Overcoming the barriers of AI deployment
Controlling risks and overcoming complex barriers are in EY’s DNA (read Data & Analytics also if you prefer), which begins with the ideation stage: bringing industry ideas, people, data, technology and trust together to shape the next best project. Scientists and engineers might jump into the better-governed AI development lifecycle and lines up the development and operational organizations towards go-live release and after-care maintenance of AI systems now fully embedded in business applications.
By “AI” we mean concretely here those relevant selection of techniques and methods being identified to solve the real business problem in hand: maybe it is a suitable combination of machine learning, natural language processing, document recognition and mathematical optimization for instance). It is key to monitor risks along all the phases of this journey, responding to any stakeholder’s concerns relating to potential conflicts of processes or transparency and data security.
In fact, the compliance departments of companies are often perceived a barrier to increased deployment of AI, incorrectly fearing that it would make operations less transparent. Hence why EY offers hand-in-hand collaboration from the start, providing the right onboarding and training if needed. This problem is still much greater in Europe, which has introduced strong data protection and transparency regulation.
Eagerness for AI throughout the entire company
It has always been essential to ensure that senior executives are eager to sponsor and drive through change. This remains the number one rule of thumb for any AI-driven initiatives too. All aspects of the business must be onboard, including the compliance and data departments. To ease this introduction, the value of AI needs to be clearly and tangibly demonstrated. While larger companies are more able to invest in the rare talents and emerging technologies, smaller enterprises may resort to outsourcing operations.