4: Download PPT and make the important edits

For the rest of tweakings, it's better be done in Powerpoint since it's quite hard to describe in so much detail for AI, rather use point and click for more efficiency.

• Remove unnecessary elements: Delete extra gridlines, redundant labels, or any clutter that doesn't add value.

• Tweak layout for clarity: Adjust spacing, alignment, and position of text or charts so everything is easy to follow.

• Add emphasis: Use color or bold for the most important number.

• Test readability: Stand back from your screen. Can you read it?

• Add or edit speaker notes: Before finalizing your report, review the AI-generated speaker notes for each slide.

Focus on:

• Explaining the "why" behind the data, not just the "what"

• Clarifying trends or surprising results

• Anticipating questions or confusion and jotting those answers in the notes

Modify AI generated speaker notes for your own

Step 5: Leave supporting slides alone Detailed backup slides are fine as-is. Focus on what you'll actually present.

Speaker Notes: The Feature I Wish I'd Used Earlier

I ignored speaker notes for a while. Then I realized I was just reading slides — not great for presentations.

Why They're Useful

Good speaker notes have:

• Context that shouldn't be on the slide

• Transition language between topics

• Anticipated questions and how to respond

• Supporting details you might need

AI-generated notes give you a starting point. You don't memorize them — you use them to prepare.

How I Use Them

Don't read them word-for-word. Instead:

1. Review before presenting to understand the flow

2. Note the transitions between slides

3. Extract the "why" behind each point

4. Prepare for questions the notes anticipate

Example of useful AI notes:

[Slide: Q3 Revenue Decline Analysis]

Speaker Notes:
Revenue dropped 15% in Q3. But let me explain what's actually happening.

Three main drivers: First, we're comparing against last year's Q3, which 
had an unusually large bulk order. Without that outlier, we're up 3%.

Second, there's a seasonal pattern--Q3 always dips before Q4 picks up.

Third, new customer acquisition is up 12%. We're building pipeline for 
next quarter.

[Anticipated question: "Should we be worried?"]
[Response: Show the historical Q3 pattern slide]

These notes give you structure without being a script.

What I've Learned: AI + Human Works Better

After trying different approaches, here's what works:

What AI Handles

• Rapid data processing (minutes instead of hours)

• Comprehensive coverage (won't miss important segments)

• Consistent methodology (same approach throughout)

• First-draft generation (getting from zero to 80%)

What Humans Add

• Strategic context (what questions matter)

• Audience understanding (how people will react)

• Visual refinement (making it presentable)

• Narrative flow (ensuring it makes sense)

• Emphasis (highlighting what's important)

How the work splits between AI and human in practice

A Practical Workflow

Here's how I approach it now:

Phase 1: Setup (1 minute)

• Define analysis goals

• Provide context and constraints

• Specify audience and format

• Set tone expectations

Phase 2: AI Generation (20-30 minutes)

• Process and analyze data

• Generate visualizations

• Create presentation structure

• Write speaker notes

Phase 3: Human Refinement (30-45 minutes)

• Review narrative flow

• Edit key visuals

• Adjust wording

• Customize speaker notes

• Test presentation

Total: About 60 minutes work that would take a day before.

Bottom Line

Good AI reports need both: good prompting upfront and thoughtful editing after.

AI handles the heavy lifting—data processing, comprehensive analysis, initial structure. Humans add judgment—context, audience adaptation, visual polish, narrative flow.

Neither alone produces the best result. Together, they work well.

What's worked for you when using AI for analysis? Any other tips I should add? Let me know.