6 things PR pros should understand about AI

pros are well acquainted with the idea that ()
will have an impact on their work. However, do they understand what impact
it will have?

To get a better idea of how machine learning may affect PR, here are seven
things to keep in mind, per Christopher Penn, @cspenn, an authority on
analytics, digital marketing and marketing technology and co-founder of Trust Insights.

1. PR pros should understand the three things that AI does.

AI accomplishes three things:

  • Acceleration – You should be able to get the information you need faster.
  • Accuracy – You should get better answers than a human might provide.
  • Automation – It should reduce the amount of work that a human has to do.

2. PR pros must learn basic AI terms.

AI terms are often used interchangeably. It’s good to have a basic
knowledge so that you know when a vendor is trying to sell you some
AI-based technology that isn’t really AI.

AI is all stats and math, probability-based at its core. It’s about
training a machine to think like a human.

With machine learning, you feed in lots of data which then self-assembles;
that’s the value of machine learning. If you tried to write software that
could predict every way someone could talk about your brand, it’s not going
to happen. When it comes to analyzing media coverage, you can teach the
machine positive versus negative.


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With supervised machine learning, you’re training it to recognize a
pattern. With unsupervised machine learning, you gather all the mentions of
your brand to reveal what words are most closely associated with your
organization. It can build clusters of understanding what the brand is
really about. For example, if it finds lots of mentions that your product
is “too expensive,” it will show you that pattern.

It’s good for PR pros to understand the differences in these, so they at
least have a baseline understanding when they talk with vendors. Be sure to

  • “What kind of machine learning are you using?”
  • “What are you doing?”
  • “How does it work?”

If a vendor can’t answer, it may not be a wise investment.

3. Know the PR tasks for which AI is useful.

If you do something more than twice, it’s a good candidate for automation.

AI can help PR pros with coverage tracking and sentiment analysis. Public
relations practitioners should be reading all the pieces of coverage about
a client to analyze them for positive or negative sentiment. However, that
process is subject to human error.

This is something natural language processing (NLP)/machine learning could
do. It could summarize the coverage more thoroughly and faster because it
can read every article perfectly.

Not only is this NLP version more accurate, but it takes the burden off the
person in this role who could be spending time building relationships with
journalists and influencers—which is ideally what they should be doing.

4. Know what products already use AI successfully.

Half of the vendors who say they have AI built in don’t really use AI.

It’s also about expectations. When a vendor claims to use AI, PR pros
should expect that you are doing supervised or unsupervised machine

There is a primitive form of machine learning that is technically falling
under the category, but be careful. If it says AI, most people are
expecting magic. They’re expecting Watson in a box. Talkwalker is an
example of a vendor incorporating true AI right now. They just won an award
for their use of AI, which is supervised machine learning/classification.

5. Learn what’s a realistic expectation for AI in current use.

You should be getting to your answers faster. They should be more accurate,
cleaner and make more sense. It should be faster than something it would
take you forever and a day to do.

6. Consider how to prepare for increased AI use.

Get educated. This will help you manage your expectations about what AI can
do, so you’ll have a better BS detector. Get comfortable with the
quantitative side of PR. Take a class.

In the next five to 10 years, if you’re a copy and paste person working in
PR, you’re not going to have a job. However, if you have basic data
analysis, data science and statistics skills, you’ll be able to fact check
the answers and know when the machine is spitting out the right (or wrong)

One area to watch is voice recognition. What is your brand strategy in this
area? For example, can the Alexas and smart assistants of the world
understand your URL? If not, you’re locked out of the AI revolution.

Another element to watch is video and image recognition, machines that can
read how people are feeling. For example, you might have cameras that watch
everyone in an audience to monitor their facial expressions, so when you’re
announcing your next smartphone model, you can see a meter that tells you
their sentiment.

This could be useful for crisis communications or at events. Machines can
track it in real time and give you instant feedback before you create a
really big crisis for your brand.

Amazon’s facial recognition technology is already in production. It just
hasn’t found its way into the PR world—yet.

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