artificial intelligence


By Kendra Barnett

The video-sharing app, which is embroiled in a legal battle with the US government over its right to operate in the US, is forging further into AI advertising, undeterred.

TikTok, the video-sharing platform with over a billion monthly active users, today unveiled new AI-powered tools for advertisers, including digital avatars and dubbing capabilities designed to translate branded messages into different languages across the globe. The announcement was made at the Cannes Lions International Festival of Creativity.

“Creativity is the core of TikTok. When brands truly lean into creativity that reflect the culture of TikTok, they are able to connect with their community and drive real results,” said Adrienne Lahens, global head of content strategy and operations at TikTok.

The new features are part of Symphony, a suite of generative AI offerings for advertisers that TikTok launched in May. Symphony includes a variety of tools to help streamline the creative development and content production processes for creators and marketers.

Brands and creators will now be able to use prefabricated stock avatars in their content. The avatars largely look and move like real people and are designed to reflect a wide range of nationalities and languages.

Symphony users can also create their own custom versions, tailoring an avatar to their own likeness or intellectual property or developing a multilingual character however they like to share content in local languages across the globe.

Meanwhile, the new dubbing feature in Symphony will enable users to translate their own content into more than 10 languages, with the aim of helping creators and brands expand their reach and build a more global audience. Using AI, the tool seamlessly identifies the language spoken in a video, transcribes the dialogue, translates and then spits out a dubbed version in the selected languages.

As part of TikTok’s efforts to further support brands and creators with generative AI tools, the platform is also launching an advisory group. The new Symphony Collective: Industry Advisory Board brings together creators and marketers from across the brand and agency worlds to provide ongoing feedback.

“At TikTok, we are building for the future of creative and are inviting brands to come test and learn with us as we look to simplify and unlock a whole new paradigm of creation,” said Lahens.

Founding members of the collective include representatives from top brands including Wendy’s, Mondelez and the NBA, as well as agency leaders from OMDTBWA\Chiat\Day, Tinuiti and more.

“TikTok has been a remarkable force for more open, diverse and bold forms of creativity,” said Anthony Hamelle, executive director of digital, social and innovation at TBWA\Chiat\Day US, in a statement. “With GenAI as a creative catalyst, this stage that welcomes thousands of creators and communities will become even more dynamic.”

Hamelle explained that the agency, which counts Discover, Jack in the Box, Levi’s and DirecTV among its clients, will share learnings from its own work, including TikTok content it develops for Hilton.

A select number of top creators are also involved in the Collective; among them are Drea Okeke, David Ma, Michelle Gonzales and O’Neil Thomas. They will experiment with TikTok’s AI tools in their own content and share feedback with the organization.

Drea Okeke, known for her content on Nigerian culture and food and her role on Fuse’s We Need to Talk About America, a comedic series on all things pop culture and the internet, said that she’s looking forward to participating – and getting more out of her own content in the process.

“As a creator, my goal has always been to bring joy and share my culture with the world,” she says. “TikTok has been a game-changer, allowing me to connect and build an online community in ways I never thought possible. I love using AI to streamline my creative process and boost productivity, so I’m especially excited to join the TikTok Symphony Collective Advisory Board. I’m looking forward to seeing the creative ways that AI can help us creators be even more innovative and grow.”

Filmmaker David Ma, meanwhile, plans to tap into TikTok’s new Symphony AI tools to enhance the commercial work he does for brands like Twix and Truff. “As a creator, I’m always experimenting with new filmmaking techniques for my craft and content. I’m constantly exploring new ways to scale my content without sacrificing my creativity,” he explained. “TikTok has been a pioneer in providing creative tools that allow me to effectively collaborate with brands and create lasting, long-term brand relationships.” He’s excited to participate in the Symphony Collective, he said, “to help myself and other creators find efficiency in our creative processes while maintaining our artistic voices.”

The news comes less than two months after President Biden greenlit a law that will require TikTok’s Chinese parent company, ByteDance, to divest TikTok’s US operations within a year or face a nationwide ban. TikTok is suing the US government over the decision, alleging that the law violates the First Amendment rights of the millions of Americans who use the app.

Feature Image Credit: Liza Summer

By Kendra Barnett

Sourced from The Drum

By Alex Kudos

Marketing has continued to evolve over the years, and with the integration of artificial intelligence, it has emerged as a transformative force. As marketers at one of the largest social discovery companies in the world, we are constantly exploring innovative ways to enhance user engagement, foster meaningful connections and drive business growth. AI has entered the space and is changing how we think about marketing within the dating industry—including localization, data-driven attribution, predictive conversations and its impact on creative endeavours—but how can it help other marketing firms as well?

Localization Bridges The Gap Between Global Reach And Local Relevance

One of the key challenges faced by global social discovery companies is the need to strike a balance between reaching a wide audience and delivering locally relevant experiences. AI-powered localization solutions are valuable tools allowing us to analyze user data to understand cultural nuances, language preferences and regional dating trends.

Through localized content, tailored recommendations, and geo-targeted advertisements, AI enables us to create more personalized experiences for users across diverse regions. For instance, with dating.com, we are able to analyse user interactions and preferences in specific locations, it is now possible to fine-tune matchmaking algorithms to better suit the preferences of users in different geographic areas, ultimately enhancing their overall experience and increasing engagement.

Data, which most companies sit on mounds of and don’t know what to do with it, is the secret weapon to make AI work for your company. By gathering data on local demographics, preferences and cultural nuances, marketers can feed this information into AI-powered tools that can capture sentiment analysis, social listening, market research, etc., and quickly adapt materials accurately across multiple languages and regions.

Data-Driven Attribution Unravels The Complexities Of User Behaviour

Multi-touch attribution is extremely important if there are a number of brands, platforms and channels under one portfolio. In these instances, understanding the customer journey and accurately attributing conversions to various channels are essential to marketers. Data-driven analytics help unravel the complexities of user behaviour, identify the most influential marketing channels, and optimize strategies accordingly. With advanced machine learning algorithms, marketing teams can analyse vast amounts of data to determine which marketing efforts are driving the highest ROI.

Predictive modelling allows us to forecast future user behaviour and tailor our marketing initiatives accordingly. By understanding the factors that influence user engagement and conversion, we can allocate resources more effectively, maximize marketing impact and drive sustainable growth.

Marketers should research a centralized system to house data for data integration and then enlist the help of a skilled team of data scientists to develop algorithms and constantly analyse the data. As this data continues to be analysed and automated, marketing campaigns can be optimized in real time.

Predictive Conversations Anticipate User Needs and Preferences

AI-powered chatbots and conversational agents are powerful tools for engaging users in personalized and meaningful interactions. Companies should consider implementing an advanced chatbot such as ChatGPT into their core product to help with user questions. Earlier this year, OKCupid integrated ChatGPT into its platform to assist with finding out the right questions that correlate directly to what’s happening in the world and pop culture. These AI-driven models can understand user intent, respond to inquiries and provide tailored recommendations in real time.

This formula can be replicated for many companies across industries. It is essential to be able to forecast customer behaviour and preferences to tailor messaging and shift strategies accordingly.

AI Use In Creative Elevates Brand Storytelling And Visual Communication

Compelling creative content is essential for capturing audience attention and conveying brand messages effectively. AI technologies such as generative design are changing the way companies approach creativity, and also speed up the process. From image recognition and content generation to personalized video recommendations, AI can create highly engaging and relevant content at scale. These services and tools are readily available and most can be integrated into existing platforms that are already functioning within an organization.

Marketers can employ AI-driven image recognition to analyse user-generated content to identify trends, preferences and brand associations. Generative design tools make it possible to automate the creation of personalized visual assets such as custom graphics, videos and interactive experiences, upleveling brand storytelling capabilities.

The integration of AI into marketing strategies within the social discovery industry represents a major shift in how we engage with users, drive conversions and foster meaningful connections. From localization and data-driven attribution to predictive conversations and AI-driven creative endeavours, AI technologies can empower marketing teams to deliver personalized experiences that resonate with users on a deeper level.

Feature Image Credit: GETTY

By Alex Kudos

Follow me on LinkedIn. Check out my website.

Alex Kudos has over 18 years experience in marketing and currently serves as CMO at Social Discovery Group. Read Alex Kudos’ full executive profile here.

Sourced from Forbes

By Georgie Everitt

Does AI pose a threat to copywriters? No, says Georgie Everitt: not if we remember that words hit differently when they come from humans.

Do you ever feel like you’re being watched? I do. At this very moment, actually, as I sit writing at my desk, in the B2B marketing agency I work for, I think – what if my colleagues see me, a professional copywriter, spell a word wrong? Googling which dash to use? Or thesaurus-ing a synonym?

Okay, they’re probably not watching me, are they? But self-consciousness is a writer’s curse and it really can disrupt our flow, which is why I’m talking to you today.

There’s been a new tool in our writing shed for a while now, and it’s time we talked about it – mainly so I can stop panic-minimizing my screen any time I’m using it.

Hopefully, it’s obvious that I’m talking about AI, a writer’s most controversial friend, but a friend nonetheless. For the time being, at least.

Don’t fear, the Terminator is not here

When AI burst into our lives, my copywriting colleagues and I immediately felt like we were in a fight against the perception that it could do our jobs, and quicker. The Terminator had arrived to deliver the news that human-writers’ days were numbered.

We creatives are already deemed to be an awkward bunch, often told that we’re overthinking, our standards are too high, and that speed is more important than quality.

Does this make us the first to go? Of course, that’s our self-consciousness talking, and what group doesn’t have its quirks?

Copywriters’ standards are high because we know that tiny tweaks can mean the difference of thousands of extra impacts, sales, or whatever we’re after.

Copywriting is writing to persuade. In usually very few words, we have to make people feel something and then want to do something with that feeling. It’s not about quantity, it’s all about quality.

How many of the ads you’ve seen today have made you want to do something?

Plenty of words sail past us, so as copywriters, we have to find the right ones, put them in the right order, and give whoever we’re talking to the feels – when we get that right, we can literally make our clients millions. There’s a reason creatives can spend weeks locked in a room to come up with a concept or strapline made of two or three words.

The danger is that, with tools like AI, we risk diluting markets with a sub-standard sameness written in grammatically correct sentences but doesn’t get results, with nobody really understanding why.

Copywriters are just like bears

Creative copywriters rely heavily on our subconscious to spark creativity. We approach creative projects like bears readying themselves for hibernation.

Yep, bears. We’ll feed our minds with the project brief, research interviews, case studies, factory tours, and incessant Googling until we’re stuffed full of enough insights and anecdotes to see us through the next stage of the process.

Then into our creative caves we go – to live, breathe, and sleep with all of that knowledge and allow our creativity to get to work.

It’s as we drift off into a well-informed stupor that the fun starts – inventor Thomas Edison actually argued for sleep as a creative technique. He’d nap upright, with steel balls in his hands and a metal plate on the floor. As he fell asleep, the balls would drop, wake him up, and allow him to withhold any creative genius that had occurred to him in his relaxed subconscious state.

While I can’t claim to have the genius of even Edison’s right pinky toe, I can still relate. I’ll always keep a notepad and pen by my bed when I’m working on a new concept. Sadly, my nocturnal scribbles are rarely of any use, but every so often there’s something.

Obviously, I don’t think my boss would be particularly impressed to find me asleep under my desk. Time is money, and that’s where a tool like ChatGPT can help.

Once we’ve stocked up on everything AI can’t do – grasp our innate understanding of who we’re talking to, our client’s preferences, unique strategic insights, and years of personal experience – then a little back-and-forth game of prompts can get us going.

AI shows us the derivative, the dull, and the done so that our brains can use that as a springboard to real creativity. And if nothing else, it can help soften any imposter syndrome – it really can churn out some very average combinations of words.

Don’t be afraid of ChatGPT

So from this point forward, I shall no longer be minimising my ChatGPT when colleagues walk past; it’s not cheating, it’s just another useful tool that has the potential to take human creativity even further.

And if you don’t want to take my word for it, here’s what ChatGPT has to say:

“Copywriting involves creativity, emotional intelligence, and a deep understanding of human communication, which are qualities that AI currently lacks. Instead, think of me as a tool that can help streamline certain tasks, generate ideas, or provide information.”

But that’s what a clever Terminator would say, right?

I believe that words in the hands of humans hit differently and, while I’ll continue to shout this from the rooftops, I do believe we copywriters need to embrace AI, just like other specialists around us, so we don’t get left behind.

Feature Image Credit: Florian Klauer via Unsplash

By Georgie Everitt

Sourced from The Drum

By Alessio Francesco Fedeli

The current landscape of digital technology is marked by the struggle to achieve visibility for your business online and target the appropriate audience amidst a wave of competition. Search engine marketing (SEM) has pivotal strategies that will allow a business to achieve this but with ongoing advancements in artificial intelligence (AI) and machine learning, more marketers have opportunities for maximum growth. These advancements are revolutionising SEM and will help enhance the efficiency and effectiveness of business campaigns significantly.

AI-enhanced SEM tools stand at the vanguard of this revolution, utilizing advanced algorithms and machine learning capabilities to transform every facet of search engine marketing comprehensively. From automating the process of keyword research to refining advertisement creation, and from optimising bid management to improving performance analysis, these tools furnish marketers with the capacity to attain exceptional outcomes. They transcend conventional tool functionality; they act as catalysts for change, facilitating precise targeting and real-time modifications previously considered unattainable.

Exploring further into AI and machine learning within SEM reveals that these technologies are not only augmenting existing methodologies but also fostering novel strategies. Marketers harnessing these tools gain the ability to predict market trends accurately, comprehend consumer behaviour with enhanced precision, and implement campaigns that are both cost-efficient and high-impact. The advent of AI-driven SEM marks a transformative era in digital advertising, reshaping the landscape in ways that are beginning to unfold.

Leveraging AI and machine learning in SEM

Leveraging AI and machine learning can revolutionise your campaigns | News by Thaiger
Photo by Steve Johnson on Unsplash

The Role of AI in search engine marketing

AI revolutionises SEM by making complex tasks simple. It sifts through vast datasets to unearth insights beyond human capability. By fine-tuning keyword research and bid optimisation, AI ensures ads hit the mark every time. It doesn’t stop there; AI tailors ad content for individual users, predicting trends and making swift, informed decisions. This not only sharpens the marketer’s toolbox but also enhances the consumer’s journey, significantly boosting conversion rates. With AI in SEM, ads become more than just noise; they’re strategic moves in the digital marketplace.

Benefits of Using Machine Learning in SEM

Although there is some apprehension from some, it is important to understand that there are benefits to incorporating machine learning into your SEM strategy.

Benefits of machine learning in SEM

Enhanced targeting accuracy By analysing user data, machine learning identifies the most relevant audience segments, improving the precision of targeting efforts.
Optimised bid adjustments Machine learning algorithms navigate the volatile bidding landscape, making real-time adjustments to maximize ROI.
Improved ad performance It analyses what works best for ad performance, from copy to design, ensuring optimal engagement and conversion rates.
Fraud detection and protection Machine learning acts as a guardian against click fraud, safeguarding advertising budgets from dishonest practices by spotting and mitigating fraudulent activities.

This integration offers strategic advantages that will enable marketers to be more effective in this competitive digital landscape. However, by implementing machine learning, businesses can not only optimise their advertising efforts but also protect their investments. This way, every dollar spent is an investment towards achieving tangible results.

Incorporating AI and machine learning technologies in SEM campaigns

Choosing the right AI tools is the first step to SEM success. The ideal tool offers a comprehensive suite for managing keywords, bids, ads, and performance, fitting seamlessly into your marketing stack. On the machine learning front, clarity in objectives paves the way for impactful integration. Whether aiming for higher CTRs or lower CPA, leveraging historical data and machine learning algorithms to predict and adjust is key. Constant experimentation and analysis refine strategies, ensuring SEM campaigns not only meet but exceed expectations. In the rapidly evolving world of SEM, AI and machine learning are not just options but necessities.

Strategies for successful implementation

Leveraging AI and machine learning can revolutionise your campaigns | News by Thaiger
This photo was generated using Dall-E

In the evolving landscape of search engine marketing (SEM), leveraging AI and machine learning can set a campaign apart, maximising efficiency and returns. Below are strategies detailing how to integrate these advanced technologies effectively.

Choosing the right AI tools for SEM

In the realm of SEM, it is critical to select AI tools that are congruent with your marketing objectives. The market is replete with a myriad of options, each purporting to transform your SEM strategies radically. Nonetheless, not every tool offers equal value. It is advisable to opt for tools that provide an extensive analysis of keywords, insights into competitors, and capabilities for automated bid management. These functionalities ensure that your campaigns are both precisely targeted and economically efficient. Furthermore, the implementation of AI-driven tools for content optimisation can notably increase ad relevance, thereby enhancing click-through rates (CTR) and reducing cost per acquisition (CPA).

Conducting trials with various tools before finalizing a decision is imperative to identify a solution that is specifically catered to your requirements. Platforms offering advanced analytics should be given priority as they afford actionable insights critical for ongoing refinement. It is important to recognize that the effective use of AI in SEM transcends merely selecting cutting-edge technology; it encompasses the strategic application of these tools to continually refine and advance marketing strategies over time.

Integrating machine learning algorithms into SEM practices

Machine learning algorithms come in as a cornerstone in the advancement of search engine marketing (SEM) strategies. With this, businesses can gain insights into consumer behaviour and preferences and to capitalise on this, it will be important to integrate it.

Machine learning algorithms constitute a cornerstone in the advancement of Search Engine Marketing (SEM) strategies, offering unprecedented insights into consumer behaviour and preferences. To capitalize on this opportunity, it is essential to integrate machine learning SEM technologies, emphasizing predictive analytics. Such an approach enables a deeper understanding of the interactions between different demographics and your advertisements, thereby improving audience segmentation.

Moreover, machine learning capabilities enable the automation of the most labour-intensive tasks within SEM, including bid management and A/B testing. This automation not only conserves precious time but also markedly elevates the efficiency of marketing campaigns. By adapting SEM practices to incorporate these algorithms, advertisements are perpetually optimised for performance, obviating the need for continuous manual intervention.

The fusion of machine learning’s predictive analytics with AI-enabled creative optimisation represents a pivotal evolution in Search Engine Marketing (SEM) strategies. This integrative approach allows for the real-time modification of advertisement components, including imagery and text, to better match user intentions, thereby markedly enhancing campaign outcomes.

Employing machine learning and AI within SEM goes beyond simply embracing cutting-edge technology; it denotes an ongoing dedication to a cycle of testing, education, and improvement. This dedication positions marketing endeavours at the vanguard of innovation during a period marked by rapid digital change.

Measuring success and ROI

Leveraging AI and machine learning can revolutionise your campaigns | News by Thaiger
Photo by krakenimages on Unsplash

Utilising metrics and KPIs to evaluate AI and machine learning impact

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into Search Engine Marketing (SEM) strategies has profoundly altered the approaches utilized by digital marketing experts.

  • For an accurate assessment of the effectiveness of these advanced SEM technologies, focusing on relevant metrics and Key Performance Indicators (KPIs) is essential.
  • These criteria provide a transparent evaluation of the performance enhancements brought about by AI and ML.
  • They enable organizations to measure success and calculate Return on Investment (ROI) with greater accuracy.

Primarily, conversion rates emerge as a crucial metric. They serve as direct indicators of the efficiency of AI-enhanced ad targeting and bid management strategies, reflecting whether such technological advancements result in an increased proportion of visitors performing desired actions, such as completing purchases or registering for newsletters.

Cost per Acquisition (CPA) represents another fundamental metric. It illustrates the effectiveness with which AI and ML tools manage advertising expenditures to secure new clientele. Reduced CPA values indicate that these advanced SEM technologies are not only pinpointing the appropriate audience but also achieving this in a financially prudent manner.

Click-through rates (CTR) hold significant importance as well. An elevated CTR signifies that the predictive analytics and automated content optimisation facilitated by AI are effectively engaging the target demographic, thereby increasing their propensity to interact with advertisements.

Moreover, Return on Ad Spend (ROAS) is an essential measure of overall operational efficacy. It quantifies the revenue generated for every unit of currency expended on SEM initiatives. An enhancement in ROAS denotes that integrating AI and ML into SEM strategies is yielding more lucrative campaigns.

Through meticulous observation of these metrics, organizations can comprehensively assess the impact of Artificial Intelligence (AI) and Machine Learning (ML) on their Search Engine Marketing (SEM) strategies. This analysis highlights not only the achievement of set goals but also identifies potential areas for enhancement. As AI and ML evolve, securing a competitive advantage in SEM requires ongoing vigilance and an adaptable methodology informed by data-driven insights.

Utilising machine learning and AI is pretty important in the pursuit of finding success in digital marketing. However, SEM is just one aspect of marketing that stands shoulder to shoulder with methods like SEO. Knowing the difference between these two will help determine which one to use or utilise together to have a more prosperous digital marketing campaign.

Feature Image Credit: This photo was generated using Dall-E

By Alessio Francesco Fedeli

Graduating from Webster University with a degree of Management with an emphasis on International Business, Alessio is a Thai-Italian with a multicultural perspective regarding Thailand and abroad. On the same token, as a passionate person for sports and activities, Alessio also gives insight to various spots for a fun and healthy lifestyle.

Sourced from Thaiger


These chilling AI trends aren’t just making waves, they’re flipping the boat entirely.

Key Takeaways

Will consumers clone your services to save money? It’s already happening! In my new book, The Wolf is at The Door: How to Survive and Thrive in an AI-Driven World, and on this podcast episode, I peel back the curtains on AI developments to help prepare entrepreneurs for a future that is already here.

Welcome to a world where AI SEO hijackers plot to seize your web traffic and customers, unauthorized cloning becomes a chilling reality, and massive AI model failures lead to unexpected domino effects like lawsuits and more.

And, to celebrate the release of my brand new book, The Wolf is at The Door, I’m giving away a Free AI Success Kit, featuring a chapter from the book to help get you up to speed on the world of artificial intelligence fast.

If listening to this show lights up your day, please take a moment to rate and review the podcast! This is a great way to support my team’s mission of empowering more individuals like you to supercharge their lives and businesses. What’s more, don’t forget to follow the podcast if you haven’t already. Thanks!

About Beyond Unstoppable

Hosted by bestselling author Ben Angel, Beyond Unstoppable is a transformative exploration of biology, psychology and technology. Learn from world-renowned experts like Jim Kwik, Amy Porterfield, Mari Smith and Jason Feifer. Dive into advanced AI tools, biohacking, and strategies to make you unstoppable.

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Tackle AI’s toughest questions with Ben Angel, mapping the business terrain for 20 years. Master the AI landscape and reach peak productivity and profits with insights from his latest work, “The Wolf is at The Door — How to Survive and Thrive in an AI-Driven World.” Click here to download your ‘Free AI Success Kit‘ and get your free chapter from his latest book today.

Sourced from Entrepreneur


Social media’s unregulated evolution over the past decade holds a lot of lessons that apply directly to AI companies and technologies.

Oh, how the mighty have fallen. A decade ago, social media was celebrated for sparking democratic uprisings in the Arab world and beyond. Now front pages are splashed with stories of social platforms’ role in misinformation, business conspiracymalfeasance, and risks to mental health. In a 2022 survey, Americans blamed social media for the coarsening of our political discourse, the spread of misinformation, and the increase in partisan polarization.

Today, tech’s darling is artificial intelligence. Like social media, it has the potential to change the world in many ways, some favourable to democracy. But at the same time, it has the potential to do incredible damage to society.

There is a lot we can learn about social media’s unregulated evolution over the past decade that directly applies to AI companies and technologies. These lessons can help us avoid making the same mistakes with AI that we did with social media.

In particular, five fundamental attributes of social media have harmed society. AI also has those attributes. Note that they are not intrinsically evil. They are all double-edged swords, with the potential to do either good or ill. The danger comes from who wields the sword, and in what direction it is swung. This has been true for social media, and it will similarly hold true for AI. In both cases, the solution lies in limits on the technology’s use.

#1: Advertising

The role advertising plays in the internet arose more by accident than anything else. When commercialization first came to the internet, there was no easy way for users to make micropayments to do things like viewing a web page. Moreover, users were accustomed to free access and wouldn’t accept subscription models for services. Advertising was the obvious business model, if never the best one. And it’s the model that social media also relies on, which leads it to prioritize engagement over anything else.

Both Google and Facebook believe that AI will help them keep their stranglehold on an 11-figure online ad market (yep, 11 figures), and the tech giants that are traditionally less dependent on advertising, like Microsoft and Amazon, believe that AI will help them seize a bigger piece of that market.

Big Tech needs something to persuade advertisers to keep spending on their platforms. Despite bombastic claims about the effectiveness of targeted marketing, researchers have long struggled to demonstrate where and when online ads really have an impact. When major brands like Uber and Procter & Gamble recently slashed their digital ad spending by the hundreds of millions, they proclaimed that it made no dent at all in their sales.

AI-powered ads, industry leaders say, will be much better. Google assures you that AI can tweak your ad copy in response to what users search for, and that its AI algorithms will configure your campaigns to maximize success. Amazon wants you to use its image generation AI to make your toaster product pages look cooler. And IBM is confident its Watson AI will make your ads better.

These techniques border on the manipulative, but the biggest risk to users comes from advertising within AI chatbots. Just as Google and Meta embed ads in your search results and feeds, AI companies will be pressured to embed ads in conversations. And because those conversations will be relational and human-like, they could be more damaging. While many of us have gotten pretty good at scrolling past the ads in Amazon and Google results pages, it will be much harder to determine whether an AI chatbot is mentioning a product because it’s a good answer to your question or because the AI developer got a kickback from the manufacturer.

#2: Surveillance

Social media’s reliance on advertising as the primary way to monetize websites led to personalization, which led to ever-increasing surveillance. To convince advertisers that social platforms can tweak ads to be maximally appealing to individual people, the platforms must demonstrate that they can collect as much information about those people as possible.

It’s hard to exaggerate how much spying is going on. A recent analysis by Consumer Reports about Facebook—just Facebook—showed that every user has more than 2,200 different companies spying on their web activities on its behalf.

AI-powered platforms that are supported by advertisers will face all the same perverse and powerful market incentives that social platforms do. It’s easy to imagine that a chatbot operator could charge a premium if it were able to claim that its chatbot could target users on the basis of their location, preference data, or past chat history and persuade them to buy products.

The possibility of manipulation is only going to get greater as we rely on AI for personal services. One of the promises of generative AI is the prospect of creating a personal digital assistant advanced enough to act as your advocate with others and as a butler to you. This requires more intimacy than you have with your search engine, email provider, cloud storage system, or phone. You’re going to want it with you constantly, and to most effectively work on your behalf, it will need to know everything about you. It will act as a friend, and you are likely to treat it as such, mistakenly trusting its discretion.

Even if you choose not to willingly acquaint an AI assistant with your lifestyle and preferences, AI technology may make it easier for companies to learn about you. Early demonstrations illustrate how chatbots can be used to surreptitiously extract personal data by asking you mundane questions. And with chatbots increasingly being integrated with everything from customer service systems to basic search interfaces on websites, exposure to this kind of inferential data harvesting may become unavoidable.

#3: Virality

Social media allows any user to express any idea with the potential for instantaneous global reach. A great public speaker standing on a soapbox can spread ideas to maybe a few hundred people on a good night. A kid with the right amount of snark on Facebook can reach a few hundred million people within a few minutes.

A decade ago, technologists hoped this sort of virality would bring people together and guarantee access to suppressed truths. But as a structural matter, it is in a social network’s interest to show you the things you are most likely to click on and share, and the things that will keep you on the platform.

As it happens, this often means outrageous, lurid, and triggering content. Researchers have found that content expressing maximal animosity toward political opponents gets the most engagement on Facebook and Twitter. And this incentive for outrage drives and rewards misinformation.

As Jonathan Swift once wrote, “Falsehood flies, and the Truth comes limping after it.” Academics seem to have proved this in the case of social media; people are more likely to share false information—perhaps because it seems more novel and surprising. And unfortunately, this kind of viral misinformation has been pervasive.

AI has the potential to supercharge the problem because it makes content production and propagation easier, faster, and more automatic. Generative AI tools can fabricate unending numbers of falsehoods about any individual or theme, some of which go viral. And those lies could be propelled by social accounts controlled by AI bots, which can share and launder the original misinformation at any scale.

Remarkably powerful AI text generators and autonomous agents are already starting to make their presence felt in social media. In July, researchers at Indiana University revealed a botnet of more than 1,100 Twitter accounts that appeared to be operated using ChatGPT.

AI will help reinforce viral content that emerges from social media. It will be able to create websites and web content, user reviews, and smartphone apps. It will be able to simulate thousands, or even millions, of fake personas to give the mistaken impression that an idea, or a political position, or use of a product, is more common than it really is. What we might perceive to be vibrant political debate could be bots talking to bots. And these capabilities won’t be available just to those with money and power; the AI tools necessary for all of this will be easily available to us all.

#4: Lock-in

Social media companies spend a lot of effort making it hard for you to leave their platforms. It’s not just that you’ll miss out on conversations with your friends. They make it hard for you to take your saved data—connections, posts, photos—and port it to another platform. Every moment you invest in sharing a memory, reaching out to an acquaintance, or curating your follows on a social platform adds a brick to the wall you’d have to climb over to go to another platform.

This concept of lock-in isn’t unique to social media. Microsoft cultivated proprietary document formats for years to keep you using its flagship Office product. Your music service or e-book reader makes it hard for you to take the content you purchased to a rival service or reader. And if you switch from an iPhone to an Android device, your friends might mock you for sending text messages in green bubbles. But social media takes this to a new level. No matter how bad it is, it’s very hard to leave Facebook if all your friends are there. Coordinating everyone to leave for a new platform is impossibly hard, so no one does.

Similarly, companies creating AI-powered personal digital assistants will make it hard for users to transfer that personalization to another AI. If AI personal assistants succeed in becoming massively useful time-savers, it will be because they know the ins and outs of your life as well as a good human assistant; would you want to give that up to make a fresh start on another company’s service? In extreme examples, some people have formed close, perhaps even familial, bonds with AI chatbots. If you think of your AI as a friend or therapist, that can be a powerful form of lock-in.

Lock-in is an important concern because it results in products and services that are less responsive to customer demand. The harder it is for you to switch to a competitor, the more poorly a company can treat you. Absent any way to force interoperability, AI companies have less incentive to innovate in features or compete on price, and fewer qualms about engaging in surveillance or other bad behaviours.

#5: Monopolization

Social platforms often start off as great products, truly useful and revelatory for their consumers, before they eventually start monetizing and exploiting those users for the benefit of their business customers. Then the platforms claw back the value for themselves, turning their products into truly miserable experiences for everyone. This is a cycle that Cory Doctorow has powerfully written about and traced through the history of Facebook, Twitter, and more recently TikTok.

The reason for these outcomes is structural. The network effects of tech platforms push a few firms to become dominant, and lock-in ensures their continued dominance. The incentives in the tech sector are so spectacularly, blindingly powerful that they have enabled six megacorporation’s (Amazon, Apple, Google, Facebook parent Meta, Microsoft, and Nvidia) to command a trillion dollars each of market value—or more. These firms use their wealth to block any meaningful legislation that would curtail their power. And they sometimes collude with each other to grow yet fatter.

This cycle is clearly starting to repeat itself in AI. Look no further than the industry poster child OpenAI, whose leading offering, ChatGPT, continues to set marks for uptake and usage. Within a year of the product’s launch, OpenAI’s valuation had skyrocketed to about $90 billion.

OpenAI once seemed like an “open” alternative to the megacorps—a common carrier for AI services with a socially oriented nonprofit mission. But the Sam Altman firing-and-rehiring debacle at the end of 2023, and Microsoft’s central role in restoring Altman to the CEO seat, simply illustrated how venture funding from the familiar ranks of the tech elite pervades and controls corporate AI. In January 2024, OpenAI took a big step toward monetization of this user base by introducing its GPT Store, wherein one OpenAI customer can charge another for the use of its custom versions of OpenAI software; OpenAI, of course, collects revenue from both parties. This sets in motion the very cycle Doctorow warns about.

In the middle of this spiral of exploitation, little or no regard is paid to externalities visited upon the greater public—people who aren’t even using the platforms. Even after society has wrestled with their ill effects for years, the monopolistic social networks have virtually no incentive to control their products’ environmental impact, tendency to spread misinformation, or pernicious effects on mental health. And the government has applied virtually no regulation toward those ends.

Likewise, few or no guardrails are in place to limit the potential negative impact of AI. Facial recognition software that amounts to racial profiling, simulated public opinions supercharged by chatbots, fake videos in political ads—all of it persists in a legal grey area. Even clear violators of campaign advertising law might, some think, be let off the hook if they simply do it with AI.

Mitigating the risks

The risks that AI poses to society are strikingly familiar, but there is one big difference: it’s not too late. This time, we know it’s all coming. Fresh off our experience with the harms wrought by social media, we have all the warning we should need to avoid the same mistakes.

The biggest mistake we made with social media was leaving it as an unregulated space. Even now—after all the studies and revelations of social media’s negative effects on kids and mental health, after Cambridge Analytica, after the exposure of Russian intervention in our politics, after everything else—social media in the US remains largely an unregulated “weapon of mass destruction.” Congress will take millions of dollars in contributions from Big Tech, and legislators will even invest millions of their own dollars with those firms, but passing laws that limit or penalize their behaviour seems to be a bridge too far.

We can’t afford to do the same thing with AI, because the stakes are even higher. The harm social media can do stems from how it affects our communication. AI will affect us in the same ways and many more besides. If Big Tech’s trajectory is any signal, AI tools will increasingly be involved in how we learn and how we express our thoughts. But these tools will also influence how we schedule our daily activities, how we design products, how we write laws, and even how we diagnose diseases. The expansive role of these technologies in our daily lives gives for-profit corporations opportunities to exert control over more aspects of society, and that exposes us to the risks arising from their incentives and decisions.

The good news is that we have a whole category of tools to modulate the risk that corporate actions pose for our lives, starting with regulation. Regulations can come in the form of restrictions on activity, such as limitations on what kinds of businesses and products are allowed to incorporate AI tools. They can come in the form of transparency rules, requiring disclosure of what data sets are used to train AI models or what new preproduction-phase models are being trained. And they can come in the form of oversight and accountability requirements, allowing for civil penalties in cases where companies disregard the rules.

The single biggest point of leverage governments have when it comes to tech companies is antitrust law. Despite what many lobbyists want you to think, one of the primary roles of regulation is to preserve competition—not to make life harder for businesses. It is not inevitable for OpenAI to become another Meta, an 800-pound gorilla whose user base and reach are several times those of its competitors. In addition to strengthening and enforcing antitrust law, we can introduce regulation that supports competition-enabling standards specific to the technology sector, such as data portability and device interoperability. This is another core strategy for resisting monopoly and corporate control.

Additionally, governments can enforce existing regulations on advertising. Just as the US regulates what media can and cannot host advertisements for sensitive products like cigarettes, and just as many other jurisdictions exercise strict control over the time and manner of politically sensitive advertising, so too could the US limit the engagement between AI providers and advertisers.

Lastly, we should recognize that developing and providing AI tools does not have to be the sovereign domain of corporations. We, the people and our government, can do this too. The proliferation of open-source AI development in 2023, successful to an extent that startled corporate players, is proof of this. And we can go further, calling on our government to build public-option AI tools developed with political oversight and accountability under our democratic system, where the dictatorship of the profit motive does not apply.

Which of these solutions is most practical, most important, or most urgently needed is up for debate. We should have a vibrant societal dialogue about whether and how to use each of these tools. There are lots of paths to a good outcome.

The problem is that this isn’t happening now, particularly in the US. And with a looming presidential election, conflict spreading alarmingly across Asia and Europe, and a global climate crisis, it’s easy to imagine that we won’t get our arms around AI any faster than we have (not) with social media. But it’s not too late. These are still the early years for practical consumer AI applications. We must and can do better.



Nathan E. Sanders is a data scientist and an affiliate with the Berkman Klein Center at Harvard University. Bruce Schneier is a security technologist and a fellow and lecturer at the Harvard Kennedy School.

Sourced from MIT Technology Review



& archive page

By Rob Davinson 

In 2024, affiliate marketing will see brand-creator alliances rise, TikTok vs. Amazon competition, programmatic opportunities, and more, says Awin’s global head of content, Rob Davinson.

Affiliate marketing mirrors the broader digital landscape, with trends at the macro level resonating in our microcosm. In 2024, we’ll see emergent trends (artificial intelligence (AI), social commerce and retail media to name just a few) that will impact affiliate marketers.

Here we breakdown the key changes (and challenges) that affiliate marketing is likely to encounter this year, and what they mean for the industry.

1. Brand-creator affiliation will rise amidst social media slowdown

With global digital ad spend growth slowing (Dentsu predicts only 6.5% growth in 2024, after a historically low-growth year in 2023), and social media facing a similar slowdown as new user growth plateaus, brands can combat this by directly partnering with creators, as influencer marketing proves more resilient than paid social.

Major brands like The Body Shop and Walmart are two examples that launched large-scale creator affiliate programs in the last year, tying social awareness to controlled marketing outcomes. We see this trend further developing in 2024, as it not only counters platform-dependent risks, but benefits influencers seeking stable incomes,

Awin’s platform witnessed a surge of registering influencers in 2023 (over 10,000), foreshadowing continued growth in 2024.

2. TikTok vs. Amazon: Affiliate model’s value amid new competition

As major tech giants mature, Amazon transitions from a shopping marketplace to an ad space, while TikTok evolves from entertainment to a product purchasing platform. This encroachment on each other’s territory is likely to intensify competition, with TikTok employing an affiliate-type model, mirroring Amazon’s commerce flywheel.

Both platforms embracing affiliate strategies validates its efficacy. Brands may channel more ad budgets into these tech giants, necessitating a choice between entering new marketplaces or driving traffic to their e-commerce sites.

Opting for the latter requires enhancing the shopper experience, supported by affiliate tech partners, as exemplified by Nike’s livestream shopping collaboration with Contester, enhancing the Cyber period with engaging content on their site.

3. Programmatic challenges will propel affiliate ad spend growth

In 2023, the programmatic ad industry faced serious challenges, as reported in the ANA’s Programmatic Media Supply Chain Transparency Study. Among its findings was the fact that there is $22bn of wastage from the $88bn programmatic supply chain.

Advertisers often grapple with misaligned incentives, prioritizing cost over value, resulting in diminished ad quality. In contrast, affiliate marketing’s performance model, linking ad spend to tangible outcomes like sales, proves more valuable.

It says a lot that global spend in affiliate marketing last year is estimated to be around $14bn, a third less than was wasted in programmatic. As senior marketers consider their budgets this year, the data suggests affiliate marketing should garner greater consideration for its effectiveness.

4. News and media publishers will leverage affiliate commerce content

In 2024, with a record number of global elections, including the US presidential election and 40 national elections, political interest will drive traffic to news media sites.

Despite heightened ad spend forecasts, news publishers may not see increased income due to past challenges with programmatic display ads. Affiliate channels offer a solution for publishers facing declining ad monetization and brand block listing.

Additionally, major sporting events like the European Football Championships and the Olympic Games in Paris promise increased traffic, creating opportunities for affiliate efforts to offset ad revenue challenges and enhance the value of journalism amid growing demand.

5. AI revolution in search will pose a threat to affiliate longtail

When it comes to online, the significance of high Google search rankings has been paramount. As the old adage (meme caption) goes: “The best place to hide a dead body is page 2 of Google’s search results.”

Google’s search console, shaping our online information-seeking behaviour for two decades, faces challenges from Google’s monetization motives and emerging AI-powered search consoles, like ChatGPT. These AI consoles provide instant answers, diminishing the reliance on external links and altering the traditional internet ecosystem.

Google’s Search Generative Experience (SGE) introduces AI-generated responses, potentially reducing organic traffic to publisher websites. Publishers face limited options – allow crawling for SGE or risk exclusion from Google search. SEO adherence to E-E-A-T values becomes crucial for publishers navigating this transformative shift, emphasizing the affiliate industry’s need to adapt and maintain audience-centric effectiveness.

6. Travel resurgence will inspire pop culture-inspired trips and affiliate growth

While some predicted its near-extinction after the 2019 lockdown, the travel industry is booming as we begin 2024.

IATA predicts that this year will exceed 2019’s travel record, with 4.7 billion people expected to board airlines in 2024. Awin observes a surge in affiliate-driven travel bookings, a trend set to continue as consumer confidence rises, airline capacity grows, and major events drive demand.

Expedia and Amadeus foresee a significant year for experience-based tourism (think set-jetting and music festivals). Affiliates play a crucial role in the complex shopper journey, offering inspiration, comparisons, and personalized options.

Brand partnerships, where one advertiser promotes another complementary one as part of the customer’ booking experience, thrived in 2023. Travel brands are well set to capitalise on this growth with lots of potential match-ups from other brands keen to tap into consumers’ resurgent appetite for travel.

7. As cheap fashion challenges sustainability efforts, green affiliates will emerge

Despite Cop28’s pivotal agreement to shift from fossil fuels, inertia persists around climate change. In 2024, the rise of ultra-fast fashion platforms like Shein and Temu, fuelled by the TikTok trend of buying cheap dupes, contributes to growing landfill fashion.

Even impacting Amazon, Teemu users spend nearly double the time compared to Amazon, prompting the e-commerce giant to lower fees for clothes under $20. However, some affiliates continue to promote mindful consumer choices innovatively. Examples include Refoorest, planting trees for site visits, and Axon Mobile incentivizing eco-friendly commuting. And another new promising solution for 2024 is spearheaded by Birl, who are introducing the circular economy to e-commerce through their smart resale system.

By Rob Davinson 

Sourced from The Drum


Realistic-looking shampoo bottles and seltzer cans are popping up on videos from digital creators on TikTok and YouTube in a new form of old advertising.

Melissa Becraft, wearing a pink workout outfit, strikes a dance pose in a kitchen.
A screenshot of a recent TikTok from the dancer Melissa Becraft that used A.I to digitally superimpose a poster for Bubly, the sparkling water brand owned by PepsiCo, onto the wall of her apartment.Credit…via TikTok

Product placement, one of the oldest tricks in advertisers’ toolbox, is getting an A.I. makeover.

New technology has made it easier to insert digital, realistic-looking versions of soda cans and shampoo onto the tables and walls of videos on YouTube and TikTok. And a growing group of creators and advertisers is grabbing at the chance for an additional revenue stream.

A recent TikTok from the dancer Melissa Becraft featured a poster for Bubly, the sparkling-water brand owned by PepsiCo, hanging on the wall of her apartment as she shimmied to a Shakira song. A duo known as HiveMind chatted about bands while an animated can of Starry soda, another brand owned by PepsiCo, landed on a table between them. And a YouTube video of the “AsianBossGirl” podcast recently displayed a table of Garnier hair products.

Virtual product placements have been offered by start-ups and streaming services like Amazon Prime and NBC’s Peacock in recent years. But a recent wave of them on social media, in which brief, animated messages disclosing the sponsorships appear on the videos themselves, is the work of a start-up called Rembrand.

The ads provide a glimpse into one way A.I. might shape advertising in the future, especially as marketers look to reach younger viewers who are apt to skip or ignore standard ads.

Rembrand’s executives say their technology could transform product placements, which have often been used to cut production costs on bigger projects and can take weeks, months or sometimes years to negotiate.

For creators, it’s a way to make money from advertisers without physically handling products or discussing them.

“This feels like I’m making my own genuine content, but it doesn’t scream that I’m making an ad,” said Ms. Becraft, 28, who has made two TikTok videos that featured Bubly. “There’s no obligation for me to talk about it.”

Product placements in the United States are estimated to be a nearly $23 billion industry, according to PQ Media, a research firm. It has become increasingly appealing to advertisers, which have grown worried about consumers skipping commercials or the ads before YouTube videos.

The shifting viewership to social platforms and advances in technology have opened a new frontier for this work, moving it beyond getting Coca-Cola cups on the “American Idol” judges’ table or cereal brands into WB shows.

Our business reporters. Times journalists are not allowed to have any direct financial stake in companies they cover.

Rembrand, which has 42 employees and is based in Palo Alto, Calif., believes it’s on the forefront of these changes. It raised $14 million in seed funding from the likes of Greycroft and the venture arms of UTA Ventures and L’Oreal since it was created in 2022. One of its founders, Omar Tawakol, 55, spent years in programmatic advertising and is best known for founding and selling BlueKai — which helped marketers track users’ online behaviour for ad targeting — to Oracle in 2014.

Mr. Tawakol said he saw an opportunity to use A.I. to insert virtual products in influencer videos and make it a fast and easy ad buy.

Rembrand uses a form of generative A.I. that can “take an existing scene and figure out how to put a product in it,” Mr. Tawakol said. “The product has to look exactly right — Pepsi is not going to be forgiving if you screw with their logo,” he added.

The company “had to train the laws of physics into the network,” Mr. Tawakol said, so that objects would properly respond to things like light, camera distance and motion. Rembrand started placements with podcasts on YouTube because “they tended to be indoors, they tended to have fixed cameras, and they tended to have a table and a wall,” he said.

It then expanded to LinkedIn and TikTok; Instagram is next. (The company said it went with the name Rembrand — an allusion to the Dutch artist, who spelled it Rembrandt — because it wanted an artistic bent while also sounding like shorthand for “remember the brand.”)

Rembrand is still asking creators like Ms. Becraft to film indoors as they improve the technology. “The things I’m more famous for are dancing outside in the rain and dancing in Times Square,” she said. “They told me that if you do that our technology might have a heart attack.”

The placements are not as subtle as the ones in TV shows. Starry and Bubly cans wiggle before entering videos, and logos hover above them. The company shared a demo in which a digitized Tide Pen danced onto a podcast host’s shirt and wiped away a stain before vanishing, “Fantasia” style. The company experimented with “what animations were acceptable,” after realizing they could spike attention on the products, said Cory Treffiletti, 50, Rembrand’s chief marketing officer.


Madison Luscombe, chief marketing officer of the Creator Society, an agency that works with Ms. Becraft, said that while the use of A.I.-generated product placement was in its early days, the deals could be valuable for “entertainment creators” who are focused on performing, podcasting or playing games, and aren’t necessarily approached by brands as often to extol mascara or new snacks to their fans.

Advertisers use Rembrand’s marketplace to connect with more than 1,000 creators from agencies it works with. Creators upload their videos to its platform and receive them within 24 hours with the product placements. Rembrand has someone check for quality and someone else for how the brand appears. Then creators upload the clips and eventually get paid from the brands based on video views. Rembrand declined to share specific figures around payouts.

The company said it expected to turn into a “self-service platform” by the middle of this year, where any creator or brand could connect and run digital product placement campaigns without Rembrand’s involvement.

When asked why YouTube, TikTok and Instagram wouldn’t just offer this option directly to creators on their platforms, Mr. Tawakol said he would “love” if they wanted to work with him. “I designed my business to integrate it with platforms,” he said. “We want to be the world’s best at this one very specific problem.”

Sourced from The New York Times


By Webb Wright 

If you’re considering launching a new AI-centered brand or product, you may want to go beyond simply adding ‘AI’ to the end of the name.

The AI Gold Rush is in full swing and brands of all stripes are rushing to establish their particular niches in this hugely profitable and increasingly crowded industry. New AI-centered brands, departments and products are cropping up by the day, each requiring a name that is, ideally, both memorable and unique.

“Every single company, whether a candy bar manufacturer or a software company, seemingly has to show that it is doing something to leverage AI,” says Jonathan Bell, founder and CEO of Want Branding. “And that often requires some kind of adjacent brand, which, of course, then needs a name.”

Several brands, as you may have noticed, have simply taken to adding ‘AI’ (or ‘.AI’) to the ends of their names. Think Stability AI, Spot AI, Mistral AI, Shield AI, People.ai, Otter.ai, Arize AI, Crowd AI, Toggle AI and so on. And, of course, there’s OpenAI, the company that has become something of a flagship for the entire wave of AI innovation that’s currently underway following its hugely successful launch of ChatGPT in late 2022 and that has probably helped to establish the ‘AI‘ suffix as the name du jour for up-and-coming brands looking to make a name for themselves in the industry.

Adding ‘AI‘ to the end of a brand or product name “is an easy but often perhaps a cheap way of doing it without much thought,” says Bell.

A parallel can be drawn between this naming phenomenon and a similar one that followed in the wake of the dawn of the internet in the late 90s when scores of new brands with ‘.com‘ at the ends of their names began to emerge. In those early days of the world wide web, it made practical sense for companies to make unambiguously clear that they were technologically savvy enough to have an online presence. (Remember, this was back when ‘online‘ was itself a new, hip word.)

Over the slow process of many years, however, the internet became so deeply embedded into most of our day-to-day lives, into the very fabric of popular culture and commerce, that it became more or less superfluous to add ‘.com‘ to the end of a brand name. Most people these days automatically assume that any given brand – unless it‘s incompetent beyond belief or run by a group of Luddites – has a website and probably some degree of social media presence.

The ‘.com‘ naming trend, in other words, began as a worthwhile marketing tactic, but “at a certain point that was eroded and it became meaningless,” says David Placek, founder and CEO of Lexicon Branding. There are still, of course, some brands (Hotels.com, for example) that have chosen to use their domain names as their official names, but such a strategy is far less common today than it was when the internet had the shiny-new-toy factor.

AI could follow a similar trajectory of cultural adoption as that of the internet: today, it’s all anyone can talk about; tomorrow, it’s basically taken for granted. Just as people today assume that brands today have an online presence – even when they don’t have ‘.com‘ in their names – we could soon reach a point as a society in which AI is so ubiquitous, so deeply integrated into our devices and our modes of working and communicating with one another, that adding ‘AI‘ to a brand or product name becomes passé. Placek says he’s “absolutely positive” that we’ll cross that threshold sometime within the next two years, after which point “everybody will assume that there’s something AI-related” built into most brands and products.

Given that forecast, adding ‘AI‘ to the end of a name “can be a disservice for building brand strength over time, because [the market] becomes crowded,” says marketing agency Tenet Partners CEO Hampton Bridwell. “There are a lot of names with a similar sound or styling and that creates a situation where you don’t have differentiation or memorability within the name.”

Anthropomorphic names and the sad tale of Clippy

There have, of course, been other naming trends that have recently emerged around AI. For example, many AI-centered products have been given human-sounding names, apparently in an effort to make the underlying technology – which could potentially come across as a bit threatening to a culture that’s been weaned on films like 2001: A Space Odyssey and The Matrix – feel a bit less alien and intimidating.

Consider IBM’s Watson, an AI model originally designed to answer questions that gained global fame when it won Jeopardy! in 2011. There are also more recent examples, including Siri (Apple), Alexa (Amazon) and Einstein (Salesforce).

As the journalist Charles Duhigg points out in a recent article in The New Yorker, Microsoft (which became a leader in the burgeoning AI industry following its recent multi-billion-dollar investments in OpenAI) has had to learn the hard way about the risks involved with trying to anthropomorphize AI. In 1996, the company introduced Clippy, a smiling virtual assistant with big eyes and a paperclip for a body, who could answer simple user questions on Microsoft Office platforms. The character became widely loathed by users. The Smithsonian called Clippy “one of the worst software design blunders in the annals of computing,” as Duhigg quotes in his article. Microsoft killed Clippy off in 2001.

The company once again tried its hand at anthropomorphizing algorithms in 2016 with the launch of Tay, an AI-powered chatbot whose conversational style reflected that of a typical teenage girl. Tay rather quickly descended into a fit of hate speech and was deactivated less than 24 hours after its launch.

Apparently wiser after the Clippy and Tay debacles, Microsoft is now naming its AI products in a manner that suggests utility and even a touch of fallibility. Copilot, the name of the company’s recently launched suite of AI-powered productivity tools, insinuates something that can be reasonably relied upon to provide a measure of assistance, not something into which one should invest one’s whole trust.

The curious case of ChatGPT

Perhaps the biggest irony in the realm of AI names is the fact that ChatGPT, the product that, more than any other, catalyzed the burgeoning AI Revolution, has such a widely disliked name.

For one thing, says Bridwell, the word ‘chat‘ in a brand name “is pretty limiting – it really doesn’t embody what the whole thing is about in terms of [how] it delivers value. It’s a terrible name. Over time, [OpenAI] should really think about rebranding it.”

Even OpenAI CEO Sam Altman agrees that it’s not an ideal name. During a recent podcast hosted by comedian Trevor Noah, Altman said that ChatGPT is “a horrible name, but it may be too ubiquitous to ever change.”

ChatGPT’s suboptimal name could stem in part from the fact that the OpenAI team that built it did not initially have high hopes for its prospects as an uber popular app. It was referred to internally as a “low-key research preview” in the period leading up to its launch and it was intended as a means through which the public could begin to interact with OpenAI’s GPT large language model more broadly so that the company could then collect feedback and fine-tune the technology accordingly.

Many within the OpenAI team were surprised when ChatGPT attracted its first million users in just five days, becoming the fastest-growing app in history.

Advice for marketers

According to Want Branding’s Jonathan Bell, brands that are looking to promote their use of AI through an optimized name should take their time. “It needs to be well thought-out,” he says. “It shouldn’t be something that’s done casually over a quick meeting, where you just simply add ‘AI’ to [the name]. Companies need to think about: What are they specifically doing? Can they deploy AI in a way that is really effective, or is this something that’s been done that could come across as bandwagon-jumping?”

Placek, who’s prone to referencing cognitive science and linguistics when discussing the psychology of brand- and product-naming, highlights the importance of sound symbolism – that is, the associations between particular sounds and the concepts that they evoke in the mind of the hearer. “You don’t want something too soft and you don’t want something too clever,” he says. “[You want something that’s] a little bit on the more serious side that [suggests] intelligence … sound symbolism should play a role in selecting and developing your names.”

When prompted to describe the qualities of a great name for an AI brand or product in fewer than 10 words, ChatGPT wrote: “Memorable, clear, unique, relevant, easy to pronounce, globally appealing, scalable.”

Feature Image Credit: Adobe Stock

By Webb Wright 

Sourced from The Drum

By Alon Goren

At this point, most enterprises are dabbling in generative AI or planning to leverage the technology soon.

According to an October 2023 Gartner, Inc. survey, 45% of organizations are currently piloting generative AI, while 10% have deployed it in full production. Companies are eager to move from pilot to production and start seeing some real business results.

However, enterprises getting started with generative AI often run into a common stumbling block right out of the gate: They suffer analysis paralysis before they can even begin using the technology. There are tons of generative AI tools available today, both broad and highly specialized. Moreover, these tools can be leveraged for all sorts of professions and business purposes—sales, product development, finance, etc.

With so many choices and possibilities, enterprises often get stuck in the planning phase—debating where they should deploy generative AI first. Every business unit (and all of the business’s key stakeholders) wants to own a part of the company’s generative AI initiatives.

Things can get messy. To stay on track, businesses should follow these guidelines when experimenting with generative AI.

Focus On Specific Use Cases With Measurable Goals

Enterprises need to recognize that every part of the organization can benefit from generative AI—eventually. To get there, however, they need to get off the ground with a pilot project.

How do you decide where to get started? Keep it simple and identify a small, specific problem that exists today that can be improved with generative AI. Be practical. Choose an issue that’s been challenging the business for a while, has been difficult to fix in the past and will make a visibly positive impact once resolved. Next, enterprises need to agree upon metrics and goals. The problem can’t be too nebulous or vague; the impact of AI (success or failure) has to be easily measurable.

With that in mind, the pilot project should have a contained scope. The purpose is to demonstrate the real-world value of the technology, build support for it across the organization and then broaden adoption from there.

If organizations try to leverage AI in too many different ways and solve multiple problems, it’ll cause the scope to grow out of control and make it impossible to complete the pilot within a reasonable timeframe. Ambition has to be balanced with practicality. Launching a massive pilot project that requires extensive resources and long timelines is a recipe for failure.

What’s a good timeline for the pilot? It depends on the circumstances, of course. Generally speaking, however, it should only take a few weeks or a couple of months to execute, not multiple quarters or an entire year.

Start small, get something functional quickly and then iterate on it. This iterative approach allows for continuous learning and improvement, which is essential given the nascent state of generative AI technology.

Organizations must also be sure to keep humans in the loop from the very beginning of the experimentation phase. The rise of AI doesn’t render human expertise obsolete; it amplifies it. As productivity and business benefits increase with generative AI, human employees become even more valuable as supervisors and validators of AI output. This is essential for maintaining control and building trust in AI. In addition, the pool of early participants will also help champion the technology throughout the organization once the enterprise is ready to deploy it widely.

Finally, once the project has begun, organizations have to stick with it until it’s complete. Don’t waste time starting over or shifting to other use cases prematurely. Just get going and stay the course. After that’s been completed successfully, companies can expand their use of generative AI more broadly across the organization.

Choosing The Right Technology

The other major component of the experimentation phase is selecting the right vendor. With the generative AI market booming, it can seem impossible to tell the differences between one solution and another. Lots of noisy marketing only makes things more confusing.

The best way to cut through the noise is to identify the requirements that are most important to the organization (e.g., data security, governance, scalability, compatibility with existing infrastructure) and look for the vendor that best meets those needs.

It’s extremely important to understand where vendors stand on each of these things early on to avoid the headache of discovering that they don’t really check those boxes later. The only way to do that is by talking to the vendor (especially its sales engineering team) and seeing these capabilities demoed first-hand.

Get Ahead Of The Competition With A Strong Start

Within the next couple of years, I expect almost every enterprise will employ generative AI in production. Those wielding it effectively will get a leg up on their competition, while those struggling will be at risk of falling behind. Though the road may be uncharted, enterprises can succeed by focusing on contained, valuable projects, leveraging human expertise and selecting strategic technology partners.

Don’t wait. Embrace this unique opportunity to innovate and take that crucial first step now.

Feature Image Credit: GETTY

By Alon Goren

Follow me on LinkedIn. Check out my website.

CEO and Cofounder of AnswerRocket. Read Alon Goren’s full executive profile here.

Sourced from Forbes