2025 AI trends from DataStax, a leading AI platform
Ed Anuff, Chief Production Officer at DataStax, and Davor Bonaci, Chief Technology Officer and Executive Vice President at DataStax, share their views on Artificial Intelligence (AI) trends for 2025.
Ed Anuff, Chief Production Officer at DataStax:
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.
So, 2025 should be the year when we see companies actually launch and have to make good on their promises around AI, both internally and to the market. Those companies that do this successfully will see huge market impact.
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. So we should see some strong launches in 2025 and beyond.
When ChatGPT launched, it was impressive to a lot of people. But Gen AI needed development tools around it, and around the other LLM tools that launched after, in order to become something that enterprises could take and use at scale. It needed approaches like vector data embeddings, vector search, integrations, and all those other elements that go into making technology work at scale. Those tools are getting into place, and 2025 should be the year when those deployments start coming through.
What industries are expected to benefit most from Generative AI?
Industries that rely heavily on engagement—like customer service, retail, and support functions—are poised to see the most immediate benefits. As well as industries that are limited by cognitive burnout of highly specialised people. AI-powered tools can enhance customer interactions, improve support efficiency, and provide real-time advice for field operations. More specifically, AI-powered tools can enhance reviewing medical scans, delivering highly technical features and drug discovery.
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.
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.
Imagine an executive working with their GenAI Assistant: One of our KPI’s is dipping. Help me figure that out. The chatbot says “Okay. based on what this KPI represents and the data available for analysis, I have three hypotheses”. AI agents could then test the hypotheses.
Davor Bonaci, Chief Technology Officer and Executive Vice President at DataStax:
Do you agree that 2025 will be the year of production AI?
Gen AI is poised to enter production at scale in 2025. While AI’s potential has been clear for some time, achieving widespread production use has required more than technical readiness alone. Over the past year and a half, as an industry, we have found that there are some significant technical challenges to running Gen AI at scale, and those have largely been solved.
The remaining hurdles relate to issues like how to design the best user experience, manage costs, deploy the technology effectively to users or customers, that are actually common to all IT projects. We also don’t yet have the defined regulatory frameworks in place that are settled enough. These issues will be largely addressed in the coming months, positioning 2025 to see substantial growth in production-level AI.
Why has production AI taken longer than anticipated?
While the core technology for production AI is ready, it’s the people, processes, governance, and operational complexities that have required more time. Technology alone isn’t sufficient; organisational dynamics, such as integrating AI into existing workflows and addressing regulatory concerns, tend to move at a slower pace. Additionally, many organisations have preferred a “fast follower” approach, allowing others to test and refine AI applications before committing fully themselves. This cautious stance is not due to a lack of capability but rather reflects a strategic choice to avoid perceived risks.
What are your predictions for AI platforms as a service (AI PaaS), and why is this concept emerging now?
As a company increases the number of use cases it needs to support, teams are pushed to standardise the approaches because knowledge of how to build AI apps isn’t distributed across the org, this is why AI platforms as a service (AI PaaS) become important. To help companies scale to many more use cases.
Because of this AI PaaS are definitely gaining traction, and they’re set to become increasingly important in the coming years. AI PaaS centralises AI tools, processes, and expertise within a dedicated team, which then enables other departments to leverage AI efficiently and securely through a vetted platform. This model addresses organisational challenges by standardising AI resources across the company, ensuring they’re compatible with existing infrastructure and governance standards.
In larger companies, this approach is likely to involve multiple AI platforms from different vendors, allowing companies to build a robust AI stack that’s well-suited to various use cases.
What kind of AI apps do you see emerging next year? Will chatbots continue to dominate, or will we build on top of them?
Next year, we’re likely to see both better chatbots and new AI use cases. Chatbots will become much more capable, evolving from merely answering questions and directing users elsewhere, to actually performing tasks within the conversation. For example, instead of just telling you how to reset your password, future chatbots will be able to help you reset the password directly within the chat interface.
Beyond chatbots, we’ll see AI-driven information retrieval and synthesis play a major role. For example, instead of browsing through hundreds of product reviews on an eCommerce site about something you are interested in, AI will synthesise relevant insights and provide personalised summaries to help you make that decision on whether it is right for you. This focus on synthesising and adapting information will significantly improve user experiences, making it easier to digest large amounts of data in a meaningful way. These innovations will go beyond chatbots to incorporate more sophisticated AI applications across various industries.
