In an era where technological advancement shapes the competitive landscape, traditional organizations face the dual challenge of preserving their legacy while embracing innovation. Rohit Masthipur who is Sales Leader: Analytics and AI at Amazon Web Services (AWS) sheds light on this pivotal junction, particularly focusing on the integration of Generative AI (Gen AI) within these longstanding entities. Distinguishing between digital-native companies “born in the cloud” and traditional organizations established well before the digital age, Masthipur delves into the complexities and opportunities that Gen AI presents.
As these organizations grapple with technological adoption, the conversation explores the nuances of transitioning from being technology-enabled to technology-driven. Masthipur’s insights offer a deep dive into the generational shift towards Gen AI, emphasizing its potential to revolutionize operations, customer experiences, and the very fabric of traditional business models.
AIM: What is your understanding of generative AI and traditional organizations?
“Generative AI, in particular, showcases the potential to perform complex functions, powered by models trained on vast datasets—encompassing billions of parameters—and requiring substantial computational resources.”
Rohit Masthipur: Traditional organizations refer to those that have been established for an extended period, often not originally designed with cloud technology in mind. These include entities that have been operational for the past 50 to 60 years, notably within sectors such as manufacturing, financial services, and healthcare. For these organizations, technology serves primarily as an enabler rather than a central focus. This contrasts with newer companies that have emerged in recent years, which are often built around digital-native technologies. Such companies are typically described as being “born in the cloud,” with a strong emphasis on technology driving their operations. This distinction outlines the primary differences between traditional organizations and those created with cloud integration from the outset.
Now, turning our attention to Generative AI (Gen AI). It’s important to recognize that AI technology has a history spanning over 70 years, marked by significant research and development. What distinguishes the current era from the past is the unprecedented access to data and the computational power available to analyze it. The advent of recent innovations such as ChatGPT, DALL-E, and other notable services has brought to light the capabilities of AI beyond mere predictive tasks. Generative AI, in particular, showcases the potential to perform complex functions, powered by models trained on vast datasets—encompassing billions of parameters—and requiring substantial computational resources. This evolution signifies the emergence of what is referred to as Gen AI.
AIM: Have you observed that traditional organizations struggle with adopting new technologies, and do you think similar challenges will affect the adoption of generative AI?
“The role of digitization in this shift is complex. It’s not merely the process of digitization that’s transformative but the inherent structure of the industries themselves.”
Rohit Masthipur: Certainly, as previously mentioned, many organizations have historically prioritized business operations, with technology playing a supporting role. Initially, as these organizations expanded, either through acquisitions or organic growth, their business strategies were distinct from their technology strategies. However, over the past two decades, technology has shifted to the forefront, becoming central not only to these organizations but to all sectors. This technological evolution has been gradual, with certain industries, such as banking, quickly adapting and integrating new technologies. Conversely, some sectors have been slow to adopt technological advancements, often due to the high costs associated with change.
The role of digitization in this shift is complex. It’s not merely the process of digitization that’s transformative but the inherent structure of the industries themselves. Take the banking industry as an example: it’s structured in such a way that integration and seamless operation are incentivized. For instance, a credit card issued by a bank can be used effortlessly worldwide, regardless of currency, merchant, or denomination differences. The transaction process, from purchase abroad to billing in one’s home currency, is designed to be smooth and efficient.
In contrast, the healthcare industry operates differently. The structure of healthcare organizations and the insurance process complicates cross-border medical services. Even within the United States, seeking healthcare in a different state can present challenges due to the industry’s unique setup. This structural difference is why adapting to technological changes is particularly costly and complex for traditional businesses like healthcare. The entrenched systems within these sectors are not easily modified, making technological transformation not just difficult but also a less immediate priority.
AIM: How does the adoption of generative AI differ between traditional organizations and new-age companies, particularly considering the excitement and push from senior management to leverage this technology to avoid falling behind competitors?
“The pace of change is rapid and beneficial, but organizations must be discerning about where and how to invest in this technology.”
Rohit Masthipur: First and foremost, the magnitude of change and disruption that is forthcoming is significant. Generative AI (Gen AI) represents a substantial advancement, offering numerous benefits for organizations. Its capabilities range from processing data to generating code and images, among other functionalities. However, despite its potential, Gen AI also presents challenges, particularly concerning the content it generates and its inherent biases. Many of the current Gen AI models are trained primarily on data from Western sources, such as the United States and Europe, resulting in a bias that leans heavily towards North American perspectives.
This bias poses a dilemma for organizations striving to innovate and enhance areas like customer experience and productivity. While Gen AI offers vast opportunities for innovation and efficiency, it also requires careful consideration of its biases and the ethical implications. The pace of change is rapid and beneficial, but organizations must be discerning about where and how to invest in this technology. This discernment could become a barrier to rapid change and scalability for some. Certain organizations, well-prepared and strategically aligned, may find it easier to adapt and thrive. Conversely, those not adequately prepared may need to invest in upskilling or reskilling their workforce to navigate these challenges effectively.
AIM: What factors related to technology and organizational hierarchy impact the adoption of generative AI in organizations, and how can these challenges be overcome?
“The challenge often lies in the processes and the ability of these organizations to adopt new technologies.”
Rohit Masthipur: Traditional organizations are often characterized by their hierarchical structures, which can influence how they adapt to change. When discussing change, it’s essential to consider three key dimensions: people, processes, and technology. Technology is readily available, made accessible by hyper cloud providers like AWS, ensuring that the necessary tools for transformation are within reach. However, the challenge often lies in the processes and the ability of these organizations to adopt new technologies.
From a process perspective, it’s crucial to examine how digital-native businesses or newer companies have embraced technological advancements. Traditional organizations frequently lag in this area, lacking the agility required to integrate new technologies swiftly. This gap encompasses not only the adoption of technology but also the aspects of enablement and training. There’s a need to educate existing staff on leveraging new technologies and to onboard new, tech-centric talent who can understand the business and contribute effectively.
Hence, for traditional organizations, the path to transformation and scalability is marked by a longer adoption curve. This is due to the challenges of updating processes, training personnel to utilize new technologies, and integrating tech-centric talent into an established, hierarchical structure.
AIM: How do scalability challenges impact the adoption of generative AI, and what role does strategic decision-making from the top down play in addressing these challenges?
“Organizations face the challenge of finding the right balance in personalizing these technologies.”
Rohit Masthipur: That’s an insightful question with many layers to consider, particularly regarding the technology and changes it brings. As you highlighted, trust is a critical factor. AI has the potential to significantly enhance productivity, whether through generating code, designing, or streamlining organizational processes.
The benefits of AI extend across various domains, such as medicine, banking, and other business functions, facilitated by advancements in Generative AI (Gen AI). However, organizations face the challenge of finding the right balance in personalizing these technologies. It’s crucial to avoid overreliance to the point where it could erode trust. Ensuring these AI models are managed carefully to prevent potential issues is a delicate act of balance that organizations often grapple with. The challenge lies not only in adopting AI but also in scaling and deploying it confidently in production environments, trusting that the models will perform as expected.
My advice is to aim high. The realm of AI is filled with potential, encouraging organizations to adopt a grand vision for its application. With the competitive pressure and the pervasive nature of change in the industry, adopting AI is no longer optional. However, a critical consideration is the integration of human oversight. Deciding where AI can be used autonomously and where human intervention is necessary to augment its capabilities is key. Starting with small, manageable steps towards a broader goal can facilitate growth and allow organizations to navigate the complexities of AI integration effectively.
AIM: In the context of traditional organizations, what role do you see generative AI playing in enhancing customer experience moving forward, and how can companies develop and quickly implement a strategy to effectively utilize and scale generative AI capabilities?
“The key to growth lies in harnessing generative AI and tailoring it to meet the specific needs of traditional organizations.”
Rohit Masthipur: Indeed, the advent of Generative AI (GenAI) has marked a significant shift from the previously available tools, which were designed for specific tasks. Earlier, we had distinct tools for different purposes, such as writing or weather forecasting, but none that could integrate these functionalities and interact at a level now possible with GenAI. While the tech industry has been aware of AI for a long time, the recent developments have unveiled its potential to the general populace, demonstrating the extensive capabilities of machines.
There are two critical aspects to consider. Firstly, AI can achieve remarkable results when properly trained. However, there’s also a downside known as “hallucination,” where AI generates convincing but inaccurate information. This poses a significant concern for organizations leveraging AI, emphasizing the need for caution to ensure that the AI’s outputs align with the organization’s brand values, culture, and reputation. The challenge is to harness AI’s capabilities without compromising on these elements, potentially becoming a hurdle in its application.
The vision of having a personal tech assistant that enhances human capabilities is within reach, provided there’s a mechanism to distinguish between accurate outputs and AI-generated errors or “hallucinations.” As technology progresses, the opportunities for organizations to create value using their unique data sets become apparent. Every organization, especially traditional ones, possesses distinct data that offers a competitive edge, such as hospitals that each provide a unique patient experience despite operating in the same sector.
The key to growth lies in harnessing generative AI and tailoring it to meet the specific needs of traditional organizations. This approach allows AI to operate within set parameters, efficiently and effectively serving the organization’s customers. Thus, the focus is on ensuring that AI works for the organization, adhering to its established guidelines and contributing to its service delivery excellence.
AIM: Could you provide suggestions or recommendations for traditional organizations on how to approach the adoption and implementation of generative AI?
“AI is a constant presence, poised to significantly aid organizations in becoming more efficient and seizing the immense opportunities available.”
Rohit Masthipur: It goes without saying that embracing change, especially disruptive change, is challenging. This is a message we often emphasize when educating the field: adopting new technology is never straightforward. However, AI is a constant presence, poised to significantly aid organizations in becoming more efficient and seizing the immense opportunities available. We collaborate with numerous organizations and are eager to assist in any transformation required. It’s been a pleasure discussing this with you today, and gaining insight into your organization. I hope my input has been beneficial in guiding your transformation efforts.