How To Build AI To Generate Actionable Insights

When we talk to companies that want to implement AI they struggle mostly with

What to implement?

Most companies still struggle with AI implementation — not because they don’t want to use AI, but because they don’t know what exactly to implement or how to do it.

Implement This

AI is one of the best at generating actionable insights that users can trust and act on.

Most companies stop at the data level, metrics, dashboards and useless reports.

Nobody wants this, not your customers, internal team members.

Everyone wants data to be used to be able to act on OR make a decision.

This is one of the lowest-hanging fruits right now that you can implement in your company, your product, so you achieve better decisions, lower churn, and of course, higher revenue.

For example:

SaaS SEO company -> Customers are desiring recommendations, next steps of experts to improve their ranking not just data on what happened?

Internal Sales teams -> Don't want just what happened, what performance John had, but the ability to suggest improvement in their sales process, drop off in conversion rates, and more.

This is one core AI feature you can implement now to generate insights from your data and achieve higher user retention and insights others don't have.

Step 1: Start with the End User

Before you write a single line of code or train a model, talk to the people who’ll use your AI. The end user.

Ask:

• Who is this for? (Sales manager, marketer, customer success?)

• What decisions are they struggling with?

• What’s their end goal? (Close more deals? Rank higher in search? Reduce churn?)

• What actions do you want them to take based on the AI’s output?

💡 Example:

If you're SaaS SEO platform building an AI SEO assistant, your user might be a B2B SaaS marketing manager. Their objective? Improve organic traffic and fix underperforming content. Your AI should recommend SEO actions — not charts or raw keyword data

Step 2: Define Your AI Persona

Here’s what most companies skip: they build AI with no defined personality, boundaries, or purpose.

AI, you are building should model of "a person", their way of thinking, and actions at the end of the day.

So that's why it's called an AI persona.

You need to create an AI persona — a clear definition of how your AI thinks, acts, and helps. Think of it like designing a virtual teammate.

Ask:

• Is your AI a coach, analyst, assistant, or operator? What is his persona?

• Main objective to achieve?

• What tone does it use?

• What knowledge base does it draw from?

• What decisions is allowed to make (and not made)?

• What fixed (universal) and dynamic (user-specific) context does it need?

• Memory types? Long, Short?

• Capabilities, Tools & Functions? Web search? PDF download?

💡 Example:

“You are an expert SEO Marketer Assistant trained in the latest SEO best practices. You help SaaS marketers diagnose SEO gaps and give 3 specific recommendations per issue. You reason with available data, act with precision, and never guess blindly.”

Step 3: Data Preparation

This is where most AI dreams crash.

Why? The data is a mess — scattered across tools, unstructured, outdated, or not unified.

🔧 What to do:

• Set up a data warehouse (Snowflake, BigQuery, Redshift)

• Implement ELT pipelines (using Fivetran, dbt, or Airflow)

• Structure all relevant data into usable models (semantic layer)

• For unstructured data (emails, call transcripts), build an RAG pipeline with a vector DB (like Weaviate, Pinecone, or Chroma)

💡 Example:

For an SEO AI, data might include:

• Ahrefs or SEMrush keyword and backlink data

• Google Analytics traffic trends

• CMS metadata (titles, H1s, etc.)

Step 4: Build the Insight Layer (Not Just the Model)

Once your data is structured, you need an insight layer — the layer that analyzes and pre-processes data before the AI responds.

This is not you sending semantic data, directly to the prompt. You can but not advised for the performance and accurcy.

This is where the AI gets “the facts” it needs to reason over.

Think:

• Detecting a 30% drop in page views

• Flagging a keyword that fell out of top 10 rankings

• Highlighting pages with low CTR and high impressions

This layer helps the AI know what matters before generating recommendations.

Example of output from insights layer that detects pages with high traffic drops and poor CTR:

Step 5: Send It to AI – Generate Actionable Insights

Now comes the fun part: pass the insights to your custom AI, "trained" with the right prompts, persona, and domain knowledge.

Whenever i say trained, doesn't mean fine-tuned model. Not necessery in this case.

Your AI should return:

• Clear recommendations

• Prioritized actions

• Just enough context to make the decision feel confident

• Apply markdowns and formatting.

💡 Example Output:

The page “/email-marketing-guide” dropped 30% in traffic and now ranks #12. 🔧 Fix #1: Rewrite the meta description to improve CTR 🔧 Fix #2: Add internal links from high-authority posts 🔧 Fix #3: Refresh content with 2024-relevant stats

That’s actionable insight, not just data.

Step 6: Close the Loop – Capture Feedback

This is what separates gimmicks from long-term value.

You need a feedback mechanism that tells you:

• Was the AI output helpful?

• Did the user take action?

• What needs improvement?

🔧 How to do it:

• Add thumbs up/down on each recommendation

• Ask “Was this useful?” or “Was this actionable?”

• Track what users click, ignore, or ask about

• Use that data to improve prompts, logic, or models

Want a free AI Worksheet Template that includes all of the above? DM “AI Worksheet” and I’ll send it your way.

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