Artificial intelligence is quickly changing how companies analyze information, make decisions, and respond to businessBusiness-to-business (B2B), also known as B-to-B, is a form of transaction between businesses, such ... More challenges. One of the most promising developments in this space is agentic AI, a new generation of AI systems designed not only to answer questions but also to take action, assist with workflows, and support decision-making across departments.
For many businesses, the idea of adopting agentic AI may sound complex or intimidating. It can feel like something that requires a complete technology overhaul, a large data science team, or a major investment in new systems. In reality, the journey can often begin with something far more practical: the data, analyticsAnalytics are used for websites, as well as in social media and email campaigns. When reviewing or t... More tools, and business systems the organization already uses every day.
Instead of rushing into advanced AI projects without preparation, companies can start by strengthening their existing data foundation. This approach allows teams to build confidence, improve data quality, and gradually move toward more advanced AI capabilities. Platforms like Zoho Analytics demonstrate how businesses can begin this journey in a structured, accessible, and low-risk way.
Understanding Agentic AI in Business Intelligence
Agentic AI refers to AI systems that can perform tasks with a higher level of autonomy. Unlike traditional analytics tools that simply display reports or dashboards, agentic AI can interpret requests, reason through data, suggest next steps, and potentially trigger actions based on business rules or user goals.
It can help sales teams identify at-risk customers, support finance teams with forecasting, alert managers to unusual performance trends, or guide customer support teams toward priority issues.
However, agentic AI does not work effectively in isolation. It depends on the quality, structure, and reliability of the information it uses. If business data is incomplete, duplicated, inconsistent, or poorly connected, AI agents may produce weak or misleading results. This is why the journey toward agentic AI should begin with the data foundation.
Start with the Tools Already Available
Making AI More Accessible Through Natural Language
A practical first step is to use AI features that are already built into existing analytics platforms. Sales managers, marketingBusiness-to-business (B2B), also known as B-to-B, is a form of transaction between businesses, such ... More teams, finance staff, and operations leaders can explore data more independently, without always relying on analysts or IT teams to build custom reports.
Building Team Confidence with AI
Introducing tools like Ask Zia also helps employees become more comfortable with AI-driven data interaction. By starting with simple natural language analytics, employees can gradually understand what AI can do, where it is useful, and where human review is still necessary.
The more familiar employees become with AI-assisted analytics, the easier it becomes to introduce more advanced agentic AI tools later.
Use AI to Evaluate Your Data Readiness
When AI Answers Reveal Data Problems
One of the most valuable benefits of using AI-powered analytics early is that it can reveal weaknesses in the organization’s data model. When a team asks a business question and receives an incomplete, confusing, or inaccurate answer, the issue may not be the AI itself.
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For example, a company may ask, “What is our monthly recurring revenue?” but receive inconsistent results because revenue data is stored differently across departments. Sales may track contract value, finance may track invoiced revenue, and customer success may track subscription status. If these systems are not properly connected or defined, AI will struggle to provide a reliable answer.
In this way, AI becomes a useful diagnostic tool. It highlights where data is missing, where definitions are unclear, and where business logic needs to be improved.
Turning Gaps into Improvement Opportunities
Rather than viewing imperfect AI responses as failures, businesses should treat them as signals. Each unclear answer can point to a specific area that needs attention. Perhaps tables need to be linked more accurately. Perhaps key metrics need clearer definitions. Perhaps certain fields are missing, duplicated, or formatted inconsistently.
This process helps organizations improve their analytics environment step by step. Over time, the data becomes more reliable, and AI-generated responses become more accurate and useful.
Strengthen the Data Foundation
Connecting the Right Data Sources
Once teams have started using AI-powered analytics, the next step is to improve the data foundation. This often begins by connecting important data sources that are currently separated.
A business may have customer data in its CRM, financial data in accounting software, support data in a help desk system, and marketing data in campaign tools. Each system provides part of the story, but agentic AI becomes much more powerful when these sources are connected.
For example, a sales intelligence agent could become far more useful if it can access not only deal information but also payment history, support tickets, product usage, and customer engagement data. With a complete view, the agent can provide better recommendations and identify risks earlier.
Defining Metrics Clearly
Another essential step is defining business metrics clearly. AI systems need to understand what terms mean in a specific business context. Words like “revenue,” “active customer,” “qualified lead,” or “churn” may have different meanings across teams.
For example, “ARR,” “annual recurring revenue,” and “annual subscription value” might all refer to the same concept. If the AI does not understand that these terms are related, it may produce inconsistent answers. Adding synonyms and standardizing definitions helps ensure that everyone, including the AI, is working from the same understanding.
Clear metric definitions also improve human decision-making. When all departments use the same definitions, reports become easier to compare, discussions become more productive, and leadership teams can make decisions with greater confidence.
Prepare and Clean the Data
Why Clean Data Matters
Agentic AI depends heavily on clean, consistent, and well-organized data. Even a powerful AI system can struggle if the data contains duplicates, spelling differences, missing values, or inconsistent formats.
For example, one customer might appear as “ABC Company,” “ABC Co.,” and “A.B.C. Company” across different systems. Without proper standardization, AI may treat these as separate customers, leading to inaccurate reports or flawed recommendations.
This is especially important for customer and financial data, where accuracy directly affects forecasting, reporting, and decision-making.
Using Data Preparation Tools
Tools such as Zoho DataPrep can help businesses clean and organize their information before using it for AI-driven workflows. Data preparation may include removing duplicate records, standardizing company names, correcting formatting issues, validating fields, and combining related datasets.
Although data preparation may not seem as exciting as building AI agents, it is one of the most important parts of the process. Clean data creates the conditions for accurate answers, meaningful insights, and reliable automation.
Move from Analytics to AI Agents
Starting with Internal Use Cases
The best starting point is usually an internal use case because it allows teams to test the agent in a controlled environment before exposing it to customers or external users.
A sales intelligence agent, for example, could summarize customer activity, identify stalled deals, highlight upsell opportunities, or warn account managers about accounts showing signs of risk. A finance agent could compare actual results against forecasts, monitor budget changes, or help explain variances in revenue.
These internal agents can save time, improve visibility, and help teams make faster decisions without creating unnecessary risk.
Testing, Refining, and Scaling
The first version of an AI agent does not need to be perfect. Teams should test the agent’s responses, review its recommendations, identify weaknesses, and improve the data model or instructions behind it.
Once the agent proves useful internally, the organization can consider expanding into more advanced use cases. This may include customer-facing assistants, automated alerts, workflow recommendations, or integrations with other business systems.
The goal is to scale gradually, based on proven value and operational readiness.
Why Gradual Adoption Is the Smarter Strategy
Agentic AI should not be treated as a one-time project or a rushed transformation. A gradual approach allows businesses to reduce risk, control costs, and create value at each stage of the journey.
By starting with existing analytics tools, companies can introduce AI in a familiar environment. By improving their data foundation, they prepare for more advanced use cases. By testing internal agents first, they build confidence before moving into customer-facing automation.
It allows them to benefit from AI without needing to rebuild their entire technology stack.
Conclusion: The Best Place to Start Is Where You Are
Agentic AI represents an exciting new chapter in business intelligence, but companies do not need to wait for perfect systems or massive transformation projects to begin.
By activating AI-powered analytics, identifying data gaps, strengthening business definitions, cleaning records, and gradually testing internal agents, organizations can build a strong foundation for the future. This approach helps companies gain value quickly while preparing for more sophisticated AI capabilities over time.
© Image credits to Landiva Weber
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