Wondering if you could analyze your ad creatives’ performance and gain insights in under a minute? Advertisers face this daunting challenge in understanding the true impact of their creatives across platforms like Facebook, Instagram, Google, and TikTok.
The black box problem leaves them in the dark, relying on inadequate traditional methods like AB testing, leading to guesswork and inefficiencies. This lack of detailed, actionable data results in wasted resources in time with advertisers unable to optimize their strategies effectively.
The frustration of costly and ineffective testing pushes brands to seek better solutions.
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Asaf Yanai, CEO of Alison.ai, explains how AI can revolutionize creative analysis. Asaf has over 15 years of experience in driving technological innovation and transforming industries, with a background in strategic roles such as VP of growth and head of media buying at top online marketing companies. Asaf leverages his expertise to lead Alison.ai in revolutionizing creative analysis with AI. His entrepreneurial journey marked by successful ventures and a relentless pursuit of progress makes him a true inspiration in the tech industry.
Alison.ai
Mike Allton, podcast host and Agorapulse’s chief storyteller: Could you start by just telling us a little bit more about Alison.ai and the work that you guys are doing there?
Asaf Yanai: As you said, in my career of 15 years within online marketing, I’ve been fascinated by creatives and the visual elements and the visual effect that advertisements have on our audiences, our buyers on our target market, etc.
But what I’ve realized in my career—and a lot of my colleagues feel the same—what we’ve realized is that, in the past few years, there has been a shift in online marketing. There’s been a shift driven by the big media platforms, Google, Facebook, TikTok, etc. And this shift is a shift towards automation, a shift towards being from being in full control—even manual control over your campaigns, media, budgets, etc.—to an era to a place like we have today where we have very little, maybe zero control, over our media metrics, campaigns, placements, etc.
All of those platforms, those main platforms shifted their focus from media to creative by making media completely automatic.
When they make media completely automatic using automatic campaigns, dynamic campaigns using their own algorithms, etc., that means that we—as advertisers, as brands—the only real thing we can make an impact and change within our campaigns are the creatives, the visual elements that we use to engage and interact with our audiences.
Here comes the tough question, which is: How do we as brands, how do we know what’s the right video content, or what’s the right video composition that we should be engaging with our audiences?
There’s a clear focus on video from Google, being put on YouTube and other display networks, Facebook, meaning Facebook itself Instagram, TikTok, Snapchat, etc. So there’s a clear focus on video from the platform side. There’s a clear focus on videos from the user side. As users, we prefer to engage with video content—but also on the engagement side, video sells better, video assets deliver multiple messages, and hyper-personalized messaging can cater needs of specific user segments, audiences, and even specific users.
So going back to the tough question—now that we know that there is a clear focus and a tremendous focus on videos and creatives themselves rather than on the media—then the question is: How do we know what’s the right video content or video composition that we should be engaging with our audience?
And it’s a very tough question—and you’ve touched it in your intro—but it comes to be a black box because the only real way without using Allison we can optimize or gain some sort of an understanding of what’s happening within our creatives is running A/B tests. Now A/B tests are extremely costly, wasteful, and inefficient. And for those of you who know me and know my career better, I’ve been fascinated specifically with this AB test challenge.
The main challenge is that the AB test hasn’t changed in decades. I mean, in the 80s, in the 70s, when everything was offline, every company that wanted to test their online advertisements also used the A/B test. And now, when everything is digital, we call it ones and zeros. We are still using the AB test. It doesn’t make real sense, right?
The problem with the A/B test is that initially, we create and generate a lot more versions, a lot more creatives than we need, because we need to make differences and iterations between the different creatives and then put them to test in the actual real world. 90% of the creatives that we produce go to waste, meaning they will not win the A/B test process, and thus we would completely scrap them.
But what happens with the 10 percent that does win the tests? How do we know if they’re the optimal versions? So how do we know if we should be making them better? This is the main problem with the A/B test. We’re only looking at one or a few variables at a time. If we want to cover all the different variables to an optimal position, we would either have to run the A/B tests forever and still, as brands, the incremental increase that we will gain after each one of those tests is very slim.
The use of AI can revolutionize and replace this entire A/B test process by completely looking at creatives differently: looking at creatives as data points and what is within the creatives and not what is this black box asset that we call a creative or a video.
Mike Allton: And that’s something I want to come back to because as a social media marketer for over a decade this is an issue regardless of whether talking about organic or paid, we have so many different platforms and it is virtually impossible to understand why certain things are performing the way they are on other kinds of platforms. Quite frankly, many of us are just so lazy and so frustrated or overwhelmed that we simply just do the same thing across all platforms, certainly being transparent that’s what I do personally. I don’t have a lot of time for this on my side hustles to do true social media marketing the way it’s supposed to be.
The Black Box Paradox
Talk to me about this concept of the black box a little bit more, because here, at least in the U.S. [when] we think of the black box, we think like the recording unit inside an airplane. That’s not what we’re talking about here.
Help us unpack this concept of a black box.
Asaf Yanai: The reason it’s called “the black box” is because we’re looking at the creative video banner as a whole.
We’re looking at it as a box or as an asset, and looking at it at a very high level as an entire asset doesn’t really give us the insight or the way to make it better in the future. It doesn’t give us the data points that we need to optimize according to performance.
- The black box paradox—or the black box challenge—is all about how we decipher or decode those videos and those creatives into measurable data points, measurable features, elements, and components that we can correlate with performance.
- We can find different combinations that might work well or might not work well, or how do we come up with a strategy that helps us understand what we should do to consistently increase our performance?
This is the big black box challenge, and again by looking at creatives as units as assets, we’re not gaining any idea and any data points around “How do we make it better?” or gaining any insights towards “Okay, what is our strategy in the future? How do we take our current strategy and make it better?” Not just one, two, three percent better. That’s the typical A/B test uplift or the typical A/B test increase.
- But how do we come up with a 300 percent increase in our main KPIs, including revenues?
- That’s a little bit about this now, AI, and I would love to dive a bit deeper into how it’s done and how this decoding or deciphering of the creative comes to life.
- AI is completely revolutionizing this entire place. AI started as taking tedious human jobs and just making them automatic. But this was the beginning of AI.
- Now there are a lot, lot more things that we can do with AI to gather so much more data that is really sometimes even impossible to imagine that we can gain such valuable insights and such a high volume of meaningful data points.
Walk us through that process of how AI and Alison.AI is used to evaluate the creatives and come up with insights that are just so fast.
Asaf Yanai: First, we take speed into consideration.
For us, speed is a part of this efficiency revolution that we’re trying to create in the market. And the A/B test takes a lot of time. We need to produce a lot of creatives. We need to dedicate budgets. We need to publish them online. We need to wait a bit between a day or a week—sometimes even more to get a decision/output on which one is the winning version. And even then we still don’t know, as I said, why.
Again, instead of running tests off one or a few variables at a time, this is what we’re doing with AI and Alison. Basically, we’re scanning every single piece of creative from banners to the most difficult and most intricate videos you can think of. We’re scanning all those creatives and using 10 different AI engines. We run a process called Feature Extraction. We’re basically identifying 25,000 features from 30-second videos so we can then correlate those features with the actual performance. So we can see which feature, which element, and which combination of features drives our incremental increase or drives our actual performance. So this goes to sound and voiceover and background and calls to action and logos and facial expressions and even product or service attributes that are used within the advertisements within the videos to actually sell.
After we break the creatives down into those thousands of elements, ending up being billions of elements per customer. We’re then correlating the features with the actual performance of those videos, so we can tell our customers not by guessing not by brainstorming not by running experiments but by telling them exactly precisely what’s driving the performance within their creatives and we can help them come up with recipes, the best recipes for creative success.
Imagining the Possibilities
In a way, I also look at it as a chef.
Think of the world’s best chef (Gordon Ramsay, or whoever) who came up with the best recipe for a winning dinner.
Instead of a recipe for a winning dinner, imagine you would have a recipe for a winning video (specifically for your target audience for your service or products). And also this recipe that takes into account every single campaign and every single creative that you’ve ever run and ever tested wherever launched. This optimization process, insight process, and recipe process begin with a lot of research that we do using AI.
And—you’ve guessed it correctly—it sounds tough. It sounds like a long task in terms of time, but it takes less than 60 seconds. So we can go through thousands of videos, break them into billions of data points, correlate all those billions of data points with the actual performance and the actual marketing metrics, and come up with those recommendations and those recipes in less than 60 seconds.
Imagine this is so powerful for a lot of businesses because the equivalent process takes a full team, probably a full month. Also, there’s this gap of data if the full team for a full month works on the traditional methods of A/B test, the level of data and the uplifts would be nice or maybe okay—but it would never be something that you can build an actual strategy that would last and sustain for longer periods of times.
But that’s just the beginning of it.
Now imagine you also have an army of market researchers on your side working for you. So we also use our AI—not just to analyze and break down our customers’ creatives. We also use our AI to analyze and break down their direct competitors’ creatives. And, for us, we look at the direct competitors as the market. They represent the market-specific market for us as advertisers. By looking at our competitors, we can drive a lot of insights and a lot of clever things that we know are working for our competitors, but there was no way we could reach them and surface those data points before if we haven’t used an AI solution like Allison.
This is how the magic happens, and this is how the AI works. But again, it’s only the beginning of the process because what we’ve realized is that providing insights and recommendations [is] nice. It’s helpful. It’s impactful as hell. But this is not a tool.
These are data points or recommendation recipes that we still need to do a lot of work on. We need to put in a lot of resources, and we need a lot of teams and a lot of people to produce the marketing outputs based on those insights, recommendations, and recipes.
What we’ve done at Alison is we’ve taken our insights and recommendations and transformed them into a prompt—or a “smart prompt,” we call it because again, it’s not just a typed-in prompt; it’s probably 300 times stronger in terms of the amount of data and the relevancy for the customer than just a regular typed in the prompt. And we use those prompts to generate marketing outputs like the regular marketing outputs that we know. But again, these are not based on subjective experiences.
These are not based on brainstorming or guesswork. These are based on billions of your own data points, and billions of your competitor’s data points that are then correlated with the exact business goals and KPIs you’re trying to achieve, and only then we are producing the marketing outputs in the form of our marketing brief, storyboards and also a live generated video that is commercially ready. Meaning that it’s not just raw footage. It’s already a ready-made ad that takes your brand guidelines, logos, call to action, disclaimers, messaging, and even hyper-personalized components to your different audiences and segments into consideration while producing and generating those videos.
And again, this all takes less than 60 seconds, this entire process.
Mike Allton: That’s the beauty of AI today.
AI Comparing Assets
But one thing I wanted to draw out and clarify: You talked about how it’s instantly going to compare one particular video asset against everything else you’ve ever run in the history of your advertising so you can draw from that learning.
But did I hear you correctly? It’s also drawing on competitors and perhaps other videos on the market. So it’s comparing that one asset against a vast ocean of other assets and helping to draw insights on what should or should not be changed.
Asaf Yanai: Precisely. Yes, precisely.
And again, what we’ve realized is that by looking only at our customers’ data, we’re missing a huge part of what’s called marketing. We’re missing the market. And we’re missing what our competitors are doing, what our competitors are leveraging if it’s working for them. We’re missing all those gaps between us as brands or advertisers and our competitors. And there’s no real way, no real tool outside of Alison that can help us run this creative research on our competitors.
We’re learning that it’s comparing our assets to all of our other assets but also comparing our assets to all of our competitor’s assets. If you look at it aggregately, this is the most powerful, most robust creative gallery and insight that you can ever think of—because it goes way beyond what you think traditionally as a brand, and it goes way beyond what you used to do traditionally as an advertiser—and it also goes way beyond the limits of the different platforms because we’re looking at all the different platforms at once, we can also provide the specific iterations and the specific tweaks or modifications that we need to do to a specific creative to maximize, not optimize. Maximize its performance on every single platform with every single concept and on every single placement.
Now, think of it with your own company. Think of it with your own business. And how would you use this kind of tool and kind of strategy if you had it at your disposal? And I can already tell you that our customers are reporting back to us. And this is an average, right? On average, we’re seeing a 180 percent increase in revenues, not just clicks, not just impressions, but revenues.
Now, again, think of it as just a regular normal business that generates X dollars or X millions of dollars per year and multiply this by 180 percent. That’s very transformative to a business, I think.
Mike Allton: That’s huge. For those of you watching [or reading!], you know a couple of big points Asaf just made.
- If you’re just starting with advertising, you don’t have a rich history of advertising that you can look back on and try to compare it, then that’s okay. Their tool brings in your competitors who may be, may have been doing this for a much longer period than you, and that’s just such a great start.
- You also have so many other assets that are bringing in and paying attention to the other thing that you mentioned that I want to underline. You talked about how each asset can be adjusted, tweaked, and have different recommendations, depending on the platform that you’re advertising on.
We interviewed with Feedonomics on our Social Pulse Podcast: Retail Edition, which they do for products. If you’ve got a shopping cart on your site and you’re trying to populate a Facebook shop and a TikTok Shop and several other online shops and pushing the same thing out, the same assets, the same descriptions, everything to all those platforms—individually, they’re not going to perform as well. There are unique individual aspects that need to be customized.
So it’s interesting to hear that your tool does that as well from advertising and creative folks.
Pricing Model
Could you talk about what the pricing model looks like for Alison.ai?
Asaf Yanai: Absolutely. So it’s the regular SaaS pricing structure that we know from a lot of other companies.
With an annual subscription fee, we’ve spiced it up a little bit. So there is a—let’s call it a base fee—and then there’s a usage component on top. The reason we’ve done it is, first, to ease you into the process, you don’t have to integrate and connect all of your media platforms, and we don’t have to scan every single piece of creative, etc. We can start from a smaller baseline and then expand from there.
Two: We also wanted to make sure that we’re catering to the needs of different types and different sizes of businesses. So, obviously, our main target and our main portfolio are big brands and enterprises. But we’re also working with some fantastic middle markets and fantastic startups that are leveraging the AI capabilities to build the business and build an entire marketing strategy upon our platform and for that reason we made it.
Let’s call it a dual-track mode. So you have a base price, a base fee, and then there’s a usage component top that could be adjusted from month to month, from day to day, etc.
I hope this gives you a little bit of understanding, but there’s another thing that I wanted to point out, and I think if we talk about pricing, that might be the best way to approach it—which is right now, around 80 percent of the dollars, we spend within our marketing departments are not being utilized correctly. Some of it goes towards budgets for A/B tests. The actual media budgets for A/B tests we already understand are obsolete and wasteful.
And on the other side, there’s a lot of different creatives that we need to produce and create to feed this A/B test process continuously. So, if we talk about pricing, let’s also talk about cost reduction—not just about performance uplift, but also about cost reduction. Companies that are using Allison are experiencing a 50 percent reduction in their marketing expenses overall.
Think of your marketing expenses. Typically, it’s the second-largest cost line within your P&L.
Now imagine cutting this by 50% and, at the same time, increasing your revenues by 180 percent. So you need fewer people, fewer creatives, and less time or basically quicker to produce creatives that will perform better, so that’s why I try to explain that this is completely transformative to a business.
So, from a pricing standpoint, we also ease you into this process. You can start small and experience. And once you see the uplifts and once you see the cost reductions, you can expand and scale together with us at Alison.
Mike Allton: Love it. I’m glad you brought out that point about both the cost savings in the time savings. That doesn’t necessarily mean, folks, that you’re going to lose a job or fire somebody, but you could free up their time to focus on creating additional assets or other kinds of campaigns that they didn’t have time to do because they were so focused on running A/B tests, or they were creating that one video, and that’s all they had time to do now. So powerful.
Regarding the things that you’re sharing …
Could you go a little more specific in terms of some of your customers because you mentioned them before?
The amount of revenue that they’re seeing is incredible. Maybe you’ve got a case study or a success story that you can share with us that will help illustrate how this is impacting their ad performance.
Asaf Yanai: Look, so we have dozens of dozens of customers that are already experiencing this increase or uplift in performance and decrease in costs at the same time, we call it the dual effect: increasing this efficiency gap rather than decreasing it. But I can give you a few examples.
Google. It’s one of the most powerful case studies that we’ve ever had—and we’re still having it—with Google. So Google is one of our partners, but not just a partner, Google is a channel partner for us. Meaning that Google understands and recognizes the powerful impact that Allison could have on their own customers gaining better visibility towards what’s driving the performance within their creatives—not having the need to produce so many different creatives and just being blind about what’s working and what should we put as a strategy and whatnot. But, also, they’re not shy about the media impact.
Imagine that you’ve touched it a little bit in your previous sentence. So it’s not about replacing people and making them worry about their jobs or job security. Not at all. It’s about augmenting their entire workflow around creatives. Well, the very first campaign creation or campaign analysis to an optimized creative and optimized video at the end of the process. So it’s about giving the power back to the teams and providing them with intelligent tools that can make an impact.
Going back to Google, what we have with Google is basically: Google doesn’t have internal tools that can gain such visibility towards creatives and videos in particular. And more importantly, they’re not acting as consultants when guiding their customers on how to create better-performing creatives. Yes, they have benchmarks, they have a lot of data, but we also know that one-size-fits-all is a thing of the past. Right now it’s looking at a specific campaign, looking at a specific customer, looking at a specific brand, and finding the right things that move the needle and in increased performance introduces efficiency for this brand.
So Google is working with us and subsidizing, believe it or not, the Alison analysis for some of their key customers, those key customers who can gain impact and that Google would see a fantastic impact when it comes to their media budget and ad spend on the platform as well.
Zynga. Another example I can give you is from one of the biggest gaming companies in the world called Zynga. So Zynga has been working with us for a long time, a few years already. And we’ve done a tremendous, tremendous process.
In the beginning, only a few of their studios used Alison, but as I said earlier, once they saw the impact and felt it in their P&L and felt it in their actual ad performance, they were able to expand significantly.
So even with Zynga, we’re looking at a 50 percent decrease in their production costs, specifically production costs. A 50 percent decrease in their A/B test expenditure. But as I said earlier, a 180 percent increase is the average. We have cases, we have specific campaigns, and specific studios. Well, we even see 300 percent increase in their main KPI, which very often is ROI, which is the bottom line, the holy grail, the cherry on top—what every single company is looking for is to increase their return on investment.
And that’s exactly what we’re seeing with Zynga after working with them for a few years and analyzing tens of thousands of their creatives and tens of thousands of their direct competitors.
Mike Allton: Love those stories. Those are fantastic. We’ll have Google here on the MarTech show in October as one of our live shows. So make sure you guys come back for that. We’ll be talking about Google Workspace in particular.
Misperceptions about AI
I’m wondering if you could share what you think some of the common misperceptions are about AI when it comes to creative analysis and perhaps ways that Alison is overcoming those.
Asaf Yanai: I think it’s still happening. Those misconceptions and those trust issues—let’s put it this way around AI. But I think it’s about to be changed.
I think next year, 2025 is going to be the real breakout year for the use of AI within marketing. Yes, we’ve seen AI do some copy generation. Yes, we’ve seen AI do some campaign structure or briefing, but we haven’t yet seen an AI that does the entire process from A to Z. And I think the big misconception around AI is that it has hallucinations. It lies a bit. It’s not 100 percent accurate. And also that it’s not intuitive and it’s very difficult to gain full control over AI tools.
And I think these are all misconceptions. These are all trust issues, not technology issues. Let’s put it this way. And I think that the companies, the brands that would be wise enough to adopt and integrate and use AI tools (specifically for marketing and specifically robust tools that take the entire workflow from A to Z).
I think they will very quickly realize that the past thoughts were misconceptions and trust issues and not technology issues, adoption issues, or performance issues within AI tools.
In some ways, I even feel sorry for a lot of people who are refraining from using it heavily. They use it as an addition to their toolbox. They use it as another tool in their tool toolbox rather than making AI their toolbox.
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Once you do this shift, change in perception, and you’re not thinking about AI as a replacement for a team member, but thinking of AI as the framework as the base for everything you do, I think this is the turning point. This has transformed a lot of different businesses that we’re working with right now.
We’re seeing this across a variety of different verticals from fintech companies to CPG to e-commerce, gaming, and even fashion and retail. And I think there’s also another missed opportunity of a misperception. AI tools are niche—or AI tools are for specific, simple, so to speak, tasks—or they’re specific for copy. They’re specific for gaming. They’re specific for retail, but I think if, again, this is based on past terrible experiences or past challenges within AI.
Today, the industry and technology is far more advanced than what we can even think of and what we can even grasp.
I think again, once companies start to adopt and use some resources, spending some time and efforts on adopting AI tools for marketing, they will see this huge impact and use huge belief in the tools that will change the perception and would definitely revolutionize the business and would be the stepping stone for everything that will come after.
Very exciting and very innovative. I think we’re also looking into the future. We should be looking forward to a very innovative, creative and exciting future if we’re wise enough to adopt and integrate AI tools again, not as another tool in the toolbox, but being the entire toolbox.
The Future of AI
Mike Allton: I couldn’t agree more. We absolutely should not be focusing on adding an AI tool just for the sake of adding AI. We need to think about how AI can be a partner and everything that we’re doing. Robert Rose put it so well on our podcast recently, where he talked about [how] you shouldn’t have an AI strategy any more than you should have an electricity strategy.
Electricity is just part of how we go about doing business today. AI very soon is going to be the exact same way. It’s permuting everything that we’re doing, and you talk about the future, so I’d love Asaf as for my last question if you could just think about how you see AI evolving in the future, particularly when it comes to AI driven creative analysis, because this is just wild.
I know so many of us are super excited about the potential here.
Where’s [AI] going?
Asaf Yanai: It’s a very good question.
And from my personal and humble opinion and perspective, I think that AI for creative analysis is going to go a long way. It’s going to go a long way. And we’re going to see this impacting a lot of different industries and a lot of different media that we haven’t seen the impact yet, for example, CTVs. Live streaming. Everything is video. Everything is creative.
If you think about dynamic billboards, think about advertisements, or online digital advertisements within transportations, trains, buses, etc—think about, again, connected TVs. It goes a long, long way. It goes way beyond just the regular media platforms that we think of. Everything is creative. Everything is visual. I think we’re gonna see AI avatars and AI people even let’s put it this way. It’s not really people, but AI figures of people that will be incorporated. A lot more in creativity.
I think that the level of the analysis is going to improve even more than it is right now. And I think that the AI for creative analysis specifically is going to be a lot more autonomous. “A lot more autonomous” means, in my opinion, that we would just be guiding, monitoring, and supervising the AI for creative analysis rather than putting in the work. Meaning: Think of it as a different company, even one that works on your behalf solely for you. And you just need to navigate.
So basically it turns regular employees and us as regular people. It turns all of us to managers. It turns all of us to supervisors, and it frees our minds to think about innovation, creativity: “What can I, as a person, as a human being with the best brands in the market, what can I do more to help my business and not what do I do today to stay in the business?”
I think I would help us stay in the business. AI would help us increase our performance steadily throughout time so we can really build a strategy upon it. And then us as humans, we would definitely need to guide and supervise those AI tools—but also to be welcoming and open to new things and new ideas that even might sound or look crazy.
Imagine—last example, I promise—we’re working with one of the biggest pet food companies in the world. Every single one of their creatives for cat food has real cats in it. Every single one of their creatives has real cats. Now, when we used Allison to run the analysis and insights for them, Alison suggested using an animated cat, not a real cat at all.
The customer, the team thought we were crazy. Said, “No, there’s no way we would use animation and not real cats because we’re serving real cat food, not just animation cat, right? We want to make it as relevant to our target audience.”
So we said, “Why not try? Let’s just try it. Let’s give it a shot. Let’s see if it works. Let’s go beyond the traditional understanding of what we think is working, what we think would resonate.”
Believe it or not, within one week—one week—this animation cat performed 50% better in terms of revenues and purchases. And now it’s a top performing creative, and it’s a one of a kind one out of hundreds.
That’s the real impact I think that we’re about to see even more so in the future: coming up with crazy ideas that might look or sound crazy, but they’re not crazy. They’re intelligent. They’re smart.
They’re based on real data that we as humans don’t have the capabilities to dig deeper and unveil.
Mike Allton: I love that story. And I couldn’t agree more with your vision of the future.
I was attending Podcast Movement recently. And so I was spending time digging into the analytics of this show and my AI in Marketing: Unpacked podcast so, folks, you understand what I’m talking about. We’re talking about dozens and dozens of episodes. 30 to 45-minute-long audio and video files. Each of them has a unique title. Each of them has a unique description, unique guests, unique topic and text. And as a human, I’m trying to look at baseline metrics of listens, downloads and consumption and trying to determine if there are patterns in there. And it’s complicated. It’s hard, probably impossible, for most humans to look at that level of data and see any kind of trend amongst that content.
But the AI is going to be able to do that. And, to your point about software soon, I won’t have to ask AI to do that. I won’t have to feed those video files or session information into a particular tool. The agents will be there to surface that information for me on a weekly, monthly, daily basis. Say, “Hey Mike, we’re seeing this trend,” or “I’m seeing this, and I think you should do this or that with your show, with your content, to your point, with your videos and your creatives.”
Such a fascinating time to be alive.
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