Artificial intelligence may be transforming manufacturing, but Onahira Rivas believes its greatest potential is not limited to sophisticated robotics, fully automated production lines or the world’s largest manufacturers.
Writing for Forbes Business Council in “Building a Modern Factory: How AI Can Enhance Manufacturers,” Rivas turns the conversation toward small and midsize manufacturers and the practical ways artificial intelligence can support the people, systems and decisions behind a modern factory.
Her argument begins with a distinction. Building a modern factory is no longer only about selecting machinery and increasing production capacity. It is also about creating an organization where information, systems, people and technology work together.
The objective, Rivas writes, should not be to create factories without people, but factories where people can accomplish more because technology supports them.
Building the Systems Before Adding the AI
For Rivas, artificial intelligence is only as useful as the operation behind it.
Manufacturing produces information across purchasing, inventory, production, quality, sales, logistics and finance. Having that data, however, is different from understanding what it means and knowing how to act on it.
Before implementing AI, Rivas argues that manufacturers need organized processes, including standard operating procedures, inventory and quality controls, production workflows and financial systems. Adding artificial intelligence to a disorganized operation will not automatically make it more efficient.
Strong systems, on the other hand, can give AI reliable information to analyze. From there, the technology can help identify patterns, support forecasting, improve planning and reveal operational inefficiencies.
Rivas illustrates the idea through a familiar manufacturing decision: determining how much raw material to purchase for the next production cycle.
Historical sales, current orders, seasonal demand, available inventory, supplier lead times, production capacity and cash flow can all influence that decision. AI can analyze those variables faster, allowing management to consider different scenarios before reaching a final decision.
The same principle applies to growth. If a manufacturer producing 1,000 units already has an inventory problem, increasing production to 10,000 units may simply create a much larger version of the same problem.
For Rivas, scaling intelligently means first understanding what is limiting growth, whether that is production speed, purchasing, equipment downtime, inventory allocation, product mix, demand or margins.
AI as a Tool for Human Intelligence
The role of people sits at the center of Rivas’ argument.
As conversations about artificial intelligence continue to raise questions about jobs, she proposes a different question for manufacturers. Rather than asking which employees AI can replace, companies can ask how AI can help employees perform their jobs better.
“The greatest value of AI will not come from asking technology to think instead of us,” Rivas writes. “It will come from using technology to help us think better.”
People bring knowledge and context that data alone may not capture. Employees understand machinery, supplier relationships, customer expectations, workplace dynamics and operational details. Artificial intelligence can process large amounts of information, identify patterns and compare scenarios faster than a person working manually, but Rivas argues that human expertise must provide context and final judgment.
A production supervisor, for example, could spend less time manually compiling reports and more time improving workflow. A quality employee could use AI-assisted information to recognize patterns in defects and focus on correcting or preventing them. A purchasing employee could evaluate supplier information and purchasing trends more efficiently.
The result, according to Rivas, is not necessarily a diminished role for manufacturing workers. AI can reduce repetitive administrative work and allow employees to dedicate more time to judgment, creativity, communication, problem-solving, and leadership.
That also requires responsibility. Rivas stresses that employees must learn to evaluate AI-generated information, verify outputs, protect company information and recognize when human judgment should take precedence.
Giving Smaller Manufacturers Greater Capabilities
The opportunity may be particularly significant for smaller manufacturers.
Large corporations can maintain specialized departments for data analysis, procurement, forecasting, finance, engineering, quality, marketing and supply chain management. A small factory may rely on only a handful of people to oversee several of those responsibilities at once.
Rivas argues that AI can help narrow part of that capability gap.
A smaller manufacturer could use the technology to analyze spreadsheets, prepare forecasts, compare operational scenarios, organize documentation, develop training materials, review production information and identify trends.
That does not transform a small company into a multinational corporation. It can, however, give a smaller team access to analytical capabilities that were once more readily available to larger organizations.
For entrepreneurs building factories with limited capital, Rivas sees that distinction as significant.
Building Smarter Factories From the Beginning
Rivas ultimately brings the argument back to the factory itself.
Entrepreneurs entering manufacturing today have an opportunity that previous generations did not. They can design factories with systems, artificial intelligence, automation and human collaboration incorporated into the business from the beginning.
But for Rivas, a modern factory is not defined by having the most machinery or using AI everywhere simply because the technology exists.
It is about identifying where technology creates measurable value and implementing it with purpose.
The manufacturers of the future, she argues, will still need people who understand products, customers, quality, machinery, operations and strategy. What will change is the set of tools available to those people and the scale of what they can accomplish.
That is where Rivas sees the real potential of artificial intelligence in manufacturing: not diminishing the importance of people, but giving manufacturers and their teams better systems, better information and greater capabilities to make smarter decisions and build companies prepared for sustainable growth.
About Onahira Rivas:
Onahira Rivas, a visionary entrepreneur whose indomitable spirit and strategic prowess have propelled her to the forefront of global business. Driven by a profound determination to revitalize American manufacturing, Onahira founded Florida’s Cotton Clouds, the first factory of cotton products in Florida.
Her relentless passion doesn't stop there; she also established Legacy Architects, a platform designed to inspire and empower entrepreneurs to build enduring legacies that resonate through the ages. Onahira Rivas's remarkable journey is a shining testament to resilience and unwavering dedication. No matter where you come from, if you set your mind and heart to it, you can achieve greatness!
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