Once a conference for graphics experts, SIGGRAPH offers much to marketers with generative AI and 3D asset creation.

Traditionally an event focused on computer graphics and attended by the tech-savvy, SIGGRAPH doesn’t hold a privileged place on marketers’ agendas—yet. While the conference has historically been research-focused, potential applications of the technologies showcased are too significant for advertisers to ignore, and I expect I will see a larger marketing presence at the event for years to come.

Marketers seem very interested in applying new generative AI technologies in their work, but are struggling to discover the immediate, impactful utility of these technologies in their day-to-day life. It’s clear that the technology is outpacing what the marketing industry is capable of today, and this makes it more important than ever for marketers to show up, learn, and understand the benefits of cutting-edge AI innovations for their workflows.

AI made a significant impact at SIGGRAPH 2023, showing up in the contexts of interoperability, asset management, and generation, and importantly, in 3D asset creation. We’re seeing a lot of progress in terms of creating and managing 3D assets, especially from NVIDIA. The advancements in AI infrastructure, with new chipsets, workstations, and devices dedicated to AI, were also noteworthy. Additionally, licensing and ethics were prominent topics, with Shutterstock and Getty playing key roles in enabling brand-safe, licensed model training.

High-profile products and services showcase compelling, AI-powered marketing use cases.

One such platform explored in-depth at the conference was NVIDIA’s Omniverse, designed for collaborative 3D content creation and real-time simulation. This means artists, designers and developers can work together seamlessly on complex 3D projects regardless of their physical location.

The focus at SIGGRAPH was Omniverse’s integration with OpenUSD, a universal scene description framework that enhances interoperability and collaboration across different 3D tools and applications. With an entire day of sessions dedicated to OpenUSD, and with tool vendors like Blender and Autodesk involved in the Alliance for OpenUSD, it's clear that interoperable assets for 3D pipelines are no longer a thing of the past.

Showcases included 3D scene generation and rendering with Edify, as well as innovations in safe asset creation with Shutterstock and Getty using Omniverse. These developments will undoubtedly enhance our capabilities in creating and managing 3D assets efficiently—and offer immense potential in streamlining the production process through integration into AI-powered workflows like our own Monks.Flow.

Another standout innovation from the show was HP Z Captis, a material-scanning tool that has just launched. It works in tandem with Adobe Substance to instantly digitize fabrics and other textures for digital workflows, greatly enhancing 3D-to-content workflows that we’re already deploying for apparel brands. Today, we can take 3D renders of products and create thousands of variations of hyper-realistic product shots (even including generated models in photoshoots), but soon, with partners like HP, Adobe and Autodesk, we will be able to ship entire marketing campaigns before the first product even leaves the factory—a velocity that brands have only ever dreamed of.

SIGGRAPH paints a bright future for further marketing innovation.

Looking ahead, the potential for leveraging AI innovations presented at SIGGRAPH is immense for marketers. As partners of tech leaders like HP, Adobe, and Autodesk, we are at the forefront of integrating these advancements into practical marketing solutions.

For instance, our collaboration with Adobe plays a critical role in transforming end-to-end content supply chains. Through the Brand Model Practice, we utilize Adobe GenStudio, a generative AI-first offering, to help marketing teams quickly plan, create, manage, activate and measure on-brand content. Integration Monks.Flow allows for generative AI brand models that can significantly reduce costs, enhance creativity, build business intelligence and maximize the impact of your marketing efforts to drive growth. The overarching theme between this tool, NVIDIA Omniverse and HP Z Captis is that your marketing workflows are about to become a lot more integrated—and a lot faster.

Innovations in AI and 3D asset creation presented at the SIGGRAPH have significant implications for AI workflows and our own tools, like Monks.Flow. As marketers, it’s crucial to stay informed and adapt to these rapid advancements to maintain a competitive edge. The future of AI in digital marketing is developing rapidly, and I’m excited to see how these technologies will continue to transform how we approach our work each day.

Recapping what Google is rolling out this year.

AI Overviews are designed to provide users with summarized, informative answers directly on the Search Engine Results Page (SERP). These overviews are aimed to offer a general response to the user's query and display clickable sources for deeper exploration. Beyond just answering queries, they will allow users to continue the interaction, refine their search and dive into specifics without leaving the SERP, or to view and click into the specific sources that informed that AI response.

Google is also bolstering AI Overviews with related features fed by its AI technology, Gemini. Among the notable new features rolling out over the coming months, their announcements included:

• "Simplify" and "Break it Down" to make complex answers more digestible
• Multi-step question handling, to tackle more intricate queries
• Organizing search results into AI-curated categories
• Enhanced planning features, to generate complex answers for leisure and travel activities
• Integrated video search using Google Lens

With the announcement of ad placements for AI Overviews, marketers finally had confirmation of how ad placements would be affected by the rollout of the generative content above-the-fold. The ads will include both search and shopping results; the algorithm will coordinate the chosen ad variant or chosen SKU with the user’s query and the content generated in the AI overview, prioritizing relevance. The ads eligible to serve in AI Overviews will be selected from brands’ Broad match search campaigns and Performance Max (PMax) campaigns (which they’re terming “the Power Pair”).

Google also shared that at first, AI Overview Ads will be more focused on retail, travel, home improvement, moving, and similar consumer verticals—this is unsurprising given these are industries that rely heavily on feed-based data, giving the algorithm more data to assess ad relevance from than just some ad copy and a landing page.

With AI Overviews rolling out for both organic and paid search results, marketers are facing several key questions about how to position their brands best and avoid getting knocked down below the fold. Here’s everything you need to know about setting your brand up for success.

Will AI Overviews push my ads lower on the SERP? Not necessarily.

The announcement of integrated ad placements within the AI Overviews section of the SERP changes the initial understanding that many marketers had after Google I/O earlier in May. We can see now that AI Overviews do not inherently push paid results lower on the page, but will fit them into a new context, as part of the generated response. Since the public launch, regular ad placements have been continuing to serve below the AI Overviews as well.

We currently see in our testing that ads appear with the same frequency as before, but we are seeing more experimentation from Google post-GML. We’ve recently seen examples in the wild of AI Overviews showing below-standard ad units (i.e. ads that are not integrated into the AI Overviews content), as well as AI Overviews content serving for mid- and lower-funnel queries (more on that below, see our example images). Marketers should expect ad positioning to keep evolving this year and beyond.

Since ad placements are dynamic to each search, Google itself is continually gathering data about what layouts lead to more engagement and satisfaction from users. The launch of all the new ad features will keep impacting this; therefore, it remains to be seen how ad placements will be affected when AI-based search categories and multi-step answers roll out later in the year.

What about organic results? Yes, expect to appear lower and expect traffic decreases.

This has been one of the chief concerns from publishers and marketers since SGE was first announced: if users are getting information straight from the SERP, won’t that mean less traffic to my site? Yes, zero-click content from AI Overviews will take up real estate on the SERP, meeting some searchers’ needs directly.

However, this is part of a longstanding trend of SERP layout changes, from the shift of Shopping results from the right rail to the top of the page, to expansion of Knowledge Graph content like Featured Snippets, People Also Ask (PAA), and other panels. Because of this, “traditional 'Top 10' organic ranking positions no longer exist,” says Dang Nguyen, Sr. SEO Strategist at Monks. “Webpages ranking below the fifth position will be pushed even further out of searchers’ view, and inevitably will see a drastic drop off in CTRs.”

However, there will be more chances for brands to get content to appear in the AI Overviews, to balance out for the decrease. “Since Google is providing multiple links to sources in AI Overviews, there are more chances to get included compared to Featured Snippets, which only provide one source link,” says Nguyen. “If users want more than the brief AI Overview summary, they’ll click through.” This shifts the focus for SEOs and brands to optimize to what the AI Overviews algorithm seeks. More on that below.

Will this affect all queries? Mostly upper-funnel terms…for now.

Our testing shows the most impact at the top of the funnel. Our team has been monitoring key terms for our clients in both the Labs environment throughout Q1 2024 and now in the public SERP. In our live testing, we’re primarily seeing AI Overviews serve for head terms and informational queries; they’re typically mapping to users in a top-of-funnel research journey. For the last few months, we have seen less frequent use of AI Overviews when queries are more “consideration” oriented (typically aligning with mid-funnel or bottom-funnel searches), or especially when users seek out a particular brand.

However, we are seeing early shifts since AI Overviews moved from Labs to public usage: recently, more AI content is appearing for consideration queries, especially those comparing different types of product/service offerings or seeking instruction. See the example images below, captured only days after GML.

Most of the other features announced at GML 2024 focused on queries with high commercial intent; advertisers should expect that brand queries and bottom-funnel queries are more likely to be impacted by Visual Brand Profiles, for example, than AI Overviews.

Should advertisers be concerned about brand safety?

No, not enough to justify any resistance to these changes. With the public rollout came fresh scrutiny, including inaccurate AI responses going viral, causing a stir in the marketing and tech worlds. It’s not surprising that an LLM would generate some buggy content while being brought up to scale for the first time, or when the user base expands so broadly (from Labs users likely testing it for professional purposes, to any user) that it would need to adapt more human elements, like satire. At the end of the day, marketers should remember:

1. The generated content is not a black box, for a reason. Google’s search results and Featured Snippets have always relied on user input to validate that they’re serving the right content, and this is no different. No matter what, AI Overviews list sources so users have full access to the original information, and can judge what source sites they trust.
2. Google is incentivized to improve this product. Since other tools like Microsoft Copilot or Perplexity AI are regularly making headlines and receiving favorable reactions, Google has a competitive need to ensure AI Overviews are meeting users’ needs as quickly and seamlessly as possible—otherwise they’d be jeopardizing their search revenues in the long run.

How can marketers optimize their SEO to appear as sources for AI Overviews?

With the recent SEO algorithm "leak", uncovering documentation about Google’s ranking factors, marketers everywhere are buzzing with “gotchas” about how to hack the organic ranking algorithm; however, at its heart, content SEO is still about understanding your target audience’s needs and buying journey to produce valuable content that they will seek out. Even when AI-generated content was rolled out last year as the Search Generative Experience, we were already talking about how it doesn’t change the fundamentals for a strong SEO strategy as much as people might assume.

First, technical SEO principles are still critical for Google to even be able to effectively find and interpret your site’s content, and must always be addressed first. But beyond that, here are tips to evolve your content strategy for the AI-first SERP:

1. Focus on depth and own your niche to be considered a topical authority. While many brands are leaning on generative AI to churn out surface-level content quickly and cost-effectively for a wide variety of keywords, Google will be more likely to view your content as meaty enough for the LLM to learn from if it goes deep and offers unique value. “Now more than ever, publishers need to provide much more value-add to their content to succeed,” says Nguyen. “Brands should produce niche-relevant content that doesn't exist anywhere else, eg. new knowledge and facts, interviews with industry experts, verification of accuracy and fact checking, etc… basically everything that good journalism represents.” Websites that show a deeper level of knowledge and expertise will thrive in the new Google search landscape.
2. Instead of a discrete list of keywords, built content around topics in a hub-and-spoke model based on the buyer journey and natural language. Google touted at GML that searches are continually getting longer, as users query the search engine with more complexity. As searching becomes a more immersive experience, this behavior will be reinforced, until searches more closely mirror conversations. For example, instead of users searching “smartphone with best camera,” in the future they will be more likely to query “currently have a google pixel phone and looking for something better, price point max of $1,000, and a device where I can take great landscape pictures.” Basic keyword analysis won’t cut it anymore in that environment. “It’s important to understand the entire customer journey and how it relates to the user’s intent in that moment, then offering that exact information they’re looking for,” Nguyen adds. “Ensure that your content is covering the entirety of the customer journey.”
3. Don’t neglect third-party credibility. Brands must reinforce overall site authority with good backlinks from other highly relevant and authoritative sites. This doesn’t just include press coverage and classic backlinking tactics, but also engaging where users are already having conversations. “Don’t forget to distribute content across various social platforms and UGC forums (serving the appropriate format), like Reddit.com and Quora.com,” Dang Nguyen suggests.
4. Learn from the AI-generated content itself. Unfortunately, at this time Google Search Console won’t differentiate the traffic coming from AI Overviews specifically, but we can learn from the answers they receive when performing our own searches. Marketers should engage with AI Overviews themselves. On a recurring basis, see what content serves for your most relevant queries and analyze AI Overview responses to identify gaps and opportunities for enriched content creation.
   
    

How can advertisers serve in AI Overview Ads, and what’s the “Power Pair”?

AI Overview Ads are not fueled by their own campaign type the way Demand Gen and Performance Max campaign placements are. Instead, eligible ads come from the “Power Pair”— PMax campaigns and Broad match keywords in search campaigns. Therefore, it’s critical for advertisers to lean into both tactics, prioritizing budget allocation and testing to increase your odds of serving. To make sure you’re hitting performance goals when leaning into the Power Pair, advertisers should:

• Pay attention to the top and the bottom. Monitor your top-performing keywords for any immediate performance losses across core KPIs as AI Overviews roll out. This would signal that users only sought a surface-level answer and didn’t get added value from clicking to your site. Also, reinforce your foundation by continuing to monitor for poor-performing queries on your Broad terms, maintaining robust negative lists.
• Share knowledge more frequently. Continually align on cohesive keyword and topic strategies with SEO to share learnings about which paid keywords perform best and to supplement any burgeoning site content with paid ads.
• Check ad strength, and perform more copy tests as needed. To combat expected decreases in CTR, ensure your ads have strong ad strength or you’ll risk serving even less frequently. If strength ratings aren’t as high as you’d like to see, launch new RSA tests that prioritize relevance (more directly work in the query to the ad copy and landing page experience).
• Maximize audience signals to feed the algorithm. To thrive in this new landscape, brands must feed the ever-hungry algorithm as much data as possible. First-party audience data from a CRM or CDP is worth its weight in gold, in addition to increasing the number of (meaningfully different!) creative variants, and making your product feeds as robust as possible. All of these signals will point the algorithm the right direction towards users most likely to convert.

What other changes do advertisers need to make to gain a competitive advantage?

Nimble advertisers who can act quickly in these other areas will have the most to gain:

• Utilize more visual ad formats to stand out as Google makes the SERP layout even more dynamic. Enhanced personalization means marketers must compete even more for relevance.
• Establish new performance benchmarks. With CTR and traffic volume likely to shift rapidly, it’s important to update the SEM/SEO KPIs you measure against, to proactively understand MoM and YoY differences. Google Search Console won’t necessarily shed light on this directly: “At this point, clicks and impressions shown in GSC will not discern between regular SERP clicks and AI Overviews clicks,” says Nguyen. Therefore, to quantify the impact of zero-click content, leverage incrementality testing, such as matched market tests or holdout tests. Historically, most SEM marketers have focused their holdout tests on Brand terms, but with AI Overviews impacting terms at every stage of the funnel, it's now crucial for broader categories.
• Combat rising CACs from lower traffic with CRO. While we anticipate a decline in CTR and click volume, the clicks that do occur will likely be of higher intent, leading to better conversion rates (CVR). To bolster this, brands should invest in conversion rate optimization, A/B testing their landing pages and site experiences to reduce friction or leaks in their conversion process.
• Consider the impact to other media channels. For example, with lower site traffic from some search terms we can expect remarketing pools to decline; to avoid oversaturating your consideration audiences if they shrunk, set frequency caps on your remarketing campaigns.

The SERP of 2024 and beyond is personalized and interactive; are you ready to embrace the change?

Google's rollout of AI Overviews marks a significant evolution in the search experience. This AI-driven shift promises a more personalized and interactive SERP; if this pays off with  enhanced user satisfaction and engagement as Google projects, it’s ultimately a win for marketers hoping to reach engaged audiences. To benefit from this, it necessitates strategic recalibration for both advertisers and SEO professionals. Embracing this change with innovative approaches and enriched content will be key to thriving in the evolving search landscape. Welcome to the future of search—where AI Overviews lead the way.

Among these are the successful implementation of artificial intelligence in marketing strategies, the advent of new interfaces for engaging with technology, and the creation of a new class of experiences that are more immersive and personalized than ever before. Let’s dive in!

Data foundations remain crucial for successful AI implementation.

“Enterprise clients will continue to emphasize building internal AI capabilities, which will vastly increase the demand for data and the systems required to manage it at scale,” predicts Tyler Pietz, Global EVP, Data. Indeed, a solid data foundation is the price of entry for successful AI implementation, especially as brands look beyond third-party cookies to better understand their audiences and build ownership of their data. By first creating fresh, predictive and prescriptive data streams that feed into AI workflows, brands can ensure their AI implementations are not only successful but are also self-evolving and constantly improving the accuracy of insights and predictions.

One can see this with our recently announced Monks.Flow professional managed service, which connects talent trained in AI, the latest AI tools, enterprise software and microservices into efficient, automated workflows. By integrating Monks.Flow into the Salesforce ecosystem, for example, we bring the creative capacity that brands need to personalize customer journeys at a granular level. This partnership allows us to fulfill the promise of digital: one-to-one personalization at scale.

The marketing structure will evolve to enable content at scale.

Brands are preparing for another great shift in 2024: transforming their marketing structure. “Clients are shifting from traditional models to content at scale structures. These content models continue to be bespoke, some of which are built in-house while others rely on third parties,” says Dave Carey, Global EVP, Studio & Embedded Solutions.

Carey, who has a background in helping brands spin up in-house studios and embedded services and leads the Studio.Monks, sees an opportunity to deliver a tech-first vision to brands looking to shake up their marketing structure. Rather than focus solely on the talent portion of the implementation, for example, brands can organize themselves around AI-centered workflows designed to enable seamless collaboration. “AI is changing the way content is created. Clients are looking for transformation AI agencies to help them understand and implement new ways to create content,” he says.

Marketers will aim to integrate media, content and commerce.

AI can help with far more than content creation, serving as an integrative force across content, data and media, a key goal that VP, Media Enablement - GTM New Business Victoria Milo sees emerging in 2024. Prompted by a reorganization of the media landscape—including consolidation of media partners and agencies—and a slowdown of growth in the media industry, brands will increasingly seek to solve media challenges with non-media solutions fueled by AI.

AI-powered innovations in platforms will connect customer insights and user experiences.

A brand’s own platform is a great spot to better align behavioral insights and user experience. Thankfully, digital experience platforms like Adobe Experience Manager are integrating AI features in service of these goals, says Business Lead Platforms & Ecommerce Remco Vroom, who leads the Platform.Monks. These features enable brands to apply dynamic learning capabilities to anticipate and adapt to user needs with remarkable speed and precision.

Adobe Sensei is one example, which combines generative AI with integrated workflows to deliver customer experiences at a scale previously unimaginable. Meanwhile, Salesforce is incorporating generative AI features that enable teams to produce enriched content and journeys at scale by utilizing customer data. This integration of AI within digital experience platforms will continue to revolutionize how brands respond to user needs in real time.

These features help underscore our own approach to platform development: generating actionable behavioral insights, then applying those insights to craft a superior user experience. “As we develop more intelligent digital solutions, our aim is to lead the change by establishing a new standard for digital ecosystems that think, learn, and grow. The future is performance-first, data-driven and AI-empowered," says Vroom.

The first global, AI-powered influencer will enter the chat.

Over the years, virtual influencers have, well, significantly grown their influence. What these digital humans do, say and share with their audiences are still crafted by teams of real people, much like fictional mascots of old—but this year, EVP, Global Head of Social Amy Luca, who leads the Social.Monks, expects to see a truly global, AI-generated influencer spring onto the scene. “This generative AI personality will be able to communicate in any language at scale, connect with cultures, and will be trained to be brand safe,” she says. They will likely be a lifestyle of fashion influencer, similar to Lil Miquela, but will enable a completely new level of one-to-one consumer communication at scale. Another possibility: bringing people back to life.

Marketers will elevate the role of immersive brand experiences.

Virtual humans are just one way that brands can better fuel one-to-one interactions at scale. And overall, Global EVP, Experience and leader of the Experience.Monks Jordan Cuddy expects to see brands focus more strongly on elevating consumer experiences in 2024 and beyond, citing a recent report that 71% executives plan to increase brand marketing in 2024, while only 46% intend to do the same for performance marketing.

But marketers face a dilemma: performance is easy to measure—think impressions, click-through rates and impressions—but the death of the cookie will challenge those strategies. Meanwhile, it’s harder to measure the effectiveness of an immersive brand experience. “Agencies need to be able to provide the feedback loop on ROI (loyalty, conversion, engagement, sentiment and opinion) in the shift toward more immersive experiences,” says Cuddy—an opportunity that will likely be helped by Milo’s earlier point about integrating strategies under one roof.

Emotional intelligence will be key to striking a chord with audiences.

Likewise, Head of Brand Design and Brand.Monks leader Jonny Singh sees an opportunity for emotionally resonant brand experiences in 2024. “As the trend towards personalization intensifies, brands will naturally elevate interactions to a new level of intelligence, tailoring experiences intricately to individual preferences,” he says. “However, amidst the technological wave, the emotional element remains pivotal, emphasizing the importance and power of storytelling.”

With a backdrop of global uncertainty, emotionally resonant content across the customer journey will go a long way in helping brands maintain relevance and resilience. “Brands that will come to the forefront this year will be ones that combine function and emotion, using logic and magic to think, make and create new futures, now,” says Singh.

Ambient computing will begin making its way into the mainstream.

In addition to virtual humans, ambient computing is another compelling evolution of how we will engage with technology. Brady Brim-DeForest, CEO of Formula.Monks, our technology services practice, envisions the dawn of truly pervasive and transparent software powered by AI. "I see 2024 as the beginning of the rise of true ambient computing. I think we’ll look back at the middle of the 2020s as ‘peak screen’—the future of work is coming fast and it will largely be screenless," he says. If you’ve interacted with a voice assistant on a connected speaker or wearable, then you’ve already dipped your toes into the screenless future. Now, generative AI will make these agents smarter and more reactive to user needs.

Brim-DeForest expects the way we interface with technology will evolve beyond verbal speech, as seen with emerging technologies like Neuralink. Brain implants may be a while away, but ambient computing isn’t far-fetched; in a recent episode of the Nex6 Project, Brim-Deforest explained how software has followed a trend of abstraction that will make these screenless experiences inevitable—and more natural, meshing seamlessly with our everyday routines.

AI innovation will focus less on hype, more on practicality.

Building on Brim-DeForest’s perspective on seamless technology use, Sander van der Vegte, VP, Emerging Tech and R&D on the Labs.Monks, sees an imminent shift towards more accessible and practical AI applications after a year of hype.

Van der Vegte notes a move away from the hype surrounding AI towards a more pragmatic approach that more realistic about AI's capabilities and use cases. “Its output is pretty good, but not great out of the box," he adds, creating a need for bespoke models that integrate with a brand’s production pipeline.

Here’s to the year ahead!

Time will tell if these predictions will come to fruition in 2024. Regardless, the convergence of AI, new interfaces and immersive experiences is providing new opportunities for brands to connect with their audiences in meaningful and innovative ways. Brands who successfully navigate this terrain will be those that adapt quickly, build robust data foundations, and embrace the potential of AI.

Brands often run into one of two common problems when making early moves in AI: they either understand the benefits of the technology but don’t know where to start, or they stitch together a Frankenstein’s monster of point solutions and tools that are ultimately disconnected from each other—inhibiting AI’s potential. Solving these operational challenges is our bread and butter as a consultative partner because we use AI each day—and have built our credentials in the technology over the last decade.

Integration lays the groundwork for AI transformation.

One of the major opportunities in AI is that it serves as a systems integrator, ingesting data and insights from across business units and customer touchpoints to enhance the customer experience and build new efficiencies. At least, that’s the ambition. Each element of the marketing mix—data, media, creativity and technology—must combine for AI to be deployed at its fullest potential.

In the past five years, we’ve made the case for an integrated marketing services partner who can unify each of these practices. That process has laid the groundwork for building an AI-powered, end-to-end workstream extends across and merges each of our capabilities. Our recent release of Generation AI, a formative report launched in collaboration with Salesforce, offers a window into how the technology is helping teams shape the entire marketing remit from insight to idea to execution.

Breaking silos and building a culture of experimentation have accelerated our ability to build this mature AI offering for brands. In fact, experimentation with AI has long been part of our business, with Co-CEO, Content Wesley ter Haar telling VentureBeat in 2017, “[AI] will allow us to refocus our efforts on what’s really going to impact the project in a meaningful way––which is design vision, design thinking and real creative leadership.”

But experiments in AI have long been relegated to the realm of R&D rather than the hands of everyday talent. When the generative AI boom suddenly ignited a year ago, making the technology far more user-friendly to people regardless of their knowledge in AI, our team enthusiastically rallied around Slack channels dedicated to brainstorming ways it could help them in their work.

This collaborative spirit has matured from casual experiments to innovation sprints that push tech to its limits, to products and services built bespoke for brands. Just look at our work with BMW Group, in which we built an AI-powered music creation platform that celebrated the brand’s partnership with EDM festival Tomorrowland. Music fans worldwide could create their own custom festival track by selecting different options in mood, pace and feeling, enabling creative expression on an unprecedented level.

A cohesive, end-to-end workstream beats random acts of digital.

Every brand may have a different need for AI: enhancing the customer experience, overcoming production constraints, activating customer data, or even a combination of those needs. That’s why we’ve built a flexible, AI-driven pipeline that connects a wide range of proprietary and third-party microservices across a single workstream.

In addition to making it easier to develop content at scale, the workstream critically has the potential to break down siloes between marketing and adjacent parts of the business. For example, imagine if your product design team could share 3D renders used to iterate assets that are then tested for performance, ultimately identifying the product angles and other variables in creative that best fit customers’ interest. Marrying product design, creative production, market mix modeling and customer insights, this workstream results in a flywheel that can inform future product designs and content.

"Experimenting with AI is important, but what’s more important are the business workflows,” says Michael Dobell, Co-Founder and EVP Innovation. “AI has sparked the need for workflow transformation, and we guide them through that change, unlocking performance gains, lowering costs of production and finding new ways of working." While marketing may be the most natural place for AI to make an impact right now, it’s actually a harbinger for wider business transformation—and this view, in turn, sets a new expectation for the role that agency partners will need to play to help brands get there.

For Kraft Heinz, we took a strategic approach to helping their in-house agency, The Kitchen, identify where and how AI could drive high value by increasing efficiencies, saving costs and elevating creative output. Together with the team, we developed a roadmap that walked through four key takeaways: data security, internal adoption, testing use cases and establishing adaptable frameworks. Overall, the engagement resulted in actionable, initial steps for the brand’s own internal team to take calculated steps toward AI maturity.

Here's to many more wins in the AI race.

We’ve always called ourselves a new age, new era partner to the world’s most innovative brands. Earning the title of Adweek’s AI Agency of the Year demonstrates that we’re already ahead—and just in time at the dawn of what’s been called the fourth Industrial Revolution.

See what else we've been up to with AI here.

Performance marketers have been an underutilized resource to date, but their years of experience using AI for marketing success make them well suited to play a large role in broader AI adoption. Blind disciples of every generative AI shortcut will get burned and those resistant to change will become irrelevant. Nobody knows this more than performance marketers. 

As it relates to the digital marketing AI arms race, Google, and to a lesser extent Meta, weren’t nearly as proactive at highlighting their work relative to Microsoft (the largest investor in OpenAI, the company responsible for GPT-4). The irony is that Google and Meta had been at the forefront of incorporating their long-standing investments in AI, which was already deployed in almost every corner of Google and Meta Ads platforms and products.

Google and Meta represent nearly half of all digital ad spending in the US and represent an even larger share of the typical performance media budget. AI integration in Google and Meta has most prominently centered around machine learning algorithms for bidding and ad serving. That said, there are examples of generative AI as well (suggesting ad copy and creating distinct ad copy from permutations of existing headlines and body copy), and AI’s tentacles can be felt everywhere in the Google and Meta ad ecosystem. Prominent examples include:

• Performance Max (Google) and Advantage+ (Meta) are effectively end-to-end automated campaigns that use AI to target, generate ads and optimize toward set goals.
• Automated bidding sets dynamic bids in real time using machine learning to more efficiently optimize toward the highest ROI.
• Responsive Search Ads (Google) uses AI to mix and match different portions of copy to deliver the best permutation for the individual searcher (right ad to the right audience at the right time).
• Recent Google Marketing Live (GML) and Meta Connect 2023 conferences announced products around AI-powered assets, AI-generated images, generative AI to create ad copy and auto enhancements to text placement, brightness, etc.

In that same vein, performance marketers, most of whom earned their stripes running or overseeing Google and/or Meta Ads, are particularly well suited to guide advertisers through this next major stage in digital transformation. The nearly half decade of experience most performance marketers have both harnessing and reining in AI tools justify them playing a central role guiding marketing teams in developing and deploying generative AI adoption.

What about this experience gives performance marketers an advantage? 

• Threading the needle between uncritical adoption and complete resistance to change
• Understanding of the importance of high-quality data inputs 
• Understanding the importance of setting guardrails and tweaking those over time 

Bringing healthy skepticism to the table.

Seasoned performance marketers have had to adapt and learn new types of automation many times over, and can share their war stories. From broad match keywords, Meta auto-placements and iteration after iteration of automated bidding on Google gone awry, we’ve seemingly seen it all. Google and Meta were trailblazers in incorporating AI into ad products, and reps would very earnestly push adoption of products that could be buggy and at worst underperform manual alternatives. However, Google and Meta were also diligent about refining those products over time and performance marketers who were not willing to continue testing at all over the last few years were quickly left behind. Broad match keywords, automated bidding, Advantage+ shopping campaigns and many more products delivered more scale at comparable efficiency to non-AI driven products. 

As AI plays a more permanent role across creative, customer journey, audience identification and more, this balance will be crucial. Blind disciples of every generative AI shortcut will get burned and those resistant to change will become irrelevant. 

Garbage in = garbage out.

One of the biggest distinctions in a strong performance marketer versus a mediocre one is her understanding that the inputs to automation can have a profound effect on outcomes. Performance marketers who press the easy button and switch from hundreds of manual bids per week to auto-pilot don’t get strong results. Worse yet, they’re quick to declare, “It doesn’t work!” Data volume and quality are the foundation of an effective AI deployment strategy. Knowing which data sources to use and exclude, and which campaigns to match with each specific type of automated bidding, is a crucial skill. Performance marketers know to incorporate lead quality data to B2B auto-bidding, initiate testing on campaigns with higher conversion volumes, and not to launch immediately after a strong holiday or back to school period.

In this sense, performance marketers have years of “prompt engineering” reps without even realizing there was a name for it. Marketing organizations stand to get AI into market faster, and benefit sooner from the positive results, by tapping into that experience. 

Performance marketers are masters at fine tuning.

The last level of mastery that performance marketers have achieved has to do with learning the intricacies of the algos. We have applied max CPCs, cost caps and negative keywords to rein in the occasionally deleterious effects of AI unchecked. At a high level, AI can be fickle and human intelligence is crucial to avoid these blips. We have seen a top performing ad set stop delivering seemingly out of nowhere, only to have a minor 5% increase in ROAS target return it to normalcy. We’ve learned to mine for insights around how, why and where AI is working:

• Is stronger performance because we’re seeing increased CTR or conversion rate?
• Are we getting in front of the same audience more cost effectively or reaching a better audience?
• Did we create better ads, or did the platforms get better at matching them to the right people?

We ask these questions daily. That curiosity bordering on paranoia allows performance marketers to squeeze the most out of AI, as well as limit downside risk. 

Performance marketers have a feel for AI’s rhythms, like a mechanic knowing just which bolt to tighten to get the rattling sound in the car to stop. This mileage, or put anachronistically “human intelligence,” is tough to replicate. 

This AI mileage and its broad applications are why performance marketers should have a seat at the table. As an agency leader I’m better equipped to weigh in on how we utilize AI to address tasks, reporting, data integration, scripts and implement processes around AI because of that performance DNA.