Today’s business landscape is arguably more competitive and complicated than ever before: Customer expectations are at an all-time high and businesses are tasked with meeting (or exceeding) those needs, while concurrently creating latest products and experiences that may provide consumers with much more value. At the identical time, many organizations are strapped for resources, contending with budgetary constraints, and coping with ever-present business challenges like supply chain latency.
Businesses and their success are defined by the sum of the choices they make day-after-day. These decisions (bad or good) have a cumulative effect and are sometimes more related than they appear to be or are treated. To maintain up on this demanding and continuously evolving environment, businesses need the flexibility to make decisions quickly, and lots of have turned to AI-powered solutions to accomplish that. This agility is critical for maintaining operational efficiency, allocating resources, managing risk, and supporting ongoing innovation. Concurrently, the increased adoption of AI has exaggerated the challenges of human decision-making.
Problems arise when organizations make decisions (leveraging AI or otherwise) with no solid understanding of the context and the way they’ll impact other facets of the business. While speed is a very important factor with regards to decision-making, having context is paramount, albeit easier said than done. This begs the query: How can businesses make each fast and informed decisions?
All of it starts with data. Businesses are aware of the important thing role data plays of their success, yet many still struggle to translate it into business value through effective decision-making. This is essentially because of the proven fact that good decision-making requires context, and unfortunately, data doesn’t carry with it understanding and full context. Subsequently, making decisions based purely on shared data (sans context) is imprecise and inaccurate.
Below, we’ll explore what’s inhibiting organizations from realizing value on this area, and the way they will get on the trail to creating higher, faster business decisions.
Getting the complete picture
Former Siemens CEO Heinrich von Pierer famously said, underscoring the importance of a corporation’s ability to harness its collective knowledge and know-how. Knowledge is power, and making good decisions hinges on having a comprehensive understanding of each a part of the business, including how different facets work in unison and impact each other. But with a lot data available from so many alternative systems, applications, people and processes, gaining this understanding is a tall order.
This lack of shared knowledge often results in a number of undesirable situations: Organizations make decisions too slowly, leading to missed opportunities; decisions are made in a silo without considering the trickle-down effects, resulting in poor business outcomes; or decisions are made in an imprecise manner that just isn’t repeatable.
In some instances, artificial intelligence (AI) can further compound these challenges when corporations indiscriminately apply the technology to different use cases and expect it to mechanically solve their business problems. That is more likely to occur when AI-powered chatbots and agents are inbuilt isolation without the context and visibility vital to make sound decisions.
Enabling fast and informed business decisions within the enterprise
Whether an organization’s goal is to extend customer satisfaction, boost revenue, or reduce costs, there isn’t any single driver that may enable those outcomes. As a substitute, it’s the cumulative effect of fine decision-making that may yield positive business outcomes.
All of it starts with leveraging an approachable, scalable platform that enables the corporate to capture its collective knowledge in order that each humans and AI systems alike can reason over it and make higher decisions. Knowledge graphs are increasingly becoming a foundational tool for organizations to uncover the context inside their data.
What does this appear like in motion? Imagine a retailer that desires to understand how many T-shirts it should order heading into summer. A mess of highly complex aspects should be considered to make the most effective decision: cost, timing, past demand, forecasted demand, supply chain contingencies, how marketing and promoting could impact demand, physical space limitations for brick-and-mortar stores, and more. We will reason over all of those facets and the relationships between using the shared context a knowledge graph provides.
This shared context allows humans and AI to collaborate to resolve complex decisions. Knowledge graphs can rapidly analyze all of those aspects, essentially turning data from disparate sources into concepts and logic related to the business as an entire. And for the reason that data doesn’t must move between different systems to ensure that the knowledge graph to capture this information, businesses could make decisions significantly faster.
In today’s highly competitive landscape, organizations can’t afford to make ill-informed business decisions—and speed is the secret. Knowledge graphs are the critical missing ingredient for unlocking the facility of generative AI to make higher, more informed business decisions.
