From 50+ requests/month to zero: Using AI dashboards for self-service product management

We talk to hundreds of small companies and lean teams a month about their need to access product usage data. These organizations need to answer all the same questions that larger companies do:

Who's using my product?
How engaged are they?
What features do they need (or aren't finding)?

The problem is that most product managers we talk to don't have the time and/or resources to build the reports needed to pull this data. Traditional BI is either too expensive or not accessible enough to meet their needs.

Traditional BI excels at delivering high-quality outputs. But the process required to get there is so laborious that it requires specialized engineering talent. That ultimately results in an engineering bottleneck that holds up valuable analytics projects.

The good news is, we've also seen a solution that works well for most companies. Self-service data analysis fueled by AI dashboards gives resource-strapped organizations the data they need in a fraction of the time and cost that mid- to large-sized enterprises spend on tailor-made assets.

We've seen our own customers solve some of their nastiest reporting bottlenecks with this approach. Some have eliminated up to 50 data requests a month and slashed data analysis time by a whopping 92%.

How did they do it? In this article, we'll dig into what makes product reporting so hard and how companies have employed AI dashboards to get the product data they need today - not a month from now.

The problems that founders and product managers encounter with product reporting

Good reporting - and the actionable insights it generates - can make the difference between building your business and losing it.

Product reporting is essential for both acquiring and retaining customers. With it, you can:

• Discover where in their customer journey prospects are turning into customers - and what the top conversion triggers are
• Report on what features users are most utilizing, and see how the feature defect rate may be affecting engagement and adoption
• Detect anomalies in behavior - such as an unexpected drop in a customer's usage - that can be used to trigger a retention campaign or alert the customer's account manager

The problem, of course, is creating these reports and dashboards. Traditional BI requires a lot of up-front investment and ongoing maintenance.

This isn't due to any failure of imagination on the part of data engineers. Rather, they've been forced to develop a complex process to address several real-world obstacles.

Scattered data across multiple systems

The data needed to answer complex product questions has never been in one place. With the advent of the cloud and Software as a Service (SaaS), it's become even more scattered.

Data must be collected from HubSpot, Google Analytics, Salesforce and other CRM platforms, and your own data warehouses hosted in systems such as Snowflake, Redshift, and BigQuery. Then, it has to be coalesced, rationalized, and joined to create unique records that tie customer data together across systems.

Manually pulling data without automation

Many teams lack the engineering support needed to automatically pull and clean data. So they yank it out manually every time they need a new report, crunching CSV data in Excel spreadsheets to get it into the shape they need.

This manual approach can result in errors and inconsistencies that negatively impact decision-making. Even when this is done well, however, it takes time. This can take 1-2 hours per request. If product teams create 50 reports a month, that's a total of 10 or more workdays per month spent just on reporting.

Inconsistent metrics across teams

With data from different systems, it can be hard to deduce which key figures and metrics are the "real" numbers. This is because different teams may have different approaches to calculating basic metrics such as "revenue" or key performance indicators (KPIs). Without a single source of truth, teams can't synchronize their decision-making and settle on a common strategy.

Slow time to insights creates backlogs

Data volumes are exploding. AI workloads demand even more high-quality data. The increase in work means there's no such thing as a data team with free time on its hands.

Even if you have data engineers and BI engineers on staff, they're likely already bombarded by data requests. That means the average time to service new requests may stretch out into days, weeks, or even months. By the time the team gets to a custom request for data, it might no longer be relevant. This backlog creates bottlenecks that slow down product teams when they need insights most.

Broken dashboards and maintenance overhead

If you build it, they will come. But if you leave it alone, it'll fall apart.

That rule applies equally to data dashboards as it does to baseball fields. As the underlying data changes, the code that drives BI dashboards can break. That can leave your business high and dry, unable to make critical decisions.

Demand for executive-ready reporting and summaries

The C-suite needs its own data that gives it a useful, mile-high view of the business. These reports and executive summaries differ from the daily and weekly reports that matter to line managers. Like all reports, they take time to craft, test, and maintain - time that takes away from other immediate data needs.

Accessibility for non-technical users and stakeholders

Dashboards are static assets. They only answer the questions that their makers saw fit to answer.

Most business users and stakeholders want - indeed, need - to go beyond these static views. However, that requires advanced knowledge of SQL that most business users don't possess. This means these users become even more dependent on your data team, creating a vicious cycle of backlogs and delays.

How AI dashboards eliminate data bottlenecks and enable automated workflows

All of these issues - scattered data, manual data pulls, broken dashboards - are symptoms of a larger problem: the limits of traditional BI.

Traditional BI dashboards require manual creation and maintenance of complex data transformation pipelines and dashboards. These assets are brittle and subject to breaking at a moment's notice. They require moderate to considerable engineering effort to update and edit to keep pace with the business.

Modern product managers and business users need three things from their data:

• A cohesive, singular view of the metrics they most care about
• The flexibility to change standardized metrics and dashboards as the business evolves
• The ability to ask unique new questions of data and rapidly iterate on the results, resulting in novel new insights into the business

AI dashboards meet all three of these needs. Leveraging generative AI (GenAI) and, specifically, Large Language Models (LLMs), users can utilize natural language queries to create new reports, dashboards, and data visualizations from your existing datasets automatically, without manually building data pipelines.

Traditional BI dashboards excel at standardization, governance, and complex data modeling, driven by highly technical data engineers. By contrast, AI dashboards excel at data exploration, self-service, and rapid iteration, and can be driven by data engineers, business stakeholders, or both working closely in cooperation.

AI-powered dashboards bring a host of benefits compared to traditional BI, including:

Provide self-service data access for product teams

With AI dashboards, business users don't have to log requests with the data team every time they need a new report. Instead, they can use natural language prompts to the AI to generate the dashboards they need within minutes. This functionality is especially powerful for startups and busy founders who have to move fast and don't have time to wait on data. Self-service analytics eliminates handoffs between teams and accelerates decision-making.

Pull fresh data automatically with real-time integration

AI-driven dashboards make it easy to connect to your data sources, pull in external data, and clean it for use in reporting immediately. This eliminates the days of effort involved in pulling data manually, without the time and expense involved in hand-crafting tailored data pipelines. Real-time data connections ensure product managers always have access to current information.

Detect new patterns through AI agents and advanced analysis

SQL (or some variant thereof) is the default language used to query data in today's modern enterprises. That means that your ability to derive new insights is constrained by your ability to express them in SQL.

By contrast, both data engineers and business users can leverage natural language queries powered by AI agents to perform exploratory data analysis (EDA) and ad hoc analysis on data. Users can leverage AI tools to ask LLMs open-ended questions, leading to the discovery of data patterns that might otherwise be hard to detect or express directly in SQL, such as a new type of financial fraud against credit card purchases, or a statistical variance in a website's performance that indicates a latent system issue.

Regenerate dashboards quickly when the data changes

The underlying data for a dashboard may come from multiple data sources owned by other teams. If one team makes a change - changing the formatting of a string variable, removes a column - it can break traditional data pipelines.

Detecting and repairing this can take hours or even days. With AI-generated dashboards, however, it's easy to re-run the original natural language query against the revised source tables and regenerate the merged tables and reports in a matter of minutes.

Support flexible workflows and templates for common use cases

AI dashboards enable product teams to create standardized workflows and reusable templates for common reporting use cases. Whether you're tracking user onboarding metrics, monitoring feature adoption, or analyzing churn patterns, you can build automated workflows that product managers can customize to their specific needs. These templates can integrate with collaboration tools like Slack and email to share summaries and insights with stakeholders in real time.

Getting AI dashboards working for non-technical teams and end users

It's easy to get started with AI dashboards and data visualizations. We've written a guide on how to select the best tooling and introduce it gradually to your teams.

Rolling out this functionality for non-technical users requires a little thought and planning. Done successfully, however, it can have a palpable impact on your business and user experience.

For example, Aisle, a marketing omnichannel software vendor, was spending over two hours per request on 50+ data requests per month. By introducing an AI dashboard solution, they eliminated those requests by giving business users the power to generate them directly. That resulted in a 92% faster analysis time and reduced the pilot period for new data-driven initiatives from weeks to a few hours.

Here are some of the best practices and pain points we've seen Aisle and other successful customers address when rolling out AI dashboards to non-technical personnel.

Make sure the underlying data is AI-ready with proper validation. Thankfully, it doesn't have to be perfect. AI can help smooth over anomalies and cleanse data so it's easier to process. However, the core data needs clear metadata (good field names) and to be well-documented for LLMs to handle it accurately. Data quality matters for generating reliable outputs.

Focus on key workflows and high-value use cases. Focusing on high-value workflows can cover 80% or more of your use cases. Rather than trying to replace every dashboard at once, identify the most common product management requests and build AI-powered solutions for those first. This approach reduces change management complexity and demonstrates value quickly.

Emphasize the time saved and optimization benefits. No one cares that the new tech you introduced is "cool." What they care about is whether it saves them time and makes them better at their jobs. Communicate the concrete benefits in terms of hours saved, faster iteration cycles, and reduced dependencies on technical teams.

Create a rollout playbook for onboarding. Document your implementation process, common workflows, and best practices in a playbook that new users can reference. Include examples of effective prompts, tips for working with AI-generated dashboards, and guidance on when to validate outputs with more technical team members.

Common questions and issues around AI dashboards

There's a lot of doubt and fear around AI in the workplace, from concerns about tools like ChatGPT to questions about OpenAI's capabilities. Naturally, this makes many people hesitant.

Here are some common questions and doubts you and your colleagues might have, along with some (reassuring, we hope) responses:

Q: Will the AI hallucinate and give me wrong answers?

A: Poor AI tools may deliver poor results. A good AI data visualization tool with proper validation, however, consistently returns accurate results if the underlying data is sound and clearly described.

Good AI dashboard tools won't just output the result of their work - they'll also show you the SQL and Python code they generated to create it. When in doubt, or when developing a mission-critical dashboard that requires greater scrutiny, ask a data engineer to review the LLM's output and make any recommended tweaks to increase accuracy. Think of AI as a copilot that accelerates your work, not a replacement for human validation.

Q: When should I use traditional BI vs. AI dashboards?

A: AI tools are gradually changing how we approach analytics and decision-making. Over time, BI will continue to do what it does best - provide authoritative keystone reports with good data governance - while incorporating more of the dynamic features of AI dashboards, such as support for natural language queries. For Microsoft Power BI users or those working with established BI platforms, AI dashboards complement rather than replace existing investments.

Q: Do AI dashboards replace my data engineering team?

A: Absolutely not. The goal of AI dashboards is to reduce the amount of repetitive, automatable work shoved onto your data engineers’ plates. That way, they can spend more time focusing on higher-value work, such as critical data infrastructure improvements, that demand their unique skill set.

Q: How do I keep my data secure and promote good data governance?

A: Make sure all users have adequate training in your company's data governance procedures and how they should handle data belonging to the company's different data classifications. For your company's most sensitive datasets, use connected data sources and implement role-based access controls and proper API security. Use a single, standardized AI dashboard and enable audit logging to track who's accessing which data from where. Treat AI dashboards with the same security rigor you'd apply to any business intelligence tool.

Q: How do AI dashboards handle forecasting and predictive analytics?

A: AI-powered platforms excel at identifying trends and patterns in historical data, which can inform forecasting models. Many AI dashboard tools support advanced analytics including time series analysis, trend forecasting, and predictive modeling through natural language commands. Product managers can ask questions such as "forecast next quarter's user growth based on current trends" and receive data-driven predictions with visualizations.

Getting started with AI dashboards with Fabi.ai

The challenges of product reporting don't have to slow you down. AI dashboards offer a fundamentally different way to work with your product data, making scattered data, broken dashboards, and week-long waits for insights a problem of the past.

Fabi.ai is explicitly built for this new paradigm of automated, self-service analytics. Unlike traditional BI tools retrofitted with AI features, Fabi was designed from the ground up to put AI agents at the center of how you analyze and report on data.

Using Fabi.ai, product teams can:

• Connect your data in minutes, bringing in data from your data warehouses - Snowflake, BigQuery, Databricks, or others - or pull data from SaaS tools like HubSpot, Google Analytics, Salesforce, and Airtable through straightforward API connections. Don't have everything connected yet? Upload a CSV or Excel file to start exploring immediately.
• Ask questions immediately in natural language to explore your data, ask piercing questions, and discover new patterns. Our AI-powered platform understands context and generates accurate SQL queries automatically.
• Collaborate with other team members regardless of their technical savvy via shared notebooks that support versioning, real-time collaboration, and cross-platform sharing. Create workflows that work for your entire product team, from technical stakeholders to business users.
• Automate reporting workflows that run on schedule or trigger based on specific events, delivering summaries and insights where your team needs them most - whether that's Slack, LinkedIn, email, or support tickets.

Create a free account at app.fabi.ai and build your first dashboard in minutes. Connect your data sources, ask your first question, and see how AI-powered analytics changes what's possible for your product team.