Third, the extent of barriers - regulatory or otherwise - to adoption and what techniques and tools can enable safe use of this technology. Fourth, an assessment of firms’ perceptions of the risks, to both their own safety and soundness as well as to their conduct towards customers and clients, arising from AI/ML. And fifth, the extent to which the appreciation of these risks has given rise to changes in risk management, governance and compliance frameworks. The full results of the survey will be published by the Bank and FCA in Q3 2019, and are likely to prove insightful. Responses were returned to the Bank in late April, so some early indicative results are emerging. Overall, the mood around AI implementation amongst firms regulated by the Bank of England is strategic but cautious. Four fifths of the firms surveyed returned a response; many reported that they are currently in the process of building the infrastructure necessary for larger scale AI deployment, and 80 per cent reported using ML applications in some form. The median firm reported deploying six distinct such applications currently, and expected three further applications to go live over the next year, with ten more over the following three years. Consistent with the McKinsey survey, barriers to AI deployment currently seem to be mostly internal to firms, rather than stemming from regulation. Some of the main reasons include: (i) legacy systems and unsuitable IT infrastructure; (ii) lack of access to sufficient data; and (iii) challenges integrating ML into existing business processes.
Nevertheless, promoting the right outcomes, even if framed as principle-based expectations, will require appropriate, up-to-date systems and controls across the three lines of defence to ensure an appropriate control environment throughout the firm. Further thought is needed. The third challenge posed by greater use of AI/ML to boards is around change. As the rate of introduction of AI/ML in financial services looks set to increase, so too does the extent of execution risk that boards will need to oversee and mitigate. It appears to supervisors, and consistent with the early results from the Bank of England/FCA survey, that some firms are approaching the introduction of AI/ML piecemeal, project by project; others appear to be following a more integrated, strategic approach. Either way, the transition to greater AI/ML-centric ways of working is a significant undertaking with major risks and costs arising from changes in processes, systems, technology, data handling/management, third-party outsourcing and skills. The transition also creates demand for new skill sets on boards and in senior management, and changes in control functions and risk structures. Transition may also create complex interdependencies between the parts of firms that are often thought of, and treated as, largely separate. As the use of technology changes, the impact on staff roles, skills and evaluation may be equally profound. Many of these interdependencies can only be brought together at, or near, the top of the organisation. Conclusion  I noted at the beginning that I would conclude by trying to extract three principles for governance from the observations I had made.