Our AI Adoption Process is an introductory service that is increasing globally among organizations seeking to improve efficiency, optimize operations, and rise production standards.
AI Adoption requires a thorough process, which ensures you are reaping its benefits: reduced risks and costs. With this process, we help you analyse your data and advise you on the best strategy to get started with AI in your company.
We follow a multidisciplinary approach which consists of 4 stages:
1. Research
Briefing: We carry out a workshop with the team to present the business
vision and give you a chance to provide a global overview of the system and data infrastructure of your
business, your constraints and limitations. We also request access to the data models and data, if
available.
Data Sources: Here, we identify and analyse the available data sources
(client-owned data, public data, amongst others), the services and systems to check POC integration
viability at the end of the process; we also carry out research on potentially useful data sources that
might complement current data sources in place.
2. Viability
Use Cases: At this stage, we identify the expected business value for the model, assess the viability and
align expectations, and formalise the problem through:
Inputs and Outputs
Validation
MetricsConstraints
Imaginary Cloud’s AI Adoption process will bring your business closer to the edge of technology, ensuring you stay competitive and are ready for the future.
3. Ideation
Exploratory Analysis: We then proceed to mine the datasets for insights into the problem at hand, including data pre-processing, visual data analysis, finding correlations, identifying data biases, and performing unsupervised learning; this will provide a comprehensive vision of the available data.
Predictive Analysis: Finally, we review technical and business risks and provide quick wins for possible implementations based on the use cases.
4. Execution
Proof of concept: Here, we work on developing extra features necessary to build a proof of concept of the proposed AI solution; this ensures that the product’s functionalities are according to expectations and meet the business needs.
MVP Project Plan: Finally, we plan out the major milestones, providing a general understanding of the project’s structure, phases, intersections and interdependencies. This allows for a good understanding of how to build the product, how much effort it will require and the expected costs.
Our AI Adoption Process is an introductory service that is increasing globally among organizations seeking to improve efficiency, optimize operations, and rise production standards.
AI Adoption requires a thorough process, which ensures you are reaping its benefits: reduced risks and costs. With this process, we help you analyse your data and advise you on the best strategy to get started with AI in your company.
We follow a multidisciplinary approach which consists of 4 stages:
1. Research
Briefing: We carry out a workshop with the team to present the business
vision and give you a chance to provide a global overview of the system and data infrastructure of your
business, your constraints and limitations. We also request access to the data models and data, if
available.
Data Sources: Here, we identify and analyse the available data sources
(client-owned data, public data, amongst others), the services and systems to check POC integration
viability at the end of the process; we also carry out research on potentially useful data sources that
might complement current data sources in place.
2. Viability
Use Cases: At this stage, we identify the expected business value for the model, assess the viability and
align expectations, and formalise the problem through:
Inputs and Outputs
Validation
MetricsConstraints
Imaginary Cloud’s AI Adoption process will bring your business closer to the edge of technology, ensuring you stay competitive and are ready for the future.
3. Ideation
Exploratory Analysis: We then proceed to mine the datasets for insights into the problem at hand, including data pre-processing, visual data analysis, finding correlations, identifying data biases, and performing unsupervised learning; this will provide a comprehensive vision of the available data.
Predictive Analysis: Finally, we review technical and business risks and provide quick wins for possible implementations based on the use cases.
4. Execution
Proof of concept: Here, we work on developing extra features necessary to build a proof of concept of the proposed AI solution; this ensures that the product’s functionalities are according to expectations and meet the business needs.
MVP Project Plan: Finally, we plan out the major milestones, providing a general understanding of the project’s structure, phases, intersections and interdependencies. This allows for a good understanding of how to build the product, how much effort it will require and the expected costs.
