www.britcham.com
manage such implementations. Consequently, we find the best answer is for both functions to work hand in hand. An AI and analytics leadership group combining business, operations, risk, and IT leaders enables this collaboration. This group oversees progress against the approved AI roadmap for cohesive governance of AI enterprise wide.
3. Build a centre of excellence for a sustainable, scalable journey AI is a journey, not a destination. AI technologies and concepts are evolving rapidly, and companies must identify and seize on ongoing advances. To do so, they need a flexible, skilled and multidisciplinary project delivery team to guide them through the journey. As a result, a centre of excellence (CoE), which reports in to the AI/analytics leadership group, is critical to successful AI deployment, sustainability and scale. Beyond managing AI implementations, the CoE develops enterprise-wide AI policies, maintains common code and other assets that can be used across initiatives and creates a culture centered around digital innovation.
4. Integrate skills and technologies to achieve a multiplier effect Digital technologies, AI and advanced analytics, natural language user interfaces, and process automation have innovated at a rapid pace, which continues to accelerate. Unfortunately, companies often deploy these fields independently from one another, or with limited synergy. This discord prevents them from delivering highly innovative solutions and achieving scale and ROI. To solve this issue, companies need to not only build a multidisciplinary CoE but also transition to “enterprise
Pascal Bornet Associate Partner, McKinsey & Company, Singapore
AI,” where integrated project teams consider and combine the full breadth of technologies and concepts as they build solutions to business problems. One example is combining RPA to collect and reconcile millions of data points from different systems with data analytics that use this information to create insights for decision making. This integrated approach enables a multiplier effect that unlocks more value from use cases and ensures a smoother integration of new technologies.
5. Place people at the centre of the transformation Finally, it’s crucial for the CIO and other top leaders to anticipate and address the impact of AI on workplace dynamics, culture, communication, labour relations, and structure as employee acceptance and buy-in is vital as organisations scale AI. To build a scalable workforce for the future, the C-suite should consider identifying: • The best combination of people and machines. AI performs some tasks better than humans—but not all. Understanding what each does better is crucial to effectively augment employees—whether automating mundane tasks so staff can focus on value-added work or using advanced analytics to process millions of data points in seconds. • How new AI technologies, such as deep learning, and flexible team structures and approaches, such as agile, can enable greater innovation. • Ways to organise and train employees based on capabilities instead of skills so they can more easily adapt as AI evolves. This article appeared in CIO Advisor APAC, April, 2018.
Thomas Carlsen Partner, McKinsey & Company, Brisbane
McKinsey & Company is a global management consulting firm, deeply committed to helping institutions in the private, public and social sectors achieve lasting success. With consultants in more than 100 offices in 60 countries, across industries and functions, work closely with teams at all levels of an organisation to shape winning strategies, mobilise for change, build capabilities and drive successful execution.
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