AI promises to revolutionise the way companies do business. The variety of solutions based on generative AI is certainly as many as the number of providers, and the number of needs as many as the number of organisations considering the implementation of AI. In this post, we will share some ideas on how to plan for the deployment of an AI solution and what it takes to deploy an AI solution based on organisations own data.
Before an organisation deploys an AI-based solution, it is important to consider a few factors to ensure the effectiveness, security and ethics of the technology deployment. Here are a few key things that every organisation should consider:
Strategic goal and need: what business need does AI address? Does the adoption of AI support the strategic objectives of the business and does it really add value?
Data protection and security: there are significant data protection and security risks associated with the development and use of AI. Where is the data stored and does the organisation contribute to the development of the language model with its own data?
Skills and resources: the organisation must have sufficient skills or a partner to develop, deploy and manage the AI technology.
Reliability and accuracy of the technology: it must be ensured that the system works reliably and produces accurate results. This requires extensive testing and validation.
Scalability and maintenance: is the chosen solution scalable so that it can evolve with the organisation to meet future needs? Can the solution be maintained in-house or will maintenance be outsourced to a partner?
Legislation and regulation: Does the solution comply with legislation or other industry regulation?
Costs and ROI: It is important to assess the costs and predict the potential return on investment (ROI) before making a decision.
Culture and change management: implementing AI may require significant changes in the culture and processes of the company. Planning is needed to ensure that everyone is committed to the change and that staff are supported and trained to adopt the new technology.
Partners and ecosystem: often the adoption of AI requires collaboration with external actors. I must be made sure that the selected partners support the organisation's objectives and invest sufficiently in development.
Using your own data with AI
One of the most effective ways to leverage AI is to build a solution based on the company's own data. Such an approach can provide a significant competitive advantage, but successful deployment requires planning, time and resources. None of the AI solutions is directly ready to be used based on a company's own data without some customization, training, fine-tuning or preparation. The extent of the work required depends on the needs of the organisation, the AI solution used and the complexity of the data.
Collection and management of training data
Data is the foundation of an AI solution. Without high-quality, timely and relevant data, AI cannot deliver accurate and reliable results. The first step is to ensure that you have a sufficient amount of high-quality data.
Data quality: This may require significant pre-processing, such as filling in missing values, correcting erroneous data and removing duplicates.
Data diversity: in addition to high quality data, it is important that the data covers a wide range of business areas that will be supported by the AI solution.
Data management: data must be stored securely and be easily accessible. This requires well-designed data warehouses where data is logically organised and easily retrievable for analysis.
Selection and development of AI models
Once the data is in place, the next step is the selection and development of AI models. It is important to clearly define the problem that the AI solution is solving and the expected results. This will help to steer the development of the models in the right direction. The choice of AI model will depend largely on the problem to be solved. For example, predictive models can be used to predict, say, sales, while classification models can help improve the customer experience. The chosen model needs to be trained with the company's data. This process can be iterative and require several experiments and fine-tuning before the model reaches sufficient accuracy.
The deployment of AI solutions also requires the right infrastructure. This can mean both physical hardware and cloud-based services. Training and running AI models requires significant computing power. This can be done either on the company's own servers or by using cloud services. Data platforms, or cloud-based data warehouses, are essential for processing and analysing large amounts of data. AI solutions often handle sensitive data, so security is a critical part of the infrastructure.
Skills and team
The development and deployment of an AI solution requires multidisciplinary skills and close collaboration between different teams.
Data analysts are responsible for processing and analysing data, developing and training AI models.
The IT team ensures that the technical infrastructure is in place to support the deployment and maintenance of the AI solution.
Business experts will ensure that the AI solution meets the business needs of the company and that the results can be used effectively.
Deployment and integration into the business
After development, the solution is integrated into the company's business processes. Before large-scale deployment, it is recommended to pilot the AI solution on a smaller scale to identify potential problems. Users who will work with the AI solution need to be trained in its use. This will ensure that the solution is implemented efficiently and effectively. The deployment of an AI solution based on proprietary data is not a one-off project, but requires continuous monitoring, maintenance and optimisation to keep it up to date and deliver added value constantly.
Ethical and legal considerations
The deployment of an AI solution must also take into account ethical and legal considerations. AI decision-making must be transparent and fair. This means, for example, that the material produced by the AI solution must be correct, justified and explainable. The data and the way it is processed by the AI solution must also meet the requirements of data protection legislation. It is important to clearly define how the AI-generated material is used and who is responsible for its application.
An AI solution based on proprietary data requires commitment to development
Implementing an AI solution based on a company's own data is a multi-step process that requires careful planning, the right technology, skilled staff or partners and, above all, a commitment to continuous development. At its best, however, a solution based on proprietary data can provide a significant competitive advantage and open up new opportunities for business development.
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