Artificial Intelligence (AI) is moving quickly from experimentation to practical deployment across Sustainability, Environmental Health and Safety (EHS) and Operational Risk Management (ORM). Organizations increasingly use the technology to demystify dynamic regulatory expectations, manage complex supply chains and mitigate a variety of operational risks.
The success of AI for these use cases hinges on high-quality data, which is often harder to obtain than expected.
Even when companies believe they are working with reliable datasets, operational realities can gradually erode data quality. Changes in processes, differences in terminology across global teams, staff turnover or shifts in organizational priorities can introduce inconsistencies that accumulate over time. Left unchecked, these issues build up and diminish AI’s ability to run analyses, synthesize information or make recommendations over time.
Understanding how these issues emerge is the first step toward preventing them.
How Good Data Becomes Unreliable
Data problems are rarely detected right away. In most cases, degradation occurs gradually as operational complexity introduces small inconsistencies and issues into systems. What begins as a minor discrepancy, such as a mislabeled field, misused term or incomplete data point, can quietly propagate across datasets.
Human bias is often underestimated. AI systems are designed, trained and guided by people, which means bias can influence outputs. Results can be impacted by how models are structured, what questions are asked, and how prompts are written. Even when datasets are objective, the way they are interpreted or prioritized may reflect underlying assumptions.
Global organizations face another challenge: inconsistent language and terminology. For example, poor oversight may mean the same safety observation is recorded completely differently in two different regions. Another example could be supplier information documented differently across regions depending on geography or regulatory expectations.
Leadership dynamics may also influence data quality. When priorities differ across departments, data collection or reporting practices may evolve in conflicting ways. In some cases, reporting structures unintentionally limit what information is captured, emphasized or released.
A single inaccurate data point can spread across systems and influence outputs before it’s discovered by audits or regulatory reporting. By then the damage may already be done, leading to flawed decisions, inaccurate reporting or regulatory nonconformance.
Preventing these problems requires deliberate, consistent oversight. The following five practices can help organizations maintain reliable, AI-ready data.
1. Keep Humans in the Loop
Despite its growing sophistication, AI is not a set-and-forget solution. Human expertise remains essential.
AI tools can identify patterns and generate recommendations, but they often lack sufficient operational context. Experienced professionals must still interpret results, evaluate anomalies and determine if recommendations make sense within real-world environments.
Organizations deploying AI should establish early governance frameworks that define what AI can and cannot do. Decision points around safety, regulatory or financial implications should always include human validation.
2. Implement Guardrails Around Data Inputs
It’s much easier to prevent poor data from entering systems than it is to correct it later.
Organizations can reduce risk by establishing guardrails around data entry and validation. Automated checks can flag incomplete records or inconsistencies before they are accepted into systems. Standardized definitions, training and accessible examples can ensure teams across regions use consistent terminology and reporting structures.
Periodic verification is equally important to maintain accuracy. Teams should occasionally validate and confirm the accuracy of AI-generated results by using traditional analytical methods. These checkpoints help ensure errors do not propagate unnoticed through automated workflows.
3. Align Leadership Around Data Strategy
Successful AI adoption requires more than technology. It requires organizational alignment.
Leadership teams must agree on what insights they expect AI to generate, what data will support those insights and how that information should be structured across organizations. When departments operate with different assumptions about data definitions or reporting priorities, AI systems can produce inconsistent or misleading results.
Establishing shared standards across sustainability, safety, procurement, operations and risk management helps maintain coherent datasets and reliable analytics.
4. Apply a Thoughtful Approach to AI Deployment
AI is sometimes presented as a technology that delivers immediate value. It is also commonly overestimated in its capacity to interpret unstructured or inconsistent legacy data. In reality, effective deployment requires preparation, oversight and ongoing management.
Organizations need a clear understanding of how their data is collected, its limitations and where inconsistencies may arise. Preparing data for AI typically involves cleaning records, standardizing definitions and establishing governance processes.
While this preparation takes time, it is essential for generating reliable insights.
5. Work with Experienced Partners
Not all organizations are equipped to go it alone on AI deployments. Organizations that work with knowledgeable advisors often develop stronger oversight and greater confidence in their data-driven decisions.
AI can transform how organizations analyze information and respond to emerging risks. But technology alone cannot guarantee reliable insights.
Effective AI depends on data quality and the expertise of the people using it. AI may process the data, but humans remain responsible for ensuring it is accurate, complete and trustworthy. This is where experienced partners can make a meaningful difference.
For more than three decades, Sphera has supported organizations across industries in strengthening data foundations for Sustainability, EHS, Product Stewardship and Operational Risk Management.
Sphera AI is built on proprietary, trusted datasets that enable organizations to generate more reliable insights and make defensible decisions. Unlike solutions that rely primarily on publicly available data, Sphera combines exclusive datasets with third-party validated sources.
Sphera Life Cycle Assessment data undergos rigorous vetting and validation by DEKRA, the world’s largest independent, non-listed expert organization in the testing, inspection, and certification (TIC) sector Combined with more than 30 years of collaboration with globally recognized academic institutions, this trusted data foundation allows Sphera AI to connect critical risk signals and transform complex information into decision-ready intelligence.
To learn more about how to transform and optimize data for AI, contact Sphera.
