AI and machine learning can be revelatory for organizations inundated with a sea of data and grappling to find ways to generate meaningful insights from it. AI tools help identify patterns and trends within large datasets that are often challenging for humans to discern. These tools can be trained to make predictions based on historical data, enabling organizations to anticipate trends, forecast outcomes, and make informed decisions. And over time, AI algorithms have also learned to capture diverse data formats. Whether it is structured data in the form of databases, spreadsheets, or tables, or unstructured data that isn’t as straightforward to encode, AI tools can be trained to handle them all. With the help of AI and machine learning, organizations can have the confidence to know that their data will never just be sitting dormant.
Nonetheless, challenges still exist because data isn’t always uniform in format. It also comes in the form of implied and internalized enterprise knowledge – the collective intelligence, information, and expertise that an organization possesses across departments, systems, and individual employees. The complex intricacies of enterprise knowledge require a different approach from traditional AI.
Numerous AI tools are precise and advanced in how they process data, but they fail to account for contextual factors derived from enterprise knowledge. This hinders the subsequent generation of actionable insights and knowledge. So, how can organizations fully capitalize on their proprietary expert knowledge, and what are the expected barriers?
The variables are endless, and a thorough understanding of the complexities of enterprise knowledge is needed so it can be layered with machine learning findings and truly transform enterprise knowledge into power. Effective management and utilization of enterprise knowledge can take organizational decision-making, problem-solving, and innovation to the next level.
The Complicated Nature of Enterprise Knowledge
Most companies readily invest in tools that simplify collaboration and knowledge-sharing to ensure company goals are widely communicated and that effective teamwork is possible. However, that has never been enough to fully allow critical and valuable knowledge to be centralized and accessible. A lot of the value-added enterprise knowledge is owned by individual employees. These subject matter experts (SMEs) have knowledge that is crucial to decision-making. The challenge often comes in codifying this knowledge in a way that makes it accessible to others within an organization. To make more informed decisions, SME knowledge needs to be effectively captured and centralized across the enterprise for companies to seamlessly integrate it into machine learning findings and, in effect, make more informed decisions.
Another issue that raises complications is the fragmentation of knowledge in organizations. This is attributed to the myriad of apps individually used by employees. Data is often on multiple applications that work on incongruous platforms and systems, further complicating the process of centralizing it for knowledge sharing. In fact, it is estimated that 175 apps are installed on the average large enterprise employee’s computer. Expectedly, companies often don’t even know what data they are missing, so the process of extracting knowledge is daunting and overwhelming.
Additionally, when knowledge is inherently owned by employees as opposed to organizations, this means that it also has the same level of transience. It also becomes lost whenever the employee leaves their position and whenever corporate structures shift. This fleeting nature of knowledge also manifests itself in daily situations. If the expert isn’t available, or the knowledge hasn’t been properly indexed or organized, enterprise knowledge is then intrinsically unavailable. This leads to frequent knowledge bottlenecks for companies, which translates to losses. This notion especially rings true in oil and gas companies that are currently experiencing a noticeable generational shift in their workforce. The older demographic is retiring and aging out, and inevitably taking their expert knowledge with them. That knowledge needs to be immortalized, especially when it’s estimated that the average loss incurred by companies when an employee departs due to ineffective knowledge sharing is $11,000.
Turning Enterprise Knowledge into Power
Companies must proactively deliver expert guidance to boost the speed and quality of team decision-making, reducing operating costs and non-compliance. Expert knowledge needs to be popularized across the company and made readily available. For that to happen, SMEs must have the tools to easily capture and convey their expert knowledge to all teams in a way that is efficient and streamlined. Because many SMEs come from non-technical backgrounds and teams, it’s important that any technical barrier is removed. Knowledge-based automation needs to be as fluid as possible, with no-code tools that waive software engineering or data science support, so SMEs can capture, test, and maintain knowledge faster and more effectively. With the help of large language models (LLMs), expert knowledge that is often in unstructured formats (e.g., texts and PDFs) can also be easily captured and added to knowledge bases.
Similarly, expert knowledge needs to be an active component of machine learning operations. On their own, machine learning findings often don’t have a significant value-added impact. Without SMEs in the loop, there is limited ongoing user input and visibility into machine learning model accuracy and maintenance. They devolve into black boxes that don’t demonstrate how they arrived at their recommendations, reducing trust and creating confusion.
Hybrid AI – which provides a knowledge trace that explains why and how each recommendation was generated – serves as a powerful asset to achieve that. Hybrid AI solutions empower SMEs to view, update, and deploy changes without technical support. There is also the risk of LLM systems going off-script and “hallucinating,” resulting in significant losses and resistance to adoption. The involvement of SMEs through hybrid AI helps apply guardrails to ensure machine learning outcomes are reliable, deterministic, and repeatable.
AI Takes Enterprise Knowledge to the Next Level
Enterprise knowledge is an invaluable asset, but because of its tacit nature, fragmentation, and dependency on its owners, it often loses its value. AI can help codify it in a way that makes this knowledge easily shareable, without requiring advanced coding skills from SMEs. It also facilitates its integration to machine learning findings, so that AI recommendations can become more holistic and reliable. Hybrid AI tools, in particular, add a layer of transparency and trust that organizations need from AI in the future.