Nathan Knight’s Post

Artificial Intelligence, mastering the art of data collection is paramount to unlocking the full potential of machine learning algorithms. By adopting systematic methods, overcoming challenges, and adopting best practices, organizations can harness the power of data to drive innovation, gain competitive advantage, and provide transformative solutions across various domains. Through careful data collection, Globose Technology Solutions remains at the forefront of AI innovation, enabling clients to harness the power of data-driven insights for sustainable growth and success.

Why your AI models stumble before the finish line - InfoWorld: Failed AI projects waste time and resources, damage reputations, and stifle innovation. · More data isn't always better · The price of poor data quality.

💡 The backbone of AI’s capabilities is #data, and enterprises rely heavily on vast data sets to train and deploy AI systems. However, leveraging #AI effectively within an enterprise comes with data-related challenges that must be addressed to realize its full potential. Effective AI/ML deployment relies on equally good data governance tools, processes, and culture. Read on to find out more… #idc #thoughtleadership #collibra

At the operational level, AI and the use of machine learning (ML) tools can augment human capabilities to monitor, predict, target, and accelerate the capacity to detect and respond to an adversary. AI can also be used to automate logistics and predict the need for repairs, improving operational readiness. At the organizational level, advanced analytics allow organizations to see and understand themselves and their processes better, identifying potential efficiencies of cost and time. AI-augmented automation can take over repetitive tasks, freeing personnel to take on more complex and demanding activities.

Digitizing documents today is more than just conversion. The typical digitization we all know scans physical documents into digital copies. Those files are saved as JPG or PDF, which have limited use for content. Simply put, they are basically just an image of the document. AI takes it up a notch. Not only does it do the paper-to-pdf thing, but it goes a step further with character recognition, a capability we at Fintelite (YC W22) tap into :) Leveraging AI, we present a solution that automatically digitizes documents into datasets you can easily work with—something that a basic scanner tool can't offer. This means data is now fully analyzable for informed business decision-making💡

Robust AI: The Future of Operations Incorporating advanced AI technologies into operational frameworks is reshaping the landscape of industries. Robust AI Ops enhances efficiency, decision-making, and innovation across sectors with key features such as automation, intelligent decision-making, enhanced collaboration, and continuous learning. Automation streamlines tasks, reducing manual effort and costs, while intelligent decision-making leverages data-driven insights for strategic decisions, enhancing agility. Enhanced collaboration fosters seamless communication, and continuous learning ensures operations remain cutting-edge. The future implications of Robust AI Ops are vast. Organizations will experience increased efficiency, improved innovation, scalability, and enhanced risk management. By harnessing generative AI, companies can accelerate product development and market responsiveness, ensuring competitiveness in dynamic markets. As businesses embrace Robust AI Ops, they unlock unparalleled levels of efficiency, innovation, and agility, shaping the future of operations.

"2025: Time to Call BS on AI in Manufacturing" Let’s be real—most AI in manufacturing is like a broken pencil: pointless. Why? 💥 Bad data = bad AI. Simple as that. In 2025, it's all about: 1️⃣ Scalable AI or goodbye. 2️⃣ Low-code, but make sure the data’s clean. 3️⃣ No more pilots—prove it, or quit pretending. AI can’t save you if your data’s a mess. Unless, of course, it’s AI that fixes that mess. 😏 What’s your take? 👇

Most organizations are at least dabbling with AI and there's a lot of hand wringing over technical talent, but as with most new technology the realization of strategic value requires much more than technical capabilities: "The broader, more enduring tasks lie in the realms of organizational structure, strategy direction, right-sizing the use case for effective deployments, and cultivating a culture of change" #AI #ML #artificialintelligence #DigitalTransformation #FasterWay https://lnkd.in/geR9C54Z

I made a huge mistake by underestimating AI workflow challenges. AI workflows promise efficiency and adaptability, but they come with challenges. Ensuring seamless integration with existing systems, enabling effective decision-making, and maintaining a feedback loop for continuous learning. Key challenges include: → Integration barriers: Ensuring compatibility with legacy systems. → Decision-making limitations: Handling ambiguity in complex scenarios. → Feedback loop gaps: Building robust systems for continuous improvement. Practical examples: • Data silos can hinder seamless integration. • AI might struggle with nuanced human decisions. • Lack of proper feedback loops can result in stagnant models. To overcome these hurdles: → Foster cross-departmental collaboration to break down silos. → Combine human oversight with AI for better decision-making. → Use regular evaluations to ensure workflows adapt and improve. AI workflows are powerful, but they’re not plug-and-play. What challenges have you faced when deploying AI workflows? Let’s share strategies for anyone else reading!

2025: Manufacturing’s Defining Moment for AI (What’s your take on these trends?) I’ve been working closely with manufacturing experts, and one truth has come to light... most "AI" in manufacturing is BS because of one thing. Data quality... Without clean, reliable data, even the most advanced AI tools can’t deliver. And in manufacturing, data is complex, fragmented, and often messy. Here are three pivotal trends reshaping the future: 1. Economic Pressures Demand Scalable AI Inflation may be easing, but energy costs and supply chain challenges persist. Manufacturers need AI to adapt—but success depends on reusing proven use cases and ensuring the data behind them is sound. 2. Workforce Gaps Meet AI Democratisation Labor shortages aren’t going away. Low-code platforms are empowering domain experts to solve problems with data. But here’s the catch: the output is only as good as the data accuracy and accessibility. 3. Pilots Aren’t Enough—Proving Impact is Key Generative AI and digital twins are game changers, but scaling them across operations demands trustworthy data foundations. Without tackling issues like compatibility and integration, they’ll remain potential, not reality.