Generative AI implementation checklist – non-interactive
Defining use cases
- What specific problem are we trying to solve?
- Have we assessed if GenAI is the right tool for the problem?
- Have we spoken to vendors and conducted research?
- Have we engaged stakeholders across the organisation?
- Does the GenAI tool align with our strategy and policies?
- Can we collaborate with other organisations for shared resources?
- Have we identified a non-critical area for initial testing?
Data management
- Is our data high-quality and well-prepared for training AI?
- Do we have data management systems in place?
- Are we ensuring that no confidential or personal data is included?
- How do we know when we are training the model versus when we are not training the model?
- Are we compliant with GDPR and other relevant legislation?
Model selection and customisation
- Have we evaluated pre-trained versus custom models?
- Have we conducted consequence scanning?
- Have we trialled and tested models before committing?
- Do the contractual terms (T&Cs, IP, data rights) work for us?
- Have we obtained legal advice on the contractual terms?
- Have we considered costs – both upfront and ongoing?
- Do we have an exit plan in place if the model no longer serves us?
Deployment and integration
- Have we included staff in our decisions on using this model/tool?
- Have we trained staff on effective prompting and ethical AI use?
- Have we defined what data can be input into the tool?
- Do we have a process for reviewing AI outputs for accuracy?
- Are we piloting the tool before full-scale deployment?
- Have we created an AI policy?
- How does the AI tool integrate with our existing systems?
- Are we transparent with clients about AI use in our services?
Monitoring and risk management
- Do we have continuous monitoring in place (issues around complacency)?
- Have we established feedback channels for performance assessment?
- Are we meeting legal and regulatory compliance?
- Have we addressed cybersecurity concerns?
- Do we have insurance coverage for AI-related risks?
- Is there a business continuity plan in case of failure?
- Have we implemented responsible AI policies to mitigate bias and misinformation?
Future-proofing
- Do we have a life cycle management process in place?
- Is our AI strategy agile enough to adapt to evolving technology?
- Do we have a plan for upskilling staff or partnering for AI expertise?
- Are we staying updated on regulatory changes and industry trends?
- Are we conducting horizon scanning?
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