AI in the Enterprise: 3 Key Application Areas
Artificial intelligence is now part of the enterprise and it isn’t likely to go anywhere soon. Today, we're seeing AI make a difference in application development, predictive analytics and customer service.
Artificial intelligence (AI) is firmly in the enterprise mainstream these days, working mostly behind the scenes to streamline and automate business processes. In the coming year, however, we should start to see it take a more visible role as it learns to communicate directly with both the knowledge workforce and the public at large.
AI is an extremely sophisticated technology that, so far, has remained confined to the world of data science and high-end analytics. This tends to inhibit its use in typical production environments, however, because of the highly specialized skillsets needed to exploit its full value. Even in high-tech fields like finance and health care, AI has shown it can augment key functions, but only at the hands of those who know how to use it -- a class of professionals that is still in short supply. (Also read: The IT Talent Shortage: Separating Myths from Facts.)
In order to truly propel the digital transformation of the enterprise, AI will need to be democratized across the knowledge workforce so that even those with little to no training can use it to increase their productivity.
This transition has only just begun, however, with Deloitte noting recently that only in the past year has the technology evolved from a “bothersome critic” in the workplace to a “copilot independently executing on insights and trends.” Based on extensive interviews with leading enterprise executives, the firm has concluded that AI is changing the way the enterprise functions on a number of key fronts, including strategy, operations, culture, change management and overall ecosystems.
Here are three areas in which AI is being applied in the enterprise today:
1. Application Development
Perhaps one of the most significant changes AI is making in the enterprise right now is the quiet revolution taking place in application development. Forrester estimates that within the next year, 10% of all code and tests will be conducted by TuringBots -- a desktop-level software that uses symbolic regression to find mathematical formulas from data values. In addition, reinforcement learning and large language models (LLMs) are accelerating the development, accuracy and deployment of new tools, providing for the automatic generation of cleaner, more effective code.
Elsewhere, AI is quickly infiltrating key enterprise applications like analytics, augmented reality and condition-based maintenance of data and manufacturing infrastructure, according to database applications platform developer Teradata. This should revolutionize data-intensive industries like finance and health care, both of which are rapidly putting the technology into production environments. (Also read: How Explainable AI Changes the Game in Commercial Insurance.)
2. Predictive Analytics
Another key enterprise AI application is the mimicry of human reasoning through neural networking. Using multi-layered deep learning algorithms, advanced AI projects are able to employ various feedback loop architectures to make informed decisions on things like stock performance, sales projections and other vital functions. More than anything, this will propel AI’s role from a simple tool to an active participant in the business model.
3. Customer Service
Neural networks and generative AI will drive a wide range of business and customer-facing applications to improve both the experience and outcomes of AI’s interactions with people -- something that is still seen as the greatest inhibitor to wide-scale deployment.
At the moment, the top AI celebrity is ChatGPT, OpenAI’s newest chatbot trained under its large language model to interact in a natural, conversational manner. Rather than the stiff, rather limited performance of earlier chatbots, ChatGPT offers a more dialogue-driven approach that allows it to ask questions, challenge premises and even admit its own mistakes. This style of communication will likely accelerate the democratization of AI because it allows users to query databases and otherwise manage digital environments using everyday language rather than the complex jargon required of traditional interfaces.
While this is impressive, it is important to distinguish between what ChatGPT, and other similar models, can and cannot do. Ram Menon, CEO of conversational AI platform Avaamo, notes that ChatGPT relies on the use of LLMs, which cull data primarily from the public internet. This may work for the consumer, or maybe prosumer, ideation and creative purposes but it can be problematic when it comes to analyzing internal content -- particularly for industries that deal with high volumes of proprietary or private information.
Menon also added that organizations should be wary of any form of AI that engages in what is known as “stochastic parroting,” the tendency to draw improper conclusions based on statistical analysis of text strings. This may prove effective for a large part of the problem-solving process, but the final 10% is often the most difficult to accomplish correctly -- and the price of failure on the enterprise level is very high.
AI in Cybersecurity
Another key area in which AI is transforming the enterprise is cybersecurity. Because of its ability to sift through incomprehensibly large volumes of data, AI can detect patterns that could indicate a security breach -- patterns humans would never be able to recognize.
Moreover, AI is finding its way into areas like identity and access management. When AI-powered cybersecurity systems detect potentially malicious activity, they can automatically and instantly block the user from file access. (Also read: Artificial Intelligence in Cybersecurity.)
Conclusion: Enterprise AI is Here to Stay
One thing seems certain, however: AI is now part of the enterprise and it isn’t likely to go anywhere soon. Impressive as it may be, it is still just technology -- a means to turn numbers into useful information. In this light, it will follow the same trajectory as all other technologies: tremendous enthusiasm at the outset, followed by the practical reality that it, too, has limitations, and then finally a comfortable existence within the enterprise mainstream as organizations sort out its practical applications. (Also read: The Ultimate Guide to Applying AI in Business.)
And by then, of course, something new and even better will hit the channel.