The Role of AI and Information Architecture in Modern Manufacturing
The Role of AI and Information Architecture in Modern Manufacturing
Discover how AI and robust data architecture revolutionize manufacturing by streamlining decision-making and boosting efficiency.
By
Ramila Peiris
March 28, 2025
7
min read
In this article
7
min read
In today’s fast-paced manufacturing environment, leveraging artificial intelligence (AI) and data analytics has become crucial for optimizing processes and reducing costs. However, many companies struggle to fully realize AI’s potential due to foundational gaps in their information architecture. Ramila Peiris, an expert in AI-driven manufacturing solutions, sheds light on these challenges and the importance of establishing a strong data foundation.
In today’s fast-paced manufacturing environment, leveraging artificial intelligence (AI) and data analytics has become crucial for optimizing processes and reducing costs. However, many companies struggle to fully realize AI’s potential due to foundational gaps in their information architecture. Ramila Peiris, an expert in AI-driven manufacturing solutions, sheds light on these challenges and the importance of establishing a strong data foundation.
In today’s fast-paced manufacturing environment, leveraging artificial intelligence (AI) and data analytics has become crucial for optimizing processes and reducing costs. However, many companies struggle to fully realize AI’s potential due to foundational gaps in their information architecture. Ramila Peiris, an expert in AI-driven manufacturing solutions, sheds light on these challenges and the importance of establishing a strong data foundation.
In today’s fast-paced manufacturing environment, leveraging artificial intelligence (AI) and data analytics has become crucial for optimizing processes and reducing costs. However, many companies struggle to fully realize AI’s potential due to foundational gaps in their information architecture. Ramila Peiris, an expert in AI-driven manufacturing solutions, sheds light on these challenges and the importance of establishing a strong data foundation.
AI and Decision-Making in Manufacturing
Experts across industries rely on data to make informed decisions and solve complex problems. According to Peiris, if companies can provide data and analytics in a ready-to-use format, what he calls “analytics at speed”, they can significantly reduce problem-solving times and, in turn, cut manufacturing costs. AI plays a pivotal role in streamlining this process by enabling faster, data-driven decision-making. However, its effectiveness depends on having well-structured data systems in place.
Experts across industries rely on data to make informed decisions and solve complex problems. According to Peiris, if companies can provide data and analytics in a ready-to-use format, what he calls “analytics at speed”, they can significantly reduce problem-solving times and, in turn, cut manufacturing costs. AI plays a pivotal role in streamlining this process by enabling faster, data-driven decision-making. However, its effectiveness depends on having well-structured data systems in place.
Experts across industries rely on data to make informed decisions and solve complex problems. According to Peiris, if companies can provide data and analytics in a ready-to-use format, what he calls “analytics at speed”, they can significantly reduce problem-solving times and, in turn, cut manufacturing costs. AI plays a pivotal role in streamlining this process by enabling faster, data-driven decision-making. However, its effectiveness depends on having well-structured data systems in place.
Experts across industries rely on data to make informed decisions and solve complex problems. According to Peiris, if companies can provide data and analytics in a ready-to-use format, what he calls “analytics at speed”, they can significantly reduce problem-solving times and, in turn, cut manufacturing costs. AI plays a pivotal role in streamlining this process by enabling faster, data-driven decision-making. However, its effectiveness depends on having well-structured data systems in place.
The Importance of Information Architecture in AI Implementation
A common mistake in AI adoption is focusing on the technology without addressing the foundational data infrastructure. Peiris emphasizes that “there is no AI without information architecture (IA),” meaning that organizations need a structured data system before they can effectively implement AI at scale. Without proper IA, AI initiatives often fail to deliver sustainable solutions.
To illustrate this, Peiris compares data infrastructure to plumbing in a house. Just as homebuyers expect functioning water pipelines, businesses should ensure that their data systems are properly structured from the outset. Unfortunately, many industries overlook this step, resulting in inefficient AI implementation. Companies investing in AI must also invest in data pipelines that clean, organize, and contextualize information for long-term success.
A common mistake in AI adoption is focusing on the technology without addressing the foundational data infrastructure. Peiris emphasizes that “there is no AI without information architecture (IA),” meaning that organizations need a structured data system before they can effectively implement AI at scale. Without proper IA, AI initiatives often fail to deliver sustainable solutions.
To illustrate this, Peiris compares data infrastructure to plumbing in a house. Just as homebuyers expect functioning water pipelines, businesses should ensure that their data systems are properly structured from the outset. Unfortunately, many industries overlook this step, resulting in inefficient AI implementation. Companies investing in AI must also invest in data pipelines that clean, organize, and contextualize information for long-term success.
A common mistake in AI adoption is focusing on the technology without addressing the foundational data infrastructure. Peiris emphasizes that “there is no AI without information architecture (IA),” meaning that organizations need a structured data system before they can effectively implement AI at scale. Without proper IA, AI initiatives often fail to deliver sustainable solutions.
To illustrate this, Peiris compares data infrastructure to plumbing in a house. Just as homebuyers expect functioning water pipelines, businesses should ensure that their data systems are properly structured from the outset. Unfortunately, many industries overlook this step, resulting in inefficient AI implementation. Companies investing in AI must also invest in data pipelines that clean, organize, and contextualize information for long-term success.
A common mistake in AI adoption is focusing on the technology without addressing the foundational data infrastructure. Peiris emphasizes that “there is no AI without information architecture (IA),” meaning that organizations need a structured data system before they can effectively implement AI at scale. Without proper IA, AI initiatives often fail to deliver sustainable solutions.
To illustrate this, Peiris compares data infrastructure to plumbing in a house. Just as homebuyers expect functioning water pipelines, businesses should ensure that their data systems are properly structured from the outset. Unfortunately, many industries overlook this step, resulting in inefficient AI implementation. Companies investing in AI must also invest in data pipelines that clean, organize, and contextualize information for long-term success.
Overcoming AI Adoption Barriers
One of the primary barriers to AI adoption is the temptation to embrace AI solutions without first establishing a robust information architecture. Peiris notes that many organizations become enamored with the promise of AI but fail to create the necessary data systems to support it. This oversight leads to inefficiencies and prevents companies from scaling AI solutions effectively.
To successfully integrate AI into manufacturing, businesses must prioritize building sustainable data systems, including:
Contextualized data pipelines
Scalable and maintainable data architectures
Seamless integration of AI with existing workflows
By addressing these foundational issues, organizations can move beyond the “buzzword” stage of AI and harness its true potential.
One of the primary barriers to AI adoption is the temptation to embrace AI solutions without first establishing a robust information architecture. Peiris notes that many organizations become enamored with the promise of AI but fail to create the necessary data systems to support it. This oversight leads to inefficiencies and prevents companies from scaling AI solutions effectively.
To successfully integrate AI into manufacturing, businesses must prioritize building sustainable data systems, including:
Contextualized data pipelines
Scalable and maintainable data architectures
Seamless integration of AI with existing workflows
By addressing these foundational issues, organizations can move beyond the “buzzword” stage of AI and harness its true potential.
One of the primary barriers to AI adoption is the temptation to embrace AI solutions without first establishing a robust information architecture. Peiris notes that many organizations become enamored with the promise of AI but fail to create the necessary data systems to support it. This oversight leads to inefficiencies and prevents companies from scaling AI solutions effectively.
To successfully integrate AI into manufacturing, businesses must prioritize building sustainable data systems, including:
Contextualized data pipelines
Scalable and maintainable data architectures
Seamless integration of AI with existing workflows
By addressing these foundational issues, organizations can move beyond the “buzzword” stage of AI and harness its true potential.
One of the primary barriers to AI adoption is the temptation to embrace AI solutions without first establishing a robust information architecture. Peiris notes that many organizations become enamored with the promise of AI but fail to create the necessary data systems to support it. This oversight leads to inefficiencies and prevents companies from scaling AI solutions effectively.
To successfully integrate AI into manufacturing, businesses must prioritize building sustainable data systems, including:
Contextualized data pipelines
Scalable and maintainable data architectures
Seamless integration of AI with existing workflows
By addressing these foundational issues, organizations can move beyond the “buzzword” stage of AI and harness its true potential.
How AI Platforms Like Katalyze AI Drive Transformation
Platforms like Katalyze AI aim to tackle the most challenging aspects of AI-driven analytics, delivering contextualized, analytics-ready data. Peiris argues that the hardest part of AI isn’t the analytics itself but ensuring that data is prepared in a way that allows end users to extract meaningful insights efficiently.
Many AI solutions claim to revolutionize industries, but true transformation comes from platforms that solve fundamental data challenges. Peiris envisions a future where AI platforms seamlessly integrate with manufacturing processes, providing:
Faster access to actionable insights
Streamlined data integration
Practical AI-driven solutions tailored to industry needs
Platforms like Katalyze AI aim to tackle the most challenging aspects of AI-driven analytics, delivering contextualized, analytics-ready data. Peiris argues that the hardest part of AI isn’t the analytics itself but ensuring that data is prepared in a way that allows end users to extract meaningful insights efficiently.
Many AI solutions claim to revolutionize industries, but true transformation comes from platforms that solve fundamental data challenges. Peiris envisions a future where AI platforms seamlessly integrate with manufacturing processes, providing:
Faster access to actionable insights
Streamlined data integration
Practical AI-driven solutions tailored to industry needs
Platforms like Katalyze AI aim to tackle the most challenging aspects of AI-driven analytics, delivering contextualized, analytics-ready data. Peiris argues that the hardest part of AI isn’t the analytics itself but ensuring that data is prepared in a way that allows end users to extract meaningful insights efficiently.
Many AI solutions claim to revolutionize industries, but true transformation comes from platforms that solve fundamental data challenges. Peiris envisions a future where AI platforms seamlessly integrate with manufacturing processes, providing:
Faster access to actionable insights
Streamlined data integration
Practical AI-driven solutions tailored to industry needs
Platforms like Katalyze AI aim to tackle the most challenging aspects of AI-driven analytics, delivering contextualized, analytics-ready data. Peiris argues that the hardest part of AI isn’t the analytics itself but ensuring that data is prepared in a way that allows end users to extract meaningful insights efficiently.
Many AI solutions claim to revolutionize industries, but true transformation comes from platforms that solve fundamental data challenges. Peiris envisions a future where AI platforms seamlessly integrate with manufacturing processes, providing:
Faster access to actionable insights
Streamlined data integration
Practical AI-driven solutions tailored to industry needs
The Power of Data Storytelling
Beyond technology, effective AI adoption requires stakeholder buy-in. One of Peiris’ key strategies is data storytelling, crafting narratives that resonate with both technical and non-technical audiences. He emphasizes the importance of explaining AI concepts in a way that anyone can understand, using relatable analogies like the plumbing example.
In addition, recent news highlights that businesses are realizing to effectively utilize modern generative AI tools, they must supplement them with human intelligence, especially in organizing and updating data. This approach leads to improved AI outcomes and productivity gains. For more details, please refer to the article on WSJ.
Peiris believes that the best stories are those that make complex ideas accessible. By simplifying explanations and highlighting tangible benefits, companies can secure leadership support for AI initiatives and drive meaningful change.
Beyond technology, effective AI adoption requires stakeholder buy-in. One of Peiris’ key strategies is data storytelling, crafting narratives that resonate with both technical and non-technical audiences. He emphasizes the importance of explaining AI concepts in a way that anyone can understand, using relatable analogies like the plumbing example.
In addition, recent news highlights that businesses are realizing to effectively utilize modern generative AI tools, they must supplement them with human intelligence, especially in organizing and updating data. This approach leads to improved AI outcomes and productivity gains. For more details, please refer to the article on WSJ.
Peiris believes that the best stories are those that make complex ideas accessible. By simplifying explanations and highlighting tangible benefits, companies can secure leadership support for AI initiatives and drive meaningful change.
Beyond technology, effective AI adoption requires stakeholder buy-in. One of Peiris’ key strategies is data storytelling, crafting narratives that resonate with both technical and non-technical audiences. He emphasizes the importance of explaining AI concepts in a way that anyone can understand, using relatable analogies like the plumbing example.
In addition, recent news highlights that businesses are realizing to effectively utilize modern generative AI tools, they must supplement them with human intelligence, especially in organizing and updating data. This approach leads to improved AI outcomes and productivity gains. For more details, please refer to the article on WSJ.
Peiris believes that the best stories are those that make complex ideas accessible. By simplifying explanations and highlighting tangible benefits, companies can secure leadership support for AI initiatives and drive meaningful change.
Beyond technology, effective AI adoption requires stakeholder buy-in. One of Peiris’ key strategies is data storytelling, crafting narratives that resonate with both technical and non-technical audiences. He emphasizes the importance of explaining AI concepts in a way that anyone can understand, using relatable analogies like the plumbing example.
In addition, recent news highlights that businesses are realizing to effectively utilize modern generative AI tools, they must supplement them with human intelligence, especially in organizing and updating data. This approach leads to improved AI outcomes and productivity gains. For more details, please refer to the article on WSJ.
Peiris believes that the best stories are those that make complex ideas accessible. By simplifying explanations and highlighting tangible benefits, companies can secure leadership support for AI initiatives and drive meaningful change.
AI has the power to revolutionize manufacturing, but its success depends on a solid information architecture.
Organizations must move beyond the allure of AI’s promises and focus on building the necessary data infrastructure to support scalable, sustainable solutions. By prioritizing contextualized data pipelines and leveraging platforms like Katalyze AI, manufacturers can unlock the full potential of AI, driving efficiency and innovation in the industry.
AI has the power to revolutionize manufacturing, but its success depends on a solid information architecture.
Organizations must move beyond the allure of AI’s promises and focus on building the necessary data infrastructure to support scalable, sustainable solutions. By prioritizing contextualized data pipelines and leveraging platforms like Katalyze AI, manufacturers can unlock the full potential of AI, driving efficiency and innovation in the industry.
AI has the power to revolutionize manufacturing, but its success depends on a solid information architecture.
Organizations must move beyond the allure of AI’s promises and focus on building the necessary data infrastructure to support scalable, sustainable solutions. By prioritizing contextualized data pipelines and leveraging platforms like Katalyze AI, manufacturers can unlock the full potential of AI, driving efficiency and innovation in the industry.
AI has the power to revolutionize manufacturing, but its success depends on a solid information architecture.
Organizations must move beyond the allure of AI’s promises and focus on building the necessary data infrastructure to support scalable, sustainable solutions. By prioritizing contextualized data pipelines and leveraging platforms like Katalyze AI, manufacturers can unlock the full potential of AI, driving efficiency and innovation in the industry.
Ramila Peiris
Ramila Peiris
Ramila Peiris
Ramila Peiris is Global Head, Data Management, ML & AI Platform, MSAT at Sanofi, a pragmatic and strategic leader with a strong track record in problem-solving, driving innovation, and delivering impactful data science solutions. He excels at engaging diverse stakeholders, breaking silos, and implementing digital capabilities that drive real business impact. Passionate about improving processes, scaling innovations from proof of concept to industrialization, and building high-performance teams, Ramila is dedicated to turning data into actionable insights that transform organizations.
Ramila Peiris is Global Head, Data Management, ML & AI Platform, MSAT at Sanofi, a pragmatic and strategic leader with a strong track record in problem-solving, driving innovation, and delivering impactful data science solutions. He excels at engaging diverse stakeholders, breaking silos, and implementing digital capabilities that drive real business impact. Passionate about improving processes, scaling innovations from proof of concept to industrialization, and building high-performance teams, Ramila is dedicated to turning data into actionable insights that transform organizations.
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"With Katalyze AI, we can analyze data in real-time and make informed decisions to optimize our processes." Chris Calabretta
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