Today’s business landscape is arguably more competitive and complex than ever: customer expectations are at an all-time high, and companies are tasked with meeting (or exceeding) those needs while creating new products and experiences. that will provide consumers with even more value. . At the same time, many organizations are short on resources, facing budget constraints, and facing ever-present problems. business challenges such as 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 or are discussed. To keep up with this demanding and ever-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 to maintaining operational efficiency, allocating resources, managing risks, and supporting continuous innovation. At the same time, 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 affect other aspects of the business. While speed is an important factor when making decisions, having context is paramount, although it is easier said than done. This begs the question: How can companies do both quickly? and informed decisions?
It all starts with data. Companies are acutely aware of the key role data plays in their success, but many still struggle to translate it into business value through effective decision making. This is largely because good decision making requires contextand unfortunately, the data does not carry complete understanding or context. Therefore, making decisions based solely on shared data (without context) is imprecise and inaccurate.
Below, we’ll explore what’s stopping 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 knew only what Siemens knows, then our numbers would be better,” underscoring the importance of an organization’s ability to leverage its collective knowledge and experience. Knowledge is power, and making good decisions depends on having a comprehensive understanding of every part of the business, including how different facets work in unison and impact each other. But with so much data available from so many different systems, applications, people and processes, achieving this understanding is a difficult task.
This lack of shared knowledge often leads to a number of undesirable situations: organizations make decisions too slowly, resulting in missed opportunities; decisions are made in isolation without considering side effects, leading to poor business results; or decisions are made in an imprecise way that is not repeatable.
In some cases, artificial intelligence (AI) can further exacerbate 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 needed to make sound decisions.
Enable quick and informed business decisions in the enterprise
Whether a company’s goal is to increase customer satisfaction, increase revenue, or reduce costs, there is no single factor that will achieve those results. Instead, it is the cumulative effect of good decision making that will produce positive business results.
It all starts by leveraging an accessible and scalable platform that allows the company to capture its collective knowledge so that both humans and artificial intelligence systems can reason about it and make better decisions. Knowledge graphs are increasingly becoming a critical tool for organizations to discover context within their data.
What does this look like in action? Imagine a retailer who wants to know how many t-shirts to order heading into summer. A multitude of highly complex factors must be considered to make the best decision: cost, timing, past demand, forecast demand, supply chain contingencies, how marketing and advertising might affect demand, physical store space limitations physical and more. . We can reason about all of these facets and the relationships between using the shared context that a knowledge graph provides.
This shared context allows humans and AI to collaborate to solve complex decisions. Knowledge graphs can quickly analyze all of these factors, essentially converting data from disparate sources into concepts and logic related to the business as a whole. And because data does not need to move between different systems for the knowledge graph to capture this information, companies can make decisions much faster.
In today’s highly competitive landscape, organizations cannot afford to make ill-informed business decisions and speed is the name of the game. Knowledge graphs are the critical missing ingredient to unlocking the power of generative AI to make better, more informed business decisions.
