Eric Shamlin, our EVP, Strategic Partnerships, joined with industry veterans on the panel to highlight the exhilarating journey of experimenting with AR, VR, and AI over the past several years. His team's foray into emerging media has not only been fun but also incredibly fruitful, showcasing AI's potential to revolutionize creativity.

Together, the panel painted a picture of how AI has transitioned from a mere experimental tool to an integral part of the creative process: whether to speed up pre-production, ideate concepts, or create assets at scale, AI workflows have become indispensable for the enterprise. In fact, these technologies not only facilitate creativity but also democratize it, allowing more people to express themselves in ways previously unimaginable—while also giving creative teams the ability to reach far more diverse audiences with personalized, relevant content.

Still, the panelists agreed that the role of human touch remains key. They shared experiences where AI-generated assets underwent manual refinement to meet quality standards, highlighting that while AI can significantly enhance efficiency and creativity, it does not replace the nuanced skills of human designers and creators.

“For creators, AI gives them superpowers,” Shamlin explains. “More people can create, like my good friend who is not classically trained as a creative–he's an accountant–but he can now let his dreams run wild.”

Looking ahead, the conversation touched on potential design trends influenced by AI, such as mashup culture and a renaissance of creativity across various mediums. The panelists envisioned a future where AI empowers creators to explore new horizons without being constrained by traditional limitations.

For brand executives who are curious about incorporating AI into their operations, this discussion offers valuable insights. It’s clear that embracing AI can lead to remarkable efficiencies and unlock new creative possibilities. However, success in this evolving landscape requires a willingness to experiment, learn continuously, and adapt strategies in line with technological advancements.

As we stand on the brink of this new era in creativity powered by artificial intelligence, one message resonates strongly: the future belongs to those who embrace change enthusiastically and leverage these powerful tools to enhance their creative endeavors.

Those who engage with AI will be better equipped to deliver personalized, relevant and effective content that engages users and helps them stand out in an increasingly competitive digital landscape, and they may do so by investing in first-party data integration, testing AI-driven bidding and creative tools, experimenting with more visual content, and preparing for the eventuality that AI will change the search engine results page (SERP) ad landscape as we know it.

Late last year, ChatGPT took the world by storm, becoming the fastest product in history to accumulate one million users in just five days. This same technology went on to power the launch of Microsoft’s new Bing search experience. Since then, Bing launched an ads experience that surfaces ads and recommendations based on relevance to the conversation. This seems to be working, as the search engine's audience has grown significantly to over 100 million daily active users.

Unsurprisingly, Google is beginning a limited release of the Search Generative Experience, with ad formats that are highly focused on travel and shopping experiences. Meanwhile, the traditional Google SERP will bring in generative AI responses to further improve how we search for, engage and ingest information we seek. Search ads will continue to show in traditional ad slots, but there will be a totally different experience based on the conversation, rendering relevant links and ads.

Still, artificial intelligence isn’t new to search; in fact, it influences a variety of factors that influence results, from bidding to search query matching to creative optimization. But the introduction of large language models (LLMs) into the equation will significantly impact not only the user experience, but also how content is valued and ranked on the results page and how we buy media. They’ll also significantly change the way we create content. Here are three big ways the future of search will force brands to adapt—and what you need to do now to stay ahead of the curve.

AI-generated content will be a double-edged sword.

 Conversational search opens the possibility of delivering highly relevant, personalized responses to users on the fly. While the benefits to this are obvious, AI’s talent for spinning up content on its own presents a double-edged sword. Some verticals—like healthcare, pharma and finance—will struggle to keep up with the pace of automation given the various rounds of legal and regulatory approval required for their creative before it goes live.  

AI-generated content risks circumventing these hurdles. It’s also vulnerable to spreading misinformation. But brands can mitigate these concerns by ensuring human review before the approval and publication of ads. Through proper tuning and training of AI models, brands can quickly spin up content that incorporates regulatory guidelines that they are beholden to.

Search will be more engaging, visual, and interactive.

The future of search isn’t all text. Search is also skewing toward more visual and experiential content. Sure, image extensions make search more visually engaging to users. But also consider more sophisticated platforms like Google Lens or Snapchat Scan, which use computer vision to make a user’s surroundings searchable. AR is another format that will add a new dimension to search and is already offered by Google, allowing users to engage directly with virtual animals, objects and places in real time.

The idea is to build a more immersive experience versus the infinite scroll. Travel, retail and lifestyle brands may benefit most from this because they already have robust libraries of visual assets to draw from. Others, like B2B brands, healthcare, pharma, and finance, will need to catch up by building libraries of visual and experiential content that engage users to avoid stock images. At the recent Google Marketing Live, new products for asset creation using generative AI were announced, making it easier for those without libraries to build creative in Google’s advertising platform. Generative AI can certainly help brands develop assets at speed and scale, although it’s important to remember that they aren’t yet production ready on their own. There may also be open questions of legal ownership and intellectual property rights.

Data streams will continue making search more predictive and proactive.

Search is already steering in a direction where it can serve more personalized results based on previous activity or what the search engine already knows about you—for example, suggesting local restaurants when searching for food on Google, or recommending related products on an Amazon product page. These experiences generally help users find what they’re searching for faster and keep them coming back for future searches.

It’s not a stretch of the imagination, then, to envision a future in which search engines anticipate user needs before they are typed. They will go beyond keyword query and apply previous behaviors and contextual information—like the intent unlocked by a conversational interface—to generate entirely unique responses for each user. That sounds amazing, but the more conversational search improves, the better it will be at delivering answers that satisfy users’ queries without their having to click through to another website—reducing opportunities for ads in the traditional sense.

The data streams that enable this experience will play an outsized role in how search continues to evolve. Brands who have first-party data will have opportunities to use it to enable even greater predictive and personalized experiences. While we don’t know for sure how this space will evolve—concerns about privacy and transparency, especially globally, may interrupt progress here—it seems likely that search experiences will continue to evolve in this trend. The lesson is clear for brands: the accumulation of data assets and the ability to deploy AI will be differentiators as the SERP ad landscape changes.

Don’t wait to update your search strategy.

Unsurprisingly, a strong data foundation will be crucial to keeping ahead of these changes. Maintain a competitive edge by investing in first-party data integrated across touchpoints in the conversion cycle. Apply conversion modeling to help fuel more relevant ads and higher returns. These insights will prove critical as brands adapt to conversational search, providing them with the insights and tools they need to deliver more personalized, relevant and effective content.

Speaking of content, brands can also future proof by updating their approach to activation and creative. Test AI though bidding, ad creative and playing with broad matches. Experiment with tools like Google’s Performance Max—an AI feature deployed in the GMP suite that allows for cross channel campaign launches and optimizations all from a single campaign configuration—and automated asset generation.

Finally, break away from relying on text by testing more image extensions and invest in performance creative to help stand out. Leveraging AI to optimize and find the best creative combinations will help brands adopt a more asset-based approach and prepare for search’s increasingly experiential, visual and conversational interfaces.

All of these developments are happening right now, and brands will need to adapt through experimentation with emerging AI tools.

By doing so, they will be better equipped to deliver personalized, relevant, and effective content that engages users and helps them stand out in an increasingly competitive digital landscape. And lastly, AI is far from perfect, so check the sources and verify the generative responses.

To stay ahead of the curve in customer engagement, brands must move beyond delivering AI-generated content (AIGC) to delivering AI-powered consumer experiences (AICX). While AIGC levels up content by creating content at scale, AICX levels up the customer experience by creating personalized interactions. Applying AI to CX is not just about chatbots for customer service, it's about adopting a customer-centric approach across all functions, from product development to marketing, and across all touchpoints, from digital to retail, all enabled by AI. AI has the ability to create intimate and hyper-personalized one-to-one interactions across all touchpoints of the consumer decision journey, and will be a key factor in how brands are perceived, valued and ultimately loved by consumers. Here’s how, in three ways.

AICX levels up storytelling.

Applying AI to consumer experience means moving from advertising a brand world, to participating and engaging in a brand world. The most discerning audiences in the world are demanding nothing less than a seamless and immersive brand experience: 73% point to brand experience as an important factor in their purchasing decisions, right behind price and product quality. Here are the ways brands are already leveling up engagement via immersive brand experiences powered by AI, and developed by Media.Monks:

• Building smart and immersive flagship stores. Chinese EV brand JIDU launched the world's smartest showroom in Beijing, powered by Baidu AI technology. This immersive space offers a unique brand experience with life-size avatars and captures valuable user data.  
• Embedding consumers directly into any story. Intel’s AI-powered technology connects young people in China to their remarkable heritage by scanning and mapping their faces in real time onto historical figures in a series of animated films that were projected on the city walls of Xi’an.

AICX levels up personalization.

The benefits of personalized customer experiences—one-on-one interaction between a customer and a brand—helps to massively uplift sales and loyalty. Research suggests that personalized and tailored CX drives over 66% of customer loyalty—more than price and brand combined. Meanwhile, 78% of consumers are more likely to make repeat purchases and recommend companies that personalize their interactions. 

• Creating hyper-personalized celebrity content at scale. In collaboration with Spotify, we created an interactive listening experience with a deep fake version of The Weeknd giving users a personalized greeting using data from their Spotify accounts. We created an AI to synthesize the artist’s voice allowing for an interactive listening experience between the fan and the artist himself. Over 600K fans visited in the first few hours—and The Weeknd knew every one of them.
• Personifying an AI personal shopper. What if your customers could have a conversation with an AI personal shopper that could recommend style tips and products, creating cross-selling and repeat purchase opportunities for higher customer lifetime value? For a long time, brands have tried to do this with chatbots to handle questions or complaints in a customer service capacity further down the funnel, but now conversational UI creates a smoother experience and will be used earlier in the funnel for ecommerce exploration. 

AICX levels up co-creation.

Involving consumers in product development very early on in R&D is proven to drive product success, advocacy and conversion. Companies that involve customers in the product development process see numerous benefits, including a higher customer advocacy, than those that don't. The proof is there, but brands find it very difficult to execute on immersive co-creation because it typically involves different departments and different organizational structures. That’s where AI comes in. Here are some AI-powered co-creation applications that we’re helping brands experiment with today.

• Generating consumer insights. At the Estee Lauder R&D Experience Center in Shanghai, the beauty brand engages consumers and key opinion leaders in product testing, validation and co-creation sessions for new products. Touch tables and magic mirrors powered with AI technology enhance workshops and ideation sessions and support the generation of consumer insights from participants in the room.
• Co-creating new products with customers in real time. What if a fashion lifestyle brand could run live design sessions with their community, their best creative directors and celebrities in a livestream that could generate the must-have sneaker, handbag or you-name-it based on their input…in real time? Not only would it speed products to market, but community co-creation and input would virtually guarantee success.

Get ahead by embracing AICX now.

The evolution of AI technology—from analyzing and processing data, to creating more content faster and at scale, to facilitating personalized storytelling through experiences that deepen the collaboration and intimacy between brands and its customers—is extremely exciting…and disruptive. There are new developments in the technology and its application for brand engagement literally every day, and brands that are able to harness AI’s power will be able to build stronger, more meaningful relationships with their customers and drive business growth like never before. Again, this will mean looking beyond AI’s power to merely build efficiency; in an environment of content overload, delivering AICX is how brands can truly deliver value.

In this report, you will learn how to handle key considerations like data governance, efficiency management, virtualization principles, consent management, unification, and activation to build a holistic view of what is happening in your business.

What’s supercharging these AIs are large language models (LLMs). LLMs are language prediction tools that can read, summarize and translate text by predicting upcoming words in a sentence, allowing them to generate new text that closely resembles human speech and writing. They’re adept at both writing and interpreting text, and that cognitive ability means they can do far more than just write the first draft of an email or summarize your meeting notes.

ChatGPT, built by OpenAI, has gained incredible popularity thanks to its simple conversational interface and its ease of use. This accessibility has inspired multiple teams within Media.Monks to experiment with LLMs, and GPT in particular, to find better ways to work and create. The result is a series of prototyped innovations that demonstrate the ability of LLMs to aid in internal collaboration, streamline information gathering and self-service, and make highly technical metrics more accessible for everyone.

Enabling collaboration through multi-user experiences.

The Labs.Monks, our R&D team focused on technology and innovation, built a chatbot designed to streamline brainstorming and collaboration across teams. Charmingly named Brian (originally from an internal pun of BrAIn but renamed for simplicity), the GPT-powered bot integrates into Slack and serves as an intelligent, active participant in team channels. The idea for Brian came from the realization that most applications of ChatGPT are task-based, which inspired the team to consider other ways LLMs can support teams, like serving as a creative collaborator.

Brian has two modes. In facilitation mode, it keeps group brainstorms going by offering questions and providing summaries on the discussion. In contribution mode, Brian serves as another collaborator who thinks along with the team and adds to the discussion.

“During one of our tests, it was able to help us brainstorm a fictional brief on how to create an experiential activation for a soft drink brand catered to seniors with some interesting results! Though ultimately we ended up coming up with an idea ourselves, the input from Brian helped us get to other outcomes we might not have thought of otherwise,” says Angelica Ortiz, Senior Creative Technologist. Being able to field a discussion among a group of users (and even address individuals by name) separates Brian from other chatbots, which are typically limited to one-on-one conversations.

The team originally built Brian as an exercise to gain hands-on knowledge and experience with LLMs, the focus of their recent Labs Report. Now, the team is exploring how to roll it out as a tool for wide use by the Media.Monks team.

An alternative to fine-tuning GPT.

After seeing the potential of LLMs, many brands are exploring the idea of fine-tuning those models to better match their tone of voice or the kinds of content they create. Generally, fine-tuning an existing model can be cost-effective, removing the need to train a model, program a chatbot or write new content from scratch. But for some use cases, fine-tuning can be prohibitively expensive compared to another method of generating more brand-unique results: prompt engineering.

Our Tech Services practice developed a method of prompt engineering that makes it easy to build a GPT-powered chatbot that can answer questions based on content from a specific domain. The example they use is turning a company’s internal wiki into an assistant that saves employees the trouble of searching and sifting through long documents to find the information they need. The key technology behind this method are OpenAI’s embeddings, a feature that allows matching user queries with answers from the most relevant source content.

Embeddings unlock some incredible features. Users can ask questions and receive responses in their language of choice, regardless of the source content’s original language, meaning there’s no need to localize. They also don’t rely on exact word matches; if someone asks our hypothetical company wiki bot about “vacation time” policies, the bot will know to pull information from a document about “paid time off.” Adding more content to the chatbot is also easy, as all it takes is a simple webhook to enable the bot to answer questions about new content as its published.

If you want to learn more about how to use embeddings to prompt engineer a bot of your own, check out the full writeup. You’ll also see a video demo that walks you through how embeddings achieve each of the outcomes above.

Digesting information at speed.

Sifting through data can be overwhelming—especially if numbers aren’t your forte. That’s why our enterprise automation team developed Turing.Monk, a chatbot affectionately named after Alan Turing, the 20th century computer scientist who developed the Turing test, which tests a computer’s ability to exhibit intelligent behavior. Turing.Monk help teams quickly find the answers they need about their campaigns by answering queries in three formats: lists, summaries and graphs.

The bot functions a lot like a marketing assistant, helping marketers draw conclusions about a campaign’s performance. Want to see how the media cost has changed on a week-by-week basis? Just ask Turing.Bot to “provide a written summary of how the media cost is changing” for the campaign in question. It’s that easy.

The ability to ask questions in natural language helps puts analytics and data science at the fingertips for those on the team who might not know SQL or Python. “It’s early in development, but today an account manager can keep prompting and fine-tuning the prompt to get the outcome they desire,” says Michael Balarezo, Global VP of Enterprise Automation. “We’re now working on improving the analytical capability of the tool, leveraging the power of LLMs to understand the nuance of the ask, and translate that into more complex insight generation”

More potential has yet to be unlocked.

While much has been said about LLMs’ abilities to generate text, their skill in interpreting queries and surfacing up helpful, contextual information—all in a conversational format—will make them incredible tools in the workplace and beyond. From facilitating creative collaboration, to making information easily accessible for all, to giving people superpowers by putting digestible data at their fingertips, the potential for LLMs like GPT is great—and you can bet we’ll continue to experiment and find even more applications and use cases to benefit our team and the brands we work with.

And while various point solutions and startups have hopped onto the scene, a growing collection of enterprise solutions is offering newly accessible ways for teams to boost productivity—all within platforms they already use and trust. The generative-AI-scrawled writing on the wall is clear: there’s no better time than now to begin upskilling teams in artificial intelligence.

The rise of enterprise AI is upon us.

Enterprise AI can be divided into three categories: martech, of which many marketers are already familiar in the data and media space; general productivity and collaboration tools; and tools aiding in creativity. Let’s look at high-profile examples from the latter two categories that have only recently been revealed.

On March 14, Google announced its plans to add generative AI features to the Google Workspace suite, with Docs and Gmail being the first platforms that will make use of the new tools. This isn’t Google’s first foray into applying artificial intelligence to work; Smart Reply and Smart Compose were introduced in 2015 and 2019, respectively, to streamline email communication by recommending contextually relevance responses or auto-completing lines as they’re written in real time. 

Just a week after that announcement, Google opened up the waitlist for Bard, its chatbot alternative to ChatGPT. Unlike Workspace, Bard cannot be used with a corporate account; users must be logged into a Google account that they wholly control. Still, the application for business is significant, with Google suggesting use cases like generating blog post outlines or snappy taglines.

Just days after the Google Workspace reveal, Microsoft introduced Microsoft 365 Copilot to the world on March 16. Similar to Google’s offering, Copilot applies natural language to unlock productivity, like translating a product spec sheet into a drafted product announcement in seconds. A key feature behind Copilot is Business Chat, which works across a user’s data to surface up information and insights that are needed at a given moment. These moves come after a potential $10 billion investment in OpenAI, the company behind ChatGPT and other AI innovations, following previous investments in 2019 and 2021. 

Getting creative with AI.

Like the enterprise tools mentioned above, Adobe Creative Suite is no stranger to AI; Sensei, a feature uses AI and machine learning to help users quickly make key edits to photography and design, launched in 2016.

Now, Adobe is launching Firefly, a family of generative AI models to assist in visual creation of all kinds, including still imagery, video content and 3D models. Examples include adding context-aware elements to an image you’re already working on—like speaking a castle into existence within a drawing of a beautiful landscape—or generating a whole image or design based on a sketch or a few words.

What’s interesting about Adobe Firefly is that it’s trained on images from Adobe Stock and works that are either in public domain or open license, avoiding the challenges of rights and ownership that affect models trained from content sourced across the internet.

Embrace experimentation to build AI maturity.

The rise of enterprise-ready AI is a turning point of the technology, which is now becoming even more accessible for employees looking to supercharge their productivity and creativity. But simply adopting a new technology isn’t a silver bullet that will help people work better with the click of the mouse.

Consider the paradox of choice: this phenomenon explains how when presented with more options, people expend more energy to make the right choice. Everyone has felt the paralyzing dread of a blank page waiting to be filled, and opaque platforms that rely on natural language can easily overwhelm, particularly for employees who aren’t used to issuing directives. To make the most out of these tools, then, brands will need to upskill their teams to better understand their potential and how to act on it.

Our team has been eager to embrace these tools to experiment and share that knowledge with our partners. Following a successful AI hackathon last winter to kickstart that maker’s attitude, Jam3 hosted another AI hackathon in March across its global offices, with the goal of answering the question: how can (and should) creatives in our industry use AI-powered tools? Challenged to build a brand strategy and product offering for a fictional fashion and beauty brand, each team employed AI as a sparring partner to gain an unconventional creative perspective and accelerate results. AI had to be used as a tool to generate ideas, insights, visuals, scripts and code—ultimately resulting in a campaign to launch the fictional brand and a storyboard and prototype to bring it to life. These events go a long way in helping employees envision the role that AI can play in achieving their everyday responsibilities.

Don’t wait to get started.

We’re only in the beginning of the AI-augmented workplace, but these tools and platforms are becoming increasingly sophisticated each day—meaning if you haven’t embraced AI on your team yet, you’re already falling behind. 

That’s why we put together a quick, single-page guide mapping out areas where brands can begin building their AI maturity right now, while also gearing toward future goals as technology continues to improve. Whether you’re looking to do more with less, personalize marketing on a grander scale, or something else altogether, find out how to get started with our quick guide.

In this report, we’ll cover everything you need to know about LLMs: what the competitive landscape looks like, how these models can be applied to your work, what changes with the release of GPT-4, and considerations for responsible use. After taking stock of the present, we’ll also look at what the future of text might look like as LLMs become more sophisticated and tailored for specific uses across industries. In addition, we’ll share our prototype built using GPT and Slack’s API that’s designed to jumpstart brainstorms among a group of collaborators.

In this report, you will learn:

• Why LLMs are so effective
• How GPT-4 changes the game
• Ways to apply this technology now
• Important considerations to keep in mind
• The upcoming impact of LLMs and how to prepare

Brian: A creative co-collaborator brainstorm bot powered by chatGPT.

To make GPT more accessible across our team and to integrate it within commonly used workflows, we created a chatbot that integrates into our widely used communication tool, Slack. Unlike typical chatbots that only engage in one-on-one conversations with users, Brian is designed to actively contribute and facilitate brainstorming sessions among teams.

Ultimately, the chatbot streamlines team brainstorming and collaboration within the Slack platform by guiding the conversation, sharing its own ideas, summarizing the discussion and more.