Gen AI Unleashed: Trends and Challenges for Enterprises in 2024


Staying ahead of the curve requires more than just embracing innovation—it demands a proactive approach to harnessing technologies that can bring tangible business value.

One such technology that has emerged in 2023 is Generative AI. The technology is in its infancy, but trends are rising that we expect to see develop throughout 2024.

Whether your enterprise operates in finance, healthcare, manufacturing, or any other sector, the transformative potential of Generative AI is simply too significant to be ignored.

1. GenAI is Transforming the Technology Stack

The advent of Large Language Models (LLMs) has ushered in a paradigm shift in how enterprises approach their technology stack. These sophisticated models are not mere tools; they are strategic assets that can revolutionize the way businesses operate. From automating mundane tasks to unlocking new use cases, LLMs have the potential to reshape the entire fabric of enterprise operations.

2. Navigating Cost Pressures with Efficiency

Cost pressures for Enterprises have been relentless in 2023. As 2024 looms in, efficiency remains paramount. GenAI offers a way to automate tasks that were once time-consuming and resource-intensive, such as structuring your data pipelines. The ability of these AI systems to learn and adapt ensures that operational efficiency is not a one-time gain but a continuous, evolving process that keeps pace with the ever-changing business landscape.

3. The Crucial Role of Fresh Data

For AI in enterprise, the adage “garbage in, garbage out” holds true. Access to a continuous stream of relevant and fresh data is the lifeblood of any successful AI implementation. Generative AI thrives on the ability to process, understand, and learn from the latest data inputs, and can unlock new use cases. Enterprises that leverage Generative AI and streaming data gain a competitive advantage by making informed decisions based on real-time insights, rather than relying on outdated or incomplete information.

4. The Evolution of Data Teams into Software Teams

The rise of Generative AI is transforming the role of data teams within enterprises. No longer confined to data analytics, teams are evolving into software teams with new responsibilities. They are now tasked with designing, running, and monitoring data applications in production—a testament to the integral role that Generative AI plays in reshaping the very fabric of enterprise IT structures.

5. Data Products and the Synergy of Multiple Sources

Generative AI facilitates the creation of data products that seamlessly combine insights from multiple sources. This synergy enables enterprises to gain a holistic view of their operations, customers, and markets. The ability to extract meaningful patterns and correlations from diverse datasets empowers decision-makers to make informed, data-driven choices.

6. Embracing Event-Driven Architectures

Event-driven architectures are gaining momentum as enterprises recognize the need for real-time responsiveness. By leveraging Generative AI in event-driven systems, enterprises can unlock new levels of agility, responsiveness, and proactivity, ensuring that they are always one step ahead in the fast-paced business landscape.

The strategic imperative for enterprises is clear—Generative AI is not a luxury but a necessity for maintaining a competitive edge.  As event-driven architectures continue to gain prominence, embracing Generative AI becomes not just a choice but a strategic imperative for enterprises that aspire to thrive. 


While the promises of the AI and live data combination are immense for enterprises, the challenges emerging from this powerful synergy are numerous. As organizations grapple with the complexities, it is crucial to navigate these hurdles strategically to unlock the full potential of AI and live data integration.

Complexity: A Multifaceted Challenge

The combination of AI with live data introduces a level of complexity that cannot be underestimated. From dealing with multiple programming technologies to managing numerous workflows, enterprises find it challenging to maintain a scalable and cohesive system for batch and streaming use cases. The pursuit of obtaining high-quality outcomes becomes incredibly difficult, requiring not only technical expertise but also a nuanced understanding of the interplay between algorithms that were conceived in a static way and dynamic live data streams.

Time to Value: A Prolonged Journey

One of the persistent challenges faced by enterprises embarking on AI projects with live data is the extended time required to reach tangible value. In a landscape where agility is key, the fact that such projects often do not make it to production is a cause for concern. 2 weeks in traditional Agile was a sprint, in AI evolution it is an epoch.

Cost: Balancing Expertise and Infrastructure

Acquiring and retaining a specialist skill set able to deal with  the convergence of AI and live data demands a substantial financial investment, and the costs associated with setting up and maintaining the required infrastructure further add to the bill. Striking the right balance between expertise and cost-effectiveness becomes a critical factor in ensuring that the pursuit of innovation does not become a prohibitively expensive endeavor that would not make sense from a business perspective.

Consistency: The Unyielding Demand for Reliability

Consistency is the linchpin that determines the reliability of outcomes and the functioning of systems dealing with dynamic data updates for mission-critical use cases. The inability to achieve reliable and predictable results hampers the use of event-driven systems for mission-critical use cases.

To navigate the challenges emerging from the confluence of AI and live data, enterprises must adopt a strategic and holistic approach. This involves seeking integrated platforms and solutions ensuring a cohesive and scalable system to maximize your return on investment. Up for the challenge in 2024?