Artificial intelligence is fueling enormous change in business, allowing for more accurate predictive analytics than ever before, so-called autonomous data-center systems and new services in areas like finance, customer relations and healthcare.
AI can optimize business processes and IT operations, automating routine jobs and allowing staff to focus on high-value tasks.
AI, though, may be at once the most transformative and least understood areas of technology today. Adding to the confusion, AI comprises concepts and terms that overlap and create misunderstanding about the field
What is artificial intelligence?
AI is a blanket term for various algorithms, methodologies and technologies that, broadly speaking, allow software to accomplish tasks that historically were associated with human intelligence, such as responding to natural language queries, recognizing images and sounds, making decisions and predictions. Machine intelligence is a more recent synonym for AI.
Perhaps the biggest point of confusion surrounding AI involves machine learning, a term that has become well-known outside tech. Non-specialists often use it as a synonym for AI since it’s responsible for some of the more spectacular recent advances in the field, such as self-driving cars. Machine learning is however a subset of AI that is itself an umbrella term, embracing methods and algorithms – such as neural networks — that let computer systems better their performance as they ingest more data.
How you can implement AI in your own business
AI technology has been baked into many off-the-shelf packaged software products. It’s used for predictive analytics in ERP, CRM and networking software from vendors like SAP, Microsoft, Salesforce and HPE. It’s used for self-maintenance of Oracle’s Autonomous Database. Help desk and customer service software from vendors like BMC and ServiceNow use AI to classify incidents and route tasks. An exhaustive list would fill a book-length manual.
Many enterprises, though, have rolled out their own AI-based applications. There are pre-built APIs you can incorporate into your own systems, cloud-based machine learning offerings for building data models and a wide range of programming tools you can use to build AI applications from the ground up.
To be successful with AI, you need to understand, among other things, the techniques that can be used, the tools and services that are available, the skills required and the relevant data you have. Above all you need a concrete plan for putting AI to work for specific business goals.