Agile, flexible, and resilient operations supported by tangible technological advances are increasingly driving business success.
This, as the U.S. Chamber of Commerce predicted in a report yesterday (March 8) that “virtually every business” will one day use artificial intelligence (AI) in some manner.
That’s because as future-fit tools like generative AI-based engines continue to gain momentum, enterprises across industries are activating the benefits of new modalities and moments of next-generation data sets with responsive, deep-learning tools that can effectively leverage contextually primed neural networks to a degree previously only thought possible within the executive seat of our own frontal lobes.
These emergent algorithms, still in the early innings of marketplace deployment, have the potential to drastically change commerce, payments, and business processes more broadly.
The full scope of that impact and its accompanying set of risks is impossible to know.
“We really are surrounded by AI in our daily lives,” Michael Haney, head of Cyberbank Digital Core at FinTech platform Galileo, the sister company of Technisys, said during a conversation with PYMNTS earlier this week.
Until recently, most AI engagements were limited to a set range of what were traditionally binary option-1, option-2 responses.
Now, those legacy intelligent solutions are getting exponentially smarter as advances in AI and machine learning now provide AI, and all of the various technologies that fall under its broad moniker, the capability to learn from and adapt to circumstances on their own by activating high dimensional data sets in what is called “deep learning,” rather than requiring a manual intervention when a hurdle in the process appears.
Haney went on to note that this newer ability of AI to move across multilayered data set “worlds” of images, speech, text and more is what makes today’s solutions worthy of being described as “truly intelligent.”
As reported by PYMNTs, Microsoft, which has been at the forefront, for better or worse, of integrating AI tech into enterprise solutions, this Monday (March 6) announced the rollout of an AI assistant for customer relationship management (CRM) and enterprise resource planning (ERP) tasks for sales, marketing and customer service operations.
The increasingly widespread application of these smarter AI tools will allow for the creation of “highly personalized and targeted customer segments by having a dialogue with their customer data platform using natural language,” as well as offer a window into the next-generation conversational capabilities sophisticated AI can now support.
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If AI for enterprise is still in its early innings, then AI-powered voice technology is just beginning to take its first few at-bats, but the tech is already showing hints of its transformational promise.
The evolution of voice commerce is accelerating as years of experimentation and right-now advances in data activation reshape initiatives around increasing smart connected homes, vehicles, and shopping experiences.
It is possible today for AI solutions to understand relevant contextual clues, such as tone or intent, and take appropriate actions based on that context through the adaption of complex neural networks to understand natural language and phraseology.
This offers businesses huge opportunities for bespoke recommendations and verbal engagements and provides a whole new way of distilling analytical insights and engaging internally across business processes.
AI voice tech, which does not require the use of a keyboard, gives users and business leaders a faster input method for queries and provides more broadly a fast, reliable, and convenient means of interacting with both digital technology and the stored data it collects.
By turning an enterprise’s knowledge base searchable in the most natural way possible, voice tech can serve as a future-fit gateway to organizational knowledge and empower front-line and field employees and internal HQ leaders. Because AI voice tech can provide easy access to information and insights in real-time, it removes the need for many historically time-consuming and low-value tasks while freeing up teams to focus on high-value initiatives.
Still, the next generation of AI and its accompanying future-fit chatbots composed of AI Humans, Virtual Humans, and Digital Twin Avatars aren’t without pitfalls or risks.
AI is inherently dependent on the data set used to train it. Maintaining robust controls around the information fed into each algorithm is necessary to ensure the technology doesn’t go off the rails and stays focused on the task.
At the same time, it’s important to remember that while it may be challenging, at least right now, to train an AI to do something properly and compliantly — it can be equally challenging to properly equip a human employee to perform the same task.