Knowledge AI is a new solution that can help contact centers dramatically. Namely, this technology combines conversational AI with cognitive search to extract data stored across multiple systems.
It combines Natural Language Processing (NLP) and cognitive search to bring forth information presented in different formats, notably knowledge graphs.
Customer Service Boost
Learning about knowledge AI will help businesses polish their processes in the customer service sector. While conversational AI has already established itself via chatbots, virtual assistants, and voice assistants, knowledge AI promises to up the game a notch.
Conversational AI combines NLP, cloud, and machine learning at the very least, with some advanced solutions even deploying biometrics.
App Fatigue and Knowledge AI
So far so good, but there’s one considerable issue to keep in mind. Scilicet, even though customers have been “spoiled” by new tech and the virtual world, people are increasingly witnessing the so-called app fatigue — the phenomenon that sees users lose interest in using new apps.
Obviously, the main culprit is the insane number of apps that are needed for this and that or, in other words, for any benefit businesses are offering or are pretending to offer.
Knowledge AI can help mitigate the trend as it combines multiple functionalities in one powerful service that can assist both businesses and customers.
Enhanced Customer Relationships
Building “meaningful” relationships with customers has become a mantra of sorts, and no wonder. With fewer and fewer chances to communicate with a real person when contacting CS departments, there’s been a surge in the need to make every conversation count.
Digital interactions have already become a standard. Instead of relying on AI evolution for context, businesses would do well to provide a touch of actual human sentiment to communicate empathy and customer satisfaction.
Of late businesses have put in focus the intuitive customer care, which translates into:
- · Faster issue resolution
- · Personalized self-service
- · Humanized customer interactions
- · Lower rates of customer issues
- · Higher customer engagement scores
- · Extended customer lifetime value
Understanding Knowledge AI
Knowledge AI adds contextual understanding to the mix of already existing AI technologies. However, while other AI tech relies on pattern recognition and data processing, knowledge AI tries to replicate human-like cognition. Basically, it is mimicking reasoning, learning from experience, and applying knowledge to new situations.
As mentioned above, the technology focuses on knowledge graphs, which are basically interconnected databases of structured information that mimic the associative thinking of the human brain. These graphs enable knowledge AI to connect the dots between disparate pieces of information.
In other words, knowledge graphs provide a structured overview of knowledge scattered across various sources. The most notable use of knowledge AI so far is in medicine, where the technology has been deployed to cross-reference symptoms, patient history, and the latest research findings to assist in diagnosing various medical conditions.
Similarly, finance can benefit from the tech as knowledge AI can cross-reference vast amounts of market data, historical trends, and economic indicators to help decision-makers predict future trends.
Ethical Considerations of Knowledge AI
AI has been scrutinized ever since its inception. With generative AI and interactive AI in the making, the debate is likely to keep getting livelier.
As for knowledge AI, the chief issues concern data privacy and algorithm bias, which necessitates cross-referencing and double-checking.
There have been many appeals to make the AI decision-making processes more transparent, without any reasonable results so far.
It should be noted that knowledge AI shouldn’t replace human intelligence. Instead, it should focus on complementing human capabilities by way of information processing.
The Future of Knowledge AI
Knowledge AI is the latest newcomer in the field of AI. With all trends pointing to the rise of generative AI, ongoing development can take any turn.
Knowledge graphs are likely to become more sophisticated as contextual reasoning of the technology advances. Typically, knowledge AI is expected to further influence healthcare.
Don’t miss the main catch: knowledge AI represents a shift in the AI department, as it is a move beyond data processing. The technology tries to interpret knowledge rather than analyze it, meaning its future success will depend on its ability to understand context, draw connections, and mimic human thinking patterns.
Ethical considerations are significant and will become even more pressing in the future, seeing as the technology is already being used for unethical conduct across the globe. Proper legislation is in order but so far humanity hasn’t come up with a proper answer. Stay tuned, I guess?
