It's only been two years since generative AI (GenAI) burst onto the public scene. Early adopters – mainly individuals – were quick to embrace the technology, impressed by its ability to competently generate texts and code. Organizations followed soon after, kicking off experiments and proofs of concept (PoCs) to explore how GenAI could transform their operations.
While many businesses experimented with the technology, far fewer succeeded in scaling their experiments to production. So, as 2024 wraps up, let's examine what has held them back and what it will take to bridge the gap between experimentation and truly transformative real-world impact.
Many organizations began experimenting with GenAI by directing large language models (LLMs) at challenges they faced in their daily operations, from automating repetitive tasks to streamlining customer support. These early trials quickly highlighted the complexities of working with LLMs at scale.
First, there's data ingestion. Enterprise data exists in a variety of formats, spread across disparate data silos, and often lacking the structure needed for seamless integration into an LLM. Bringing all this data into a single GenAI platform, particularly as the volume of data increases, is no small task.
Compounding the data ingestion challenge, there's access control, required to ensure that sensitive information is visible only to authorized employees. Without robust mechanisms for managing access rights, deploying LLMs across an organization can bring serious compliance and security risks.
Retrieval augmented generation (RAG) emerged as a promising solution to these challenges. By enhancing LLM queries with data retrieved in real time from internal or external data repositories, RAG eliminates the need to retrain LLMs in order to get up-to-date insights. Moreover, it enables organizations to leverage the power and versatility of commercial LLMs without giving up sensitive data for model training.
But RAG, too, has its own limitations – primarily its precision. Both retrieving relevant data and generating coherent responses are probabilistic processes. Combine them in sequence and the uncertainties stack up, resulting in responses that may lack the accuracy needed for high-stakes applications.
At Squirro, 2024 was all about addressing these shortcomings, which we did by introducing a range of enhancements to RAG. We acquired Synaptica to bring knowledge graphs into the core of our enterprise GenAI platform. We invested in MeetSynthia.ai to spearhead the development of AI guardrail management systems for large-scale GenAI deployments. And through external partnerships and internal developments, we extended our platform to provide operational data access, AI agents, and robust privacy layers.
Enhanced RAG promised to bridge the gaps in precision, reliability, and security, opening new doors for enterprise-scale adoption. As anticipated, it delivers. Still, scaling enhanced RAG to large-scale production continues to hold back genAI deployment roadmaps as organizations contend with the complexities of managing terabytes of information and millions of documents.
This brings us to the central question facing enterprises today: How can they integrate GenAI at scale to deliver lasting business value? To succeed, they need to more than just deploy models – they need systems that are accurate, secure, scalable, and cost-effective.
As we look ahead to 2025, we're at the cusp of what might be the most interesting and exciting phase of GenAI adoption. The experimental era is giving way to serious, large-scale deployments. For the first time, enterprise GenAI adoption is set to outgrow the hype, delivering real results that transform industries.
This gives organizations an unprecedented chance to innovate and solve real-world challenges, from streamlining operations to delivering highly personalized customer experiences. Only now, the entry ticket is the ability to maintain deployments at scale while ensuring regulatory compliance and operational efficiency.
At Squirro, we've been on the frontlines of this transformation, helping organizations move from experimentation to full-scale adoption. We've worked with some of the most demanding industries, from financial services to government agencies, demonstrating how GenAI can be scaled successfully. By leveraging our experience, we help businesses navigate the complexities of data ingestion, vector indexing, and regulatory compliance – ensuring they achieve real, measurable impact.
With the hype around GenAI finally cooling, the focus is shifting to pragmatic applications. Companies are now leveraging AI to tackle deeper business challenges and unlock new opportunities. But this transition isn't easy. It requires strategic investments in technology and partnerships, as well as a commitment to balancing solution quality and speed of deployment.
As we enter 2025, our mission at Squirro remains unchanged: to revolutionize enterprise decision-making through AI-driven solutions that empower organizations to act with confidence and foresight. By driving operational excellence and accelerating AI-powered transformation, we will continue to create value for our customers and all their stakeholders.
With that, we thank you for your trust and wish you a prosperous new year!