Despite its popularity, ChatGPT poses risks as the face of artificial intelligence, especially for companies that rely on real-time data for insights and analysis. Aside from biases, simplifications, and inaccuracies, its training data is limited to 2021, rendering the free version unaware of current events and trends. With no external capabilities to verify facts, relying on outdated data for infrastructure management is akin to launching a new app on a flip phone. If you wouldn't do it there, why would you build new technology on old data now? For industries like manufacturing, where real-time data insights are essential, the effectiveness of AI hinges on the quality and timeliness of the underlying data.

As leaders across Industry 4.0 contemplate, scramble, or pivot to this new era, it's important to get their data to use AI effectively before all else. Tools like ChatGPT can be counterproductive if they require constant error-fixing, but using AI can be revolutionary if you're ready.

To unlock AI's true potential, we must address the core issue: data infrastructure readiness.

Clean, Centralize and Combine

As companies make acquisitions, they inherit different sites and systems, resulting in data fragmentation and inconsistencies that pose significant challenges for centralized data management, especially when using AI. Organizations must prioritize cleaning and aligning data across systems to address these data discrepancies and ensure consistency and accuracy. By centralizing and consolidating data into a unified system, such as a data warehouse, manufacturing companies can streamline data management, facilitate efficient analysis, and avoid inconsistencies from disparate sources for improved operational efficiency.

For Industry 4.0, innovative IIoT solutions are needed to merge, automate, and process the massive volume of timestamped data that needs to be shared, centralized, and analyzed. Large companies likely have a mix of different data systems, meaning that modern systems still need to interoperate with legacy infrastructure over common protocols like MQTT and OPC; ripping and replacing existing data systems to install one uniform system is difficult or impossible for most industrial enterprises.

For more efficiency and better collaboration among key stakeholders, combining data connectors with cloud services provides a powerful tool for leveraging open systems and seamless data sharing. With the combined data, organizations can now have one source of truth, making it easier for AI integration.

Data Sharing and Governance

It is important to audit current data sharing processes and develop standardized procedures to prepare data infrastructure for AI. Data subscription allows real-time sharing without repeated queries, providing partners with only predetermined data. This avoids potentially exposing sensitive information to outside parties. Companies can securely share data by implementing access controls, monitoring usage, and working with reputable vendors.

Next, a data governance strategy establishes procedures, policies, and guidelines for integrity, quality, compliance, and seamless transformation. By defining ownership, enforcing protections, and maintaining standards, manufacturers can create a strong foundation for AI insights. This helps teams use AI efficiently instead of fixing mistakes.

Embrace Open Systems

Sharing data externally is critical for AI success, and open systems are key to providing data sharing. Open systems provide flexibility to work with different AI providers and technologies, assisting the product selection process and letting enterprises choose the solutions that are best for their particular use case.

Transitioning from closed to open or semi-open systems enables effective data sharing across stakeholders while avoiding rip-and-replace scenarios. Open systems allow seamless data sharing via APIs while ensuring security. In addition, they allow third-party products and services for data management to be implemented to leverage AI and Industry 4.0 without extensive in-house infrastructure.

Are You Ready?

In the AI era, data infrastructure readiness is more important than ever. Outdated systems and inefficient tools will hold you back from reaping the benefits of the latest technology. Now is the time to position your organization for better decision-making and more advanced analytics by embracing the transformative effects of AI. The future belongs to the AI-ready. Are you?