Author: Hannah Wehrly

A Napier Webinar: Which AI Should Your Boss Hire?

Large language models (more commonly known as AI) have provided marketers with a wide range of opportunities to improve and make their marketing/PR activities more effective.

But which platforms should you be using? And how do your goals or priorities shape which is the best for you?

In our on-demand webinar, 'Which AI Should Your Boss Hire', we explore how different AI and large language models (LLMs) perform and compare the results for different LLMs for a range of B2B marketing tasks. We will cover:

• What are LLMs?
• How LLMs work
• The differences between LLMs
• Our tests - a comparison of different LLMs
• How to get the best out of AI and LLMs in marketing

Register to view our webinar on demand by clicking here, and why not get in touch to let us know if our insights helped you.

Napier Webinar: ‘Which AI Should Your Boss Hire?’  Transcript

Speakers: Mike Maynard

Okay, good afternoon, and welcome to the latest Napier webinar. Thank you all for attending. It's great to have you here today. We're going to have a bit of fun, and we're going to talk about artificial intelligence, and in particular, we're going to try and find out which AI your boss should hire to replace you. So hopefully we're going to enjoy this, and it's going to be quite entertaining. I would encourage you, if you've got any questions, to put questions into the Q and A that's a tab by your chat window. So if you select Q and A and let me know whether that's whether you've got any questions that would be perfect. Thank you. What I will do is I will cover the questions at the end, so if you could just let me know that you've got questions, and then we'll pull them out from the list of the Q and A, okay, so what we'll do is we'll kick off and we'll start looking at AI. So this is an interesting question. What we really wanted to do was to find out how close we are to replacing marketers with AI. I think there's a lot of discussion, and certainly, we see different opinions.

So some people will say AI can replace a large percentage of marketers activities. Other people today are much more skeptical, and to be honest, what we've seen at Napier is there are areas where AI work and areas that AI doesn't work, and in particular when it comes to generating technical content, AI struggles quite a lot, particularly content around new products where there's no existing training data. So obviously, AI is effectively coming in, as you know, almost if you think of it as a person, like someone who doesn't know anything about a product, and then we're asking it to write about a product without really giving it training data. So we're going to talk a little bit about how we can address that and maybe what we can do. But I think the most important thing about this webinar is really that we're going to actually benchmark a number of different AI tools so you can see you know, what the level of differences between those tools are and also which tools are working the best for us.

It's a somewhat arbitrary test, but hopefully it'll prove useful. So before we start, let's have some fun. And the first thing we want to say is we're not actually going to go out and sack all the Napier people that are listening to the webinar at the moment, hopefully we're going to keep you all there. And AI is not brilliant. It can do some amazing things, and it can really struggle. So here's a good example for the last webinar, which was an API digital playbook. I wanted to create a kind of playbook image for American football. So this is a classic image I've taken from an image library, and we thought we'd just ask chat GPT to create it. So I said, you know, could you draw a picture of a play from an NFL playbook? And it came up with this, which doesn't really look like that, Oz and x's play at all, you know. And also it has some issues, you know, one of them being 20 players on a team, which, if you can get 20 players out there on the field for American football, probably is a great way to play. But I'm sure there'll be lots of penalty flags being thrown for that. So anyway, so we don't want the images of the players. So let's ask chat GPT to remove the players. Make this picture much simpler with that image of the players. Fairly simple prompt, yeah, that didn't work.

So then we thought, well, let's try and explain it. So we had a prompt, draw a football play in the style of this image and provide an image that was a good example image, and it still didn't work. So then we said, well, just use the OS and x's style. That's got to work. It's got to understand that it still doesn't work. And I think, you know, here we're up to, like, 22 players on the team, or something. So you know, more and more players, less and less realistic. So eventually we start getting frustrated, and we start telling chat GPT what we think your diagrams are nothing like we're asking for. Suddenly, there's a moment of illumination, and chat GPT responds and says, I understand you want something more in line for the OS and xs. Clean, minimal, yes. This is it. This is fantastic. Exactly what we want, and then it created exactly the same thing again. So whilst AI is amazing, and a lot of those diagrams are quite fun and quite impressive to be created by AI, to be quite honest, and sometimes it doesn't get what you want. And in the end, for the last webinar, we gave up and we just took a stock. Image as being the best approach. So anyway, there are there are issues, but I think there's also some things that AI can do quite well, and one of them is generating content around topics that AI already understands, or topics that you give information about.

So we're going to have a look, particularly around written content. For this this exercise, we're going to have a quick look at what llms are, how they work. We'll talk a little bit about the difference between llms, and we're going to do some tests so comparing different llms, and ultimately, we're going to summarize with how to get the best out of AI and llms. Now, in this case, llms, if you don't know, are large language models. So what are large language models? Well, they're basically AI models that are trained on lots and lots of data, actually, vast amounts of data. I mean really huge amounts of data. So literally, the internet, all the public domain books in the world, you know, social media sites, Wikipedia, all of this is fed into the into the neural network, so a mass amount of data, almost all the written data that's available. But what happens is, you generate a model that understands and can generate natural language as well as others types of data.

So how do they work? I mean, it's pretty complex, but let's try and get a bit of an understanding of how they work. Well, the first thing to say is, there is an amazing tutorial on how generative AI works on the Financial Times website. It's not behind the paywall. So if you want to know how AI and particularly large language models, work, I recommend going to this web address here. We're going to pick out a couple of key concepts. We're going to talk about tokens, we'll talk about mapping and vectors, and we're going to talk about prediction of the next word. And I promise this is a typo from me and not a typo from Ai. It's not the next work, it's the next word. So tokens are really important. So the first thing to say about large language models is they actually don't understand words. They understand tokens, and a token is quite often a word. So as an example here, we've we've typed in was Mike Maynard, an international speed skater, into the tokenizer that chat GPT uses, and it shows you how it breaks it up. And it's very interesting, because has Mike and international and speed are all tokens in themselves.

So they're all considered single tokens, but may and nod are split into two separate tokens, and SK and ATA are split into two as well, which is interesting. So I have no idea why it splits like that. That's how the algorithm works and but you can see some words are tokens of themselves, and some words are split into multiple tokens. And this token tokenization allows processing. So you know, you can much more easily process compound words because you split them into tokens. So you build these maps of words by using or maps of tokens by using these words and working out what's related to something else. So we put the tokens into the system, and it tries to map the words. It tries to say where words are related. So this is, you know, an example of the sort of thing might happen. We've put in the section on the top right, ride, cycle, fly and drive. They're all fairly close together.

Obviously, ride and cycle are much closer than drive is to ride or fly is to ride and cycle. You know, equally, car taxi, bus and train would all be together, because they're all kind of ways of creating, effectively the same sort of thing. So car bus, taxi and train are all forms of transportation. So you get these groups. So this is how, if you like the large language, Your Honor, begins to understand what things mean, because it understands how one word relates to another. So now we've built a model. Very quickly we understand how it works. Now the real thing is to predict what's most likely to happen. And so you know, as an example, you know, if you're predicting words, ride and cycle could be options in the same situation, but also similar words would follow ride and cycle in a sentence.

So as an example, we could enter into chat. GPT Mike Maynard is a speed skater who and the question is, what would be a likely word to follow? Now don't forget chat. GPT is not as such, looking up facts. It's trying to work with probabilities. So if you start saying Mike Maynard is a speed skater who quite often you know Mike Maynard might have represented. Represented a team. And so, in fact, it might come up with represented then Canada is quite likely as somewhere to represent in speed skating. And then you might have actually represented Canada if you skate for Canada at several World Cups. So anyone who knows me knows that I'm a speed skater, but I am definitely not an international speed skater. And so these words all feel quite likely, but factually they're wrong. And this is the issue that we see with large language models with AI generated content, is you get what people call hallucinations. And genuinely, this is a hallucination that previous versions of chat GPT actually had, and because these algorithms don't just produce what they think is the most likely web but it was a little bit of randomness as well, around 50% of the time with older versions of chat GPT, and it used to claim very confidently that I was an International speed skater who represented Canada, sadly, now with chat GPT four and four, oh, I just get back Mike Maynard, who never heard of him, so my opportunity to fame has gone. But you can see how we get these hallucinations, but you can also see how words are predicted, and so this prediction is how content is generated. If we look at, you know, the differences between large language models, there are some things that are really key at differentiating so the number of parameters, which basically the size of the model. And we're talking about, you know, many millions of different vectors that are used within it to create the model. So these are different floating point numbers. So the parameter size is very important. The context window size is basically the number of tokens that a large language model can take in at one go. So that represents how much data you can give it to process. The bigger the context window size, the more complex the prompt you can generate, and also, generally, the longer the amount of content you can produce that's effective. The training data is very important. The producers it, you know, and in particular, we're starting to see some large language models being trained on synthetic data.

So as I said, we basically used all the real data in the world to train these models. So one of the approaches is that AI is being used to generate training data to train AIS, which is very bizarre and has huge potential problems. The geeky people will immediately be saying, there's a potential over fitting problem here, it's very difficult to train AI on AI generated content and get good results. And also I mentioned parameters. So effectively, a model is a very large number of floating point numbers, and so this requires a huge amount of storage. And actually, what people do is they'll take the model they generate and they'll compress it, or they'll do what's called post training quantization. And for those who are a bit mathematical, literally, what it's doing is taking a very precise floating point number and it's rounding it and it's rounding it to a certain number of decimal places, so it's getting an approximation. And this actually works. So it actually keeps most of the data, but it compresses the size, so you're not keeping, you know, exactly the same level of information in the AI model, but it still works effectively, and it means you can run it on a smaller system. And then, of course, one of the other big differences between large language models is whether they run in the cloud, something like a chat GPT, or whether they run locally. And the important thing to remember, if it's running in the cloud and you're sending data to the cloud, is that potentially, the AI can train on that data. And there are various opt outs with different systems, but there is a risk, once you start providing data to the cloud, that your confidential information will then be used by the AI and become part of the AI's knowledge, and that, obviously, is potentially dangerous in terms of protecting trade secrets. So a lot of people like to use local AIS, and a lot of companies now are mandating the use of local AI. So that's a really quick run through about how large language models work. I appreciate it's a very, very simplistic I'll once again refer you back to the Financial Times and their very interesting, interactive presentation. Let's go on to the main bit of this webinar, which is really to talk about how we tested AI and compared some so we're going to look at the different ais that we used.

So we used five different models. We used chat, GPT, the latest version, app, four. Oh. We use the latest version of Claude, which is anthropics AI, and the latest version of Gemini, and all of these were run within the last week or so, so all very much up to date. They're all cloud based AIS, so they're all ais that potentially have some degree of security risk. We also ran a couple of local models. So these are models literally run on my laptop. If anyone's interested in learning about local AI models and being able to run your own model, please do ask me. We used a tool called GPT for all, which is great, and we use basically two AI models, Lama, which is a Facebook model, and five, three, which is a Microsoft model and a very small model. So if we look at this, the cloud based models are much, much bigger than the local models. And the reality is, is that the world of agency is not hugely profitable. So it's run on a very old laptop with pretty moderate specifications. It's about a five or six year old core. I seven model. It did have 16 gigabytes of RAM. One of the challenges is these models, as I mentioned, are quite large. If you try and run them, for example, on you know, a Mac Mini with eight gig, you will run into trouble. So running on a laptop with decent specification. The memory is quite important. And as I say, the most important thing is to make sure that you understand you're running smaller models locally just because of the limited processing power. If we look at the speed, I mean, llama RAM is considerably slower than the cloud based models in terms of interaction time. And obviously these cloud models, they're being run on very high performance server farms. Lama ran on my laptop. It ran much slower. Phi three actually was fairly similar in terms of speed, but as we'll see, is not as good in terms of what it produced. One last thing to mention is that we did do some processing of data sheets, and one of the big issues was running locally, is incorporating documents into your models. And actually we uploaded a very large data sheet, around 300,000 words, and it took about two and a half hours to process on the laptop. So some things do take quite a while to process locally.

So let's move on, and let's find out which of these models is going to be replacing us tomorrow. So our first test was fairly simple to write an 800 word blog post about how variable speed motor drives will increase energy efficiency in the future. One thing worth mentioning is, obviously, this is a presentation about AI. The images are all AI generated, apart from when we get to one of our clients, images, and this was a low voltage variable speed drive, according to chat GPT in terms of image generation, those of you who know anything about variable speed drives and motor drives will know that maybe there's something around this, but there's a lot wrong with this image, from size to number of breakers to All sorts of things. So this is not a great image. It shows again that once you want to do something specific and technical, AI sometimes isn't the greatest thing.

So anyway, we want an 800 word blog post. That's the first thing we wanted to do. Should be fairly simple, variable speed drives as a pretty generalized product. All of these models will be trained on content about variable speed drives. So it should be okay. Well, the first thing we came with, up with is an obvious love for bullet points, Claude, which is quite often recommended as the best AI for writing content, pretty much just writing bullet points. There's almost no pros. It really wasn't like a blog post. Interestingly, Gemini, at the other end, actually wrote directly in prose from the first one. It was the only one to not fill the blog post with bullet points. So Gemini came out best on that in terms of the style, more of a blog style, but certainly some issues with that first pass, none of the models really discussed the future. All of the models were pretty high level and pretty non technical. And actually, for something, you imagine, computers were good at generating a right the correct word count. Only fine Claude were within 10% of that 800 word word count, and Gemini was like your lazy writer on a Friday afternoon looking to get home. Then he gave us 477 words for us for an 800 word blog. So a little bit disappointing there. Fay also had a thing where it seemed to like inserting word count. Accounts that were random and completely wrong for sections. So there were some bad artifacts going on here. So what we did was we actually went through another two revisions.

So the first revision we asked to remove bullet points and make the article more prose. The second was to then take the content and to target variable speed drive experts, and we have mixed results. Fi, basically just produced an outline, not a blog post. At the end of this, we were still way off the target word count. Gemini was still the laziest. AI, producing only 40% Oh, sorry, producing 60% of the words, 40% below the target count. And chat GPT was super enthusiastic. Actually produced a third more. So not necessarily good for what we want, because you want to know 100 words. But you know, chat GPT was again, way out, but the other way. And the focus was, was very bizarre. It was kind of all over the place. So 20% of chat GPT blog post was around harmonics, which is not a particularly major factor going forward. And also the articles can contradict themselves. So depending on which section of the article that Claude wrote, you'd either be reading that drives that don't need sensors are absolutely the most important thing, or you could read that the development of sensors for drives that need sensors was the most important thing. So again, this, this is something that quite often we see with AI, is that the overall narrative is kind of, you know, not very well structured. And I think this is a good example where it talks about senseless drives and also pregnant sensors. Both are great, but Claude was unable to create a narrative that explained, you know, why some would would go for sensors less, and some would use censors, and it kind of just put two opposing opinions in the same article. So not exactly the greatest blog post, I would say. However, the content wasn't terrible.

So if we look at the chat GPT one, which I personally found the best and was generally seen to be the best quality article, it produced a pretty coherent, pretty good article in terms of talking about some of the trends going on. And, you know, really gave you a bit of a background. Claude also did a pretty good job, as I say, overall, the structure wasn't great, you know, and it was really almost as though you had random paragraphs just banged together. But the actual content of those paragraphs was pretty good, and it identified, again, a lot of the most important things. So we had some reasonable results. I mean, whether anyone would want to put the blog post either of these, you know, two better blog posts out on their website without having a, you know, a sub edit by someone who's human, to improve some of these issues is a question. And certainly we're seeing with SEO, and particularly where people are looking to rank on SEO queries where AI gets involved, it's all about ranking at the top, and frankly, the quality of the blog post, it's okay for someone to read, but it's not going to be seen as the most authoritative blog posts. And so, you know, there are certainly issues in taking blogs directly and not editing them. So one thing you can do is something called ragging. So ragging basically, rather than training an AI on something that lets the AI look things up, so we can provide content for an AI, and then that AI will actually use it as a reference. So what we did was we took a family of three products, the fabulous NRF 50 4l series, which are new products from our clients, Nordic, and we provided the full data sheet. As I mentioned, it's a big data sheet, around 300,000 words, and we asked the AIS to write a press release about the date sheet. Now, press releases are fairly standard in structure, but unfortunately, it didn't produce a press release that could be used again. Jake chat, GPT and Claude went bullet crazy, and this time, Gemini also just absolutely flooded the press release with bullets as well. Now it may be because the data sheet has a lot of bullets in it kind of copied that style, I don't know, but certainly it wasn't the sort of thing you would say looked really like a press release. That's basically three key messages that need to be pulled out. And if you look at the human written press release that's been done for this product, it pulls those out really clearly, performance, efficiency or low power and security.

Only one of the AIs really did a good job of identifying which was chatGPT, Lama and phi three. These are the smaller, locally run ones. They missed the security message completely. Claude almost missed security it was buried towards the end. It just about squeezed it in, but not very well. And then Gemini completely missed the performance message, which is interesting, they're missing different things, even though they're processing the same content. Bizarrely, Gemini also talked about the structure of the data sheet and gave kind of a summary of the table of contents of the data sheet, which seemed a bit weird. And then we had some hallucinations. So chat GPT and llama invented availability of the products, and literally wrote, you know, these products available, or these products are sampling now, with absolutely no reference to whether they were or not. So completely made it up. And then Lama made up a spokesperson as well, which I thought was awesome. So Sven Norden. Sven Norden Toft, apparently, is their spokesperson, so completely made up. Person doesn't exist within Nordic but Lama decided to create this person to give quotes, so hugely risky in terms of creating false information. So what we decided to do was do another test, and we thought we'd do something a bit simpler, so we'd write a LinkedIn post about the launch of these products. So taking the same products again, the NRF 50 4l and writing a LinkedIn post. Interestingly, all the LLN choose quite long posts. I mean, Gemini was the shortest, at nearly 1000 characters. That's quite long for a LinkedIn post. So that's kind of an interesting thing. I would have expected some of the produce much more shorter, succinct posts.

Chat GPT and Claude had rather dubious attention electronics engineers type openings. I can't remember that those words were literally from chat GPT or Claude, but one of them literally started out with a post which felt a bit cringy. I'm not sure I'd be posting that on my LinkedIn. One positive thing, though, was clap chat GPT and Claude included emojis, so that did look quite cool. Llama started well and really crashed and burned at the end. Just basically wrote an ad at the end. Fi obviously continued its focus on completely the wrong features and interesting. Llama didn't have any hashtags. The others did, so it was okay if you wanted a longer blog post. It's not too bad. But you know, and this is the chat GPT blog post on the left, they weren't great again, not something you'd necessarily want to use directly without some editing, but certainly you might want to pull some of this content directly into a post. So lastly, we thought we'd go and we do Google search ads, because that's got to be an easy thing to do, right? So it's shorter, it's easier, hopefully we can get some good results.

So first thing that happened was all the llms ignored the maximum character count for Google ads. Now this is interesting because Gemini actually told us what the maximum character count was and then promptly ignored it on the content it generates it. So it's kind of interesting. None of them did well, it is correct that you can actually go in and then ask to recreate those headlines and descriptions under the character count if you re prompt it normally. That works with these AIs. But for first pass, it wasn't great. Claude was probably the best. The headlines were okay, but some of the descriptions were too long, as I said. That also highlighted the maximum characters. We also got some bizarre extras as well. So Gemini actually gave us a short tutorial on how to optimize Google ads. Claude talked about Call to Action phrases, which I would have hoped was actually in the ads they wrote, rather than something separate, Lama started talking about the audience we should target, and also gave some not particularly great keywords, and then fi, and poor old fi, I mean, the smallest, least comprehensive model. They actually gave us an ad for different Nordic products as well. And I'm not entirely sure where that came from, but their power management ICS got a free ad there. And also, interestingly, Gemma and fight only gave three options for each of the headlines and the descriptions, which is relatively small for for Google ads.

So we've done that. I mean, one question that I, you know, I know people who ask is, yeah, okay, so you're criticizing this. But can you jet, you know, can you genuinely tell it's AI? Well, the answer is, there's lots of tools to tell whether content is AI. And interestingly, we looked at the LinkedIn post, we used a tool called. Copy leaks, which uses Open Source AI generator and Claude, Gemini, Lama and faiz, LinkedIn post with detectors being 100% AI, only 66% so two thirds of chat GPT post was detectors AI, but again, the vast majority of it was so quite clearly, if people are penalizing AI, or if you're submitting to a publication that's not happy to take AI generated content, do be warned, because people can detect this, and it is scarily good, much better than humans at detecting AI. And the other risk, of course, is plagiarism. Interestingly, we had very little plagiarism identified, and in fact, the only plagiarism we had was the first draft of the chat GPT blog post, and 11% of that was identified as plagiarized. So one good thing that does seem to be happening is these tools do seem to be moving away from dumping phrases and sentences that are directly copied, and they do tend to be generating much more things in their own words. So that is certainly a positive.

So anyway, I know I've covered a lot, I've talked about a lot of data. I think you know, the easiest way to summarize this is to basically score the different AIS and give them a rating. So what we did was we looked at rating. So basically we rated based on privacy, ease of use, and then on the first test the blog post, which was quite a long exercise, we actually measured based on quality and whether it met the brief and then the three subsequent tests, we just gave an overall score. And it's quite interesting. We looked at this and almost went back and changed the numbers so they weren't also similar. But what we got in this particular test I mentioned a couple of times, chat GPT did quite well in generating content that's not that common. Generally speaking, most people testing for written content, flying Claude generates great results. In our case, it didn't. So it may be different for you, but we found chatgpt slightly better than the others. Fi being slightly worse, which is not surprising. It's the smallest model by far. So I wouldn't be I wasn't surprised that that works less well, and then really nothing to choose between the others.

As I said, almost went back and edited this to make it look less like a dead heat. But the reality was, Gemini Claude and Lama all have pros and cons. Do well some places less well in others. So you know, really the answer is, is that there's not one that's winning in that group. So overall, I mean, what do we think? Well, I mean, it is amazing when you see content being written. It is less amazing when you try and optimize it, or you see content from multiple llms being produced. That all sounds pretty much the same. It begins to feel a bit bland when you see multiple pieces of content on the same topic, from from different llms, or from the same LLM so it can become bland. It becomes samey, but it's still impressive, and it's still good for ideas. I mean, that certainly is the case in terms of picking up a structure, identifying main points without doubt. You know, the AI tools today are incredibly powerful and useful, but I think if we're really trying to get the best out of AI and lions, you know, we've got to be honest, they're incredibly useful, but probably not today for producing the final copy of technical content. And I think that's important. You know, an early draft, a structure and outline some ideas. They are awesome, but trying to produce a final copy, and the more technical you try and get, the less good they are. Because it's more specific, they aren't particularly creative, as I say, you know, everything began to feel the same. You began to see similarities. When you looked at what happened. Hallucinations absolutely do happen, and we were quite surprised at how many, how much, you know, problem we had with hallucinations. They're terrible at writing to word counts, which is surprising. You'd think computers would be good at that, but they are terrible at that.

There are other ways to get around that. We'll mention that in a minute. But I think the most important thing to say is they're incredibly fast. The speed they can produce drafts is, you know, many times quicker than a human. And so their use as kind of a brainstorming tool, or as an ideation tool, or as a, you know, basically just a tool to get over writer's block, I think is going to be essential for a lot of people. And then the last thing I'd say is, you know, we've tested general purpose, large language models here. We haven't tested specialized tools. So there are specialized tools, for example, for generating Google. Lads and those tools will guarantee to hit the maximum word count. So specialized tools can give better results. Also, there are tools such as market mate, which are designed to help you create much more complex prompts that should, in theory, produce much better quality output. I mean, as we saw, Ragging is another way to do that, where you provide files that are used as reference. And that didn't always work very well, but generally speaking, these specialized tools will produce better results. So hopefully that's helped. Hopefully it's giving you some insight into the effectiveness of AI tools.

If you're interested in other webinars, the next webinar we're running is all about B to B research, so completely different topic. And if you're planning for next year's marketing campaigns, this could be a great webinar, because we'll be talking about how to get customer research that really helps you understand what is the thing that should be guiding your marketing for the next year, so it'll help you with your annual planning. So I'll leave that up there for a minute. It'd be great if you're able to attend. Just scan the QR code or put in the short code at the bottom, and hopefully I can talk to you at the next webinar. So thank you very much for listening. I know this has gone a little over our 20 minutes target that we normally 2025 minute target. I'm really interested to know if anyone has any comments or questions. I'll just leave it open for a couple of seconds to see if anybody's got anything,

Okay, so the first question I've got is around cost. So the question is, were any of the tools we used free? And the answer is yes. Actually, everything we used was free of charge apart from chat GPT. And the only reason chat GPT is paid is because I just have a paid account already, so I use existing paid account, but all the other tools were the free of charge versions. So it's something you can definitely use yourself. It's not expensive at all. Okay, let me just see if there's any other questions coming through.

We've got one question here. This is a good question. So are there any tools that could potentially outperform those tested today? I think it's a great question. If you talk to anybody in AI, the answer is, is there are amazing tools that are so good that they will terrify you, that are just around the corner, but this has been the case for, you know, really a couple of years, I think the answer is, is that what happens, generally speaking, is with any AI tool, and for simpler tasks, this has been mapped and documented very clearly by academics, the improvement in performance tends to be pretty fast. So a tools improve very, very quickly, and then they hit a plateau, and they round off and almost stay flat. They don't improve, and they stay flat just below what academics call the average person. Now, the average person writing content around, for example, variable speed motor drives is not the average person. They're clearly someone who's specialized. They've got knowledge. They're a good writer. So, you know, just below average is still going to be scarily good. But I think what we're seeing, and most will probably agree, is that we've seen this rapid ramp up to, you know, what feels like almost human qualities, but now we're not seeing a particularly big increase. So unless there's something that we can do to either change the way that AI works in terms of the models, or somehow find masses of new training data that doesn't exist, or to find training data that's of much better quality, I think we're going to stick it at a bit of a plateau. So my gut feel is, what's going to happen is that actually it's going to be much less around the improvement of the AI engines themselves, and it's going to be much more about how AI is embedded into specific tools. So I mentioned earlier, for example, Google ads, you know, a standard, general purpose, large language model like chat GPT isn't great at writing Google ads, but if you use that engine and do some very specific coding around it and generate some very specific prompts and embed that into a Google Ads tool, then it can become incredibly powerful. And to me, that's where we're going to see some of the biggest improvements. Is AI that's embedded and optimized to do particular tasks, and I think that's going to be the most exciting thing for marketing over the next year.

Well, thank you very much everyone. I really appreciate your time on the webinar. If anyone is interested in more information about what we did or seeing some of the content we produced, please contact me. My email address, Mike at Napier, B to b.com. Is there on the screen, and hopefully we'll see you all for our next webinar in December. Thank you very much. Bye.

by Hannah Wehrly

A Napier Webinar: The Top 10 Marketing AI Tools

Although AI can be over-hyped, today, you can get real benefits from the technology. In fact, there is a wide range of marketing tools available now that use AI to help you complete tasks in less time, with better results.

In our on-demand webinar 'The Top 10 Marketing AI Tools', we explore the AI tools marketers have available to them, and how different tools can support different areas of marketing. We cover:

• What AI can do
• The risks of using AI
• The top 10 marketing AI tools
• How tools can support different areas of your marketing
• The future of AI tools

Register to view our webinar on demand by clicking here, and why not get in touch to let us know if our insights helped you.

Napier Webinar: ‘The Top 10 Marketing AI Tools’ Transcript

Speakers: Mike Maynard

Hi, and welcome to the latest Napier webinar, I hope you're looking forward to finding out a little bit more about some of the AI tools that we use. And that we've seen our clients use successfully, it'd be great if someone could just pop a note into chat just to let me know that everything is working, okay. And that you can hear me. So someone could do that, that would be brilliant. I'm actually presenting this from home today.

And we'll crack on and we'll start talking about some AI, and some of the tools that that we've used.

Okay, so first, let's look at the agenda, what we're going to talk about.

So we are going to start by covering you know, a bit of an overview about what AI can do.

And make sure that, you know, everyone's you know, at the same level, what we think the expectations are. But also we're going to take a breath and make sure that we know, you know, what actually isn't practical.

So, you know, I think there are some things happening with AI and some hype that is perhaps over egging the ability of AI to do things that we want it to do.

We're going to talk about GPT models. Now. GPT is the model underlying chat GPT, but also used by a lot of other tools. So it's very important to understand that, once we've done all this, we'll get straight into the top 10 AI tools, which I know a lot of people are very keen to see. So what we're going to try and do is we're going to try and cover what we think are the most important tools that people can use today in marketing. So this isn't going to be a webinar that in two years time is going to be relevant. This is about what you can use today.

But lastly, we will look forward and we will wonder what is next and what's going to happen.

So let's start and let's start by thinking about what AI can do what we all kind of know what AI can do. We've all heard the stories, you know, AI is great with chat GPT giving advice.

It was certain whether I should give up my career in marketing or not. So I decided to keep going on. But you can ask chat GPT all sorts of things. You can also use generative AI to create images. And I thought that Spaceman dancing and orange is an image that the world really needs. So we can actually do more than create images, we can create videos, this is a training video here. And it's actually created by an AI avatar. So what we're seeing more and more is companies using AI to create avatars. For things like training, this can be really effective, because all you have to do is feed the text into the AI system. And it will generate a high quality recording of someone presenting some training material. So that's clearly good. And I think you know, as avatars will start seeing more and more going forward.

Images again, I mean, we had to put a couple of images in. So here's some ketchup bottles created by Heinz that basically is AI versions of ketchup bottles for bottle a campaign. We thought that was fun. And of course, you know, we've mentioned chatting before, but chatbots are a particularly big area for AI. And a lot of companies are deploying AI chatbots. And I think those of us that have used them have sometimes been really impressed. And sometimes we're really frustrated by the ability of AI to actually deliver the answers we need. And I think this is it is that, you know, there's some really cool stuff that's going on with AI, it certainly does a lot of amazing things. But sometimes there are things that it doesn't quite do.

So let's take a breath.

AI is really good at taking content and putting it together. And people have called it the ultimate plagiarism machine.

If you happen to be super geeky, like me, it's a stochastic parents or a parent that repeats things, based upon probability. But the problem is, AI is not that creative, much of the content is very similar. And I think this is something that we're all beginning to realise, you know, the first time I saw AI generated content, I was blown away, I just could not believe how good it was. And I thought, you know, this is it's the end for content generation, you know, we can't tell the difference, but very quickly, you begin to spot AI content, you begin to get a feel of what's AI generated and what's not.

And perhaps I think, you know, one of the best examples of this is, you know, could AI generates a Shakespeare sonnet? You know, let's have a look at the first line, Shall I compare thee to a summer's day? Well, no, AI is not going to do that, because a summer's day is 24 hours long, and you're five foot six. And I just can't do that comparison, because I'm very literal. So I think what's going to happen is, we're going to increasingly see that at the top end, where people want high quality, very creative content, AI is going to continue to struggle. Of course, at the low end, where maybe people have been using low cost content farms, just to churn out material that covers a particular subject. Absolutely. I mean, I can see AI taking over from that sort of low quality, mid quality content. But at the moment, we're not seeing AI really generate things that are truly creative, unless you have someone spending a lot of time doing what people call prompt engineering.

So actually asking the AI to generate specific things. So what I think is going to happen is AI is going to have a role, but it's going to have a role in certain areas. And let's have a look at another thing. You know, one of the things that AI is great to do is to give you ideas, and perhaps ideas for marketing campaigns. So I asked it for a couple of marketing campaigns make a marketing plan for an electrical infrastructure manufacturer. And here's its ideas, you know, goes from market analysis and target audience all the way through to actually sending a sales goal of 20%. And you can see that it's not quite perfectly it thinks of infrastructure as being construction rather than electrical infrastructure. And fundamentally, it looks ok, you think, well, that's quite good, it's really thought about it.

But then I asked it to generate a marketing plan for a semiconductor manufacturer. And you can see it's incredibly similar. In fact, it's a very formulaic marketing plan that's going to come out and look similar to every industry. So clearly, we have an issue that AI is not generating something creative. I'm intrigued, it thinks the electrical infrastructure manufacturer is going to get 20% sales growth. You know, that's an interesting number, it seems like a reasonable number. But you know, without knowing the the amount being invested, or the current market share, that's hard to know. And then the semiconductor manufacturer is going to get 15% market share increase. So effectively 50% growth in the first year, why it's 15% or not 20%, who knows, he seems to be random numbers plucked out there. But then given a sort of veneer of authority, by the confidence in which AI tends to present text. So I think we need to be careful about thinking AI can do everything, because sometimes it's just not great.

I mean, there are also other challenges as well, you know, particularly around the fact that AI fundamentally is taking things that's been trained on and reciting them again. So copyright plagiarism, or an issue, we've talked about bland content, some of the data is out of date. Obviously these AI models take a long time to train. And whilst open AI is building models that can be continuously trained. A lot of people are still using the AI models that were trained on a certain date. And so we see this classic art. So you know, that says that chat GPT was trained on a particular date doesn't know anything more recent I'm sure we've all seen that. And ultimately, you know, the thing that everyone's worried about is hallucinations and inaccuracy. And this you know, the researchers think appears to be an issue.

That, actually AI makes stuff up. And it's inherent in the way AI works. So these neural networks work. And here's an example. There's many, many different examples. But here's an example where two lawyers and law firm were fined because they got chat GPT to write a submission, and chat GPT that just literally made up some cases that didn't exist. So it's very easy to use check GPT you see that very confident presentation of what you think of facts, but unless you check them, it's very risky.

So AI has got some drawbacks, but it still has that potential. And I think, you know, we need to look at how people are using AI, in different tools, to, you know, give you the potential of AI without giving the downsides.

So firstly, let's try and understand, you know, what AI is doing. And we're gonna look specifically at the text based models. So this is GPT. So GPT was created by Well, she wasn't created by open AI. But the most recent models were created by open AI. And they are a neural net. So basically a network of connections that tries to mimic how we roughly think the brain works.

The most recent ones are GPT, four, or GPT, 3.5, if you don't want to pay for it, the cost to create these models is huge, you know, potentially 100 million dollars or more in terms of energy and compute time to build GPD for so they're very, very expensive, very complicated. So we're not seeing lots of people create these models, some people are beginning to, but many applications use the standard models. And so many of the AI text applications will use GPT models. And so what we're seeing is underlying, effectively the same brain the same model, but with a different wrapper on it. And so a lot of what we see in tools is people placing wrappers around it, to make it easier to use the API or to make the API deliver better results.

So now we know that, you know, sometimes we're looking at the same thing, just with a different cover. Let's go into what we really care about. And let's go and have a look at the top 10. So this is an absolutely subjective, completely unreasonable and probably not entirely accurate selection of the top 10 AI tools that we felt should be featured in any marketing toolbox.

So the first one is chat GPT. Now, a lot of people talking about chat GP to generate content. And that is exciting and getting chat GPT to write blog posts and things like that is pretty cool. But that's not why we're excited about chat. GPT. I mean, really, there's a couple of things that chat GPT is great at, it's great for helping you answer questions, explaining things. It's fantastic for summarising content. So if you've got meeting notes, you want to summarise it is really good at that. And it's fantastic at analysing data.

And this is something maybe people aren't aware of is chat GPT actually can do some incredible things. So I'm going to try and demo this, and we'll see how this works with with our webinar tool. But hopefully I can do a quick demo of chat GPT doing some analysis.

So what we've got, oops is we've got chat GPT here. This is a painful version. And I think if you see this and you hate Excel, and let's face it, most people in marketing do, you'll suddenly see why people pay for chat GPT. So in the GPT, four model, there's something called advanced data analysis.

And what I've done is I've enabled that advanced data analysis, which lets me upload files. So I'm going to upload a file.

And then I'm going to ask it to do something. So this is a file taken from our Google Analytics data. So it's Google Analytics data. And I'm going to say which sources produce the highest engagement time and have at least one conversion. So those you know, Google Analytics, you know, that typically you set a goal and objective, which is the conversion, and you can measure things like time. So this is gonna say, which sending traffic that not only converts to something we think is valuable, but also has the highest engagement time.

This is something you do in Excel. And as I said, typically people are not super excited about trying to do these sorts of analysis in Excel. And so what it's doing is it's going to churn through it's going to try and work out the data set. You'll notice I haven't told it anything about the data. So what it's doing is it's reading the data. And it's trying to understand that you can see here chat GPT tries to understand the data. And then hopefully, in a couple of seconds, it's going to give me an overview of what are the most engaged sources. So what's driving web traffic to our site that is very engaged when it's on our site, but also creates a conversion.

So here we go, sorry, I can't make GPT run any quicker, it actually tells you how it's going to do it, it's going to do some filtering and sorting. So you actually understand how it's working this out, which again, is really useful.And we just have to wait a second.

And here we see chat, GPT has done the analysis. And it's found out what sources produce the highest engagement time. And this is kind of interesting, because actually being is producing slightly longer engagement than Google, whether it's enough to actually be significant is something we need to dial, dial down into, and find out. But you know, first thing we've learned from here is actually being produces traffic that stays slightly longer on our website than Google.

And then we've got, you know, systems that are sending out our press releases, we've got referrals from websites, we've got some direct traffic, etc, etc. I can stop this going now. And I can also show something else. So what I can do is say, well, actually, you know, I love the fact Excel draws graphs, but I don't like Excel. So I'm just gonna see if I can say, create a bar chart of the top 10 engagement times. You can see again, it's going to sit there and work for a little bit. And then all things being equal, we should be able to see the top 10 engagement times.

And chat GPT is not being quick today, which is not helping in the webinar.

And there you go. So you've now got a bar chart, you can cut and paste into a presentation, you haven't had to do anything complicated, you've literally just got to tell it what it wants, and chat GPT produces it. So hopefully, this has given a few hints into what Chappie GPT can do. That is a bit beyond the standard chatting.

We have some other applications as well. So you know, one of the things we really like is the tools that place wrappers around the GPT model to make it easier to generate content. So chat GPT tends to generate, you know, very similar very bland content, you can write longer queries, but products like Jasper, or writer or Freezy, they all have a structure. And I will just very quickly show you that structure now from Jasper. So if I jump into Jasper, you can see that Jasper here I've logged in. It offers a range of different templates. And so obviously, you can type into chat GPT, you can ask it to generate different pieces of content. But if for example, I wanted to generate some Google ads, I can click on ads, I can click on Google ads. And what it's going to do is it's going to be able to generate some different content. So I could, for example, say marketing, AI tools webinar, and it pulls best for b2b marketers to use. And you can have any sort of tone of voice you'd like.

I'm going to actually pick something I've custom done, which is tone of voice that is similar to our website. This is one of the things that's really neat about these writing tools is they can match your tone of voice. We could have some examples, but I'm just going to click generate content. And in a second, you'll see that we've generated both headlines and descriptions, all compliant with Google in terms of number of character counts, and just a little bit easier and quicker than trying to use chat GPT to do the same thing. As I mentioned, you know, these these are typically tweaked versions, the GPT model anyway, so they're basically the same content, but it's just going to make it easier for you. And if you're looking to try these writer is one of the ones that has a really good free trial and free tier. So it's a very easy thing to try.

Okay, we've done a couple of demos let's actually crack on and try and look at some more of these tools and move a bit quicker.

Grammarly is a tool that actually has some AI in it. And I'd also recommend trying Hemingway, which is an online tool as well Hemingway app.

And what it does is they just help you write. So sometimes we have to write ourselves, check GPT isn't gonna do it, nor is Jasper nor is writer, but we make mistakes. So how can we fix mistakes? Grammarly is obviously great. Hemingway is really good at aims to make content more readable and more impactful. Based upon the way Hemingway wrote. However, it doesn't necessarily mean it picks great content.

So you know, a little bit of Charles Dickens here. And you can see that Hemingway just basically highlights the whole lot saying it's hard to read. It is true how that Dickens isn't necessarily the easiest thing to read. And maybe in business, we want to do this, but obviously use these tools, with a little bit of common sense.

However, they are great for using if you have written something, and you just want to get it checked.

The next one is Dall-E. You know, we all need a picture or an image every now and then, even if it is a bitmap image of a painter painting a painting, as I've asked for, here, from Dall-E. And one of the interesting things as well as Adobe is really getting into AI for image generation. And if you have a design team, Adobe, Firefly is a fascinating product to use. It can do things like, you know, effectively expand a photo beyond the frame. You know, obviously, it's making it up. It's trying to imagine what it is. But it's got some incredible capabilities that I really recommend using. So definitely look at Dall-E as being, you know, or stable diffusion or Firefly as being a tool. One thing to bear in mind, of course, is that there is a risk of copyright breaches in the images just just as there is in text when you generate. And actually I mean, the CEO of the company that runs stably fusion.

He actually said they don't know how many copyright images they've used and absorbed into their model, but they know it's in the hundreds of millions. So there's a huge amount of copyright material in there. And there's a real question of what happens if you do get some content that uses copyright content.

Also, in the US, it's been decided that AI Generated Content can't be copyrighted See, can't own the AI generated image in other countries that something's still going through the courts. And I think we're going to see a lot around copyright as we go forward with AI.

The next one, I've picked his Salesforce Einstein partly because our Head of Business Development and Marketing Hanna loves that little cartoon Einstein they use both on the page and in their promotion. But actually, I think it's indicative indicative of what's happening with a lot of tools. So they are applying AI in Salesforce, to do all sorts of things. So this is an example of actually a prediction of what the engagement is going to be like on an email you send. So it's actually predicting whether people are likely to open the email or click on the email, and how effective it's going to be. You know, the other thing that people are using a lot of AI around is scoring leads and CRMs to try and decide whether people are showing intent based on their behaviour, whether it be clicking emails or behaviour on the site. And then rank who are the most likely contacts to become customers. And I think, you know, this is something that's going to be embedded. Basically, it's every CRM and marketing automation platform we see going forward.

I mentioned chatbots. I mean, we all love or we hate, you know, a good Chatbot. You know, chat base is one that's very heavily involved in using AI to have an automatic chat bot. So it's drift. That's another very popular platform. But we're also seeing marketing automation platforms. You know, HubSpot, for example, has put a lot of effort into its chatbots and other platforms beginning to offer this chatbot functionality. So I think more and more, we're gonna see chat bots being deployed. I did find an article on the top five AI chat bots, in b2b. When I was doing some prep for this. It was written about 18 months ago, so it's a little bit old. And interestingly, I think four out of the five websites had actually taken the AI chatbot off the site. So one of the things we are seeing is training these chat bots is difficult. And I think the tools to train the chat bots. There'll be something that's developed over the next, you know, couple of years. But at the moment, it is hard to deploy an AI chatbot in a b2b situation where you've got complex products and lots of things to cover.

Chatbase seems to be one of the hot b2b marketing tools at the moment. And it's all about trying to understand which of your accounts are showing intent. And looking on the public internet as well as on your marketing, to find out which accounts seem to be doing things that suggests they could be a customer. And it's a great way of prioritising your accounts. And we were talking about this earlier in our podcasts that Hannah and I was, we're recording. And if you don't subscribe to the marketing automation moment podcast, and you use marketing automation, I'll just put a little plug in for that podcast. Now. Of course, one of the issues tools is that inevitably, marketers focus on the contacts that are labelled as either in decision or purchase phase. So almost by definition, they're going to have a higher conversion rate because they get more focus. But even so, having said that, tools like six cents seem to be pretty good at identifying, you know, which accounts are likely to buy, and which you can probably leave and just nurture in the background.

Contents, obviously another area. And we've talked briefly about generating content. But there's some fantastic AI tools, market news and surfer SEO, the both use of SEO, both use an AI to identify opportunities. And here you can see, basically, market news has tried to look at, you know, opportunities for generating content to rank for search terms around telescopes. And what we're seeing now is actually more and more of these tools are important, because they do so much analysis of the competition, and actually helps you generate content where you have a reasonable chance of ranking highly. So these tools, I think, are going to become more and more popular as we go forward.

Once you've got the content, you've ranked high in SEO, you've got someone on the website, you want them to convert. And there are a number of tools that will sit on your website and help people to convert by popping up when they think someone is interested and ready to engage. Our favourite is path monkey is actually run on the Napier website. And it's great for website engagements. And it also generates some good leads. So we've actually seen, you know, potential customers come through path monk, because something's popped up and said, you know, would you like some more information about this. It's a very complicated tool in terms of, you know, the way it works the way it tries to analyse customer journeys through the website. But in terms of ease of use, it's really easy. You just create what they call micro experiences. And then Pathmark tests, the different micro experiences to see what works, at what point. So it's a very, very easy way to do it.

Once once you've done all these you go on the website, people are now wondering, I think you know, where are we going to go next, what's going to be the number one AI tool for marketers. And this might be a bit controversial. But we've rated Google ads as number one. And the reason we've rated Google ads as number one is Google has put a lot of AI behind Google ads. But it's all kind of hidden, you get these recommendations, Google will run the headlines, the descriptions that it reckons works best.

And it's all kind of magic in the background. And I think this is where AI is going to go is there's going to be more and more AI and systems. But it's going to be less obvious. It's just going to be sat there doing things helping you be more efficient.

And you know, the interesting thing with Google is there's clearly some rules, and whether they've been worked out through experience or whether they're rules that the Google engineers have put in, you know, that they're pretty obvious, you know, longer headlines are better. And if you run Google ads, you'll find the closer you can get to that 30 character limit, the more Google's gonna like that headline. So, you know, it's interesting to see what what happens, you can kind of read those rules and make them simplistic, but there's a lot of AI going on in the background, to test different headlines and find out what works. So we really, really like Google ads, we think it's an indication of where AI is gonna go. And probably a lot of you are happily using this AI without worrying about it.

Now, of course, we've had our number one, our winner, the number one of the top 10 of marketing tools. But those of you who sat on a presentation will know that a Napier we always like to do something to give you a little bonus. So one of the things we wanted to talk about is building your own AI tool.

Putting around AI. So this is all about trying to use AI to create things that are going to be more human. And so what we've got is we've got an example here, where we will have a look. We go to our spreadsheet. And initially we've just got a few contacts in here that I've put in. But actually, if we unhide, these rows, you see, we've got a huge number of contacts. And our boss has told us, I want to know the industry, the employees and the CEO of each contact. Now there is an extension, that will actually take chat GPS API, and interface it into Google Sheets. So we've configured this extension, and we could do some cool things. So we can type in briefly identify the industry and then obviously, just like any other spreadsheet, we give it a cell to look at.

Hopefully, it's gonna tell me the industry, technology electronics. So that's not bad, it's pretty good. We can also do things like say estimate, number of employee per company. And again, we'll give it the company name. Hopefully, it will tell us roughly how many employees are there. It's interesting, actually, it's come back with 137,000 employees written out not very helpful if we want to process and sort.

So what we can do is, we can actually do a more detailed inquiry and say, provide it as a number without commas. And that should just give me the number of employees. And it does. And then lastly, we can actually say, who is the CEO of this company. With any luck, Tim Cook will pop up here.

And there you go. So you can see that this is a fantastic use of chat GPT. And obviously, once we've done this, we can just drag it down. And we are loading data.

And we can see there's some errors coming up. But slowly, what it will do is it will populate all the chat GPT answers here. So if you've got data processing, it can be really, really useful to actually go out and get data and find information, just simply by using chat, GPT and a spreadsheet. So hopefully that save some people some time in the future. If you're interested in it, you just need to Google GPT for sheets, instal it on your Google account, and it will work.

So hopefully a nice little tool if people are going to do some data analysis of customers.

The last thing to say is what's next? So I think the biggest question is what's going to happen to generative AI performance, we saw an unbelievable leap in performance in the space of about six months, with chat GPT. And now we've seen, you know, perhaps a few months of stability. The question is, you know, can AI keep getting better and better? Or is it only going to sit at the current level? And I don't have the answer to that. There's lots of arguments to say it can get better. There's lots of arguments that say, you know, we're using basically all the training data that's available, and the performance will stall and it won't keep getting better, I suspect we're going to see the rate of increase slow down. It's been crazy over the last few months. But I think equally, we're going to see some ongoing performance improvements. So these AI tools are going to become better and better.

But even if it stalls, I mean, even if we look at the worst case, and we're at PKI, at the moment, nothing's gonna get better. Hopefully, you've seen that AI tools can be very helpful. Hopefully you understand that you're going to have to experiment and try different things. And if you want some information on any of these tools, or you want me to go through how we did some of the demos, you know, feel free to contact me. But the last thing I think is, you know, even with the abilities that AI has today, AI is going to be embedded everywhere in different marketing tools. I don't think we're anywhere close to having an AI Marketing Robot, you just say go creating a campaign. As you can see, they tend to be very formulaic from the eyes at the moment. But in terms of you know, accelerating what you do, making things easier, you know, just that example of the spreadsheet where we found the industry, incredibly useful thing to do. So, AI is going to be everywhere. I think people use it, they're going to get much more out of their marketing than people who don't

Don't use AI because they're going to have that little help. The ideas, the content creation, the suggestions about you know, where the leads are coming from, all of that is going to boost. So hopefully, I've excited you about AI, you feel like, you want to try some of the tools. What I'm gonna do now is open up for questions. So I'm really interested if anyone has any questions, and they'd like to cover them on the call, please feel free to put them in the chat. And I'd be very happy to answer any questions and cover anything you'd like me to address.

So I'm just going to have a look at the chat. Okay, so we have a couple of questions. So the first question is, that is a great question. So it's really good. It's from Rachel. And it's, you know, we've seen some great AI tools. But the reality is, is most people don't have time to experiment with hundreds of different tools. So what are the top three tools that people should use at the moment?

I think that's a really interesting question. It depends upon what you're trying to achieve, and what your objectives are. So if you're somebody who's very heavily into content, I would absolutely look at some of the AI tools that help you with SEO, help you, you know, identify the keywords, generate content, find those opportunities for content. And they'll also help you write the content as well, they're starting to introduce generative AI in there. I think if you're somebody who processes a lot of data, I would probably at the moment, LOOK AT chat GPT. So chat, GPT can not only do the data analysis, I showed you, you know, pulling information out of big data files, but chat GPT can also help you in Excel, you know, enrich data. So I think, from data that that would be really important. And then the last thing is, I think if I was, you know, more focused around marketing automation, or CRM, then probably what I'd be doing is I'd be spending time looking at the AI features that my CRM platform or my marketing automation platform had.

Because I think, you know, we are going to see a lot of different features. I mean, Salesforce announced that Dreamforce that they were introducing Einstein one with all these features, including, you know, the AI lead scoring, and within a couple of weeks act on was talking about very similar AI lead scoring in their their tools. So I think it's about you know, using the tools you have, and not necessarily, you know, going out and trying to find lots of other tools.

And have one other question here.

Okay, really simple one. Will there be a recording of the webinar? Yes, absolutely. We do always make a recording of our webinars. So our webinars are on demand. So if anybody has any thing they'd like to look at, again, you will be sent a link with the information of how to access the on demand webinar.

So I'm very aware of time we try and keep these webinars to about 30 minutes or so we're run over time now. So what I'm going to do is, I'm gonna call it a day, I'm gonna say, hopefully, I've helped you understand some AI tools. If anyone does have any more questions, or think of anything that they haven't asked yet, please do send me an email, email addresses Mike at Napier b2b dot com. And I'd be really happy that walk you through how to set up some of these tools, or just chat and answer questions. So thank you very much. I really appreciate your time. And good luck. I hope AI helps you do your job. In less time you get maybe a little bit more break or a longer weekend over the next few weeks from using AI thanks very much.

by Hannah Wehrly