AI is already being used to deceive. Fraudsters are using AI to impersonate with stunning accuracy and the average person is not equipped to quickly discern real from fake. The recent U.S. elections are the first with AI driven misinformation competing with AI driven campaigning. Credible journalism is our first defense against the influence of misinformation and it must be protected.

The value of credible and trusted media brands will increase as they serve to distinguish the signal from the noise like never before. At the same time, clickbait journalism will have a second wind.

Safeguarding democracy from false narratives is hard work, sustainable journalism relies on robust revenue and people-focused experiences. While AI introduces opportunities for advertising and subscription growth, not all pathways align with the greater societal good.

The world is awash in self-proclaimed AI expertise since the public launch of ChatGPT and similar generative AI tools — experimentation is wonderful and true expertise is developing quickly.

Some will want to take things slowly to minimize risks. Others will want to move more quickly to in an effort to be the first with AGI. Thoughtful, open, and challenging dialogue is necessary to find the right balance.

We’re guiding our learnings with questions core to the value AI can create for recurring revenue customer experience:

• Does AI solve our customer's needs now and ongoing?

• What is gained and lost with the use of Generative AI content in place of human-created content?

• How does the interface evolve beyond chatbots?

• How does AI improve the customer journey and where does it make the biggest contribution — acquisition, engagement or retention?

• Should AI products be free or paid?

• What’s the risk of fraud to customers or the brand?

Feel free to share your questions and concerns in this brief and anonymous survey.

AI is capable of many things, not all of which are in service to people. We must prioritize AI’s outputs to solve people problems, rather than create new ones.

AI has biased datasets underlying the likely bias of the prompt, so we must be intentional with our efforts to make it reflective and considerate of the full diversity of people who will experience its outputs.

New things are interesting but they must also have a higher purpose beyond their newness. They must contribute meaningfully to the lives and experiences of people.

While we don’t fully understand how AI works, we do know exactly how we’re trying to use it. As such we must be fully transparent when incorporating AI in anything we do. This includes overt notations about the sourcing, citations, and fact checking efforts.

Remember that AI is based on machine learning from ‘training data’, and data is not the same as truth, so AI’s interpretations and representation of that data must be scrutinized down to its training data and sources.

Like coffee, AI data may be more or less ethically sourced. The training data that machines learn from should be questioned on how that data was obtained.

In this learning phase we need to promote experimentation while protecting key assets. In our work, these are our current guidelines:

• Do try new AI tools — experience comes from experimentation

• Don’t provide identifiable details to any AI, such as:

  • Names or pictures of people

  • Brand names or logos of clients in combination with information about that brand

  • Contact info of individual people (phone, location, email, etc)

  • Account numbers of any kind

  • Customer data of any kind

• Do ask AI for customer insights and hypotheses

• Do try to find efficiencies in your work

• Do fact check and cite sources for anything AI has stated as fact

• Do be transparent about the contributions made by AI

• Do share your learnings with the team