Marketing Insights Q&A: How will AI change social media?

Marketing Insights Q&A: How will AI change social media management in the next two to five years?

We have no way of knowing exactly what’s going to happen in the next two to five years in AI. We had no idea in 2015, that shortly thereafter, in less than 18 months, deep mind would be able to solve some of the big computing needs. We projected this as something that was 30 years away but was, in fact, only 18 months away.

Starting off with that caveat, let’s remember what the three purposes of AI or the three applications of AI, the three A’s:

Acceleration: to get more work done or to do work faster
Accuracy: producing better quality work, especially at massive computational tasks
Alleviation: learning repetitive tasks, the more repetitive the faster the machine can learn

These are not new reasons for using AI, which at a basic level, is a type of computer programming. This has been the case for software development for years. It is only now that AI is able to do this with some training on its own, as opposed to us having to explicitly spell out what the actions and outcomes should be.

Now consider what social media management entails.

How repetitive is social media management? The sourcing and curation of content to follow the 80/20 rule is a very repetitive process. You need very little human judgment to find high-quality content to share. That can be easily automated.

Similarly, you can identify and manage influencers with statistics and analytics. Running an advertising program is something that can be done using statistics and analytics. Because digital ads are numbers based, AI is already doing a tremendous amount of programmatic advertising management. The system will automatically adjust bids and placements for you based on the settings you specify up front.

Acceleration, Accuracy, Alleviation.

Think about this…

If you do it with a template today, a machine can do it without you tomorrow. How much of social media fits inside an actual playbook? Your company may even have its own social media playbook. That’s a massive template. How much of your social media management fits inside the playbook? That’s what AI will do. If it’s in the playbook today, the machines will do it tomorrow.

The question many still have is, “so what will I be doing if the machines doing everything in the playbook?” You’ll be responsible for everything that’s not in the playbook. That includes the grand strategy and the integration of that social media strategy into the overall business.

In a lot of cases, social media strategy is still very simplistic because of organizational silos. If social media is not integrated into marketing, and marketing is not integrated into sales, sales are not integrated into customer service, etc, – you end up with simplistic corporate social media goals that won’t necessarily move the business forward, like more followers. You don’t need a human for that.

What won’t machines be able to do in the next two to five years? Tasks that involve understanding the nuance of behavior. Machines won’t be able to integrate and disseminate the social program into the overall ecosystem of the company. We will still need people to design campaigns because machines can’t learn that the nuances of empathy, judgment, reason and cultural significance. Machines are are are not good at that today and they probably will not get good at that in the next two to five years. Eventually they will, and eventually, they will figure out how to manage those nuances as the training data sets get bigger and bigger.

Another example is the overall architecture and connecting the pieces of AI. What we mean by that is, how do you coordinate all of the pieces. Today, you’re thinking about what to buy off the shelf and what to build internally. Many companies will have a blend of systems that are custom built and off the shelf. The average midsize or enterprise business isn’t going to spin up a 100 million node computer cluster – you would go to a company like IBM for something like that because it’s their core competency. Those same companies would absolutely leverage the power of that supercomputer cluster in the overall marketing technology infrastructure. If you look at a product like Watson Studio, IBM structured it as a drag-and-drop modeling interface to help companies build their own AI models. It’s accessible and easier for data scientists, and eventually, businesses, to design their own models. The eventual goal is for businesses to create their own AI architecture. The role of the social media manager will be to help those system architects figure out what the architecture should do, what the data sources are, and the overall corporate architecture of how to handle that data.

Bear in mind that AI works best in situations where there’s not a great human experience. If your current human social media experience is terrible, AI is going to be able to replicate it really easily. For example, If the social media experience that you give to customers and audience is best in class, it’s interactive, and you have true real in-depth conversations with people every single day; that’s going to be much, much harder to automate. Conversely, if you have someone who just posts five times a day with links, never responds to anybody, ignores customer complaints, that that can be automated out of existence easily.

The quality of service you give is the last metric of what a social media manager will or won’t do in two to five years. These days as people start thinking, ‘what will I be doing as humans’ we have to double down and commit to the best in class customer experience. Otherwise, a machine will be able to do the work for us and not and we won’t be needed anymore.

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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.