The clamor for artificial intelligence is growing. Demands for this technology—to elevate customer and employee experiences via intelligent interactions—are escalating. Yet manyorganizations 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 abilitiesas 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.
