Here’s what we’re looking at as we navigate toward an AI-powered future
By Jason Mayde, Head of Information Technology at Highwire
AI is the new frontier in tech. At Highwire, we’ve been exploring ways to leverage the latest emerging tools under the AI umbrella — generative AI, machine learning, transcription, automation, and much more — to make our workflows more efficient. Our goal is to give our team of expert communicators more time to be creative and productive so they can provide better service for our growing roster of clients.
However, this space is moving fast — so fast, in fact, that it can be challenging to keep up. Between advancements in faster, more powerful chipsets from NVIDIA, new players like AMD entering the space, and updated models from the likes of OpenAI, it can be difficult to separate genuine innovations from the noise.
To provide deeper insight into what’s going on in the world of AI, here’s what IT leaders like myself are keeping an eye on: the trends, the sources, and the next big challenges to overcome.
How I keep up with the latest AI news and trends
There’s a lot to keep track of, so I read and watch as much as possible to get a sense of the whole picture. Adding even one of these sources to your media diet will help you stay on top of the latest trends and advancements. Feel free to pick and choose the ones that suit your learning style, and you’ll get up to speed quickly.
- For industry publications, I regularly check in with websites and blogs like TechCrunch, Wired, Technology Review, Medium, KDNuggets, AI Magazine, and The Verge. These sources give me a wealth of insight into the latest updates on cutting-edge technologies, as well as their implications for our business.
- I also check out company blogs, newsletters, and announcements from the biggest developers and manufacturers in the AI space, such as AWS, Microsoft, NVIDIA, and OpenAI. These feeds usually provide firsthand insights into the direction leading AI companies are taking. They’ll lay out how they’re approaching everything from building new data centers to meet demand, how they’re going to power those data centers, what new chips and processors are coming down the pike to make these compute requests more efficient, and model advancements that will increase generative AI’s overall capabilities.
- To see how the rest of the IT community reacts to industry news, I browse social media and community websites like X (formerly Twitter), Reddit, Hackernews, and LinkedIn. One of my favorite follows on LinkedIn is Steve Mnich, the Head of Product & Partner Communications at Anthropic, an AI research and development company focused on making generative models safer and more reliable. He has a wealth of insight on how AI developers and companies using AI must navigate its unique challenges as the tech matures.
- Webinars are also a fantastic way to learn about the latest updates in AI and how businesses can more efficiently apply them to their workflows. I’ve been attending webinars from The PR Council, The Institute for Public Relations, Public Relations Society of America, and Gartner. Gartner’s webinars, in particular, are always pretty interesting. They also tend to be high-level instead of focused on a specific industry, so they’re great for anyone looking to dip their toes into the AI space.
Keeping an eye on generative AI advancements
By sifting through these sources, we can piece together the puzzle and figure out which evolutions will have the most significant impact in the coming months and years.
Of course, there are the broad moves, like Open AI’s rollout of GPT-4o back in May 2024. This is an updated version of its flagship model with improved image and video recognition capabilities. Users can feed in an image containing a foreign language, and the model can translate the text much faster than previous models.
Then there’s the upcoming o1 model, which is currently available in a preview state. This model takes the next step on the road toward artificial general intelligence (AGI). Rather than merely regurgitating answers, o1 can offer deeper analysis and solve mathematical or scientific problems.
All of this is leading toward the evolution of the generative AI agent (a tool that merely answers questions) into an assistant that can actually make decisions and complete tasks for you. Companies like AWS are already making great strides in this space. There’s so much intelligence in its ability to autoscale microservices for web applications, as it analyzes past data usage and traffic loads to spin up or wind down services to keep everything running smoothly while also keeping costs optimized.
I’m very excited about how this technology can be applied more broadly, especially as we look for ways to continue optimizing our own data analysis and creative workflows.
AI ethics and governance is the next big problem to solve
These advancements are, of course, compelling. However, the biggest challenge I see isn’t technological — it’s ethical.
Keeping our clients’ data protected goes beyond cybersecurity best practices. It’s about being ethical stewards of that data, ensuring personal or proprietary information isn’t added to training models or exposed to the public. As such, there’s an increasing emphasis in the AI community on building security guardrails and data governance frameworks that align with ethical standards, regulatory requirements, contractual obligations, and organizational values.
Companies that can figure this out quickly without sacrificing process efficiency will put themselves in a position of strength within the next few years. For example, there’s Jasper, an AI marketing platform that doesn’t train on any of the data you put into it. It’s SOC 2 compliant, GDPR compliant, and features robust data governance tools — all huge in the face of increasing concerns about keeping client data private and secure while still getting something meaningful out of AI.
Then, there’s the legal aspect of the AI equation. Generative AI needs a vast corpus of data to provide more reliable answers, and some companies don’t necessarily enjoy their data being used to train these models without permission. Even the Federal Trade Commission is starting to get involved, publicly warning AI companies to avoid “quietly” using customer data to train AI.
It’s difficult to see how this ultimately shakes out — whether a lawsuit sets a legal precedent or the AI companies themselves attempt to self-regulate to keep government watchdogs at bay. In the meantime, it will be imperative for us and anyone who uses these solutions to develop internal processes to protect clients and ourselves from legal risk.
Preparing the future AI-powered workforce
Another aspect my peers and I are concerned about is how we integrate these tools into our organizational workflows to maximize our team’s efficiency while keeping costs manageable. Right now, this is still an unsolved problem.
Engineering the right prompt or developing automated processes can take significant time, which can be an issue — especially in a services company like ours where time is money. We can’t just roll out a new solution and expect everyone on staff to put the time in to experiment with getting the best results. We need to integrate these solutions to allow our team to hit the ground running while also offering education to those who need further assistance.
However, the business integration of AI is in a very experimental phase at the moment. Part of the process is figuring out what challenge we want AI to solve and then finding a way to inject those solutions into our agency without increasing workloads.
This process reminds me of nascent industries like virtual reality, where the application developers who create the solutions and the IT professionals responsible for integrating them are constantly throwing new ideas at the wall to figure out what sticks. The more we discover during this process and communicate what works, the more best practices are likely to emerge that will make integration more efficient in the future.
Ultimately, the goal is to give people more time to do the work they’re passionate about. If AI can get to a point where we can spend less time worrying about the right prompt and more time just letting it do its thing, we’ll be in a pretty good place.
If you’d like to join us on that journey, feel free to reach out. Our team of communications experts and data scientists are more than happy to show you what we’ve been up to, and we can help you find a PR and marketing strategy that leverages AI to get you the results you need. Contact us today to learn more.
