Achieving Returns Through Artificial Intelligence
Every second of every day, digitized organizations across the globe generate an abundance of consumer information and business statistics, and the need to disseminate this data and derive actionable insights from it has never been greater.
With its far-reaching capabilities, the global business ecosystem is shifting to develop the infrastructure required to account for large volumes of business data, much of which cannot be analyzed through traditional business analytics models and tools. That’s where Artificial Intelligence (AI) comes into play. AI is transforming the business sector with its wide spectrum of applications, which can include aggregating corporate data, developing personalized marketing strategies, or improving consumer experiences.
Even though AI continues to make strides in redefining business analytics, skepticism remains surrounding the observance of tangible returns from AI tools. So, what’s holding AI back in the business sector?
Ren Zhang, Director of Selling Partner and Development Science at Amazon, joined the third annual Women in Data Science @ Penn conference on February 9-10, 2022, and offered what she views as the four largest challenges businesses face in adopting and scaling with AI:
Organization and Culture
Lack of empowerment and support for development team that makes data science possible – companies are not willing to embrace change
There is a deficit of individuals with the niche skill set required to bridge business and technology/AI
Businesses struggle with limited availability of curated, high-quality data that is suited for the current infrastructure
Minimal time and financial investments prevent AI from being implemented in a useful or sustainable way
With these roadblocks identified, Ren then provided advice on how businesses can best introduce, sustain, and scale AI implementation to achieve positive results.
Ren Zhang, GrW ’00 & ’02
Director, Selling Partner, Data Science, Amazon
Strategies to Achieve Returns Through AI
Introduce AI in increasingly more impactful “phases”
Starting with Proofs of Concept (POCs) and more digestible AI-based projects can mitigate what some might view as risky or uncertain investments into AI while still proving its value
Focus on partnerships with AI-friendly business units
By targeting business units/problems that are receptive to AI, you can achieve early wins, and build upon those experiences to expand possibilities
Select projects with tangible values and a clear path to production
Focus on “low hanging fruit” projects to streamline systemic implementation and scale with greater efficiency and synergy
Foster stakeholder trust and sponsorship in advance of development
Developing clear lines of communication can ensure you have the necessary investment needed to build your infrastructure
Build reusable AI products
Reusable products drive scale in business development, shorten your time to market in the future, and reduce implementation cost
Manage the project pipeline toward full implementation
By keeping an eye toward the future, you can increase the reliability of individual components and open possibilities of building new solutions
Although it can be difficult to see the immediate benefits of investing in AI, taking advantage of this technology early on can improve business decisions and supplement organizational functionalities. The evolution of AI will indeed transform the structure of the modern economy – being ready to adapt these systems is pivotal for driving business growth.
– Daniel Hwang
Watch “The Path to Scale with AI”
About Women in Data Science @ Penn
Women in Data Science (WiDS) @ Penn is an independent virtual event to coincide with the annual Global WiDS Conference held at Stanford University and an estimated 150+ organizations worldwide. All genders are invited to attend WiDS regional events, which emphasize the diversity of data science, both in subject matter and personnel. A celebrated interdisciplinary event, WiDS @ Penn will welcome academic, student, and industry speakers from across the data science landscape. WiDS @ Penn is co-hosted by Analytics at Wharton, Penn Engineering, Wharton Customer Analytics, and The Wharton Department of Statistics and Data Science.
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