AI is the conversation we can’t get away from, so we’re doing our best to bring you as many perspectives, experts and insights into how enterprises are adapting, incorporating and utilising its rapid advancements.
Molham Aref is CEO of RelationalAI, an organisation building intelligence into the core of the modern data stack. He’s had a more than 15 year career in AI where he has been investigating and implementing how knowledge graphs and covers benefits the build of intelligent data applications.
M.R.: Generally speaking, how do you see AI advancing enterprise?
Molham Aref: AI is an expansive concept that encompasses a wide range of predictive and analytical technologies. Gartner coined the term Composite AI to reflect the fact that AI in the enterprise is combining these technologies to help build intelligence into organizations’ decision making and applications. AI provides great opportunities to drive smarter and more insightful outcomes.
Using AI, organizations can improve their decision making and achieve more reliable outcomes. The emergence of large language models (LLMs) has driven AI to an inflection point that requires a combination of techniques to generate results that cannot be achieved by point solutions.
By leveraging AI, organisations can make accurate forecasts, anticipate customer behavior, and optimize resource allocation. This allows them to proactively address challenges, identify opportunities, and ultimately become more profitable.
M.R.: How are you incorporating knowledge graphs working with AI and Enterprise?
Molham: Knowledge graphs were pioneered by technology giants like Google early on to improve search results and LinkedIn to understand connections between people. The technology models business concepts, the relationships between them, and an organization’s operational rules.
Specifically, a knowledge graph organizes data designed to be human-readable, augmenting it with knowledge about the enterprise in a way that allows organizations to take their data, reason over it, and create inferences with the goal of making better decisions. This can be done in a variety of ways, including with graph analytics, which focuses on connections in the data.
Organizations can augment their predictive models with an understanding of the relationships that exist between their data, for example, inventory and profit. These enhanced models enable organisations to arrive at decisions that make them more effective, more competitive, and more successful.
Knowledge graphs are proving to be one more tool in the toolbox that will significantly advance the enterprise.
M.R.: What do you see the future benefits being for organisations who build intelligent data applications?
Molham: Imagine a world where applications seamlessly adapt to your data, driven by intelligent capabilities. Where your applications can take action on your behalf, notify you to make important decisions, and dynamically make recommendations in response to sudden changes.
Once organizations understand the potential impact of AI, they start to embrace technologies like knowledge graphs and data clouds. And with the modern AI stack complete, they can start building applications that let them automate workloads.
With intelligent applications making the easy decisions, humans are freed up to work on the things that are more interesting and complex. Intelligent applications take the drudgery and tedium out of business operations, so that experts can focus more of their time and energy on decisions and tasks that will have a bigger impact, are harder to make, or require more human ingenuity than can be codified in software.
M.R. Rangaswami is the Co-Founder of Sandhill.com