For the 95% of publishers who haven’t yet adopted AI, identifying a use case they can start with is usually the biggest roadblock. But the tools also need to be available for publishers to be able to support their unique revenue streams.
This is the latest in our Media Briefs series of short, sharp sponsored episodes – just 10 to 15 minutes – with a senior executive from a vendor working with publishers to make their businesses better.
In this episode, Peter speaks with Maanas Mediratta, CEO at Bridged Media, about how and why making AI tools more accessible to publishers will help the whole ecosystem. Much of the AI innovation is being driven by big tech companies and industries like eCommerce, fashion and finance. But publishers have such a diversity of business models, even tools designed for media companies often won’t satisfy individual companies’ goals and needs.
Here are some episode highlights:
The need to empower publishers with AI
AI, whether you’re talking about generative AI, NLP [Natural Language Processing] or machine learning algorithms, they’re primarily used by very, very few players, mainly Google and Facebook, to really understand the audience, see what they want, engage them, and so on. That has resulted in the media pie being shared, or being taken away from publishers. So we started Bridged Media with this objective, this vision of, how can we empower publishers by democratising AI?
If you think about how AI is being used, especially in the media sector, it’s mainly for understanding what the visitor really wants, whether its’ to engage them, show them an offer, really analyse them and maybe sell them ads, whether that’s through direct ads or programmatic ads. But all of these algorithms are currently very, very inaccessible.
When I say inaccessible, there are two things to it. One is talent; I think the talent resides mainly in these big tech companies that can really understand the algorithm and tailor it for a publisher. The second thing is definitely the cost, in terms of the way you can build a proof of concept – it’s not a simple thing. Publishers who have been successful with AI – think about the Wall Street Journal or the New York Times – have taken years to build a proper algorithm and spent millions. 99% of publishers cannot do this.
To make it accessible, we feel that there are two main things we need to do. One is to reduce the amount of experience that is needed to create a proof of concept for any use case that they have in AI. And the second is pre-defined modules, out-of-the-box solutions that can be utilised for any content infrastructure by doing some tweaks and configurations.
Why publishers need specific tailoring
Most of the AI tools that exist are for eCommerce, where you’re having an eCommerce store, or for other big market value industries like fashion or finance. But when you talk about publishers, there is an intrinsic problem in the industry when you think about creating no-code solutions.
Number one is that publisher revenue has decreased pretty much for each and every publisher from the Western world. And number two is that publishers are unicorns – not from the startup unicorn – but unicorns from the point of view of the way they have created their own business model. I don’t know if I’ve met two publishers that have identical business models. Some are collecting emails, some are promoting events, some are even adding eCommerce, affiliate, you name it there are at least 10 business models that a medium to large-scale publisher is employing.
So creating B2B tools is not that easy. You need to know when to engage the audience, where they’re likely to interact with you. Two, you need to give them the right offer, whether the offer is a subscription offer, or an ad offer, or just a value exchange for giving the email. And number three is really analysing what happened in a session.
Advice for publishers who aren’t using AI yet but want to be
There is this primary research we have done with 300 publishers in the US and Europe. 95% of them actually want to adopt AI.
For me, the biggest roadblock is really identifying what is the one use case that they can start with, and how to ensure that it is an ROI-positive use case. I want to keep people in my infrastructure, I’m going to invest in an ML recommendation system. This is how I’m going to monitor it. This is the ROI that I see.
If you’re able to crack that, then it becomes a flywheel. You can create different proof of concepts, given the accessibility of AI, and really take the benefit of the AI ecosystem and not being that 95%. You’ll be that 5% that has already implemented.
Note: Media Voices are currently working with Bridged to advance our own goals of driving sign-ups to our daily newsletter, and resurfacing relevant content from our site. You may well see some of our engagement tests over the coming weeks, so if you’re interested in trying Bridged for yourself, details are below.
This Media Briefs episode is sponsored by Bridged Media, democratising AI for publishers. Through no-code AI solutions, Bridged lets publishers access the power of machine learning and Gen AI to meet their engagement and revenue objectives.
Publisher-first AI tools detect where the audience is most likely to engage and through a single line of code, introduce action cards that prompt readers to register, subscribe, or read more, helping publishers establish richer relationships.
Learn more about Bridged Media’s no-code AI tools on their website.
