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Redesigning Workflows to Integrate AI
Organisations are redesigning workflows to integrate AI. Instead of layering AI onto existing processes, leading firms now reconfigure how work gets done. That means that companies automate routine tasks and assign them to AI agents, while human roles shift towards oversight, training AI systems, and solving complex problems that machines cannot yet handle. Hybrid teams—made
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AI as a New Factor of Production
In today’s evolving business landscape, industry analysts see the emergence of AI as a new factor of production alongside human effort. Rather than simply adding more people to increase output, organisations can boost productivity by equipping individuals with AI capabilities—effectively adding “digital labour” to each role. Some experts argue that AI’s ability to reason, code,
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Interviewing Machine Learning Engineers
Machine Learning Engineers specialize in implementing ML models in real-world applications. They combine software engineering and data science skills to train, deploy, and maintain models that perform reliably in production. They thrive on solving practical problems with AI and delivering working solutions that improve products and user experiences. Here is a guide on interviewing Machine
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Interviewing AI Research Scientists
AI Research Scientists drive innovation by advancing the state of the art in artificial intelligence. They operate much like academic researchers—even in industry settings—and thrive on intellectual challenges, autonomy, and the opportunity to make lasting contributions to the field. Here is a guide on interviewing AI Research Scientists. What Motivates AI Research Scientists Solving Cutting-Edge
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Interviewing AI Platform Engineers
AI Platform Engineers bring a unique blend of infrastructure expertise and machine learning fluency. They often lead initiatives like building centralized ML platforms that support data ingestion, model training, and deployment—or integrating open-source tools like Kubeflow or MLflow into enterprise environments. Here is a guide on interviewing AI Platform Engineers. AI Platform Engineers Interview Strategy
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Interviewing AI Talent Guide
Hiring and interviewing AI talent demands a clear understanding of each position’s unique motivations, skill sets, and interview focus areas. This guide breaks down three of the most sought-after roles in AI—AI Platform Engineer, AI Research Scientist, and Machine Learning Engineer. It outlines how to effectively interview candidates for each. We explore what drives professionals
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Eightfold Lessons Learned
Deploying Eightfold platform in a large organisation reveals valuable insights. Drawing from industry research and the experiences of AstraZeneca, Eaton, HSBC, and Vodafone, here are the 10 Eightfold lessons learned and success factors for deployment: 1. Align Technology with Strategy and Culture Align AI implementation directly to strategic talent objectives. Define how Eightfold supports business
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Eightfold Launch and Go-Live
Eightfold launch is the culmination of the deployment project. It is when Eightfold go-live for production use across the organisation. Success requires meticulous planning and careful execution. Gartner advises creating a detailed plan that minimises last‑minute changes and preserves support continuity to ensure a seamless transition. Plan the cutover well before the launch date Well
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Eightfold Training and Enablement
Eightfold training and enablement is key for successful deployment. Technology platforms fail if users do not know how to use them or resist change. Effective training gives recruiters, coordinators, hiring managers, and other stakeholders the skills to leverage Eightfold from day one. Here are key steps to Eightfold enablement: Design a role‑based training programme Tailor
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Eightfold AI Change Management
Deploying a new AI recruiting platform is as much about people as it is about technology. Eightfold change management should run in parallel with technical work so the organisation is ready to embrace the platform. This phase covers communication, training (detailed in the next section), and ongoing stakeholder engagement to drive adoption. Here are the
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