Generative AI is no longer a future bet — it’s already becoming an advantage for businesses that adopt it successfully. From sales to HR, organizations are integrating large language models (LLMs) into operations to accelerate insights, automate repetitive tasks, and augment decision-making. The challenge is adopting AI that really works and delivers value rather than being a costly experiment.

We talked to some our customers that have already been successful implementing AI that delivers real bottom-line value. A common thread emerged: not just mandating the use of AI but building the necessary support around AI to make it work. That includes connecting enterprise data context, so AI isn’t working alone — it’s connected to live, governed business data. Robust data access for AI makes the difference between failed pilot and measurable ROI from their AI initiatives. Yet, the majority of IT leaders say lack of integration is still a top barrier, with AI unable to access and understand key data.

Below, we explore three real-world generative AI use cases already delivering measurable outcomes today — and how data enables them to scale team- and organization-wide. Each scenario highlights the "recipe" of AI components (models, data sources, etc.) required for success.

Application #1: Chatting with your data for instant insights

One of the most impactful early applications of AI is enabling employees to talk to their actual business data. In this conversational analytics approach, users ask plain-language questions (e.g., "Which products had the biggest sales increase last quarter?") and receive immediate answers grounded in live enterprise data. The key ingredients:

• An LLM to understand the question and compose an answer

• Connectivity to query the right databases and systems in real time

Example scenario: A sales team pairs an LLM like Anthropic's Claude with live CRM data from Salesforce and forecasting insights from Clari. A rep asks, "Which deals are likely to slip this quarter?" The LLM uses pipeline data from Salesforce and risk scores from Clari to return a fact-based answer, highlighting at-risk deals. The result: quick, accurate insight without needing reports.

Behind the scenes: CData Connect AI serves as the platform link between the LLM to source systems like CRMs, ERPs, and data warehouses using secure, governed access. The LLM translates natural-language questions into live data queries and submits them to Connect AI using MCP, retrieving up-to-the-moment data — no SQL skills or BI dashboards required.

In other words, the user’s question becomes a live data query, and the results are returned as a conversational response. This allows non-technical users to access analytics instantly, using everyday language.

Examples of team-level impact:

• Sales: Reps ask, "Which deals need attention?" and get real-time answers from Salesforce about likely-to-slip deals or accounts with low engagement.

• Marketing: Analysts compare campaign performance by asking, "How did our email CTR compare to Facebook ads in Q3?" The AI pulls metrics from tools like Marketo and Google Analytics.

• Finance: Leaders get variance reports by asking, "What’s the Q2 spend versus budget by department?" with data from QuickBooks or Snowflake.

• HR: Managers explore trends by asking, "What’s the quarterly attrition rate by team, and does it correlate with employee survey scores?"

The result: Faster insights with less time spent wrangling reports. Teams make decisions in minutes instead of days, and anyone who can ask a question gains access to analytics. When the data is live and trusted, the answers are always accurate—no outdated extracts or spreadsheets.

Application #2: AI agents that automate workflows

Generative AI is evolving beyond answering questions — it’s now able to take action. AI agents powered by LLMs can now perform multi-step tasks across applications, like processing an invoice or triaging a sales pipeline. To reliably automate workflows, AI agents need:

• Secure data access

• The ability to write back to enterprise tools

Connect AI enables read/write access to over 350 enterprise systems (SaaS apps, databases, etc.), governed by role-based permissions and source-level security.

This allows AI agents to retrieve data and trigger actions in enterprise systems without building custom APIs or duplicating data.

Example scenario (finance operations): At an energy provider, a multi-agent AI system running on the OpenAI LLM framework automates vendor invoice processing. The AI reads invoice data from PDFs, checks purchase orders in SAP then logs the transaction, and marks the order as complete in Salesforce, the system of record...no human needed. The result: significantly reduced manual work and faster approvals.

Another example (sales automation): Intuit’s "Intuit Assist" acts as an AI financial assistant. A business owner forwards a vendor email, and the AI creates a bill in QuickBooks, monitors receivables, and sends reminders—reducing overdue payments by 5 days on average.

Customer service: AI agents now resolve support requests end to end. A customer asks for a refund in chat. The AI retrieves order info from Shopify, verifies eligibility, initiates the refund, and sends confirmation — all within seconds. Agents connected to the systems via MCP can handle common requests without human handoff or with a human-in-the-loop.

Why this matters: AI agents can act as tireless assistants, but only if they can access and interact with business systems securely. Connect AI makes this possible. It grants secure, governed access so LLMs can analyze and act in real time — enabling AI-driven automation to launch in production faster.

Application #3: AI assistants for knowledge and communication

Another high-impact pattern: using generative AI as a knowledge copilot. These assistants synthesize information, generate content, and make recommendations using enterprise data. When connected to knowledge bases, communication tools, and documents, LLMs reduce time spent searching, reading, or drafting.

Example scenario (financial services): A top bank builds an internal GPT-4 assistant that helps advisors search proprietary research. Integrated with OpenAI APIs and internal data connectors, the assistant pulls answers from tens of thousands of documents. Over 98% of advisor teams use it, saving hours once spent manually searching. It even summarizes meetings and drafts follow-up emails logged in the CRM.

Why connectivity matters: AI assistants are only as good as their context. They must connect to the right systems—SharePoint, Confluence, document databases — to produce useful, trustworthy output. Connect AI enables this by linking LLMs to the authoritative sources of knowledge in your business.

The result: Better decisions, higher productivity. Employees spend less time searching and more time acting on insights.

A modern AI + data stack: what powers these use cases

To deliver results like these, you need more than just an LLM. A modern stack might include:

• LLMs (e.g., GPT-4, Claude) for natural language understanding and generation

• Agent frameworks (e.g., LangChain, Semantic Kernel) to manage multi-step tasks

• Context stores (e.g., vector databases like Pinecone or Chroma) for grounding in proprietary knowledge

• Integration channels (e.g., Slack bots, intranet widgets, CRM buttons) to make AI accessible

• Enterprise connectivity (e.g., CData Connect AI) to provide real-time, governed data access to business systems that universally connects to any of the above

Without connectivity, the AI is flying blind. With it, AI becomes a real-time partner grounded in your live business context.

Connect your AI to live enterprise data with CData Connect AI

Ready to bring AI that works into your business? Start with a high-impact use case and build on a strong data foundation. Here are a few pilot ideas:

• Sales forecasting: Deploy a conversational pipeline analyzer using AI connected to Salesforce and Clari.

• Invoice automation: Use an AI agent to match invoices to POs in SAP and mark them as processed in Salesforce.

• Customer support: Let AI handle tier-1 tickets by pulling order info and generating responses in seconds.

In each case, CData Connect AI accelerates delivery by eliminating integration hurdles. Connect to your SaaS apps or databases in minutes, set access rules, and focus on AI logic, knowing your data flow is handled securely and at scale.

When generative AI is grounded in your business data, it becomes more than a demo—it becomes a teammate. It surfaces insights, takes action, and helps every employee make smarter, faster decisions.

Start a 14-day free trial and start using data in AI in minutes to see the difference firsthand that data makes for AI.