Any agreement among agency executives about the future of artificial intelligence in PR seems limited to, well, its limits.
In a recent column on PRWeek, Ivan Ristic of Diffusion suggested that while AI might be good for data mining keywords across blogs and online publications to find trends for clients, it still won’t replace relationships that define the industry.
“A bot can’t lay claim to emotional intelligence, a cornerstone of all PR work. Teams employing AI handling external communication would be wise to have plans to manage reputation should anything go awry,” he wrote. “Humans build trust with humans—not bots.”
When used effectively, machines can get smarter about how to book hotels, solve customer service issues and much more. Is it really so difficult to imagine a bot that would generate pitch ideas for specific journalists? While some in the profession loathe to hear it, it’s not far-fetched.
There is good news for communications pros. The future of PR will be a blend of both technology and human insight. Most experts suggest AI will augment that strategic thinking by synthesizing details the way marketing automation has done for others.
This recent story on Ragan.com sums it up well:
By taking advantage of massive quantities of data and using AI to draw insights on it, PR pros can now . . . cut through clutter and find useful, relevant data, quantify buzz and press hits, properly attribute revenue, know which tactics are working, spot brand and revenue indicators and identify PR funnel accelerators.
Communications pros don’t need to move immediately, but they would also be making a mistake to not prepare for AI. Just as personal computers entered the workplace to reduce onerous paperwork and administrative tasks, there’s nothing wrong with taking baby steps with AI before PR professionals maximize its potential.
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A blogger on Towards Data Science suggested this is precisely the low-hanging fruit they should begin to pick. “If you have an agency full of clients, you probably already realize how exhausting it is, engaging with the public on social media, liking, replying, following, searching all over again for every single account you manage daily,” she wrote. “How good it would be if every task could be automated, saving the time of PR professionals for vital tasks such as creative work and decision-making activities?
Maybe the best way to get started is by learning how technology experts are defining AI. The below video from PBS Digital Studios explains in detail the difference between automated machine learning and AI.
One day the PR industry may want and rely on both pure and pragmatic AI, but to make that call you need to understand how a machine actually learns.
Think about how we teach children in school. A group of elementary children might be asked to learn their multiplication tables and solve a few problems but are free to ask for help here and there. Machine learning in AI works much the same way.
In what’s called “supervised learning” technology can use what it is given, or “training data,” to get from A to B on a particular task where you know exactly what the end result should be.
Imagine you want to take headlines and rewrite them for social media in a way that highlights your client’s or brand’s role in the story. The next step might be copying and pasting the text with the URL in the field of a social networking service. Then it might be cross-referencing the right hashtags, including one created for a particular PR campaign, to include in the post that is known to get the most amount of pick up.
While most people have a person do this today, supervised learning algorithms with good training data might be able to manage some social media promotion to scale some of your social media engagement. Now think of other tasks where AI could be put to work organizing work back schedules or even writing rough drafts of a press release based on your firm’s brand voice and guidelines. The Associated Press is already using AI to write earnings stories.
A more complex version of machine learning might be able to look for clues about the sentiment of earned media coverage, sending alerts about negative stories the second they appear so PR professionals can respond more quickly. The technology might also become sophisticated enough to spot “fake news” or inaccuracies in the way a company operates, or nicknames for people and organizations that might otherwise get missed when looking for where a brand is being mentioned online.
Next steps for PR pros
A few months ago, PR consultant Stephen Waddington took to his blog to chastise his peers for seemingly attempting to avoid the revolution unfolding before them.
“The impact of algorithms on discourse in the public sphere needs to be characterized and their creators held to account,” he wrote. “Public relations, like other professions, is sleepwalking into the issue of artificial intelligence. It’s an issue that is rarely addressed at events and by media in the business of public relations. That needs to change.”
In terms of how that change could take place, here are a few ideas:
1. Seek out an AI advantage. Whether you want to use advanced machine learning to solve big problems or just reducing some of the grunt work, begin brainstorming with your team, as well as though in other departments, on where AI might make sense as a pilot project.
2. Lay the foundation for automation. The transition to AI-enabled processes will be much smoother for firms that have already gotten familiar with technologies that assist in areas that have traditionally been manual, error-prone or both. If you’re a data-driven PR shop, you could have a head start on what AI could do.
3. Adopt a reporter’s mindset. The journalists you pitch are often infinitely curious—and highly skeptical—about what they see and hear. They ask a lot of questions to get at important details. They may even focus on the negative angle before they get to any “good news.” Such behaviors and characteristics could serve PR professionals well as they learn more about AI and what it could do for them.
Chris Lynch is the CMO for Cision a media monitoring and database company. A version of this article originally ran on the Cision blog.