Be ready for AI/ML solutions sooner than the majority
AI Adoption Framework
Early adopters of new technologies already started to implement AI/ML Powered solutions into their businesses. Not to lose the wave of booming artificial intelligence capabilities, make sure to adopt best practices allowing you to integrate AI/ML solutions seamlessly.
Successful implementation of AI solutions requires environment ready to tackle multiple challenges
How we work on AI/ML powered projects?
AI/ML solutions typically begin with a robust data governance process. However, when adopting any AI/ML-based technology, there are several other challenges to take into account. Assessing your readiness to effectively implement AI/ML solutions in your business is a crucial prerequisite before diving into development.
What areas does AI Adoption Framework cover?
- Data quality, availability, accessibility and management
- Data protection and privacy
- Legal and regulatory obligations (EU AI Act)
- Ethical concerns and risk management
- Continuous effectivity improvement
- Cost effectivity, sustainability and total cost of ownership
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Assess your organization's readiness
Do I have my data under control?
Do you have a designated individual responsible for data management or a well-established process to ensure the quality of the data you’re collecting? If not, it’s highly likely that your data are in a moderate shape.
Do I work with sensitive or personal data?
Handling sensitive or personal data necessitates additional security and legal considerations. Such data is also more sensitive when used in external environments, particularly when shared with third parties.
Do I have any regulatory or compliance obligations?
Is using AI tools in my use case ethical?
Are your users or employees aware of the use of AI/ML tools, and do they have concerns about its ethical implications? It’s crucial to maintain honesty and transparency regarding the utilization of AI/ML solutions.
How to make sure your organisation is ready to adopt AI/ML tools?
01 People
02 Data
03 Process
04 Technology
Ensure that you promote the education of your internal teams. A solid grasp of AI/ML at a high level will give your team a competitive edge and equip them with the ability to brainstorm AI/ML use-case ideas within your business setting.
Acknowledge the significance of data management as a crucial factor in facilitating AI, as well as the extent to which data scientists and ML engineers can collaborate, uncover, and recycle data resources and various ML components.
Recognizing the importance of establishing and incorporating processes to ensure a sustainable and effective data discipline cycle over the long term, ongoing education, and technical proficiency will be essential when developing sustainable AI/ML solutions.
Adopting the right technological skillset and migrating the obsolete infrastructure onto modern cloud-based solutions will prevent technology to become a bottleneck while adopting AI/ML solutions.
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