FAQs

Artificial Intelligence (AI) is the broadest term, referring to systems designed to perform tasks that typically require human intelligence. This includes reasoning, learning, planning, perception, and natural language understanding.

Machine Learning (ML) is a subset of AI that focuses on algorithms that can learn from and make predictions based on data. Instead of being explicitly programmed to perform a task, ML systems learn from examples and improve their performance over time.

Generative AI is a specific type of AI that can create new content, such as text, images, audio, or video, that resembles human-created content. Examples include large language models like GPT-4, image generation models like DALL-E, and music generation systems. Generative AI has gained significant attention recently due to its ability to produce high-quality, creative outputs.

At Agiteks, we work with all these technologies, helping organizations implement the right solution for their specific needs and use cases.

At Agiteks, we believe that AI systems must be developed and deployed responsibly. Our approach to AI governance and responsible AI includes:

• Ethical Principles: We adhere to key principles including fairness, transparency, privacy, security, and human oversight
• Bias Detection and Mitigation: We implement processes to identify and address biases in data and models
• Explainability: We work to ensure AI systems are interpretable and their decisions can be explained
• Privacy Protection: We implement privacy-by-design approaches and comply with relevant regulations
• Governance Framework: We help organizations establish AI governance structures, policies, and procedures
• Risk Assessment: We conduct thorough risk assessments for AI implementations
• Continuous Monitoring: We implement systems to monitor AI performance and detect issues

Our responsible AI framework is designed to help organizations harness the benefits of AI while managing risks and ensuring alignment with organizational values and societal expectations.

Data is the foundation of successful AI implementation. Key data requirements include:

• Data Quantity: Sufficient volume of data to train models effectively (requirements vary by use case)
• Data Quality: Clean, accurate, and consistent data with minimal errors or inconsistencies
• Data Relevance: Data that is directly related to the problem you're trying to solve
• Data Diversity: Representative data that covers the full range of scenarios the AI will encounter
• Data Accessibility: Data that can be accessed and processed in a timely manner
• Data Governance: Proper permissions, privacy controls, and compliance with regulations

Our AI implementation process includes a comprehensive data assessment phase where we evaluate your existing data assets, identify gaps, and develop strategies to address any data challenges. In some cases, we can also work with synthetic data or transfer learning approaches when limited data is available.

Measuring ROI for AI projects requires a comprehensive approach that considers both quantitative and qualitative factors:

Quantitative Metrics:

• Cost reduction (e.g., operational efficiency, automation of manual tasks)
• Revenue increase (e.g., improved conversion rates, customer retention)
• Time savings (e.g., faster processing times, reduced cycle times)
• Error reduction (e.g., improved accuracy, fewer defects)
• Resource optimization (e.g., better inventory management, capacity utilization)

Qualitative Benefits:

• Improved customer experience and satisfaction
• Enhanced employee experience and job satisfaction
• Better decision-making capabilities
• Competitive advantage and market positioning
• Organizational learning and capability building

We work with our clients to establish clear KPIs at the beginning of each AI project and implement measurement frameworks to track performance over time. This allows us to demonstrate value and continuously improve AI implementations.