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AI Understanding
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The business is well aware of the ongoing AI transformation in the respective industry
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The business has a well-defined AI strategy that aligns with its overall vision and goals
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The business model has been adapted to leverage AI, identifying new revenue streams or ways to optimize operations
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The business has defined key performance indicators (KPIs) to measure the impact of AI initiatives
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The business has a clear vision of how AI can enhance its products, services, and operations
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The business actively monitors industry trends, competitors, and has insights from the industry landscape on AI adoption
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Cross-functional collaboration is encouraged, enabling different departments to work together on different initiatives
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Employees are empowered to suggest innovations and new solutions
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The company's culture promotes experimentation and emphasizes learning from experiences
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There exist clear communication lines to keep the workforce engaged on different initiatives
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The organization stays updated on tech trends to identify new opportunities for innovation and optimization
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The company cultivates a culture of continuous learning, motivating employees to keep a tab on the latest developments in their relevant space
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There exists a clear set of rules, guidelines, and procedures to oversee the development, deployment, and use of AI technologies
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There are clearly defined roles and responsibilities for decision-making related to AI initiatives
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The lack of transparency and understandability of different AI technologies is acceptable for stakeholders
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The organization has a well-structured change management plan in place for AI adoption
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Employees are provided with training and support to transition smoothly to AI-enabled processes
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The business is aware of the ethical considerations and compliance with industry-specific regulations relating to AI
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The business has a well-structured data strategy in place, ensuring high-quality, diverse, and accessible data
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The data being collected is relevant to major business objectives and decision making
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The IT infrastructure is capable of handling advanced workloads efficiently, with the ability to scale as needed
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There are robust mechanisms in place for data security and privacy
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The business has a dedicated technical team comprising data scientists, engineers, and domain experts
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The business is open to collaborate with external AI experts or research institutions
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AI news sparks discussions on innovative AI applications and drives decision making
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Conscious efforts have been made to leverage AI solutions (such as ChatGPT) for business purposes
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AI tools have been integrated in business workflows
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The knowledge around overlapping concepts such as Artificial Intelligence, Machine Learning, Deep Learning, GenAI etc. is not overwhelming
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Key stakeholders, including leadership and relevant teams, have a solid understanding of AI capabilities, and limitations
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Leadership is aware of potential biases and ethical concerns associated with AI
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There are several business processes that include repetitive tasks or manual data entry
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The organization seeks input from customers, clients, and stakeholders to understand pain points and areas for improvement
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The organisation is aware of bottlenecks in current processes that hamper efficient working
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Cross-functional teams with representatives from different departments work together to brainstorm workflows improvements
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There exist several decision-making junctions which rely on organisational data (formal or informal)
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The business has identified a range of AI use cases across different functions
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