How to Use Data to Make Smarter Business Decisions

When leaders talk about how to use data to make smarter business decisions, the conversation has shifted far beyond static dashboards and periodic reports. The most forward-thinking organizations are now combining Agentic AI—AI that acts proactively and autonomously—with unified, high-quality data ecosystems to anticipate problems, seize opportunities, and execute decisions faster than ever.

This isn’t a tech trend that will fade; it’s a structural change in how businesses think, plan, and respond. In this article, we’ll break down:

1. Understanding Agentic AI: The Next Step in Intelligent Automation

Most businesses are already familiar with generative AI—systems that produce text, images, or code based on a prompt. Agentic AI goes further. It doesn’t just respond—it acts.

Agentic AI systems can:

• Identify a business goal
• Break it into actionable steps
• Orchestrate tools and resources
• Adapt plans in real time based on changing data

For example, in a retail setting, an Agentic AI system could monitor inventory data, detect an impending shortage, source alternative suppliers, and update procurement orders—all without a direct human request.

Why it matters now:
Analysts forecast that Agentic AI adoption will accelerate sharply in the next 18 months, especially in industries where timing is critical—such as logistics, finance, and energy.

2. The Critical Role of a Unified, High-Quality Data Foundation

While Agentic AI offers speed and autonomy, it’s only as good as the data it consumes. If your business data is scattered, inconsistent, or outdated, AI will amplify the noise instead of delivering clarity.

A unified data foundation means:

• All critical business data is integrated—from sales and customer behavior to supply chain and finance.
• Quality and consistency are enforced through governance rules.
• Access is controlled so the right teams and tools get the right data without exposing sensitive information.

Why it’s urgent:
Businesses that skip this step risk making fast decisions for the wrong reasons—wasting money, alienating customers, and inviting compliance problems.

3. Step-by-Step Guide: How to Use Data to Make Smarter Business Decisions with Agentic AI

Below is a proven six-step process you can adapt to your industry.

Step 1: Audit and Clean Your Data Landscape

• Identify all data sources—internal (CRM, ERP, HR systems) and external (market data, partner feeds).
• Remove duplicates and standardize formats.
• Map where critical data is stored, who owns it, and how it’s accessed.
• Document gaps and potential bias sources.

This stage can reveal inefficiencies—like redundant software licenses or missed integration opportunities—that improve ROI even before AI comes into play.

Step 2: Build an AI-Ready Data Foundation

• Invest in a unified data platform (e.g., Snowflake, Databricks, or Microsoft Fabric) to bring disparate sources together.
• Enable real-time or near-real-time pipelines so decisions are based on fresh data.
• Implement metadata tracking to ensure data lineage and trustworthiness.
• Create a governance framework—permissions, encryption, compliance checks.

Without this backbone, even the most advanced AI tools will operate in isolation.

Step 3: Define Clear Goals and Guardrails

Agentic AI thrives when given specific, measurable objectives and defined limits.

Examples:

• Objective: Reduce logistics costs by 12% in six months.
• Guardrails: No supplier contracts over 50K dollars without human sign-off; all procurement changes must meet ESG standards.

Guardrails prevent over-automation and ensure AI decisions align with company values and regulations.

Step 4: Test in Controlled, Low-Risk Environments

• Run pilot projects in areas where errors have minimal impact but wins are visible—like internal reporting or predictive maintenance alerts.
• Track:
  • Accuracy of AI-driven decisions
  • Speed improvements over manual processes
  • Cost savings or revenue impact
• Use early data to refine algorithms, workflows, and governance rules.

Step 5: Scale Responsibly with Continuous Oversight

• Gradually extend Agentic AI into higher-impact decisions—such as supply chain planning, marketing personalization, or dynamic pricing.
• Keep human-in-the-loop review for high-stakes scenarios.
• Establish periodic audits to check for data drift, model bias, or compliance issues.

Scaling too quickly without safeguards can erode trust and cause costly missteps.

Step 6: Build a Data-Driven Culture

Technology is only half the equation. People must understand and trust the system.

• Offer training on reading AI outputs and questioning them constructively.
• Create feedback loops so employees can flag anomalies.
• Celebrate successes publicly to reinforce adoption.

When teams feel included in the decision-making evolution, adoption accelerates.

4. Real-World Examples of Smarter Decisions in Action

Supply Chain Optimization

A global manufacturer implemented Agentic AI to monitor shipping delays and tariffs. The system autonomously rerouted orders and adjusted schedules, reducing late deliveries by 27%.

Fraud Prevention in Commerce

Mastercard uses large-scale AI to flag suspicious transactions in real time, preventing fraud without unnecessarily blocking legitimate purchases. This reduced false declines, boosting merchant revenue and customer satisfaction.

Financial Forecasting

A multinational bank replaced static quarterly forecasts with continuous AI-driven modeling, enabling faster product adjustments in volatile markets. McKinsey reports that AI adoption in financial services jumped from 33% in 2023 to 71% in 2024.

5. Additional Trends Complementing Agentic AI

While Agentic AI plus unified data is a major shift, several parallel trends can further strengthen decision-making:

• Prescriptive analytics: goes beyond predicting outcomes to recommending exact actions.
• Edge computing: processes data closer to where it’s generated for faster response times.
• Data-as-a-service (DaaS): subscription-based models giving teams curated, on-demand access to specialized datasets.

Integrating these with your AI strategy can multiply benefits.

6. Key Challenges and How to Overcome Them

• Data Privacy Compliance
  Ensure your AI workflows comply with GDPR, CCPA, and industry-specific regulations. Build compliance into the design, not as an afterthought.
• Change Management Resistance
  Address fears of job displacement by showing how AI supports—not replaces—human expertise.
• Bias and Fairness
  Continuously audit models for bias. Diverse data sources and governance reduce skewed outcomes.
• Integration Complexity
  Start with high-value, low-complexity integrations before tackling enterprise-wide overhauls.

7. The Road Ahead

The combination of Agentic AI and a unified, high-quality data foundation is not just a technological advantage—it’s becoming a competitive necessity. Those who adopt early, responsibly, and strategically will find themselves making not only faster but also better decisions.

As the AI landscape matures, the question won’t be whether you use Agentic AI to make smarter business decisions, but how effectively and ethically you do it.

Summary Checklist

Step	Action
1	Audit and clean your data
2	Build a unified, AI-ready data platform
3	Define goals and guardrails
4	Pilot in low-risk areas
5	Scale with oversight
6	Foster a data-driven culture

References

• TechRadar. (2024, October 15). The age of agency: Why agentic AI will redefine the future of work. TechRadar Pro. Available at: https://www.techradar.com (Accessed: 14 August 2025).
• Business Insider. (2024, September 9). Unleash AI’s potential: Transform enterprise data quality for success. Business Insider. Available at: https://www.businessinsider.com (Accessed: 14 August 2025).
• McKinsey & Company. (2024, August 7). The state of AI in 2024: Generative AI’s breakout year. McKinsey & Company. Available at: https://www.mckinsey.com (Accessed: 14 August 2025).