Enterprise AI is leading businesses into a new era of automation, efficiency, and data-driven decision-making. However, understanding the basics of this transformative technology can take time and effort.
Whether they’re streamlining their hiring process or rooting out bias in the workplace, AI tools are becoming everyday tools for managers. But they come with risks that businesses must be aware of.
What is AI?
Artificial intelligence (AI) can mimic human intelligence and perform tasks like completing complex jobs, analyzing data, and understanding speech. AI also can learn faster than humans and help free teams from routine, low-value activities.
Some of the more common applications of AI are automation, conversational platforms, and bots, and leveraging the power of AI to improve existing technologies. For example, many companies are adding intelligent capabilities to products they already sell, such as a computerized system that inspects products to detect defects missed by human inspection or the addition of Siri to Apple’s iPhones.
An enterprise AI platform enables teams to design, develop, deploy, and operate machine learning applications for practical business use at scale. These platforms offer four functionality types:
What is machine learning?
Machine learning is a subset of artificial intelligence that allows machines to spot patterns in data and make decisions without being explicitly programmed. This will enable devices to improve continuously, becoming more intelligent over time.
ML can be used to solve problems for which the development of algorithms by human programmers would be cost-prohibitive, such as finding the best sites for clinical trials or classifying credit applicants. It can also improve existing processes, such as data mining and predictive maintenance in manufacturing or making recommendations for insurance customers based on experience and other customer data.
When considering using ML, businesses should avoid looking at technology as a solution in search of a problem. They should clearly define their business goals and look for opportunities to improve or automate them. Otherwise, an organization could waste time and money on a project that fails to deliver tangible value.
What is natural language processing?
AI must possess the capacity to comprehend and interpret human language. NLP, short for natural language processing, is what this is. Computational linguistics is used with statistical, machine learning, and deep learning models in this area of artificial intelligence.
You likely interact with NLP-based technology several times daily without even realizing it. Online chatbots, voice assistants, text prediction apps, and grammar and spell-checking features are all powered by NLP.
NLP is a challenging area of computer science because human language is rarely straightforward or structured. It is a complex, unstructured dataset that requires a deep understanding of context and meaning.
As such, it is a challenge for machines to analyze. NLP performs various tasks, including speech recognition, document summarization, automatic translation, sentiment analysis, and named entity recognition. NLP also enables automated responses in email and can help automate tasks like logging into one or multiple systems, managing data silos, and performing manual searches. This frees up time for teams to focus on more high-value activities.
What is deep learning?
Enterprise AI is the application of advanced artificial intelligence techniques, including machine learning, to solve specific business problems at scale. It empowers organizations to transform their business processes to achieve step-function improvements across the entire value chain.
The recent breakthrough innovations in AI, coupled with capabilities enabled by technologies like Big data, elastic cloud computing, and IoT, create the perfect environment for business-wide expansion of AI methodologies.
Enterprises can take advantage of the capabilities offered by enterprise AI platforms to automate, optimize, and streamline business operations while reducing costs and improving performance metrics. From intelligent personal assistants to automated customer support chatbots, incorporating enterprise AI can help businesses improve the quality of their products and services while increasing employee productivity.
As a subset of Machine Learning, Deep learning refers to algorithms that learn from data without being explicitly programmed; they recognize patterns and can make predictions based on that information. A typical example is how home assistants can automatically identify STOP signs or pedestrians from their inputs.
What is AI for business?
There are countless applications for AI in business, from strengthening cybersecurity to improving customer service to saving time and effort on mundane tasks. It benefits industries that require a lot of data, such as finance or manufacturing.
For example, a credit card fraud alert system uses AI to identify and flag suspicious patterns for investigation. It’s also used in automated manufacturing processes, like computerized systems that inspect and upgrade products without human oversight.
Other examples include self-driving cars that navigate routes and avoid obstacles. AI can help businesses automate and improve processes, reduce errors, and free employees time to focus on high-value activities.
As with any revolutionary technology, there are unique challenges to effective AI adoption. To avoid these pitfalls, companies need precise alignment before a project starts, technological competency, and stakeholders’ commitment. They must also adhere to firm regulations to ensure compliance and minimize unintended bias. Getting these pieces in place will make the most of a company’s investment in Enterprise AI.
