by Neelam Keny, on Jun 13, 2025 6:16:27 AM
Key takeaways from the blog
- Data governance is the cornerstone of the intelligent automation continuum.
- A minor data inconsistency can create a rippling effect.
- High-quality data and continuous enhancement unlock high value and a quick RoI.
Digital transformation projects in the Manufacturing domain, driven by contemporary automation solutions, unlock RoI and revenue very late in the transformation journey. Intelligent automation, on the other hand, delivers RoI faster and with better results. However, the success of intelligent automation pivots on the foundation of scrupulous data and data governance.
The synergy between the intelligent automation continuum and data governance directly results in enhanced efficiency, cost savings, and seamless user experience in Manufacturing automation projects, and imparts a competitive edge. Early adopters of this artificial intelligence (AI) driven automation have realized its true potential.
What is the significance of good data governance and AI-led intelligent automation in the Manufacturing industry?
Automation solutions that work on AI/ML models have data and data training at their core. Hence, intelligent automation and all AI-driven projects, including Agentic AI, in the Manufacturing domain grow stronger only on the basis of data and a data governance foundation. A strong data governance foundation is the crux of all intelligent automation projects.
When a customer service executive serves a customer by referring to the company’s knowledge base, which is reinforced by using contemporary automation solutions, the customer expects accurate and real-time information and query resolution. If the information is either lagging or incorrectly captured, the customer-specific personalized and bundled offers will also be in vain.
A manual data capture and validation error can ruin the entire automation effort, and, hence, the customer experience. Hence, data validation and data governance are important parts of the intelligent automation journey. Automating the aspects of data capture, data validation, and data governance reinforces data sanctity across all stages of the automation journey.
Intelligent automation in Manufacturing relies specifically on data as it pivots around the just-in-time manufacturing paradigm, which heavily relies on the supplier-business-customer trust and relationship ecosystem. Data remains the deciding factor in training the underpinning AI algorithms in intelligent automation solutions.
The use of data-trained AI/ML models usually cuts across the AI-driven unstructured data extraction, the intelligent bots, and the decision nodes that leverage the actionable data insights. A lack of data sanctity and governance results in data inconsistencies and errors that derail the entire project, damage customer relationships, and erode brand trust.
Data governance lacunae and intelligent automation derailment in Manufacturing – The cause and effect
Intelligent automation combines AI/ML, RPA, BI, and IDP that connect different Manufacturing automation siloes and functions by using existing data frameworks and spinning data feedback loops in the process. As a result, an inconsistency in the data creates a Domino Effect affecting all intelligent automation project outcomes. Even though one regards it as a tiny spec of data inconsistency, as the project leverages data as its underlying framework, the data governance lacunae can throw exceptions at each juncture, at the least, or derail the entire project effort, at the worst.
Why do data governance and intelligent automation create synergy in Manufacturing?
Data governance is important in all AI-led solutions. The data that goes in should be of high quality. Having data feedback loops and reinforcement learning is one thing, and starting with proper data architecture is quite another. Here, data preparation, data curation, labelling, consistency, and compliance are crucial at the project foundation itself. Some important stages in data governance include:
- Data collection: Collect data in properly structured formats.
- Data preparation: Curate the collected data and label it as per the enterprise policies.
- Data management: Assign individuals or teams for data accountability and overview.
- Data consistency: Ensure data consistency across all the enterprise systems.
- Data compliance: Periodically assess the effectiveness of the data governance frameworks and attune, it as required.
When intelligent automation in Manufacturing automation projects gets aligned to a high-quality data pool that is continuously reinforced and refreshed, the resulting synergy unlocks the pathways to highly scalable enterprise automation dissemination that generates return on investment earlier in the intelligent automation journey.
Advantages of integrating data governance in the Manufacturing industry’s intelligent automation roadmap
Integrating data governance as an integral part of the Manufacturing industry’s intelligent automation journey strengthens the overall project outcome. The significant benefits of the resulting solution are –
- Accuracy and reliability: It eliminates data incongruence, delivering a robust and reliable automation solution.
- Consistency: It ensures seamless data integration that allows scaling up the effort, both vertically and horizontally, in a consistent manner.
- Compliance: It helps the business comply with regulatory requirements and eliminate business risks.
- Efficiency: It ensures efficient data leverage and utilization and helps to effectively monitor the automation at the enterprise level.
- Data relevance: The framework thus laid makes the data useful and highly relevant for leveraging in contextually appropriate situations. The established framework also helps to transition along the progressively higher automation continuum easily.
Intelligent automation and data governance roadmap for the Manufacturing industry
A pragmatic approach to intelligent automation that has data governance as an integral part helps to ensure a successful intelligent automation journey. The ideal intelligent automation roadmap for the Manufacturing industry should –
- Shortlist impactful areas: Select use cases that target critical business areas and deliver immediate, tangible results.
- Start small: Automate across smaller use cases first and then take on bigger projects at the enterprise level.
- Collaborate: Secure inter-team buy-ins, align the project across different teams, and focus on data governance across all nodes.
- Data framework: Create data frameworks using high-quality data and build feedback loops that integrate with the framework.
- Training: Establish training programs and set up protocols that ensure data quality through complete data governance.
- Incremental improvements: Build a minimum viable solution and then refine it through an iterative approach.
Simply put
AI/ML-driven intelligent automation has immense potential for enterprise transformation and change in the Manufacturing sector. If it is built on a robust framework of high-quality data and data governance, it can achieve significant returns early in the project. Realizing this relation between intelligent automation and data governance is crucial to any AI-driven digital transformation project. Laying a trustworthy data architecture, validation frameworks, and feedback loops enable Manufacturing businesses to ensure a successful intelligent automation journey and faster RoI to gain a competitive edge.
