Harnessing AI and knowledge graphs for enterprise decision-making

Today’s business landscape is arguably more competitive and complex than ever before: Customer expectations are at an all-time high and businesses are tasked with meeting (or exceeding) those needs, while simultaneously creating new products and experiences that will provide consumers with even more value. At the same time, many organizations are strapped for resources, contending with budgetary constraints, and dealing with ever-present business challenges like supply chain latency. 

Businesses and their success are defined by the sum of the decisions they make every day. These decisions (bad or good) have a cumulative effect and are often more related than they seem to be or are treated. To keep up in this demanding and constantly evolving environment, businesses need the ability to make decisions quickly, and many have turned to AI-powered solutions to do so. This agility is critical for maintaining operational efficiency, allocating resources, managing risk, and supporting ongoing innovation. Simultaneously, the increased adoption of AI has exaggerated the challenges of human decision-making.

Problems arise when organizations make decisions (leveraging AI or otherwise) without a solid understanding of the context and how they will impact other aspects of the business. While speed is an important factor when it comes to decision-making, having context is paramount, albeit easier said than done. This begs the question: How can businesses make both fast and informed decisions?

It all starts with data. Businesses are acutely aware of the key role data plays in their success, yet many still struggle to translate it into business value through effective decision-making. This is largely due to the fact that good decision-making requires context, and unfortunately, data does not carry with it understanding and full context. Therefore, making decisions based purely on shared data (sans context) is imprecise and inaccurate.  

Below, we’ll explore what’s inhibiting organizations from realizing value in this area, and how they can get on the path to making better, faster business decisions. 

Getting the full picture

Former Siemens CEO Heinrich von Pierer famously said, “If Siemens only knew what Siemens knows, then our numbers would be better,” underscoring the importance of an organization’s ability to harness its collective knowledge and know-how. Knowledge is power, and making good decisions hinges on having a comprehensive understanding of every part of the business, including how different facets work in unison and impact one another. But with so much data available from so many different systems, applications, people and processes, gaining this understanding is a tall order.

This lack of shared knowledge often leads to a host of undesirable situations: Organizations make decisions too slowly, resulting in missed opportunities; decisions are made in a silo without considering the trickle-down effects, leading to poor business outcomes; or decisions are made in an imprecise manner that is not repeatable.

In some instances, artificial intelligence (AI) can further compound these challenges when companies indiscriminately apply the technology to different use cases and expect it to automatically solve their business problems. This is likely to happen when AI-powered chatbots and agents are built in isolation without the context and visibility necessary to make sound decisions. 

Enabling fast and informed business decisions in the enterprise

Whether a company’s goal is to increase customer satisfaction, boost revenue, or reduce costs, there is no single driver that will enable those outcomes. Instead, it’s the cumulative effect of good decision-making that will yield positive business outcomes.

It all starts with leveraging an approachable, scalable platform that allows the company to capture its collective knowledge so that both humans and AI systems alike can reason over it and make better decisions. Knowledge graphs are increasingly becoming a foundational tool for organizations to uncover the context within their data.

What does this look like in action? Imagine a retailer that wants to know how many T-shirts it should order heading into summer. A multitude of highly complex factors must be considered to make the best decision: cost, timing, past demand, forecasted demand, supply chain contingencies, how marketing and advertising could impact demand, physical space limitations for brick-and-mortar stores, and more. We can reason over all of these facets and the relationships between using the shared context a knowledge graph provides.

This shared context allows humans and AI to collaborate to solve complex decisions. Knowledge graphs can rapidly analyze all of these factors, essentially turning data from disparate sources into concepts and logic related to the business as a whole. And since the data doesn’t need to move between different systems in order for the knowledge graph to capture this information, businesses can make decisions significantly faster. 

In today’s highly competitive landscape, organizations can’t afford to make ill-informed business decisions—and speed is the name of the game. Knowledge graphs are the critical missing ingredient for unlocking the power of generative AI to make better, more informed business  decisions.

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