This year, AI continued to make major waves, proving to the naysayers that it’s here to stay. But despite its growing presence in the mainstream and its increased utilization across fields, many are still unsure what AI actually is, how it works and the true potential it harnesses. 

AI is a complex subject, but it can be summarized as computer systems that are able to perform or assist with tasks that traditionally required human intelligence. It’s important to note, though, that there’s a distinction between artificial and augmented intelligence. Artificial intelligence often operates autonomously – a point where I will argue tech still has considerable growth to achieve  – while augmented intelligence works alongside human intelligence, essentially enhancing human output rather than replacing it.

As with most emerging tech, the discourse surrounding AI is full of buzzwords that are often not fully understood by the masses. Some of the AI terms you’re most likely to encounter are automation, machine learning, deep learning, natural language processing and prescriptive vs. predictive analytics.

Let’s quickly explore these key terms:

Automation: The act of processing based on pre-programmed rules. AI-assisted automation is becoming increasingly common as companies of all kinds look to become more efficient across customer service, market research, manufacturing and so much more.

Machine Learning: An application of AI that allows systems to process data automatically, analyzing insights without any explicit programming. Ultimately, machine learning (ML) is focused on learning functions and patterns in order to take on tasks such as prediction and classification. We encounter machine learning in action on a daily basis, ranging from search engine and email filters to personalized recommendations. 

Deep Learning: A subset of machine learning, capable of unsupervised learning from unstructured data. Essentially, deep learning is a function of AI that imitates the workings of the human brain when it comes to processing data and decision-making patterns. Deep learning is often used for image recognition tools and natural language processing (NLP) software.

Natural Language Processing: An application of AI that utilizes algorithms to train machines to respond to human communications. NLP is focused on information retrieval, text mining, question answering, intent understanding, machine translation and extracting sentiment from text. NLP is also all around us, from autocorrect to assistants such as Siri and Alexa. 

Prescriptive Analytics: This uses raw data in order to help humans make better decisions. Prescriptive analytics account for potential scenarios, available resources and past and current performance in order to suggest a course of action — both for short- and long-term purposes. 

Predictive Analytics: As the name suggests, this employs statistics and modeling to determine future performance. Predictive analytics can be utilized as a decision-making tool in a wide variety of scenarios.

Over the past few years, AI has proven to be invaluable across industries. Looking at public relations, in particular, many PR pros already see the value and potential of AI, although they don’t always have the full perspective or understanding of AI’s place in public relations. 

In a recent Harris Poll survey, completed in partnership with PRophet, findings revealed that90%of respondents believe AI has the potential to allow professionals to spend more time on higher-value tasks. However, some unknowns still exist, as 85% of professionals wish they knew more about AI capabilities with half (50%) admitting they have no idea what AI could do for the PR industry.

To bridge this gap, tools such as PRophet put AI’s power for public relations on full display. Employing a proprietary combination of NLP and ML, PRophet brings science to the art of communications, leveraging data in order to predict earned media interest and sentiment so that PR pros can pitch the right journalist at the right time with the right story.

The PRophet process begins once PR pros place their pitch, press release or crisis statement into the platform and select from a list of global sources. From there, PRophet analyzes the content in order to suggest the most relevant media categories. For example, proposed matches could include general categories such as “Food & Drink,” or more specific categories such as “Food & Drink > Cooking and Recipes > BBQ & Grilling,” depending on the content. Within seconds, users are supplied with a list of suggested journalists that they can reach out to directly; ML makes it possible for PRophetto surface and rank the top 100+ journalists to target for a particular story.

Through this sophisticated tech, PRophet can reduce pitch time by up to 50%. Considering that so many PR pros focus a great deal of their time and energy on pitching, this advantage can give them hours back into their busy schedules, ultimately allowing them more time to perform. 

Efficiencies like this perfectly encapsulate how AI can help humans do their jobs better, rather than replacing human workers altogether (as it’s long been feared to do). And in an industry as relationship-based as public relations, it’s essential that AI plays a supporting role without interfering with the human touch that makes our work so special. 

I remain optimistic that the public relations industry will continue to adopt AI – even if slowly but surely. Ultimately, increased assistance from AI is likely to lead to better results for all parties involved, moving our industry forward in the process.

PRophet is the first-ever AI-driven platform, built by and for PR professionals, to use a proprietary combination of natural language processing and machine learning to predict media interest and story sentiment before you ever send a pitch.