The clamor for artificial intelligence is growing. Demands for this technology—to elevate customer and employee experiences via intelligent interactions—are escalating. Yet many organizations are still uncertain about how to monetize AI’s learning capabilities in conjunction with their respective business models.
The key to unlocking its underlying enterprise value will come from smart data techniques that will significantly broaden the adoption of AI in 2018. Two trends in particular will emerge this year to solidify this effect.
The first is the increasing reliance on semantic technologies as interpreters for user interfaces (for both consumers and employees) based on AI. The natural language capabilities of AI to provide intelligent conversations hinge on the meaning derived from those interactions, which is optimized by self-describing, smart data approaches.
Secondly, AI knowledge graphs will transform customer 360 views and entity relationship models. The combination of semantics, visualizations, and AI will create interactive profiles (akin to social networking pages) that redefine how domain expertise and customer experiences shape an organization’s understanding of those they serve, positively impacting their ability to do so.
In both of these use cases for smart data, methodologies will create a fundamental understanding of what AI means to the enterprise, which organizations can then leverage according to their business needs.
AI’s colloquial capabilities are some of its most vaunted and are found in everything from digital personal assistants to smart speakers.
Deploying its natural language abilities as interfaces for both consumers and employees is the next progression for facilitating expedient interactions with real-time information systems. In this respect, AI has the potential to boost both customer satisfaction and employee productivity by enabling users to quickly interact with underlying data systems.
Those that leverage these capabilities can affect competitive advantage only by doing so in a sustainable manner at scale that maximizes AI’s potential for speech recognition.
In this regard the innate understanding of data’s meaning provisioned by semantic technology will prove incomparable and, even more importantly, necessary. Conversational AI requires a granular understanding of human communication, especially in relation to speech. Semantic technologies can extract meaning from such communication through standardized taxonomies and data models forming the basis of elaborate terminology systems linked to enterprise data assets.
By focusing on the underlying terminology and its consistency of meaning throughout use cases and business units, semantic technology—in tandem with AI’s advanced machine learning prowess—is ideally suited for such a challenge. Increasing deployments of AI user interfaces will expand the need for these smart data methods.
AI Knowledge Graphs
The impact of AI knowledge graphs on customer 360s and entity relationships may possibly exceed that of semantics for AI user interfaces. These graphs are able to connect any amount of data elements from a range of sources pertaining to relevant customer data. Moreover, they are also able to identify relationships between data for an enhanced understanding of elements which contain points of relevance not easily discerned. Thus, every point of interaction throughout each stage of a customer’s journey with a company—whether via online interactions, telecommunications, or even transactions in physical locations—are readily joined and accessible through an exhaustive knowledge graph.
Alternatively, organizations can join multiple knowledge graphs if need be (such as those for customers and those for employees domain knowledge, for example), to determine who’s best suited to deal with a customer or certain business problem.
Whether an organization is focused on healthcare, financial services, retail or another vertical, they can combine knowledge of their customers and domains to holistically understand them and opportunities for competitive advantage.
What will arise in 2018 is the confluence of that knowledge with visualizations and AI to create opportunities that were previously not possible. The outcome will be visual customer profiles resembling Facebook pages elucidating points of pertinence between consumers, products, services, employees, and any other apposite facet of business. These will exploit aspects of machine learning, natural language processing, and smart data technologies to produce predictions, facts, and interpretations for achieving business objectives—as well as for pinpointing new ones.
The synergy between AI and smart data will become particularly important in the next 12 months. Smart data is poised to become the universal interpreter for the language and speech capabilities of AI as canny user interfaces behooving consumers and employees.
AI knowledge graphs maximize the understanding of customers and internal resources, relying on a standards-based approach to link data for increased insight of information assets. In both instances smart data provides the means for optimizing the automation for which AI is currently desired, which is widely expected to transform data-driven processes.
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