By: Jitendra Putcha (Jit), EVP & Global Head of Data, Analytics, and AI, LTIMindtree

Early adopters are true weathervanes in the technology space. They inform us of the direction in which the wind blows. More importantly, they provide us with critical early indicators that contribute to the long-term success of technology adoption based on field experiences. The Roger theory tells us that early adopters drive industry behavior by reducing the uncertainty around new ideas[i].

The insights gleaned from early adopters of generative AI (Gen AI), can help enterprises to avoid missteps and false starts. Enterprises can take a more considered approach to the technology, set expectations, determine investments, and accelerate outcomes.

Given the potency of Gen AI to reinvent entire industries and business processes, there is significant pressure from investors and customers to make fast moves in deploying Gen AI use cases. This creates risks as missteps will result in setbacks which can derail the AI strategy. So, it is essential to get behind what early adopters are signaling:

• Are there specific use cases or functions more suited for experimenting with the new technology?
• For which use cases can we expect the best ROI with the least risk?
• Which applications can be presented to end customers, and which applications might be risky without moderation?
• What investments in people will be necessary for upskilling and cross-skilling?
• Which partnerships should be given priority?

Our study, The State of Generative AI Adoption, was designed to shed light on the thinking, decisions, and actions of early adopters of Gen AI. Among the host of invaluable insights, it extracts from users of Gen AI, the study makes three things clear:

1. Use Gen AI to amplify customer experience: Leaders who have extensively adopted Gen AI across multiple functions or the entire organization are using Gen AI to improve customer experience (81%). This is true for even those businesses with moderate adoption of the technology within specific areas of business (71%). The technology’s ability to handle and parse immense volumes of data – with its large language models (LLMs)—helps it quickly and accurately summarize reports (examples: financial, medical, sales, marketing, and compliance); from a consumer perspective, the technology can go beyond locating a product to looking for a solution; it can customize images in real-time based on customer profiles and preferences (example: show a customized thumbnail of a show on Netflix to increase viewership), etc.The applications of Gen AI in the area of customer experience are limitless. The good news, say innovation management experts from Wharton, is that APIs from big tech will make sure integrating LLMs into digital user experiences will be democratized: “Even a small healthcare start-up, or a school district with antiquated technology infrastructure, will have access to this technology.[ii]” They also warn that integrating the skills of LLMs will soon become “table stakes” – meaning, the sooner a business leverages Gen AI  to improve customer experience, the better. If your business has a use case related to customer experience, Gen AI should be a top priority.

2. Focus on talent and next-generation skills: Our study shows that in the US, the most important factor in the successful adoption of Gen AI is access to skilled personnel (57%). By contrast, Europe highlights leadership (62%), and the Nordics rate this factor even higher (68%). Strong leadership, management support, and access to skills were listed as three (of the five) top factors that dictate success. The access to skills cannot be emphasized enough. Globally, our study suggests that organizations that have extensively adopted Gen AI across multiple functions or the entire organization believe that access to skilled and knowledgeable personnel is the key to success (69%).An essential skill required to leverage Gen AI is that of a prompt engineer and librarian. Anthropic, an AI start-up founded by former OpenAI team members and backed by Google, observes, “Given that the field of prompt engineering is arguably less than two years old, this position is a bit hard to hire for!” The company was offering a salary in the range of US$250,000 to $375,000 for the position[iii]. A vital requirement of the position was “a creative hacker spirit and a love for solving puzzles with a grasp of Python.”The implications of this data on skills acquisition are straightforward: for the moment, businesses considering Gen AI as part of their strategy would do well to partner with a technology provider with a dependable Gen AI talent bank. Meanwhile, businesses must wait to create in-house talent for this function until prompt engineering is offered as an academic course with industry-specific university specializations.

3. Identify the right use case balancing risk & reward: Our study shows that leaders in Gen AI usage are exploring new use cases (80%). Not finding a proper use case can become a significant barrier to adoption, followed by data quality and availability issues (as expressed by 78% of respondents in Europe) and technical infrastructure challenges (as expressed by 81% of respondents in the Nordics). An interesting use case that showed up in the study was product development—two-thirds of leaders want to use Gen AI to accelerate new product development.Marketing and sales, corporate finance, supply chain, and human resources also showed up in the study as fertile areas for use cases. However, every industry will have its priorities that drive the adoption of Gen AI: media, tech, and healthcare will use it for new product development; travel, transport, and hospitality will use it to improve customer experience; the communication sector will use to enable data-driven decision-making and improve productivity.Aside from boosting creativity and driving initiatives around product development, customer experience, and productivity, Gen AI can drive cost reduction, process accuracy, training and growth, manage privacy and security, detect fraud, and enhance quality control and safety.

Our study distills the strategies of 450 leading decision-makers around Gen AI. It looks at who is adopting the technology, why it is being adopted, and the best ways to guarantee successful adoption.

[i] Diffusion of Innovations, Everett M. Roger, Simon and Schuster, 2003: https://books.google.co.in/books/about/Diffusion_of_Innovations_5th_Edition.html?id=9U1K5LjUOwEC&redir_esc=y

[ii] Create Winning Customer Experiences with Generative AI, by Nicolaj Siggelkow and Christian Terwiesch, Harvard Business Review, April 4, 2023: https://hbr.org/2023/04/create-winning-customer-experiences-with-generative-ai

[iii] Anthropic, last retrieved October 4, 2023: https://jobs.lever.co/Anthropic/e3cde481-d446-460f-b576-93cab67bd1ed