GenAI: The Path to Adoption and Deployment
When generative AI landed on the scene two years ago, it was clear the impact would be sizable. However, the path to GenAI adoption has not been without its challenges. From budgeting and tools to finding an ROI, organizations are figuring out as they go along how to fit GenAI in.
1. What’s the GenAI budget?
In the overall IT budget, AI will be a significant portion of any new or fresh funds that the business allocates for spending. In terms of use cases, the largest share of the Gen AI budget is likely to support applications such as implementing chatbots, getting data from knowledge bases into other conversational content platforms. The goal for this budget will be how to enhance user interaction, streamline information access, and improve support and engagement through conversational AI interfaces.
2. What is the current state of generative AI in production across industries?
Generative AI is still in its early stages of adoption, with most businesses yet to launch their first production-grade applications. While tools like ChatGPT demonstrate potential, the reality is that widespread deployment—especially for business-specific use cases within enterprises—hasn’t occurred. The delay mirrors previous technological waves, where enterprises took between two and four years to integrate new innovations meaningfully.
Chatbots are Step One in the GenAI Adoption Curve
Chatbots are step one in the GenAI adoption curve (sdecoret/Shutterstock)
3. Why do some experts criticize the “more than a chatbot” narrative?
The “more than a chatbot” narrative is seen as premature because most organizations haven’t successfully implemented even basic chatbot systems that deliver on their promises to users. Many IT leaders and vendors who advocate for more advanced applications often lack experience with actual chatbot deployments. Getting the right foundations in place is essential, and that work on GenAI projects should not be devalued in the rush to hype the next big thing in AI.
4. How does the adoption of generative AI compare to previous technological shifts like mobile and social?
Generative AI adoption is following a similar trajectory to previous innovations like mobile apps and social media. Look at mobile – Apple launched the App Store in 2008, and it took to 2009 for Uber to launch and 2010 for Instagram to launch their apps. Each of these apps disrupted industries. For example, Mobile enabled Spotify to disrupt the music industry and Airbnb and Uber disrupted the hospitality and transportation industries. Those companies are now worth billions. It took even longer for traditional enterprises to feel comfortable with mobile, yet now it is essential to them. GenAI is following that same path, and we are now in that two-year timeframe.
5. What are the challenges facing businesses in deploying generative AI?
There are four key problems – inertia in adoption, lack of expertise, getting over the hype and having the right infrastructure in place and ready. Many enterprises are slow to experiment and deploy new technologies, even when they are production-ready. GenAI is still developing, so there’s a lot of work to be done to get the foundation right.
GenAI Startups are Attracting Billions in Venture Funding
GenAI startups are attracting billions in venture funding (TSViPhoto/Shutterstock)
6. How can businesses overcome the challenges in deploying generative AI?
There are four key problems – inertia in adoption, lack of expertise, getting over the hype and having the right infrastructure in place and ready. Many enterprises are slow to experiment and deploy new technologies, even when they are production-ready. GenAI is still developing, so there’s a lot of work to be done to get the foundation right.
8. What predictions exist for the future of generative AI adoption?
2025 will be the year where we go from hype to widespread production use and deployments around AI-powered chat services or where AI gets embedded into other applications. We’ll get where we’re going faster. For Scientists, generative AI is going to reduce the cognitive burden of scientists globally and the world will be a better place for it. For technologists, generative AI will build products faster, fix bugs when we find them, and deliver experiences users love. We’ll get where we’re going faster, we’ll cure cancer faster, and we’ll combat hunger faster, with the power of generative AI in 2025.
9. Why are current chatbot use cases still relevant for 2024 and beyond?
Although conversational interfaces (chatbots) might seem like “last year’s use case,” most organizations haven’t implemented and deployed even one in production effectively. Therefore, deploying conversational interfaces remains a critical goal for 2024. For enterprises, the emphasis is on creating functional and scalable solutions for customer interactions, internal support, and field operations.
10. What is the long-term outlook for generative AI in enterprise use?
Generative AI will likely become the fourth major wave of digital engagement after web, social, and mobile. Over the next few years, it will transition from an experimental technology to a core component of business operations. Companies that embrace generative AI to enhance engagement and efficiency will gain a competitive edge.
Conclusion
The path to GenAI adoption and deployment is complex and multifaceted. With the right approach, budget, and infrastructure, businesses can overcome the challenges and unlock the benefits of GenAI. As we move forward, it’s essential to remember that GenAI is not just about chatbots but about building a more efficient and effective future.
FAQs
Q: What are the biggest challenges facing businesses in deploying generative AI?
A: There are four key problems – inertia in adoption, lack of expertise, getting over the hype and having the right infrastructure in place and ready.
Q: How can businesses overcome the challenges in deploying generative AI?
A: By focusing on the fundamentals, building the right infrastructure, and overcoming inertia, businesses can overcome the challenges and unlock the benefits of GenAI.
Q: What is the future of generative AI adoption?
A: 2025 will be the year where we go from hype to widespread production use and deployments around AI-powered chat services or where AI gets embedded into other applications.
Q: Why are current chatbot use cases still relevant for 2024 and beyond?
A: Because most organizations haven’t implemented and deployed even one chatbot in production effectively, deploying conversational interfaces remains a critical goal for 2024.
About the Author
Ed Anuff is the chief product officer at DataStax, provider of a big data platform. Ed has more than 30 years experience as a product and technology leader at companies such as Google, Apigee, Six Apart, Vignette, Epicentric, and Wired. He led products and strategy for Apigee through the Apigee IPO and acquisition by Google. He was the founder of enterprise portal leader Epicentric, which was acquired by Vignette. In the 90s, at Wired, he launched one of the first Internet search engines, HotBot, and he authored one of
