How large organisations are putting generative AI to work on their own data, from the team building AsscherAi. 487 articles.
A security manager implements red teaming by simulating an attack on their organisation's systems to identify vulnerabilities, then uses the findings to remediate weaknesses before an...
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AsscherAi's true cost lies in data preparation and integration, where a logistics manager must work with the data team to ensure accurate and reliable responses, by cleaning and structuring...
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Process mining identifies inefficiencies in the maintenance log by analyzing event logs from systems like ERP and CRM, providing insights into actual process behavior, and helping...
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Implementing AI to improve quality involves setting up the system, identifying key questions, and tracking leading measures, such as defect rates or customer satisfaction, to drive tangible...
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AI productivity in manufacturing streamlines workflows by analysing equipment sensor data, allowing a shift supervisor to identify potential issues before they cause downtime, and enabling...
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AI process automation in metals and mining streamlines workflows by automating data analysis and handoffs between teams, allowing a production planner to quickly identify deviations and...
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Enterprise AI security in logistics involves implementing least privilege access controls for assistants like AsscherAi, which reduces the risk of data breaches by limiting access to...
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AI reduces reactive work crowding out planned work by analyzing maintenance data to identify patterns and trends, enabling maintenance managers to prioritize planned work and prevent...
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Integrating AI systems for finance, like AsscherAi, with general ledgers and purchase order systems requires careful mapping of definitions to ensure consistent data, and financial...
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When a logistics manager uses AsscherAi to query carrier performance, they may find that results are inconsistent due to variability in dwell times, detention times, and exception handling...
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Handling personally identifiable information in AsscherAi requires careful consideration of access controls, encryption, and regular audits to ensure compliance with regulations and protect...
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Understanding AI cost production helps organizations allocate resources effectively by identifying areas where integration and data work dominate the spend, allowing them to plan and budget...
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A maintenance manager checks the equipment sensor data on the AsscherAi platform to identify early warning signs of failure, such as unusual temperature fluctuations, and takes corrective...
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Implementing AsscherAi in production requires a structured approach, starting with setting up the system, configuring the data sources, and training the team, which can take several weeks...
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AI productivity tools in logistics help teams like the supply chain manager identify bottlenecks by analysing data on carrier performance, dwell and detention, and exception handling, then...
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Implementing AI process automation in manufacturing involves mapping current workflows, identifying areas for improvement, and automating handoffs between teams, which can reduce delays and...
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A hospital's IT department must ensure AsscherAi's security and privacy compliance by implementing robust data boundaries, access controls, and encryption, while also training clinical...
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AI reduces lead time variance by analyzing purchase and delivery history to identify patterns, enabling decision-makers to take corrective action, such as renegotiating with suppliers or...
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Getting started with AI systems integration requires choosing the right approach and understanding the mechanisms involved, such as read paths and write paths, to produce something...
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Implementing enterprise AI governance in healthcare providers requires careful consideration of permission at retrieval, auditability, and routing to a human, as seen in the example of a...
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AsscherAi's AI observability helps a category manager track interactions between the AI system and the organisation's data, including the questions asked, data retrieved, and answers...
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AI cost in procurement is often misunderstood due to hidden costs in integration and data work, which can lead to budget overruns and reduced ROI, as seen when a procurement manager...
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AI orchestration fails when the inventory management system updates stock levels but the accounting system does not reflect the change, causing discrepancies in financial reports, this is...
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Implementing AI in procurement takes time, as the initial fortnight is spent on groundwork, getting data in order, and establishing a common language for the AI to understand, after which...
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