As enterprises seek to gain AI maturity, and move along the path from experimentation to scaling applications, greater efficiency and success can be achieved through the establishment of a holistic Responsible AI approach to identifying opportunities and managing risks.
BCG defines Responsible AI (RAI) as “an approach to deploying AI systems that is aligned with the company’s purpose and values while still delivering transformative business impact.”
Put so succinctly, it sounds simple, but in practice, it entails establishing the following, across a large and complex organisation:
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Strategy - Comprehensive AI strategy linked to the firm's values that ties back to risk strategy and ethical principles.
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Governance – Defined RAI leadership team and established escalation paths to identify and mitigate risks.
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Processes – Rigorous processes put in place to monitor and review products to ensure that RAI criteria is being met.
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Technology – Data and tech infrastructure established to mitigate AI risks, including toolkits to support RAI by design and appropriate lifecycle monitoring and management.
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Culture – Strong understanding and adherence among all staff – AI developers and users – on their roles and responsibilities in upholding RAI.
Enterprises that embed this kind of approach to Responsible AI into their organisation structure, and integrate these practices into their full AI product lifecycle, realise meaningful benefits including accelerated innovation, better products and services, improved talent attraction and retention, and improved long-term profitability.
This may also explain why we observe enterprises in more highly regulated industries to generally be faster in their approaches to GenAI, benefiting from long-established practices related to governance, rigorous processes around monitoring, and data and tech infrastructure that supports them. On the other hand, enterprises operating within industries that have remained only lightly touched by regulation, often lack such structures and are scrambling to create the governance and processes that will enable them to face the new compliance challenges that emerge when deploying GenAI applications.
For startups looking to sell either AI applications or AI tooling to enterprises, it is key to try to qualify and understand where a potential customer is at with regards to their journey and the establishment of these functions. Trying to sell to organisations that are severely lacking in e.g. tech infrastructure or governance will likely represent time-consuming dead-ends. This view across an organisation may also provide a map of sorts for the various stakeholders that may need to be addressed along a sales process.