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19.06.2024
Artificial intelligence (AI) has established itself as a cornerstone of innovation. However, as many C-suite executives have learned, procuring fit-for-purpose AI solutions is a nuanced process. The risk factor is high, and the implications of a wrong choice can be costly for organisations.
Before venturing into the AI marketplace, companies must align their AI objectives with their broader organisational goals. What does an organisation want to achieve with AI? Whether automating mundane tasks, deriving insights from data, or pioneering discoveries in a niche field, understanding the ‘why’ of procuring third-party AI solutions is the first step in the procurement journey.
Then, companies must start thinking about integrating the business and technical requirements of the AI system. Failing to do this at an early stage of the procurement process can lead to significant unnecessary downstream costs. If key stakeholders across technical, business, legal and procurement teams are not involved early on to define the performance and risk metrics, it can result in post-deployment modifications, integration challenges, and potential compliance hurdles.
For instance, an AI system that isn’t properly aligned with a company's existing IT landscape can create disruptions, compatibility issues, or even security vulnerabilities. Without input from technical, operational, and compliance experts from the outset, there's a heightened risk of overlooking critical concerns, which can translate to regulatory fines or compromised system efficiency.
Even as the AI landscape evolves, best practices for procuring AI can help provide senior decision-makers clarity in ensuring that they adopt the most suitable third-party AI systems for their organisations. Take the World Economic Forum’s “AI Procurement in a Box” framework as a reference. It is a comprehensive set of guidelines and tools designed to assist governments and businesses to procure AI in an innovative, efficient and ethical way.
One of the central best practices emphasised by the WEF is the need for “algorithms to be interpretable as a means of establishing accountability and contestability”. This means vendors should share information such as what training data was used and which variables have contributed most to the results. Vendors can do so through model cards, results from audit and assurance practices etc.
Other best practices that the WEF framework touches on include the importance of assessing AI systems for potential biases and continuous oversight and iteration of AI systems to keep up to date with changing data landscapes or evolving contexts
Below are several other factors that organisations should consider to achieve AI procurement excellence:
AI offers a transformative power that can redefine industries. However, its procurement requires a blend of technical understanding and strategic foresight. Senior management and C-suite decision-makers play a pivotal role in ensuring the responsible and effective use of AI. The key components above can help senior decision-makers make better decisions in procuring AI systems.