From Confusion to Clarity: How AI Helped Us Build Data Strategy Roadmaps That Actually Get Used

Jack
Jul 28
5 min read

We didn't set out to build another AI marketing tool. We set out to solve a painful, persistent problem for growing businesses: how to make better decisions with their data, without drowning in it.

Most business owners and operators don’t struggle because they lack dashboards or reports. They struggle because they lack a strategy for using their data. And that gap exists regardless of company size, maturity, or tech stack.

So we asked ourselves: What if we could generate a personalized data strategy roadmap for any business... in minutes?

That’s where AI came in - not as the star of the show, but as the engine that helps us scale strategy.

TLDR;

We built an AI app that helps small businesses build a strategy for their data, so they can actually be data-driven, the easiest way possible, using low cost / no code tools to help them build the dashboard of their dreams.

The Real Problem: Data Without Direction (no Data Strategy)

My firm works with companies ranging from $1M to $100M+ in revenue. And almost all of them, regardless of industry, come to us with the same pain point:

“We have a bunch of tools. We have data. But we don’t feel like we’re actually making data-driven decisions.”

As dashboard experts, we see this all the time - the vision is there, but the path to actually use all that data is not simple.

They’ve got HubSpot, QuickBooks, Meta Ads, Shopify, even Looker Studio or Power BI. But what they don’t have is a clear, structured plan for how those tools connect to business goals—and how that should inform decisions.

It’s not a tech issue. It’s a strategy gap.

Most teams don’t need more software. They need clarity.

We realized the first version of that clarity didn’t have to be handcrafted for every single business. Parts of it could be automated. That’s when the idea of an AI-powered, tiered data strategy system took shape.

What We Built: Snapshot → Audit → Roadmap

Rather than create a catch-all tool, we built a structured system that mirrors the kind of work we’d do in a traditional consulting engagement—just faster and at different levels of depth.

Snapshot

An AI-powered starting point:

Recommended tools based on their tech stack
A 1-page dashboard layout mockup
A quick-win summary & best practices to help them take action

Delivered instantly. No calls. No obligations.

Get your Free Data Strategy Snapshot Here

Audit

AI-assisted, expert-refined:

2-page dashboard mockup
Field-level recommendations
ETL flow suggestions
BI setup tips

A blueprint that helps DIYers and delegators take the right next step.

Full Roadmap

A fully customized engagement using AI to speed up production, but relying on humans with expertise to tailor it:

Manual review of data sources and structures
Custom dashboard logic
Branded multi-slide mockups
Loom walkthroughs to explain every decision

It’s everything a company needs to go from raw data to real insights, and it’s built on top of the AI groundwork laid in the earlier tiers.

How We Used AI (And What We Didn't Let It Do)

We didn’t use AI to replace the thinking. We used it to speed up the translation layer between business goals and dashboard structure.

We applied AI in four specific areas:

1. Translating business goals into technical structure: AI helps us convert goals like “improve LTV” or “track rep performance” into structured plans:

What fields need to be extracted
What KPIs to calculate
Where those metrics should show up in a dashboard

This saves hours in discovery calls and requirement gathering.

2. Matching the right tools to the tech stack: We trained the system to identify which ETL tools (Coupler, Zapier, Dataddo, etc.) work best with specific data source combinations. AI takes care of the matching and logic mapping, so we don’t have to reinvent the wheel each time.

3. Generating real visual mockups: We had AI create real dashboard mockups - not just placeholder boxes. That means clients see actual KPI scorecards, bar charts, funnel visuals, and conditionally formatted tables, even with dummy data. It moves the conversation from abstract to tangible.

4. Formatting and delivery: AI helps us generate brand-aligned, well-structured summary documents that look and read like Pineapple Consulting Firm built them -because we did. The AI just gave us a running start.

But just as important is what we didn’t use AI for.

We didn’t let it:

Create dashboards in isolation from strategy
Make architectural or security decisions
Replace human judgment for high-leverage clients

We believe AI should scale process, not replace expertise.

Beyond Marketing: Why This Isn’t Just a Lead Magnet

AI is flooding the marketing world. You can spin up landing pages, write copy, generate visuals, and automate email flows with a few prompts. But much of it feels hollow - like speed for speed’s sake.

We didn’t want to build another AI-powered “thing” that looks impressive but doesn’t deliver real clarity.

We built this because strategy doesn’t scale easily. And yet that’s what most growing businesses need: not another platform, but a plan.

The reason our system works is because we focused AI where it’s strong:

Recognizing patterns
Structuring logic
Accelerating visual output

And we left the parts that require expertise - judging data quality, tailoring KPIs, aligning visual structure to stakeholder needs - to people who know how to think strategically (aka us at Pineapple).

We didn’t remove the human element. We compressed the time it takes to reach the human element.

That’s what AI should do in service of strategy - not replace thinking, but give it leverage.

Lessons From Building an AI-Powered Strategy System

1. Use AI to create structure, not just content: The value isn’t in writing faster. It’s in converting messy inputs into usable plans. Just like data that takes the form of a dashboard.

2. Don’t fake visuals - deliver clarity: Dashboards with "lorem ipsum" or chart placeholders don’t help anyone. When users see actual layouts, even with sample data, they engage. They understand.

3. Anchor your tiers in outcomes, not access: We didn’t call our tiers “Basic,” “Pro,” or “Premium.” We called them Snapshot, Audit, and Roadmap. They describe the value, not the price. That language builds trust.

4. Scale the middle, not the extremes: Most businesses are stuck between “random spreadsheets” and “$20K BI builds.” We used AI to serve that middle - the teams who know what they want but don’t know how to structure it.

Final Thoughts

AI gave us the ability to do what we were already doing - only faster, cheaper, and with more consistency.

It didn’t replace our consulting process. It made it more accessible.

Now we can help more businesses get out of spreadsheet chaos and into data clarity -without relying on custom scoping, long discovery phases, or expensive workshops.

The future of analytics isn’t just about tools. It’s about making strategy scalable - and AI, when used well, is how we get there.