Frequently Asked Questions

AI implementation involves integrating artificial intelligence technology into existing systems or processes to automate tasks, improve decision-making, in addition to enhance user experiences.

o Identify the problem or process that AI can improve.

o Gather and analyse relevant data.

o Choose the appropriate AI technology (e.g., machine learning, natural language processing).

o Secure funding and resources.

o Assemble a cross-functional implementation team.

It depends on the complexity of the task. Some machine learning models can be trained with small datasets, while others require large amounts of high-quality data. Consider using data augmentation techniques or transfer learning if data is scarce.

o Data scientists and AI researchers to develop models.

o Data engineers to manage data pipelines.

o AI/ML engineers to integrate models into existing systems.

o Project managers to oversee the project.

o Business analysts to ensure alignment with business objectives.

o Implement data validation rules.

o Regularly clean and update the data.

o Use techniques for handling missing or incomplete data.

o Perform exploratory data analysis to understand data distributions and identify anomalies.

o Ensuring privacy and security of data.

o Mitigating bias in AI models.

o Being transparent about AI use and capabilities.

o Considering the impact on employment and the workforce.

o Assess the problem you're solving.

o Consider your existing infrastructure and data capabilities.

o Look at the maturity and support for different AI technologies.

o Evaluate the cost and return on investment.

o Data quality and availability.

o Integrating AI into existing systems.

o Skills shortage and talent acquisition.

o Ensuring ethical and unbiased AI.

o Scalability and maintenance of AI systems.

It can vary greatly, from a few weeks for off-the-shelf solutions to several months or even years for custom, large-scale implementations.

o Set clear, quantifiable objectives.

o Use performance metrics relevant to the AI application (e.g., accuracy, speed, cost savings).

o Assess user adoption and satisfaction.

o Evaluate the impact on business outcomes.

Costs can range widely based on complexity, ranging from thousands to millions. Factors include the technology used, labour costs, data acquisition, and infrastructure requirements.

Yes, there are scalable AI solutions for businesses of all sizes. Small businesses can use AI for tasks such as customer service, sales forecasting, and process optimisation.

 o Start with pilot projects to demonstrate value.

o Use APIs to connect AI models to existing systems.

o Train staff to work alongside AI tools.

o Continuously monitor and refine AI integrations.

Machine learning is a subset of AI that focuses on creating systems that learn from data, identifying patterns, and making decisions with minimal human intervention.

o Diagnose the issue (e.g., poor data quality, model overfitting).

o Improve the training data or model architecture.

o Implement fallback mechanisms or human-in-the-loop processes.

o Continuously monitor model performance.

o Regularly retrain models with new data.

o Keep abreast of the latest AI research and techniques.

o Update the infrastructure as needed.

o Collect ongoing user feedback for continuous improvement.

AI can automate tasks, potentially displacing certain job functions, but it also creates new opportunities and roles. Upskilling and reskilling can help mitigate the impact on employment.

o Use encryption and robust authentication mechanisms.

o Regularly conduct security audits and penetration testing.

o Implement strict access controls and monitoring.

o Stay updated on the latest security practices and patches.

You can send us your RFI, RFQ or RFP.  We will then issue an engagement letter outlining the scope of the agreement terms and costs, prior to signing contracts. A statement of works is issued and development work can commence.