The custom part of the proposal is a key feature. Marketing must graduate from “embedded, out-of-the-box” solutions, Frank says. He quotes Gartner research that shows that, while 55% of business leaders consider AI for every use case (rising to 71% if AI has been in use for more than four years), marketing comes seventh in the top twelve list of business functions seen by the leaders as benefitting from AI.

Why start with brand. Presenting at the Gartner Marketing Symposium, Frank made the case that brand is actually a “fuzzy, abstract” concept, and pointed correctly to the immense progress made by AI, and notably by generative AI, in handling the fuzzy. Generative AI like Chat GPT, for example, tends to sacrifice precision for broadly relevant and more-or-less accurate output. “It’s easier for them to tell you whether a story is happy or sad than whether it’s true.”

Ideal for brand, Frank says, which is not one precise concept, but a panoply of imagery, color, tone, mood and values.

“You have a brand, you care about that brand and you have been developing assets for that brand,” Frank told us. “That is actually a perfect situation to begin custom modeling.”

Of course, just starting is going to be daunting, but Frank is not calling on brands to start from scratch; “That’s out of the scope of most organizations,” he said. ChatGPT is just one of a number of foundational AI models out there, including offerings from Google and Amazon. The strategy should be to deploy one of these models and then customize it by training it on the brand’s own data. “It becomes a copy of the original model,” Frank explained, “with your own custom additions.”

As well as training data there should be human oversight and feedback, especially to represent brand values.

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This doesn’t mean that humans are themselves going to have to feed the model with what it needs to know. “The beauty of these models is, you don’t even have to understand the concepts that it’s extracting. It will do that for you. All you have to do is feed it with a corpus of examples and all of the subtle semantic connections that we consider it really hard to think about, it does that for you.”

Who’s on the team? This project will need input from both marketers and from IT and data scientists and AI experts. At the heart of the team, however, is a role Frank refers to as the Model Owner. The Model Owner will not be a hands-on data or AI expert, but she will be able to interact with the experts and translate between their operational challenges and the needs of the marketers. “It’s not a technical role at all,” Frank said. “It’s more of a supervisory role that articulates and owns the training process. They don’t have to know how the training process works.”

The operational framework for the model envisages generative AI creating paid media, content and social ads, sites, apps, videos and chatbots, but all within the parameters of the brand it has come to understand.

Why we care. Among all the use cases currently being described for AI, this is an ambitious one. It’s easy to see how branding could go off the rails without close human attention. Frank admits that. Also, once one starts introducing an IT team (with time on its hands) and data scientists, one begins to think this is primarily an enterprise project.

Nevertheless, Frank is bold enough to posit that custom training of AI by brands will be mainstream by 2026.

The post It’s time to teach AI about your brand appeared first on MarTech.

Dig deeper: Three things ChatGPT needs which only you can provide

Many marketers are using ChatGPT to write subject lines. That’s okay for a starting position. But if that’s all you’re using it for, you’re missing a huge opportunity to solve some of your biggest marketing problems and elevate your marketing team’s role as a revenue and tech leader. 

I know you have a lot on your plate right now, but with AI poised to be a game-changer, you need a voice in implementing AI and its associated technologies. As email marketers, we will work closely with AI in our companies. We can’t afford to wait and see what happens. If we don’t step up, martech could lead us down the wrong path. 

3 steps to shape AI development and use

1. Experiment with AI across platforms

Did your ESP just add an AI-driven subject line generator? Great! But don’t stop there. Boxing AI as a subject line tool can mean you won’t use it to achieve a goal that has a greater downstream impact. 

AI-generated content at scale could modify language to match unique segments or cohorts in your marketing efforts to recognize intent or activity. Subject line generation is a means to that end. It’s not the end. 

Take some time to experiment with other uses for AI and discover how different platforms can deliver different results. With AI and associated technologies in their infancy, it’s important not to get stuck on one or two uses or systems.

In this preliminary stage, you can experiment on different platforms, including ChatGPT (OpenAI), Google’s Bard and Microsoft’s Bing. This will give you a robust idea of what you can do, what you can learn, and where you can apply results. The best way to achieve this is to develop a testing plan focusing on a task or goal that supports a marketing objective. 

Next, get to know the platforms available to you and replicate your testing plan on all of them. Use both the free and paid versions of these platforms. I’ve used all three of the ones I’ve mentioned here, and I’m amazed at how different the answers are. Although it means doing extra work at this preliminary stage, you’ll learn what each platform can offer and develop a realistic vision of the future.

Learn all you can right now, and adapt as you go. This is not about lifting out subject-line or call-to-action alternatives and pasting them into your email template. You know your brand better than these platforms do, so you must use your AI results as a starting point for further testing.

2. Ask your vendors for their visions 

Pull your vendors into conversations about their plans for this technology. Yes, ChatGPT is still in its infancy, but it’s growing up fast even as we try to define it. I am interested in knowing what my vendors are planning. 

Another option: If your vendors have customer advisory boards, ask them if you could join. This will put you right in the middle of the conversation about planning in the most relevant sense. 

If your vendors don’t plan to include AI or focus on a specific use like subject lines, it might be time to start an RFP and talk to other vendors that are farther down the innovation path.

3. Take a seat at the AI table

As an email marketer, you need to be in on the conversations happening at your company. You might even know more than others at the same table, so why not leverage that into a leadership position?

Email delivers the highest ROI of all your marketing channels. If I make the most money in my marketing work, I deserve a seat at the table. I can do content generation at scale. I have more touchable consumers. I have greater functionality and a proactive messaging path directly into the inbox. I don’t have to hope my customers find my emails — they’re right in their inboxes waiting for them.

Bring all the power and authority of email to bear in your AI conversations. The ideas you generated from Step 1 and Step 2 above will flow into this process and inform your discussions. You can use this knowledge to take control without waiting for your vendors or the industry to define the role of AI.

Wrapping up  

For 20 years, we have been talking about dynamically inserting relevant content into messages to increase engagement. AI could get us there. But it will not work if we marketers don’t actively take part in deciding how to use it for its highest goals in marketing. We can’t be passive technology users. We must be active influencers on this path. 

You can participate in this great movement by testing, leading conversations and learning how you can achieve your marketing goals with it and help your company prosper. 

The post 3 steps to make AI work for you appeared first on MarTech.

But with all the hype, how can marketing leaders and their teams best determine how to utilize AI to benefit their work and customers? This four-part series will explore four categories of artificial intelligence (AI) and how they can meaningfully impact marketers and their customers. 

This first article of the series will look at generative AI, a category that includes tools you might have used — or at least heard plenty about — like OpenAI’s ChatGPT or Google’s Bard (focused on generating text responses to queries), MidJourney and Dall-E (focused on generating imagery) and many others. This category may be very familiar to you, but we’ll take a fresh look at it here before moving on to other uses of AI in the subsequent parts of this series.

What is generative AI?

Generative AI uses existing text, imagery and other information and creates new content from those sources. They use underlying technologies like GPT (Generative Pre-trained Transformer, used for text generation) or Stable Diffusion (used for image generation). They are trained on specific or more general sets of data and information. 

Some commonly used generative AI tools include:

• ChatGPT (text)
• DALL-E and MidJourney (image)

This category of AI requires the user to provide a prompt to generate output. It is important to note that everything generated is based on some prior learning or source text, images or other materials, and the base technology continues to evolve. That, combined with the materials that the tools are “trained” on, determines the quality of the output. 

Why it’s worth paying attention to 

While generative AI is still in its infancy, it can still be a great source of new ideas. Think of it as an idea generator that provides the foundation for creative work. By asking ChatGPT for 10 ideas for blog posts, you might get 3-4 solid ideas (meaning 6-7 will get discarded), but that is still quicker and easier than brainstorming when you are on a deadline.

Another way of thinking of generative AI is as a starting point for tasks, knowing that what you are getting is preliminary to even a first draft. But, if you know that going in, the tool is only meant to cure writer’s block (in the case of text-based content) or give your illustrators or designers a rough idea of the visuals you have in mind. 

Finally, generative AI is beginning to be used in the context of personalization. Looking at it as a pure personalization engine is still a bit of a stretch, but there are some interesting use cases within limitations. 

Text-based generation of personalized variations and image-based ones that restrict copyrighted images and brand-safe elements are already being used, at least in limited ways. This area may have the most potential because of the prohibitive scaling issues associated with creating content required for hyper-personalization. 

Near-term potential

Although there is still much room for improvement, marketers can look forward to a couple of things in the (hopefully) near future.

Using it as an idea generator or as a starting point for creative content presents nearly limitless opportunities. It is one of the reasons you have probably heard so much about generative AI recently, as ChatGPT has also become the most quickly adopted software platform of all time. 

Greater personalization opportunities will also continue to be available, particularly as the quality of results and the ability to restrict specific source material and keep things “brand safe” becomes more sophisticated. 

What to watch out for

Generative AI is arguably the most-hyped flavor of artificial intelligence tools at the moment. That said, it is far from perfect, and there are some areas that marketers and others that wish to utilize it should watch out for.

First, there are easily avoidable errors that generative AI can sometimes introduce into otherwise compelling or interesting text or visuals. For instance, generating images of humans with four fingers or in impossible poses made some early examples of this type of AI not quite ready for prime time.

While the image tools have improved and are making these errors less often, there are still other things you will want to watch out for that might be more nuanced. These might include the background imagery or anonymous people in a crowd with odd errors.

Another potentially larger issue is the rights of the images used as source material. Because generative AI bases its responses to prompts on existing materials — essentially whatever it was “trained” on — the sources of those materials matter greatly. Because of this, there can be plagiarism and copyright issues if care is not taken to avoid using copyrighted images as source material. 

We have already seen some lawsuits around this, and the potential for more exists. 

Conclusion

As you can see, generative AI has some compelling current uses and holds considerable potential, but marketers should also proceed with caution before wholesale adoption. 

In the next article in this series, we will look at another area where AI can play an impactful role in marketing: predictive analytics. 

The post The 4 categories of AI that impact marketing: Generative AI appeared first on MarTech.

To stay competitive, you’ll need to understand the technology and how it will impact our marketing efforts, said Christopher Penn, chief data scientist at TrustInsights.ai, speaking at The MarTech Conference.  

Learn ways to scale the use of large language models (LLMs), the value of prompt engineering and how marketers can prepare for what’s ahead. 

The premise behind large language models

Since its launch, ChatGPT has been a trending topic in most industries. You can’t go online without seeing everybody’s take on it. Yet, not many people understand the technology behind it, said Penn.

ChatGPT is an AI chatbot based on OpenAI’s GPT-3.5 and GPT-4 LLMs.

LLMs are built on a premise from 1957 by English linguist John Rupert Firth: “You shall know a word by the company it keeps.”

This means that the meaning of a word can be understood based on the words that typically appear alongside it. Simply put, words are defined not just by their dictionary definition but also by the context in which they are used. 

This premise is key to understanding natural language processing. 

For instance, look at the following sentences:

• “I’m brewing the tea.” 
• “I’m spilling the tea.” 

The former refers to a hot beverage, while the latter is slang for gossiping. “Tea” in these instances has very different meanings. 

Word order matters, too. 

• “I’m brewing the tea.” 
• “The tea I’m brewing.”

The sentences above have different subjects of focus, even though they use the same verb, “brewing.”

How large language models work

Below is a system diagram of transformers, the architecture model in which large language models are built. 

Simply put, a transformer takes an input and turns (i.e., “transforms”) it into something else.

LLMs can be used to create but are better at turning one thing into something else. 

OpenAI and other software companies begin by ingesting an enormous corpus of data, including millions of documents, academic papers, news articles, product reviews, forum comments, and many more.

Consider how frequently the phrase “I’m brewing the tea” may appear in all these ingested texts.

The Amazon product reviews and Reddit comments above are some examples.

Notice the “the company”  that this phrase keeps — that is, all the words appearing near “I’m brewing the tea.” 

“Taste,” “smell,” “coffee,” “aroma,” and more all lend context to these LLMs.

Machines can’t read. So to process all this text, they use embeddings, the first step in the transformer architecture.

Embedding enables models to assign each word a numeric value, and that numeric value occurs repeatedly in the text corpus. 

Word position also matters to these models.

In the example above, the numerical values remain the same but are in a different sequence. This is positional encoding. 

In simple terms, large language models work like this: 

• The machines take text data.
• Assign numerical values to all the words.
• Look at the statistical frequencies and the distributions between the different words.
• Try to figure out what the next word in the sequence will be. 

All this takes significant computing power, time and resources.

Prompt engineering: A must-learn skill 

The more context and instructions we provide LLMs, the more likely they will return better results. This is the value of prompt engineering.

Penn thinks of prompts as guardrails for what the machines will produce. Machines will pick up the words in our input and latch onto them for context as they develop the output. 

For instance, when writing ChatGPT prompts, you’ll notice that detailed instructions tend to return more satisfactory responses. 

In some ways, prompts are like creative briefs for writers. If you want your project done correctly, you won’t give your writer a one-line instruction. 

Instead, you’ll send a decently sized brief covering everything you want them to write about and how you want them written.

Scaling the use of LLMs

When you think of AI chatbots, you might immediately think of a web interface where users can enter prompts and then wait for the tool’s response. This is what everyone’s used to seeing.

“This is not the end game for these tools by any means. This is the playground. This is where the humans get to tinker with the tool,” said Penn. “This is not how enterprises are going to bring this to market.” 

Think of prompt writing as programming. You are a developer writing instructions to a computer to get it to do something. 

Once you’ve fine-tuned your prompts for specific use cases, you can leverage APIs and get real developers to wrap those prompts in additional code so that you can programmatically send and receive data at scale.

This is how LLMs will scale and change businesses for the better. 

Because these tools are being rolled out everywhere, it’s critical to remember that everyone is a developer. 

This technology will be in Microsoft Office — Word, Excel and PowerPoint — and many other tools and services we use daily.

“Because you are programming in natural language, it’s not necessarily the traditional programmers that will have the best ideas,” added Penn.

Since LLMs are powered by writing, marketing or PR professionals — not programmers — may develop innovative ways to use the tools. 

An extra tip for search marketers

We’re starting to see the impact of large language models on marketing, specifically search.

In February, Microsoft unveiled the new Bing, powered by ChatGPT. Users can converse with the search engine and get direct answers to their queries without clicking on any links.

“You should expect these tools to take a bite out of your unbranded search because they are answering questions in ways that don’t need clicks,” said Penn.  

“We’ve already faced this as SEO professionals, with featured snippets and zero-click search results… but it’s going to get worse for us.”

He recommends going to Bing Webmaster Tools or Google Search Console and looking at the percentage of traffic your site gets from unbranded, informational searches, as it’s the biggest risk area for SEO. 

The post How to scale the use of large language models in marketing appeared first on MarTech.

Dig deeper: Artificial Intelligence: A beginner’s guide

Here is a roundup of actual AI-powered martech products, platforms and features announced this week. 

• Wurl’s ContentDiscovery is a machine learning-powered advertising platform for streamers and content publishers. It finds high-value viewers by promoting content they will want to watch across streaming apps and on mobile devices.
• Messagepoint has added AI-powered content generation to its AI engine, MARCIE (Messagepoint Advanced Rationalization and Content Intelligence Engine). The new feature enhances the existing Assisted Authoring capabilities by providing content rewrite suggestions that align communications with desired reading levels, sentiment and length.
• Brandwatch has added ChatGPT features to Iris, its AI-powered platform for managing social media presence. These features include natural language summaries of themes and trends, a writing assistant and generating content insights into owned and competitor materials.
• Stensul has added AI-powered content creation features to its collaborative email and landing page creation platform. These features include a subject line, preheader text, and title generator; a writing style changer; and a CTA (call-to-action) text generator.

Dig deeper: Email creation platform Stensul expands its offering to landing pages

• Evocalize has added a generative AI content creator to EVOLVE, its suite of intelligent marketing capabilities. The new feature can generate headlines and ad copy and tailor the ad copy to different styles and tones of voice. 
• Seedtag has added a generative AI capability to Liz, the company’s contextual AI platform. The new feature lets users build tailored creative based on the context of the surrounding page-level content. 
• NetElixir’s audience intelligence platform LXRInsights now has an AI-powered content generator that can create ad copy and product descriptions.
• IZEA has added two ChatGPT-powered features to IZEA Flex, its influencer marketing platform. One is AI Briefs which prompts users for inputs and generates an influencer marketing campaign brief that can be converted into a Flex Campaign. The other, AI Brainstorm, generates creative campaign concepts.
• Yopto‘s new email marketing solution for ecommerce brands, Yotpo Email, includes AI-powered product recommendations tailored to each subscriber. 

The post AI in martech: this week’s new features, products and platforms appeared first on MarTech.

That was the focus of a panel at The MarTech Conference, here are some of the takeaways from that discussion.

AI requires human supervision

As AI evolves, capabilities will expand. Can AI take over a specific business function and run it unaided? Not yet, according to Ricky Ray Butler, CEO of BENlabs, which uses AI to place brands’ products in entertainment and influencer content.

Artificial general intelligence or AGI is the kind of technology that is completely automated, and that’s simply not available yet.

“There is still human supervision [required] when it comes to data inputs or [telling the AI] what the purpose is to have successful outcomes,” said Butler.

“What AI really brings to the table is when it comes to the feedback loop,” he said. “It can structure data and a massive amount of data in a way that the human mind can’t even comprehend or compute. And it can do that at a scale where it can look at millions and millions of videos and monitor, prioritize and then also…make predictions with successful outcomes or or potentially unsuccessful outcomes. We are literally building a brain when we’re leveraging this type of technology to do what the human mind does, but to be able to do it even better and even more accurately.”

Dig deeper: A beginner’s guide to artificial intelligence

Generative AI writing tools need writers

Generative AI writing tools position themselves as writing assistants, not writers, said Anita Brearton, CEO of marketing technology management platform CabinetM.

“[These tools] describe their value prop as productivity,” she said. “They can help you write faster, they can improve SEO in fact.”

They can also help writers get started when all they’re staring at is a blank page. “They’re good for refining texts and creating some A/B versions of texts,” Brearton said.

Generative AI continues to improve in order to help creatives make text-based and visual content.

“I think we’re entering a very disruptive phase for creativity for designers, illustrators, video producers and writers,” said Paul Roetzer, CEO of the Marketing AI Institute 

A marketer’s point of view is more important than ever

As AI gets adopted for more marketing functions, marketers using these tools are needed to guide the technology and point it toward specific marketing objectives.

“The issue right now is the AI doesn’t have your knowledge of your product, it doesn’t have a knowledge of your customers, it doesn’t have knowledge about the internal politics of your company,” said Pam Didner, VP of marketing for consultancy Relentless Pursuit. “[AI doesn’t] have knowledge about even the road map that you are going to produce for your company. So AI can write very well, but you still need to add your own point of view. That’s where a human comes into play.”

Leaders need to know about AI when hiring

When AI is adopted by organizations, leadership needs to know how work has changed so they make the right hires.

“ChatGPT woke everyone up to AI, so we’re all testing the tools,” said Roetzer. “There’s pressure on CMOs and CEOs from boards and investors to figure out AI. Everybody needs to have a plan, and you have a whole bunch of leaders who don’t understand the underlying technology that now have to make decisions around staffing.”

He added, “We need to rapidly accelerate the comprehension of what AI is and what it’s capable of doing, what its limitations are. But, also [we need] to come to grips with where it’s going.”

Register and watch The MarTech Conference here.

The post What marketers should keep in mind when adopting AI appeared first on MarTech.

Maybe it wasn’t exactly 10 minutes, but it sure felt that way. Since then, I’ve received countless solicitations from companies offering all kinds of AI-generated solutions. 

Will the ability to send better emails/content written more quickly by AI change the lives of B2B marketers for the better? Not yet. In fact, the greatest performance impact will not be seen in creation but in execution. 

The promise of AI’s ability to deliver mass personalization and unique experiences is only realized if we focus on gaining better insight into the audience, specifically their preferences. Here’s why.

Faster doesn’t always mean better

The ability to create more content faster will only result in lower performance. Up to 89% of decision-makers said the content they encountered through the buying process was high quality, according to a 2019 Gartner research.

Buyers were almost at the point of saturation in their cognitive ability to consume more information. Simply put, more content will not result in increased consumption or understanding. The supply of content is at maximum, according to buyers. And that was four years ago. Imagine what they would say now. 

This insight led to the discovery of the “sense-making” seller, a person with the important attributes of connecting the right information to the right person at the right time. They also possess the ability to filter out unnecessary information, giving the decision maker only the information they need in order to take action. 

It’s one thing to have a human listening and understanding buyers’ needs during the sales process, but it’s another when trying to do this at the top of the funnel with marketing assets.  This is where the opportunity for AI in B2B lies. 

AI as the ‘sense maker’ for marketers

We’ve now built ABM stacks that typically encompass dozens of marketing technologies that pump out endless contact and engagement data. Still, the performance of those leads remains poor. Why? Because we don’t have a sense-making filter to align and route the right marketing asset to the right person for the right reason.

AI personality profiling tools represent an opportunity to be the sense maker for marketers. By understanding the distinct behavior of audiences, marketers can better:

• Align content based on individual preferences.
• Understand what intent “signals” are real.
• Create messaging that appeals to specific segments of the market. 

Understanding buyer behavior offers value beyond just outbound marketing. It extends to routing and aligning business development resources. It can help sales managers understand how to align their teams based on prospects’ preferences with their engagement activity. 

Understanding personal motivations and engagement behavior gives insight into what leads hold the most potential to move forward. It can identify which targets to avoid and the most fertile ground to build relationships. 

AI sense making in action

Here’s an example. A professional services firm was getting high attendance for its webinar series, but very few attendees converted into leads. After analyzing the audience, they found that over 50% of their attendees had one dominant personality type. 

Their webinars were rich with data and research, with their content mostly white papers. That was the problem. Their audience was made up of mostly strategists and consultants. Their behavior was to learn the information to inform others. Their content preferences were for “light” content that traveled easily and could stand independently without an explanation. Think infographics and animated videos. 

After they shifted to lighter follow-up content, lead conversion post-webinar increased by 35%. This group had a personality profile of an “influencer,” or those who use the information to inform others. They were not the “lead” but pointed to the opportunity. 

The organization started tracking sharing versus downloads and followed the content to the intended audience. They found that more content was not the answer. It was personalized content aligned with how the audience wanted to use it. The “sense maker,” in this case, was the influencer attendee who was routing the relevant information to the right person at the right time. 

AI tools unlocked the insight into understanding their behaviors and motivations. A better understanding of the audience improved the performance of their outbound efforts. 

And that is where the ROI will be found. If you’re truly interested in impacting performance, find the solutions that will provide insight into buyers. You don’t need more content. Buyers already told you they have what they need four years ago.

The post Why AI will make the greatest impact on B2B audience insight, not on content appeared first on MarTech.

Do you want to learn how to harness the power of AI and use it to reach your target audience, boost your engagement, and maximize your overall digital marketing ROI?

Sign up now for a free online event hosted by Google and Ignite Visibility – AI-Powered Ads in 2023 with a Human Touch – on Thursday, May 18, 2023, at 2 p.m. EST/11 a.m. PST.

“Our amazing Google Agency lead, our VP of paid media and I will be discussing their new AI system, Google Bard. We will also cover Google Analytics 4, Performance Max, and you’ll get a look into how businesses are using AI to improve the world and increase their operational efficiency,” said John Lincoln, CEO of Ignite Visibility.

“Also, I’m going to be providing my own insights on how we’ve used AI to improve operational efficiencies within our business and, in addition to that, used AI to improve digital marketing services outside of Google Ads.”

Whether you like it or not, AI is the future, and this is one event you don’t want to miss. Get more information and sign up here. 

Attending this informational event is free, and if you cannot make it live, sign up and a recording will be emailed to you afterward.

Hosted by experts in digital marketing, AI, and paid media

In addition to John Lincoln, CEO of Ignite Visibility, speakers include Evan Trevers, strategic agency manager at Google, and Meghan Parsons, vice president of paid media at Ignite Visibility. 

Together, these industry experts will share their insider knowledge, discuss how AI’s continuous evolution will match relevant search results to user queries, and help businesses connect customers during different parts of the customer journey. They will also share why you should focus your paid media efforts on developing the right creative copy with the help of AI.

In addition, speakers will also share their thoughts on why your company should invest in Performance Max campaigns to combine AI with expert human insights, how to use AI to further your business goals, and more.

It’s no secret that the world of digital marketing has changed over the past few years. AI is just one of the many tools we have at our disposal to help take our businesses to the next level. Now is the time to learn how to use AI to generate and power ads to help you reach your specific audience, boost your engagement rates, and maximize your overall ROI.

Join Evan Trevers of Google and John Lincoln and Meghan Parsons of Ignite Visibility to learn how to get ahead of your competition by getting a handle on AI now. The best part of this event? It’s free to attend!

Learn more about AI-Powered Ads in 2023 with a Human Touch and sign up here.

The post Navigate the world of AI before your competitors do appeared first on MarTech.

AI’s inflection point was OpenAI’s decision to provide free and unfettered access to ChatGPT — the result: 100 million users in less than two months. 

As martech and marketing operations leaders, this open access is both a blessing and a challenge. It dramatically changed our 2023 plans and priorities.

That’s where MarTechBot entered the picture approximately two weeks ago. Thanks to Marc Sirkin and the team at MarTech for allowing me behind the curtain of MarTechBot, providing insider access to how it’s being trained, the underlying tech, and the real-time learnings.

Sirkin and I discussed the implications of the contextual “MMM” prompt that he posted about. That experiment demonstrated that training MarTechBot with this site’s content would result in customized answers for marketers. The result was expected but impressive nevertheless. And led to further reflection. Here are some of the insights I walked away with.

• Start now. Learning how to train an AI bot using a company-specific language model should be at the top of your 2023 to-do list. It may not be released to the public, but the potential benefits demand that we all start taking tangible steps now.
• Echochamber effect. Watching MarTechBot respond within the bubble of marketing and martech was awesome — like a two-week-old baby already knowing how to say “mom” and “dad!” However, the implications are serious. Biases may creep in just as quickly. In the world of marketing tech, would MarTechBot soon conclude that the only solution to each marketing problem is to add a new tool to the stack?
• New marketing ops roles. We discovered that training a bot comes with all sorts of new guardrails. One example is operationalizing GPT token limits. While word counts are a rough metaphor, they are not exact equivalents given the predictive feedback loops that are the foundation of large language models (LLM). Another example would be new content ops roles to edit audio/video text transcriptions. Previously, slight inaccuracies produced by real-time closed captioning would’ve been overlooked. Those inaccuracies are consequential when text is fed into training bot models.
• Pivots. If a bot can be trained so quickly to take on a tone of voice, can it be retrained instantly? What if a brand has trained a bot on messaging and tone that’s now obsolete because of a new product direction or rebranding?

But wait, there’s more! The following are just the tip of the iceberg when it comes to new MarTech and MOps challenges (e.g., unanswered questions!) that MarTechBot prompted.

• New stack without a how-to guide. Those creating generative AI systems admit that they don’t understand exactly why and how they respond the way they do sometimes. How does a marketing ops professional explain that to customers, the executive team, shareholders, etc.? 
• Balancing speed and responsibility. The race to innovate will unearth thorny legal, copyright and ethical issues. Will new content tags such as #train_on_this (or #do_NOT_train_on_this) be honored? 
• The potential rekindling of marketing-IT “infighting.” Over the last 10 years, we have established some norms in the role/responsibility splits between marketing and IT. But AI tools will be used by the entire enterprise. Will marketers need to renew their cross-functional partnership with IT, or risk losing access to important datasets that IT will and should always control for the enterprise?
• Rapid infusion into marketing automation. As I wrote and spoke about in March, these capabilities also drive reinvestment in core CRM and marketing automation platforms as the foundation of the martech stack. I’ll cover the impact on data management in part 2 of the series in June. How much will change again or be introduced between now and then? (I’ve already modified my outline three times!)

In the past, vendors and/or consultants could usually help us identify where something was awry in our stacks. That won’t be the case with the AI bot stack for the next 6-12 months. We have to be the operator behind the curtain. Start today.

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Dig deeper: Facebook agrees to revamp adtech over discrimination charges

What it does. It lets advertisers: 

• Create different versions of the same ad copy for different target groups, while maintaining the main message. 
• Generate different assets for a campaign with the background generation feature.
• Crop images to adjust visuals for different formats, such as social posts, stories, or short videos like Reels.

AI Sandbox is available to select advertisers at the moment with expanded access in July.

Why we care. Meta is lagging in the AI publicity wars and knows it. The first sentence in the AI Sandbox announcement is, “Since the earliest days of News Feed in 2006, we have used machine learning and AI to power all of our apps and services, including our ads system.”

Last September, CEO Mark Zuckerberg gathered his top execs for an extended meeting on this. “We have a significant gap in our tooling, workflows and processes when it comes to developing for AI. We need to invest heavily here,” wrote new head of infrastructure Santosh Janardhan, an attendee. 

Zuckerberg seems determined to put his foray into the metaverse behind him and is spending billions to catch up with AI. If it makes life easier for marketers, we’re all in favor of it. 

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