Presented by AHEAD
The global AI market has seen exponential growth, skyrocketing from approximately $4 billion in 2014 to an astounding $200 billion in July 2024. The number of AI startups has surged nearly 14 times since 2000, reflecting the increasing integration of AI into daily life—77% of devices are projected to utilize some form of AI, even in basic appliances like washing machines. Recognizing the importance of capitalizing on this growth, the Saudi Arabian government has announced a significant $40 billion investment in AI and is currently seeking leadership for this venture. The AI revolution is only just beginning.
"Most organizations are preparing for a rapid shift toward adopting generative AI to maintain competitiveness, despite feeling overwhelmed by the myriad of tools and potential use cases, all while under pressure to deliver substantial results," says Ryan Barker, Field CTO at AHEAD.
The pressing question is: where should one begin? The somewhat unexciting but essential answer lies in data.
Having the Data Conversation
"Every discussion about AI inevitably leads to a conversation about data," Barker states. "You must align the right data with your AI use cases to extract true value for your enterprise."
AI technology fundamentally depends on data, and with data volumes growing at an unprecedented rate, considerations of quality, lineage, and long-term storage have never been more vital. The challenge is compounded by the numerous disparate tools used to access and manage this data, which can create bottlenecks that hinder AI efficiency.
"Assessing data readiness is one of the primary areas that AHEAD addresses for clients," Barker explains. "This process begins with identifying where the necessary data resides—it tends to be abundant yet often siloed in various locations, including on-premises, in cloud environments, at the edge, or even on individual devices. Data may be obscured within legacy applications, requiring unique extraction methods, or may be securely stored yet inaccessible due to restrictive access controls."
Before embarking on the data discovery journey, it's critical to establish robust governance and quality practices as foundational elements for effective data cleaning. Fortunately, advancements in data governance and quality control technologies now allow for automated checks, ensuring data integrity as it's located and evaluated.
"The data aspect can become a daunting challenge," Barker notes. "Typically, we guide clients in selecting a manageable data set to avoid overwhelm, ensuring they make tangible progress while they grasp the basics. Our goal is to maximize the value derived from AI once we reach that stage."
Defining Technology Investments
As one of Barker's clients pointed out, AI can often appear as a solution searching for a problem. Selecting an effective direction and suitable tools can be paralyzing. "Some companies are adopting established tools like Microsoft Copilot to enhance their business processes immediately, which is a commendable first step, but it doesn't rule out more significant investments," Barker clarifies. Advanced applications may necessitate the adoption of vertical solutions and models to fully leverage these investments and optimize process improvement.
"When investing in a platform integration, it's usually tailored to that specific platform and lacks cross-application versatility," Barker warns. "Such limitations can confine organizations."
Every business grapples with knowledge management and extracting insights from disparate data sources. For many, the initial step is to create a scalable Retrieval-Augmented Generation (RAG) framework that integrates multiple data repositories for organizational use.
Retrieval-Augmented Generation (RAG) as a First Step
"We're observing many companies achieving early success by developing RAG architecture," Barker remarks. "Investing in RAG is crucial as it lays the groundwork for all AI initiatives, enabling users to extract accurate insights and value from their data—an often elusive goal over the years."
RAG architecture addresses the limitations of large language models (LLMs), which are constrained by their training datasets. By retrieving information from both external publications and internal proprietary data, RAG creates a dynamic generative AI system that remains relevant. It alleviates the need to continuously fine-tune and retrain LLMs, adding necessary context to user prompts, thus offering a more cost-effective method for integrating specialized data.
RAG provides substantial transparency and reduces the frequency of unverified outputs, as LLMs can cite the sources used for responses, allowing users to verify the information. The accuracy of answers is also improved with data that is more current than the original training datasets.
LLM-powered chatbots can utilize RAG to provide more relevant answers based on existing company knowledge bases, enhancing the customer experience by delivering less generic, more tailored responses. Internally, generative AI-enabled search engines and knowledge bases see vast improvements by incorporating company-specific data, benefiting roles such as accounting and sales by providing quick, relevant insights through natural language interactions.
"I refer to this as hybrid AI because it leverages various technological components—whether cloud-based, on-premises, or using vector databases—to build a scalable platform as organizations expand their use cases over time," Barker asserts. "This approach is an excellent starting point for companies, creating an environment where users at all levels can inquire about their data and receive prompt answers."
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