Artificial intelligence (AI) is set to be the key source of transformation, disruption, and competitive advantage in today’s fast-changing economy. Gartner estimates that AI will create $2.9 trillion in business value and6.2 billion hours of worker productivity in 2021. As a result, numerous early adopters are buying into AI within organizations across diverse industries. But many are already encountering challenges as a vast majority of AI initiatives are failing to meet their expectations or provide solid gains on investments. For these organizations, the setback typically originates from the lack of foundation on which to build AI capabilities. Enterprise AI projects end up being isolated endeavors without the needed strategic change to support business practices and operations across the organization. So, how can your organization avoid these pitfalls? It may help to first define what successful AI transformation looks like for the enterprise.
Enterprise AI entails leveraging advanced machine and cognitive capabilities to discover and deliver organizational knowledge, data, and information in a way that closely aligns with how humans look for and process information.
In order to succeed with AI, organizations will first need to identify which of their current enterprise information and data management challenges are a good fit for an AI solution, keeping in mind that AI is not a magic bullet that can solve all business problems. After selecting appropriate use cases, organizations must then build the foundational competencies to structure their information in a manner that is machine-readable. From our experience, the best suited enterprise use cases for advanced capabilities such as artificial intelligence and machine learning include:Â Â
semantic search & Natural Language Processing (NLP): semantic search seeks to understand the meaning and context behind searched terms as opposed to just executing queries against keywords. It takes into consideration language, word variation, synonymous terms, location, and user preferences to simplify user experience by describing information closer to how the user would to another person. For the enterprise, this is made possible throughsemanticenterprise knowledge graphs that provide the architecture to discover and surface knowledge across disparate data sources with the flexibility to quickly modify and improve data flows. This further makes it easier to sustainably add new data sources (without making extensive changes) and support future business questions that are currently unknown. Successful organizations leverage semantic search to develop human centered applications using simple natural language (think applications such as chatbots and question answering systems). Scaled Data Governance through automated organization: Auto-tagging and classification automatically route and organize content and data to the right channel(s) to enable findability, discoverability, optimize enterprise information and data/content governance. The most successful data categorization solutions put in place consistent follow-up processes to manage and access data in the right place, removing the manual burden (usually error prone) from humans, and enabling the enterprise to consistently organize data based on predefined access and security requirements for reliable risk management and compliance purposes. Augmented categorization and classification of data: Augmented categorization leverages machine logic to organize data based on similarities between content, context, and/or users, and further enables the automatic assignment of non-topical concepts to documents such as sentiment (e.g. positive, negative).
