AI Analytics Agents: Turn Cost-Cutting into Revenue Growth

For any company that sells data or manages it on behalf of customers (including SaaS platforms), the path to top-line growth isn't just selling more data — it's embedding AI agents that sell answers, suggest next steps and take actions.

I’ve led AI-driven product lines at Salesforce and UiPath, served as a founding member of Numbers Station (now part of Alation), and spoken with hundreds of executives. Across these conversations, one theme is constant: revenue and growth before efficiency.

Why “efficiency AI” isn’t enough

Boards no longer ask if you have an AI program; they ask, “what does this do for top-line growth?” and “how could AI expand revenues?”. Most AI initiatives still chase cost savings —automating processes, reducing errors, and freeing up resources— important work, but capped in potential. Indeed, Alation’s suite of AI agents automates many of the tedious tasks of data management, from data quality rule generation to data documentation. Such agents free up the time of data teams for more impactful initiatives. The question then becomes: how can we expand revenues and fuel growth? 

The next wave of software is AI agents embedded inside your product that customers pay for.

From tool to teammate: How AI agents act as “digital interns”

For a data or SaaS provider, the most effective way to move up the value chain is to embed agents that act as on-demand "digital interns" for your users. These AI-driven researchers don't just optimize a process; they create new, billable value by delivering insights directly within your customer's workflow.

But a good intern doesn't act without permission. Their work follows a clear path to earning trust, which we call the Autonomy Slider:

• Assist: First, the intern does the research and shows its work with full transparency.

• Suggest: Next, it uses that research to propose a specific, data-backed action plan.

• Act: Finally, once it has proven its reliability, it executes the approved plan.

This Assist → Suggest → Act model is the key to building agents that are both trustworthy and monetizable.

Here’s how this "digital intern" model applies to different users:

This model unlocks premium pricing—often 20-50% above standard subscription rates—because it moves your product from a passive tool to an active participant in your customer's success. Meanwhile, customers benefit from dramatically faster decision-making, reduced manual research time, and access to insights that would typically require dedicated analysts. The result is a win-win: higher revenue per customer and increased customer value realization.

Map these interns to your application users and their core workflows to unlock higher ACV and stickier customer relationships.

Three reasons the C-suite should care about analytics agents

Here's why embedded analytics agents represent a fundamental shift in how enterprise software creates value:

Reason #1: Immediate new SKU

Salesforce’s Einstein Analytics added roughly $250 M ARR in 2019 by charging $75–$500 per user/month. Salesforce Data Cloud + Agent Force is now a $1B business with 120% YoY growth (albeit with a rather complicated pricing model). This demonstrates an organization's ability to charge higher subscription fees for service suites that include AI capabilities, independent of the substantial ROI these teams generate through improved decision-making and operational efficiency.

Embedded analytics agents create similar premium tiers without rewriting your core app. For enterprise customers, these agents deliver immediate value through accelerated research cycles, automated insight generation, and conversational data exploration that eliminate the need for technical training or dedicated analysts.

Reason #2: Customer-centric innovation

A global real-estate service firm cut insight delivery from six weeks to 60 seconds by white-labeling Numbers Station (now Alation) in its portal. The solution unified the firm’s analytics infrastructure, creating a single source of truth that adapts to each client's unique needs. This approach gives clients instant, self-service access to real-time property and market insights—without requiring technical skills or data team involvement.

Building on this success, the firm is developing an agent that auto-kicks off lease optimization by merging Databricks occupancy data, PDF lease documents, and public market data to propose comprehensive action plans for lease cancellation, renewal, or subleasing negotiations. This agent analyzes building occupancy patterns, lease terms, and market comparables to recommend optimal lease strategies, potentially saving clients millions in real estate costs while generating new revenue streams for the organization through premium advisory services.

Deployments like this lift engagement up to 55% (Product Alliance survey), demonstrating how AI agents transform traditional software interactions into dynamic, insight-driven experiences. 

Reason #3: Defensible advantage

Agents run on trusted metadata. Competitors can copy a UI, but they can’t replicate a governed knowledge graph tied to your data estate. Unique enterprise data becomes highly valuable when contextualized through proper governance—creating a proprietary moat that's nearly impossible to reverse-engineer.

Imagine replicating similar growth and stickiness within your own customer base. The revenue potential is substantial, and it comes with the added benefits of increased user engagement, reduced churn, and a stronger competitive position.

Getting started: A practical roadmap for building analytics agents.

Step 1: Map your "digital interns"

Start by looking at how your customers use your data today. What are their most time-consuming research workflows? What questions are they trying to answer that your current dashboards or API calls can't handle? Pick one high-impact use case to be your starting point. 

The following examples show how research agents transform industry-specific workflows:

Financial services: Wealth management firms struggle with fragmented portfolio data analysis, spending hours drilling into dashboards and writing SQL queries. Analytics agents can enable conversational exploration ("Which portfolios have drifted 10% from their original asset allocation?") while automating insight packaging through auto-generated PowerPoint slides, CRM notes, and email summaries—transforming hours of manual work into minutes of strategic insight.

Healthcare operations: Clinical analysts mining data for capacity bottlenecks and readmission trends face slow insight-to-action loops due to heavy tooling requirements. Conversational queries like "Where are surgical delays exceeding 24 hours most frequently?" can trigger automated analytics workflows that generate weekly summaries for department heads and draft email-ready insights for immediate action.

Manufacturing: Operations analysts investigating production line performance must manually compile downtime logs, scrap rates, and shift schedules into leadership reports. Analytics agents can interactively explore line performance anomalies and automatically generate root cause analysis slides, summary reports for ops leads, and pattern alerts, reducing the time from raw data to packaged insight.

The outputs of these workflows aren’t just data points—they’re decision-making tools that drive both efficiency and strategy.

Step 2: Define the autonomy slider

Don't try to build a fully autonomous agent from day one. Start with Assist mode, focusing on delivering transparent, verifiable insights. This builds trust. Then, create a clear product roadmap to introduce Suggest and Act capabilities as premium, opt-in features.

Begin with a pilot program focused on a specific use case or customer segment. As you implement the pilot, meticulously track key performance indicators (KPIs) such as revenue generated, user engagement, and customer satisfaction. Use these insights to refine your approach and build a compelling business case for wider implementation.

Once you’ve proven the value, expand to other areas of your business. This phased rollout enables you to learn from each iteration, fine-tune your AI capabilities, and scale more effectively.

Step 3: Prepare for a conversational future

Consider how to make customer interactions more seamless through conversational interfaces. This shift is not just an incremental change; it’s as transformative as the move from desktop to web, or from web to mobile. Just as those transitions reshaped how businesses operate, we’re now on the cusp of another fundamental change: the era of chat-based interactions. In the near future, every business interface is likely to become conversational, with chat serving as the primary mode of interaction. 

This evolution isn’t happening in isolation. As VentureBeat notes, organizations are struggling with fragmented AI tools and platforms. To unlock the real potential of conversational AI, enterprises need unified solutions that cut through this chaos and enable trusted, coherent interactions. A streamlined platform approach ensures that conversational interfaces aren’t just novel features, but strategic drivers of productivity, efficiency, and user satisfaction.

→ See how Alation is leading the way with our Chat with Your Data capability

The engine room: Why this is hard to build alone

Together, Alation’s governed catalog and Numbers Station’s agent framework fuse into a single stack that is accurate, fully connected, and makes your “digital intern agents” impossible to clone.

• Trusted-metadata backbone: An agent is only as good as the data it uses. Lineage, governance, and PII tagging are baked into Alation’s catalog, giving every prompt enterprise-grade accuracy and compliance. This provides the context that fuels AI accuracy and eliminates hallucinations. The result is AI that enterprises can trust with their most sensitive data and critical decisions.

• Unified agent fabric: Our framework unifies different types of agents—for querying, forecasting, charting, and knowledge search—as composable MCP Servers. Agents can query any source and reason across the results to return actions, not just data.

• Proprietary moat: By building on this governed foundation, your AI product becomes unique and defensible. You’re not just enhancing the user experience; you’re future-proofing your product for the next wave of digital transformation.

Alation’s acquisition of Numbers Station marks a turning point: foundation models and agentic frameworks gain the precision, context, and governance they lack on their own. Together we deliver:

• Faster decisions – agents surface answers in seconds, not sprints.

• Smarter operations – metadata rules out hallucinations and policy breaches.

• New revenue streams – SDK lets you embed pay-as-you-go agents without wrestling with data plumbing.

The result? Non-technical domain experts can now run sophisticated data workflows through natural language, while you monetize proprietary, AI-powered features rivals can’t reverse-engineer.

Ready to plug in? Our AI Agent SDK standardizes connectors, security, and governance so your team—or partners—can spin up revenue-generating agents in weeks.

Explore Alation Chat with Your Data, see our Analytics Agents in action, or request a demo today.