AI and GenAI are currently experiencing a peak in the hype cycle. However, understanding these technologies and their practical applications is still in the early stages and requires continued development and exploration. It is also important to understand the critical role of data in driving advancements in AI technologies. While technology innovations like AI evolve and become compelling across industries, effective data governance remains foundational for the successful deployment and integration into operational frameworks.

GS Lab | GAVS recently conducted a webinar, Unlocking Hidden Efficiencies in Manufacturing & Supply Chain Operations with AI and GenAI. Mr. Michael LoRusso – CIO & Head of Shared Services at embecta and Mr. Joe Butcher – Director, Digital Strategy & Product Delivery, Digital Manufacturing at Merck were part of the panel. Mr. Vinod Sanjay – Vice President, Life Sciences at GS Lab | GAVS moderated the session.

Role of Data Quality in Business Strategy

The critical importance of data quality cannot be overstated, as it plays a pivotal role in shaping digital strategy and product delivery. From a strategic standpoint, digital initiatives must align closely with overarching business strategies, avoiding the pitfall of separate digital strategies divorced from core business goals. Starting with a clear purpose rooted in business strategy, rather than searching for perfect AI applications in isolation, is crucial. Clarity on prioritizing business challenges, framed by specific KPIs, is identified as crucial for effectively leveraging AI and digital tools to drive impactful outcomes.

Importance of Data Governance for Regular and Synthetic Data

Despite the common trend of cutting or reducing funding for data governance and archiving, companies must make data governance a core part of operations. To that end, companies must implement various strategies, including reframing data governance, demonstrating specific instances where poor data management negatively impacted business operations, to make a compelling case to executives and line managers, and working with partners to integrate governance and data control as core infrastructure elements.

While the idea of generating data from sample data is in its infancy, particularly for training machine learning models, there are significant concerns. However, there is also a major concern of data alteration rather than corruption, where generated data variations closely resemble authentic data, potentially leading to undetected errors. There is also an overarching worry about the new potential for cyber attacks through data alteration, which can be subtle and hard to detect, posing a significant risk to technology and business operations.

Synthetic data must also be cautiously approached in the manufacturing sector, particularly under strict Good Manufacturing Practices (GMP). Its use must consider the specific use cases and the implications of any decisions such data influence. As data volume increases rapidly, maintaining data quality becomes more challenging, making it easier for even experienced professionals to spot anomalies or errors just by reviewing data.

While generating data from samples is a promising technique for enhancing machine learning models, it requires careful consideration of cybersecurity, data integrity, and specific industry standards to ensure it does not inadvertently harm business operations. It is interesting to note the growing number of tools designed for data governance noting the critical role of prompt engineering in ensuring these tools perform accurately and effectively. While the upfront investment in training these tools is substantial, businesses must understand that they can yield high consistency and efficiency.

Operational Challenges in AI

There is a noted trust deficit in AI, partly because AI operations are not fully transparent, which can affect stakeholder confidence and lead to adoption challenges. AI systems must be seen as decision-support tools rather than decision-makers. AI can augment human decision-making processes by providing recommendations and explaining their rationale. Gradually integrating AI helps build trust.

For instance, AI offers recommendations discussed and reviewed in logistics in meetings, combining algorithmic output with human intuition and knowledge. The evolution into more autonomous AI systems, like GenAI, which can innovate beyond initial programming, introduces a new level of complexity and potential mistrust. This ‘trust dip’ occurs as these systems make decisions independently, without direct human input or alteration. The conversation compares the integration of AI with past experiences with technologies like RPA (Robotic Process Automation) and blockchain, emphasizing the need for careful evaluation of the benefits and impacts of new technologies.

Adopting New Technologies

While adopting new technologies within IT infrastructure, the manufacturing and supply chain industries must take a strategic approach based on process maturity to technology adoption. This helps identify the root causes of issues accurately and ensures that technology implementation directly addresses these identified problems. The ideal scenario for adopting new technology is to target long-standing, mature processes where the operations have reached ‘entitlement’ or the best they can be without a significant technological upgrade. This approach minimizes the noise and variability that can obscure the effectiveness of new technology. New technologies can act as ‘step functions’ for mature processes that dramatically improve efficiency and effectiveness rather than merely making incremental changes.

Integration of AI in Decision Making Processes

Organizations integrating new technologies, particularly AI, into their operations must follow the mantra of ‘simplify, standardize, digitize’. This approach emphasizes understanding and refining business processes before introducing technological solutions. They should also focus on integrating with business processes. Effective technology integration involves identifying key decision points within business processes and designing user-friendly applications to support these decisions. This approach helps to blend business and technology functions, avoiding silos and fostering collaboration.

However, balancing infrastructure modernization with extracting value from existing systems takes a lot of work. For long-standing companies like Merck, which has been in operation for over 125 years, transitioning legacy systems to modern infrastructure like cloud services is a gradual process that requires balancing innovation with ongoing operations. In sectors like manufacturing, especially those involving medical devices or pharmaceuticals, regulatory requirements (such as GMP) must be considered. These regulations can sometimes be perceived as barriers to adopting new technologies but are essential for ensuring safety and compliance.

Ensuring data security in AI-integrated supply chain operations involves strategic partner selection, rigorous security assessments, a deep understanding of data provenance, and stringent monitoring of data exchanges. These steps help mitigate risks associated with data security while leveraging AI technologies. embecta has integrated governance and data control principles into their foundational infrastructure with strategic guidance from partners like GS Lab | GAVS.

GS Lab | GAVS’ AI/ML services transcend mere technology; they embody the foundation of transformative solutions to reshape industries. We excel in creating customized AI-powered solutions and ML applications that empower businesses to unravel intricate data, foresee trends, and make precise data-driven decisions. To learn more, please visit here. We also bring a structured methodology to tackle varied challenges using GenAI, collaborating closely with you to develop tailored solutions that deliver tangible benefits to your customers and employees, prioritizing security throughout. To learn more, please visit Gen AI page.

While this blog offers a high-level gist of the webinar, you can watch the entire webinar here. For more such videos, please visit Webinar page and Video section.