Organizations that have enabled AI at the enterprise level are increasing operational efficiency, making faster more informed decisions and innovating new products and services. However, challenges remain for those who aren’t deploying AI in this capacity. Enterprises that lack a clear AI strategy, support from the c-suite and a specific set of metrics, struggle to make progress. At the EmTech Digital Conference, produced by MIT Technology Review Insights, EY conducted a brief survey of conference attendees and revealed how AI is being applied and enabled at their organizations.
Read on for the results of this survey.
This first question asked respondents for their views on the impact of AI on jobs – and a clear majority said that AI will transform the workplace and that more jobs will be created than lost.
Jeff Wong, EY Global Chief Innovation Officer, says: “As businesses deploy AI strategies, they’re increasingly aware of how the roles, responsibilities and skills of their talent is changing. With AI taking a leading role on tackling organizations’ simple and repetitive tasks, the human workforce can focus more on complex work that ultimately provides a greater level of professional fulfilment to employees and a more efficient use of critical thinking power.”
Next, the survey asked if AI is being used, currently, at respondents’ organizations. The majority said that AI is being piloted in one or more areas but as of yet, there is not an overall enterprise strategy. A close second said that AI is currently not a strategic priority.
Chris Mazzei, EY Chief Data & Analytics Officer and Global Innovation Technologies Leader, says: “While we’re seeing momentum in businesses deploying AI more strategically across the enterprise, its application is often fragmented across business functions, leaving much of the potential untapped.”
When asked for the top three desired business outcomes from the application of AI, the answers were: to improve and/or develop new products and services; achieve cost efficiencies and streamlined business operations, and to accelerate decision-making.
Chris Mazzei, EY Chief Data & Analytics Officer and Global Innovation Technologies Leader added: “AI technologies have been proven to streamline operations and speed-up internal processes. However, businesses should think more holistically about the competitive advantages that can be reaped from thoughtful applications of AI in product and service development, sales enablement, enhancing customer experience, or capturing business intelligence that helps impact the bottom line.”
In answer to a question about the biggest challenges to enterprise AI adoption, the vast majority reported that the biggest obstacle to building an AI program is finding those with the right skills —followed by the problem of integrating AI insights into current processes.
Nigel Duffy, EY Global Innovation Artificial Intelligence Leader says: “Despite AI’s potential to drive transformational change, adoption continues to be hampered by a shortage of talent. Businesses must invest in and create a culture of continuous learning that comprises skills programs, training sessions, and research partnerships to attract and retain leading AI practitioners.”
On the question of how organizations are measuring the value and impact of AI; most reported that they are not defining specific business outcomes at this time, but are instead focusing on piloting and learning.
Nigel Duffy added: “While businesses should be encouraged to experiment with AI methods and technologies, it is important to have an organization-wide process in place to analyze the performance and measure against set goals as AI is being applied. Only then can the future success and application of AI be fully realized.”
When asked about the impact of gender and racial diversity in AI programs, the majority reported that they believe that the diversity of AI talent affects how AI programs are designed and implemented, increasing the potential for bias to emerge through machine-learned processes.
Jeff Wong added: “There is a correlation between the continued lack of diverse AI talent and the distortions being found in some machine-learning outcomes. To mitigate this, businesses need to look for a wide variety of talent to ensure a diversity of experience, and social and professional perspectives are integrated at the coding stage.”
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