While WorkBoard and many other organizations have used AI, machine learning, and natural language processing to improve stakeholder experiences, the advent of generative AI presents tremendous new opportunities and creates new concerns. While generative AI is still in the early stages of adoption, it is already making a real difference in our ability to gather, synthesize, and author information more quickly – activities at the center of knowledge work.
To drive the most beneficial outcomes for our customers, our use of AI must be trustworthy and ethical, and as regulations emerge, compliant.
The pillars of our commitment to responsible AI are:
We accelerate teams, not replace them
WorkBoard’s use of AI generates drafts of OKRs, action plans, scorecards, and other strategy execution artifacts that enable the user to make their own decisions faster. The Co-Author and AI are collaborators with the user – visibly and transparently – rather than obviating the user’s ideas, input, or insight. Users can choose to accept, modify or reject the draft or briefs that our Co-Author generates. This "human in the loop” use of AI honors the agency of our users while providing benefits that neither AI nor a human could achieve alone.
We are transparent
We make clear documentation on our architecture and software available to our customers, including how we use AI. This includes explaining how our learning models are built, trained, and tested. We value inclusion and fairness, and our governance process monitors how we use AI for unintended consequences. As we continue to both innovate and learn, we will maintain our deep commitment and controls for explainability.
Your data is private by design
Data privacy and confidentiality are the foundations of trust for any platform, and our privacy and information security policies apply to our use of AI technologies. We grant customers control over their data's usage in our AI solutions. Privacy-by-design is a first principle of all of our development practices, and our use of AI and learning models is no exception. WorkBoard enables companies to harness the intelligence of their own strategy execution data managed in our platform while benefiting from the power of a domain-specific large language model to generate strong suggestions faster – without worrying that their data ever lands in the public domain, on ChatGPT, or in a competitors’ hands.