In the second Q& A with Appspace co-founder and Chief Product Officer, Stan Stephens, we asked him about AI’s role in supercharging the future of workplace communications.
What are some ways AI can enhance digital signage?
Alright, if we take digital signage as an example, AI’s role is going to grow exponentially. Computer vision, machine learning, and enhancing the way technologies can analyze demographics, behaviors, viewer engagement, and emotion are improving and becoming much more precise so we can better determine the viewers’ characteristics and reactions. They’re providing much more data so that we can target content and understand the engagement level when that content plays back. Machine learning algorithms are better at identifying patterns in traffic, engagement times, and other metrics that are important. As these algorithms process more data, their predictive abilities and analysis should become more reliable, enhancing the effectiveness of digital signage.
The use of AI in public spaces raises ethical and privacy issues, so we’ve got to find a balance between leveraging AI potential, but also respecting individual privacy. So again, this is probably going to lead to stricter regulations. Although what we do with digital signage can benefit from a better understanding of its use and engagement, we can’t just throw it out there and start capturing data without following whatever those guidelines or regulations are going to be.
Despite the hurdles of accuracy and privacy, AI is set to play an increasingly important role in the digital signage industry, helping to drive more effective, targeted, and engaging signage.
What can AI do for content creators?
Appspace’s innovation arm has split into three groups to look at how we leverage AI: insights, generative, and conversational.
If we look at insights, for us that’s about generating insights and a taxonomy based on what tags and skills are part of any piece of content, so you have this ontology of content that may end up using a deterministic algorithm to group things together. AI will then be able to read this material and look at what types of topics and skills it covers. It will be able to understand the context.
AI will help us deal with and understand the huge amount of material that’s out there. It’s one of the first things we look to do in how we leverage AI at Appspace. As we keep producing stories and pages – and at some point, we’ll get into the comments and sentiment – the AI will start to automatically create these different labels about it such as the topics of content and the skills that are involved in it. Not only can we use that to understand the content, but we can also use it to understand who’s creating it and associate it with them. So, if I want to connect with an expert, or someone who understands a specific subject, then the AI will surface the people that author material in that domain as well.
How will AI give us a better TL;DR experience?
Generative AI is the next piece of the puzzle and there are many use cases for it. The simplest example is the idea of a podcast that gets you caught up on company events. I open the Employee App sometimes on the way to work and I just want to put my headphones on and find out what’s going on. Today, I need to open my Employee App and swipe through and see what the latest updates are. But wouldn’t it be great if I could create a narration of all these updates? Almost like a five-minute company podcast that catches me up on what’s been posted and what’s been happening inside my company based on the content that’s been published to the app. It’s a narration feature where any article, story, or page can be narrated. It also gives you a simple button when you log into the employee app that’s tasked to summarize everything, generate those TL;DRs and at the same time, generate the audio version. Now imagine that it could capture your voice, so the content you’ve authored could be played back in your voice. That would be so cool, right?
What are some of the key benefits of integrating AI into content creation and image generation processes?
One of the great things about AI is that it can understand the context of a page, and it can understand the key points. And those key points can end up being a prompt into an image generation service. It must be a grind to keep coming up with images that are relevant to a page or article’s content. I still feel that tech is maturing and we’re not at a point where you just write a page, and software like Stable Diffusion, Midjourney or DALL.E will always generate the perfect image. But I think it’s getting to a point where we’ll be able to start offering that as a workflow to a publisher.
A lot of knowledge-based content can be helped by AI just from prompts. We recently went through a design camp exercise and looked at AI areas for generative and insights and how we integrate that into our platform. Some of the cool stuff we came back with was about generating content in different blocks so that you can use AI to go into that block and say, let’s lengthen that, and shorten that, let’s change the tone.
The age of us spending hours trying to get content, images, and graphics correct is going to slowly slide away as generative AI creates good enough or great enough output. Internal communication is going to be transformational once we get AI involved.
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