Working with brands like Chime who have sophisticated multi-channel advertising programs, my colleagues have unlocked benefits ranging from enhanced automation to scalable personalization, all while driving cost efficiency. This topic took center stage during a panel discussion at Campaign’s AI Deciphered 2024, featuring Brayden Varr, ACD at Chime; Ashwini Karandikar, EVP of Media, Technology, and Data at The 4A’s; Jesse Waldele, and SVP of Digital Operations and Client Success at Dow Jones; along with myself.

Steve Barrett, our moderator and VP and editorial director at PRWeek & Campaign US, opened the panel discussion with a pivotal question: What do the new AI-powered tools mean for the role of a media planner? Below are some key insights from our session.

Creative and media are becoming closer together.

Historically, creative and media roles operated in silos, each adhering to their own set of responsibilities. This setup is far from ideal, because the intersection of creativity and performance analysis is crucial for brands to succeed. As Karandikar put it, "The creative arm needs to speak the language of performance while still capturing the brand message.” However, reaching a point where team members could put aside their production tasks to collaborate with others was, at the very least, challenging.

Now, with AI managing repetitive tasks and various aspects of content generation, creatives can concentrate on higher-level strategic thinking and innovation. As a result, they can collaborate more effectively with media planners—and vice versa—gaining deeper insights into target demographics and enhancing campaign optimization. Plus, it opens up new possibilities for media planners who may not have the support of creatives on their teams.

Varr, approaching the topic from the creative and design side, highlighted how performance marketers are leveraging AI tools to enhance creativity on limited budgets. “If you work in the performance space, you probably have zero creative production budget,” he said. “But we have to get noticed. With tools like Adobe and Midjourney, we can create content that helps us stand out in these feeds more than ever, and it’s positively impacting our key metrics that we evaluate every day.”

What’s more, in highly regulated markets, brands often face restrictions that prevent them from using demographic data, such as age, gender, or location, to target consumers. Instead, they can rely instead on creative content as a key input. That means exploring various attributes—such as interests, behaviors, or emotional triggers—within the creative itself to connect with consumers. Media buyers are increasingly collaborating with creative teams, brand strategists and media strategists to craft campaigns that resonate with target audiences. 

It’s not just about automation; it’s about intelligence.

As mentioned earlier, automation allows creatives and media planners to focus on high-value tasks rather than routine analytics. But it doesn’t end there. Gen AI excels at processing vast datasets to reveal patterns and trends that human analysts may miss. By generating actionable insights, generative AI helps inform media strategies, allowing planners to optimize their approaches based on real-time data rather than relying solely on historical experiences.

Waldele said, “Where we really see a huge opportunity is not just in media plan automation, but in media plan intelligence. We can create media plans that are performing and infused with that intelligence.” To provide an example, working with enterprise clients with complex media programs, we can automatically tag thousands of potential attributes within our creatives. This used to be a cumbersome task that required building complex taxonomies to capture various elements of an ad. Marketers had to create detailed tags for simple attributes like “red background” or “blue background,” while also specifying the product’s context. This method imposed limits on the number of characters allowed in an ad name and often left teams struggling to log every attribute of the creative that could impact performance.

Now, the automatic tagging capability streamlines the process and enhances the depth of analysis. AI can pick up subtle details that were previously imperceptible to the human eye—such as whether a card is positioned at the top of a phone or next to it—and understand how these elements resonate with audiences. Moreover, many ad platforms integrate generative AI to dynamically alter creatives in response to immediate audience feedback. It’s a significant leap forward—not just in terms of efficiency, but in the depth of intelligence that drives media planning today.

There’s value in taking risks.

From highly personalized creative content to real-time insights, brands have much to gain by incorporating generative AI into their media strategies. Still, many find themselves caught up in the challenges, risks and considerations. As Varr said, it’s easy to say no to these tools, but doing so can be costly. Once your competitors start effectively harnessing the untapped potential of generative AI, catching up becomes a formidable challenge.

While smaller brands tend to be more agile and willing to take risks, established enterprises have just as much—if not more—to gain from embracing innovation. To secure your team’s buy-in, Varr suggests, “Find someone who believes in it, and then demonstrate the impact. If you can do that, that’s when you’ll succeed.”

We are entering a new era of collaboration, agility and intelligence. By breaking down traditional silos and fostering collaboration among creative, media, and strategic teams, organizations can leverage the full potential of their insights and automate routine tasks. This empowerment not only enhances creative quality and campaign effectiveness but also positions brands to respond swiftly to market dynamics. In a time when agility and informed decision-making are crucial, those embracing generative AI will not only stay ahead of the competition but also redefine what's possible in media planning and execution.

But a key theme was the need to unify disparate data sources, orchestrating teams around unified workflows to maximize data impact—in one word, the event focused on integration. This strongly resonated with me, because it’s exactly what my team is built to help brands achieve; as a unitary partner and systems integrator, we specialize in creating platform solutions that seamlessly integrate AI and customer data to drive growth. From new product announcements to sales stories, let’s look at the growing need for an integrated approach to customer relationship management (CRM) and the role that a unitary partner can play in helping brands maximize its impact.

Here's what Salesforce announced this year.

One of the most exciting announcements was the introduction of Einstein Copilot for Marketers, set to release in June. This tool translates customer data into actionable campaign briefs, offering generative AI features like copy creation and automated communications. Salesforce is also now orchestrating seamless handoffs between multiple Copilots to enhance team collaboration. These innovations bridge the gap between customer data insights and content creation to drive impact across the business. For example, you can pair Einstein Copilot for Marketers with Einstein Copilot for Merchandisers to uncover up-selling opportunities.

Salesforce also announced enhancements to Data Cloud for Commerce, providing a unified view of customer data from numerous commerce data points. This empowers marketers to create hyper-personalized experiences—but when paired with Einstein Copilot products, these efforts become even more impactful.

Another major announcement was the Zero Copy Data Partner Network, connecting technology, system integration, and data ecosystem partners. This network allows marketers to draw data from a broader array of sources (without that data needing to be housed on their Salesforce platform), amplifying their AI-driven efforts.

What’s interesting about these announcements is the emerging, overarching theme of integration and collaborative workflows to help marketing teams work better together. This is the bread and butter of a unitary partner who can ensure that data accessibility, unified customer views and team collaboration are optimized across the business. By bridging together expertise across disciplines like data, media, content, and technology, such a partner is best suited to deliver the full potential of these solutions as they work in harmony with one another.

Peek inside the success stories that were shaped by seamless integration.

While at Salesforce Connections, I had the chance to speak with brands—learning their needs, pain points and the opportunities they most look forward to—and got to watch the different speaker sessions that we hosted or participated in. These conversations presented a range of success stories demonstrating how integrated solutions helped brands unlock new possibilities in their marketing. Here are three goals that my team has been able to help brands achieve.

Data integration and unified customer views. In a talk that is available on demand, Alex Furth, Marketing Manager, Digital Innovation from Gatorade shared how Einstein AI and Data Cloud unified fragmented consumer data, enabling effective and tailored marketing strategies. Theresa McCombs Marketing Director, Brokerage Services and Julia Homier Digital Marketing Consultant, from Holmes Murphy told a similar story in the talk “Drive Financial Services Marketing ROI with AI-Powered Data,” where they detailed their journey with Salesforce's Marketing Cloud to enhance customer engagement and data management through centralized automation tools and Einstein AI. Both brands’ successes show how unified data solutions significantly enhance engagement and performance metrics, exemplifying the need for centralized, strategic data management.

Personalized engagement and customer loyalty. Our session with PepsiCo, “Data-Driven Engagement for PepsiCo Tasty Rewards,” focused on their Tasty Rewards loyalty program, which uses Salesforce Marketing Cloud and Einstein to drive loyalty and increase long-term value. They achieved a 100% increase in open rates and a 170% increase in click rates. If you missed the talk, you can still learn more about PepsiCo’s approach to scaled personalization from a different angle in a previously recorded webinar.

Meanwhile, Broadway Across America showcased how Salesforce’s solutions personalized customer experiences, significantly increasing month-over-month (MoM) subscriptions. “My favorite part of Connections was talking about some of the innovations we’re helping Broadway Across America with, mainly the SMS texting strategy for them in 25 different markets, and they’ve seen awesome results,” my colleague Amy Downs, VP of Commercial at Media.Monks, noted. “Their MoM subscription increase was 7%, compared to a 0.14% increase before we implemented that strategy.”

The lesson: data-driven engagement strategies drive significant increases in subscriptions and long-term customer loyalty—an important consideration for marketers who are embracing product-led growth strategies.

Marketing automation and strategic alignment. Another significant consideration that Theresa and Julia at Holmes Murphy emphasized was the importance of consolidating varied automation tools. By implementing Einstein AI and aligning marketing strategies with business objectives, together we were able to streamline operations and surpassed industry engagement benchmarks. Centralizing operations through consolidated automation tools not only boosts engagement but also enhances overall marketing efficiency, demonstrating the critical role of integrated, strategic automation in achieving business goals.

That’s a wrap on an event that’s all about connections.

Attending Salesforce Connections was an exhilarating experience, showcasing the transformative potential of integrating AI and data to drive marketing innovation. The success stories from brands like PepsiCo, Gatorade, Broadway Across America and Holmes Murphy highlighted how unifying data with Salesforce's powerful tools opens up new possibilities. These brands have achieved remarkable success by leveraging coordinated workflows and seamless data integration, and I’m excited to continue supporting brands in their journey to do the same by unlocking the full potential of their CRM and the technologies like AI that rely on it.

From founding a specialist marketing evaluation and modeling agency to co-founding the Measure.Monks—our data-driven team that builds marketing effectiveness models to help brands deliver more profit—my professional journey can be divided into four milestones: starting, scaling, merging and integrating a business. In this piece, I aim to share my experience (instead of unsolicited advice) with the hope that established and aspiring founders can draw inspiration from it.   

Starting a business 

Looking back, this might have actually been the hardest part of it all. Let’s just say it takes a pretty large leap of faith to go from the comfort of having a salary and holidays to being completely responsible and accountable for your income. People’s common perspective is that you can work when and how you like, but my reality looked quite different. No, I didn’t have a direct boss—but I did have to constantly be on hand for clients to get the business off the ground. In my mind, any day off was a day lost in growth. 

Those early days were testing times, but resilience and hard work got me through them. While resilience is required in taking the bad breaks at the start (which I believe happens to test your mettle), hard work is needed to create more leads to increase your chances of bringing in bigger projects. In hindsight, having a co-founder would’ve helped enormously, but I’ve always been lucky that my wife knows the industry and is incredibly supportive. 

Scaling a business

With more work came more income, and as the team grew, the pressure eased. Upon reflection, I realize how reliant you can be on one client in the early days, which is a very precarious position to be in. If they drop you, your business drops—and this means making tough calls about the team. Fortunately, I could always lean on my advisor and chairman Paul Edwards, who was an invaluable coach, highlighted things I’d overlooked, and helped me manage and expand the team. I’ve found that having an external advisor is not only what kept me sane, but what kept our company’s standards high. In turn, these standards drove our mission, vision and values, which proved key to attracting and retaining talent. 

Merging a business

After years of great growth, we reached the point where we had nurtured an incredible team and built amazing market-leading products. But to truly accelerate our growth, we had to go global—and needed a partner to do so. Avoiding private equity as cash wasn’t the issue, our focus was on access to clients and facilitating global growth. After a lengthy scouting process, we were introduced to Media.Monks. We were immediately blown away by their agility, sheer focus on groundbreaking innovation, and culture of entrepreneurship. Making the merger decision may have been nerve-racking, but we knew we had the support of our people, whom we kept informed along every step of the process.

And what a great decision it was! Our merger was handled superbly by SI Partners, who managed our pitch process and the offers that led to the Letter of Intent, all the way through due diligence, legal and finally signing all the agreements. Having heard horror stories about this process taking up all of the leadership’s time—with a suffering business as the result—I was not looking forward to it, but our M&A partners made it easy to navigate. 

Integrating a business

Wasting no time, Media.Monks quickly initiated integration. Turns out, they are pros at this. As a dedicated Post Merger Integration (PMI) team made everything run smoothly, we immediately felt part of the team. They provided a detailed plan of everything we needed to fall in on, like accounting practices, legal, HR, CRM software, audits and more. However, allowing us to move at our own pace was the real value of the PMI team, which made us feel comfortable in the nearly 12 months it took to fully merge.

On the business side of things, we jumped straight into the global network, sharing our story with any team that would listen, which was met with sincere interest and support. These last few years have led to significant global growth, as we’ve not only gained clients in new markets, but also expanded our Measure.Monks team. At the moment, we have talent located in New York, Toronto, the UK, Buenos Aires, Melbourne and Singapore, and this list will only keep growing. 

While our team folds into the data pillar, we seamlessly work across our media, content and technology pillars. As a result, we regularly venture into new territory, from supporting our agility-focused media teams to running creative measurement and optimization with our content teams to developing new products with our data teams. There’s so much more to be explored, created and delivered—especially given the recent uptake in usage of AI and automation—and that’s why my excitement about this journey never wavers.

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.

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.

For the new generation of consumers, a brand that keeps its distance on social media is not a memorable one. Quite the opposite. Audiences today have come to expect a degree of relatability—and above all, a real understanding of their interests and necessities. It’s not about keeping up with the top 10 TikTok trends; rather, about thinking and creating like consumers. 

Marketers who have a solid grasp of this are expanding their businesses and promoting their brand just as with word of mouth. But they are not doing it alone. Community managers are one of the main players in the game of hooking consumers—even if their role is often simplified and associated with junior professionals who are just starting their careers. As consumer behavior continues to evolve, we need a new approach to community management that understands its importance and allows us to harness its true power. Here’s what that looks like.

AI and automation meet an increased focus on being human.

As previously mentioned, consumers are more likely to engage with brands that demonstrate some sense of humanity. And if they are not afraid to show an actual human behind the screen, all the merrier. Community managers today are spending less time solving problems and more time sharing their own opinions, experiences and emotions—acting more as entertainers and relatable friends than customer service agents.

If we think about the spaces where consumers connect with brands, these are mostly global digital platforms with a demand for always-on interaction. One of the ways that world-class brands deal with this expectation is by hiring community managers in a bunch of different time zones so that they are manually working round the clock to serve them. However, by incorporating automation tools—such as social bots or other applications of AI—you can also offer on-demand attention and instant solutions so that the users feel supported 24/7 while CMs focus on being creative.

In other words, these tools manage all the liking, retweeting and answering of repetitive queries so that community managers can better direct their energy toward inspiring real connections with people.

On top of that, you can have fun with it. Working with the television series Atlanta, for example, we created their own custom AI bot to take over the show’s official Twitter account for a week. In a joint effort between Jam3, Cashmere and Media.Monks’ teams, we trained it on every tweet from the Atlanta handle. Then, we used Twitter’s new edit function to tease out a takeover that had communities on Reddit and Discord following along.

Web3 fosters a spirit of participation.

Many factors have pushed community managers to expand their roles, and as long as new platforms keep emerging, they’ll continue to adapt and evolve. With its values of collaboration, decentralization and power-to-the-user, Web3 is already changing the way we engage with communities, switching the focus from “talking to” to “participating with.” In that landscape, community managers will need to be quick on their feet and feel prepared to appropriately engage with consumers—whether that means communicating through a virtual avatar or even hosting an auction of NFT artwork. 

What’s more, commerce is going live—prompting brands to blend communities and real-time connection to offer entertaining interactive experiences. While influencers or digital creators are typically the stars of these events, community managers play a fundamental role in moderating and executing these activities. 

Community managers are becoming more involved in creative processes.

As virtual worlds evolve and virtualization emphasizes the spirit of collaboration, brands have an opportunity to give more thought to the role that community managers play within their team. Instead of simply asking ourselves what new platforms to join, we need to follow it up with, “What should be the purpose of the CM in each one?”

Not all brands need to have the same approach, but one thing is certain: when community managers are invited to creative rounds, campaign briefs and content calendar meetings, they are better equipped to create the kind of brand experience that social media managers and creatives are working so hard on. What’s more, they can provide unique insights they’ve gathered from interacting directly with consumers.

In a world where brands need to be active listeners and co-create culture alongside their audience, community managers are key liaisons between the two. As their role evolves, we need to get rid of the simplistic view of posting, responding and reacting—understanding that they have the power to create brand love and a direct impact on the brand experience. Let’s move away from the concept of community managers as an exclusively intern-level position and recognize the importance of elevating the role of those communicating directly with consumers.

CRM is crucial to any first-party data strategy because it sits at the center of every customer interaction: through behavioral and environmental triggers, your customer is feeding inputs that influence future experiences, like product recommendations and personalized messaging. Essentially, data makes personalized experiences possible—and when done right, those experiences in turn generate more data that brands can act on. With the death of the cookie on the horizon, these insights will become even more critical to your marketing strategy.

There’s no better time than now to unify data within a CRM ecosystem to improve the efficiency of teams, inform future business strategies and, of course, enhance customer experiences overall. These efforts involve building data pipelines that help them better learn about their customers and engage with the right message at the right time. With the help of automation, a powerful collaborator that helps teams pull off outcomes that only eluded them before, the sales team can focus on only the most qualified leads. 

Not sure where to get started? No worries; I’ve gathered a couple brands who have successfully transformed their CRM ecosystems to fuel personalized experiences at scale.

Translate behavioral cues to key business insights.

Beyond driving conversion, one of the most impactful results of a strong CRM strategy is being able to leverage behavior data to guide better consumer experiences—of which Australian Community Media (ACM) makes a prime example. ACM is a large media organization that operates over 140 local news mastheads across Australia, serving both free visitors and paying subscribers. That’s a lot of relationships to manage and readers to serve. To those ends, the brand relies on email marketing and onsite personalization via Salesforce Marketing Cloud to reach and continually engage with readers.

ACM wanted to better understand subscriber behavior to create more personalized, relevant experiences in the form of automated content recommendations. Previously, this content was manually selected by editors or determined by publish date. Using Marketing Cloud Personalization, we were able to pull from subscribers’ engagement and platform behavioral data (like affinities toward news categories) to build personalized recommendations—boosting not only relevance but also employee efficiency.

This data did more than simply help serve personalized content to email subscribers. Armed with insights into which topics readers enjoy the most, editors can now easily plan out future content and focus on the kinds of stories their readers care about the most. More broadly, these same insights allow ACM editors to better predict engagement across the user journey—showing how CRM data can extend beyond marketing to unlock critical business insights that ultimately serve audiences. The best part: automated content recommendations free the editors to focus more on these strategic concerns of how to build better impact.

Elicit engagement to personalize at scale.

If you struggle to glean insights from audience behavior, here’s a tip: make it as easy as possible for customers and prospects to tell you more. This simple step was the cornerstone of Woodlea’s CRM refresh. Woodlea is a master-planned community of 7,000 lots located 30km west of Melbourne. With a need to focus on buyers at the right time, their sales representatives wanted to be able to give special attention to novice buyers. But this posed a challenge: how could they personalize communication and experiences at scale?

We began by helping the brand insert forms into email sent to buyers, a move that increased engagement while generating significant user data in the process. The newly interactive emails included simple questions and a prompt for recipients to build out their profile in Woodlea’s customer portal (powered by Salesforce Experience Cloud). The fact that these forms were embedded into the actual email content made it a seamless user experience and increased the percentage of leads who engaged. This first-party data then fed back into Woodlea’s Salesforce CRM, allowing for automated lead nurturing and qualification. These efficiencies freed the sales team to focus on two key buyer personas: those ready to make a purchase and first-time buyers who needed more attention throughout the buyer’s journey.

Enhance CRM to start building your first-party data foundation now.

The best time to transform your customer experience was yesterday, but there’s still time before cookie deprecation to experiment with new ways of generating first-party data—and CRM is at the heart of the process. From eliciting user engagement to gain key insights, to building efficiencies through automation and automation, linking data and inputs across a connected CRM ecosystem goes a long way in serving stronger, more personalized customer experiences and key business goals—so don’t wait.

Need help or don’t know where to start? Reach out to learn more.