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This article was originally written for and published by CEOWorld.
Artificial Intelligence (AI) in manufacturing has reached an inflection point. While many of us were understandably marveling at ChatGPT’s abilities, early adopters were busy doing what they do best: experimenting to determine how to get the most out of AI.
As with other technology trends, those early adopters are now publicly sharing their best practices so we can all unlock the benefits of AI. It takes effort and commitment to deliver an effective AI program. Fortunately, it has become a lot easier to develop a top AI strategy thanks to these early adopters.
Today’s AI isn’t just intelligent – it’s arguably the most strategic addition to any product team. Generative AI can transform an organization’s existing data into a powerful competitive advantage. By turning complex information into actionable insights, AI fuels innovation and drives smarter, faster decision-making.
Agentic AI takes it a step further. By automating routine tasks, agentic AI empowers employees to focus on higher-value work, significantly boosting productivity without increasing operational costs.
But AI doesn’t just automate and analyze, it’s also able to anticipate and act. Manufacturers that generate large, structured datasets effectively use AI algorithms to spotlight trends, immediately identify anomalies, and provide product teams with the knowledge to proactively address things like quality issues or supply chain hiccups that are a normal part of business today.
According to Salesforce, 70% of manufacturers view technology and digital innovation as key drivers of business transformation. AI is one of the most transformative technology solutions in our lifetime. So, what’s the best way for manufacturers to capitalize on AI’s potential? A three-stage approach maximizes AI adoption success. It starts with preparing a strong data foundation, moves to employing generative AI, and culminates in the implementation of native agentic AI.
1) Establish a Core Data Framework
Data readiness is one of the biggest challenges for achieving productive AI. Most of us are familiar with the garbage in, garbage out concept, where poor inputs result in poor outputs regardless of the quality of the model. Large language model (LLM) outputs are only as strong as the underlying data they receive, which may be restricted by siloed systems, incorrect data, or missing data. Mulesoft found that 95% of IT leaders report integration as a hurdle to implementing AI effectively. Rather than being trapped in disparate applications, data needs to be integrated and linked across the company.
Without a fully unified data thread that spans the entire product lifecycle from design to delivery, companies can apply AI within isolated functions such as engineering, but, in doing so, leave substantial productivity gains on the table. Insights from supply chain, quality, marketing, field service, and other critical functional areas that work in tandem with engineering remain untapped, significantly limiting overall decision-making capabilities.
At a minimum, manufacturers’ product data needs to connect:
- Requirements: Straightforward documentation of what the product is meant to do and the constraints it must satisfy. This information functions as the baseline for downstream engineering and quality processes.
- Items and Bills of Material (BOMs): The comprehensive definition of parts, assemblies, and documents for how the product is built, in addition to evolving changes all through the lifecycle.
- Finished Products – Stock Keeping Units (SKUs): The commercial version of the product as it will be sold, usually with variants for different channels, markets or customers.
- Serialized Assets: The individual unit as it exists in the field, including all service history and traceability over time.
These data sets should not exist in isolation. It’s crucial to ensure information is both linked and structured in a way that allows the AI solution to unify it into a single, intelligent product thread. This core unified data foundation unlocks smarter business decisions throughout the value chain.
Ensure Data Security
Every company needs to protect its data, and extending this protection to a secure AI environment starts with proper data governance. This means implementing robust access controls, role-based permissions, and usage protocols that define who can view, edit, and act on specific data sets. These governance frameworks are essential not only to safeguard sensitive information, but also to maintain traceability, compliance, and auditability as AI systems leverage enterprise data.
Security isn’t something that can be bolted on after the fact. It must be built into the foundation of a manufacturer’s data architecture (again, it all starts with the core data model), ensuring that AI operates within clearly defined parameters and respects organizational boundaries. After all, an employee who does not have permission to access sensitive data directly in a system should not be able to access it via an AI query. When these protocols are in place, organizations can confidently scale AI without compromising IP protection or regulatory posture.
Another area of concern is transferring data from traditional on-premise systems to third-party AI engines. This often presents significant challenges, particularly given that data privacy rules for these AI engines are not always transparent, placing valuable IP at risk of public disclosure.
The more fragmented a manufacturer’s architecture, the greater the risk. Moving data across disconnected systems increases complexity, slows performance, and undermines control over how data is accessed, used, and stored. This is especially problematic when AI capabilities are bolted on through external engines that operate outside a company’s trusted infrastructure and data access rules.
To reduce exposure and maintain control, organizations should prioritize cloud-native, SaaS-based solutions that unify operations and data on a single platform. The ideal architecture doesn’t just support AI, it embeds it directly within the platform where a business’s data already lives and governance is already enforced. Examples of this approach include Salesforce, Microsoft and Google.
2) Deploy Secure Generative AI
With the data foundation established and trusted, generative AI models now have access to high-quality, structured, and secure enterprise data. This enables AI to produce accurate and contextually aware outputs that are also aligned with an organization’s business standards.
Typical generative AI use cases include answering questions, drafting emails, and generating content based on enterprise data. For example, by referencing a product maintenance guide and previously resolved cases, AI can deliver a detailed summary to service teams on asset-specific repairs.
The following must-haves are essential to ensure trusted, relevant AI responses:
- Dynamic grounding for LLMs ensures factual, relevant data references and prevents hallucinations or incorrect responses.
- Data masking replaces sensitive data with anonymized data to protect personal information and comply with privacy requirements.
- Toxicity detection flags toxic content, such as aggressive language.
- Zero retention ensures prompts and outputs are erased and never stored in an AI model.
- AI auditing ensures systems are working as expected, without bias, with high-quality data, in line with regulatory and organizational frameworks.
Generative AI thrives by leveraging an organization’s foundational data as its context source, dramatically improving relevance and efficiency. Establishing a secure trust layer between sensitive enterprise data and the responding AI model eliminates data leaks and ensures guardrails are followed for every prompt.
3) Implement Native Agentic AI
Agentic AI is a huge productivity driver for manufacturers because it performs real actions that are key to employees doing their jobs. These solutions leverage intelligent workflows to analyze vast datasets, surface key insights or recommendations, and execute approved actions autonomously but under the direction of humans.
Agentic AI customizes outputs based on user roles and skill levels, accelerating ROI and empowering staff to make fast, informed decisions. More than just supportive tools, agentic AI proactively initiates processes, helping manufacturers stay ahead of possible issues and optimize operations in real time. Additionally, agentic AI seamlessly integrates into existing tools, eliminating disruptive context switching, and enabling teams to maintain their focus and momentum.
At its core, AI in manufacturing thrives when it amplifies human potential. By embedding agentic AI directly within everyday workflows, a business can transform time-consuming processes into faster, more informed, and more precise outcomes.
Instead of spending valuable time sifting through data or managing tedious tasks, teams gain time to focus on strategic objectives, innovation, and customer interaction. AI agents serve as trusted collaborators, providing tools that anticipate needs, while streamlining tasks and delivering swift, reliable results, without friction.
The Five Building Blocks of AI Agents
AI agents are purpose-built to operate autonomously within defined roles and business contexts. To deploy them effectively, organizations must design around these five foundational building blocks, all built upon a foundational layer of trust:
- Role: Clearly defined purpose ensures agents specialize in tasks aligned with specific job functions.
- Data: Secure and appropriate access to relevant data streams is necessary for informed decision-making.
- Actions: Explicitly defined set of instructions to follow enables agents to act decisively and accurately.
- Guardrails: Built-in controls and instructions that tune actions to business needs maintain compliance, security, and ethical standards.
- Channel: Integrated seamlessly across existing platforms and tools, including Slack, Salesforce, or custom enterprise systems, facilitates real-time collaboration.
Integrating agentic AI throughout manufacturing operations establishes a new benchmark for efficiency and creates a strategic edge in an increasingly complex and unpredictable environment. By effectively aligning these agents with specific roles and responsibilities, manufacturers can boost output, enhance decision-making, and respond more effectively to shifting market demands.
The future belongs to those who treat productivity as a mission, where AI collaborates with people, data is protected and connected, and enterprise systems evolve alongside the businesses they support. As product companies embrace the AI-powered future, adoption of intelligent agents will position them not just to react to industry shifts, but to shape them.
