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Marketers looking out at today’s digital advertising landscape see a tidal wave of change, with new rules to play by and an increasingly elusive path to success. Many are frustrated by the sheer number and complexity of data sources, overwhelmed by the deluge of tech tools, and suspect that somewhere along the way, they’ve lost sight of the fundamental marketing goals of brand building and audience connection.

Struggling to keep pace with innovation and to meet resource constraints, marketers say they have been more focused on defending ROI than on pursuing brand transformation. In place of time spent on strategy, category disruption, messaging creativity, and the customer experience, marketing leadership is focused on how to stretch resources and often finds their teams bent on daily, rote tasks like creative rotations and bid adjustments.

As pioneers in autonomous AI, Albert’s aim is to help marketers regain their true north. We are paving the path forward in the post-digital era to unravel the evolving rules of digital advertising, the role of autonomous AI, and the new tenets that will enable marketers to drive transformative business impact.

Before we share our path forward, we’ll take a look at the critical developments accelerating change and compelling innovation in the market today:

#1 From Mass to Hyper Niche

For more than a decade, marketers have relied on paid advertising on major digital platforms such as Facebook and Google as a primary tactic to drive brand awareness, customer acquisition, and conversion. As these platforms reach advertiser and audience saturation, new platform adoption slows, and audiences split their attention across platforms and devices, we find increased spend doesn’t necessarily translate to increased return. 

Marketers in this ecosystem are realizing they can no longer win by simply increasing budgets, but that they need to deploy many micro-optimizations (at greater frequency) in order to achieve macro gains again. Winning strategies today are those that lean on actionable learnings to guide media optimizations at each phase of the customer journey, uncovering optimal and tailored methods of catching attention amidst advertising chaos, piquing interest with the right messaging shown to the right audience, and delivering utility where it’s most appreciated. It is a movement away from blunt strokes in media execution to hyper niche targeting and creative messaging. 

In order to maintain gains in a saturated marketplace, marketers have moved to invest heavily in niche approaches – and subsequently overhauled the way media operations, partner relationships, and investments look to match.

#2 Reaching for the Longtail

As audiences have shifted their attention and time spent to smaller, endemic platforms, they created a new and significant opportunity for reach and revenue generation that brands could not ignore. However, trying to reach customers in the longtail presented additional, complex challenges for marketers – now compelled to consider the pros and cons of investing in programmatic, dynamic creative, and the tradeoffs that follow. Ultimately, digital marketers betting on the promise of programmatic to deliver efficient reach against dynamic and niche audiences, have often been disappointed. In an effort to maximize efficiency, effectiveness often took a backseat. Marketers learned that substantial investment (both time and money) is required to move ad budgets manually against valuable micro audience groups, often missing the true opportunity of incremental audiences. 

Digital marketers have begun to demand more sophisticated tools to help plan, build, optimize, and measure audience activity in the face of growing platform complexity and a returning focus to effectiveness, not just efficiency. 

#3 Managing Data Complexity

The customer journey continues to move toward greater complexity and marketers find they need to do more – just to stay in the game. To supplement the hard work of developing traditional and dynamic creative, creating compelling media plans and relevant buys, they insist on more robust measurement and analysis to see their audiences in full view and take more meaningful action.

In an effort to solve for this, marketers look to new tech partners to fill in the gaps in the story. Data vendors offer reams of data on web behavior, mobile insights, shopping propensities, influencer affinity, credit card purchases – resulting in many partial views. And campaign management tools bring to market specialized capabilities that offer another snapshot of the full picture, such as measuring discrete phases of the journey, providing siloed optimization, or calculating ROI by single channel. Such additions to the tech stack often compound complexity, despite the intention to simplify. And still, holistic views such as full funnel ROI, cross-channel optimizations, and comprehensive analyses of the customer journey remain critically absent. 

Marketers continue to crave actionable insights across their efforts. This coveted state remains out of reach due, in part, to the inability to assess and take action from the data that lives across their tech stack. 

The Path Forward

The way we see it at Albert, marketers must develop innovative strategies fit for this new landscape in order to create and sustain gains. By doing so, we reclaim the greater and original function of marketing: building a powerful brand and audience connection. 

In this series, we’ve mapped the key tenets of digital marketing as they apply to brands today, with the aim of guiding advertisers through tumultuous change in the post-digital era. We’ll touch on how to hire and deploy the right teams to win the machine + human opportunity, we’ll share our approach to pursuing customer obsession successfully, and unveil methods of maximizing your data use and application. In each post, we will share our view on how marketers can employ autonomous AI to achieve renewed marketing impact in today’s landscape and ultimately, restore its greater purpose. 

Read more in our first post of the series – Tenet #1: Recruit for Human Intelligence.

Major retailers like Amazon have been using advanced technologies like artificial intelligence (AI) seemingly since the beginning. So when brands set out to go the direct-to-consumer route, they have to quickly master cutting-edge retail tactics and adopt new technologies to challenge, or at least keep up with, these established competitors as quickly as possible.
There’s an especially steep learning curve for brands that choose to execute on these new strategies in-house, using artificial intelligence to scale their team and reach rather than relying solely on an agency.

This shift is not limited to retail brands. Financial institutions, telecommunications companies and other traditional industries are also assuming the role of seller — and increasingly, of agency — to take a more direct approach to customer experience and acquisition.

My company recently commissioned Forrester Consulting to understand brands’ increasing adoption of artificial intelligence in this changing climate. Forrester spoke with 156 marketing decision-makers in retail, CPG, food and beverage, financial services, telecommunications, software and travel and hospitality to take a closer look at their use and applications of the technology.

Their research revealed an opportunity gap between how marketers are currently using AI versus how they could and should be using it. Here are three ways brands can bridge that gap:

1. Redefine the division of labor between humans and machines

In 2016, Forrester conducted a similar study (via MarTech Today) that revealed over 40% of marketers were using AI. This number has now more than doubled, with 88% using either assistive AI or autonomous AI technologies. The distinction between these two types is important as it highlights an important part of the AI opportunity gap — or, where brands are versus where they have the potential to be.

Where brands are: Seventy-four percent of respondents report using assistive AI technology, which surfaces insights for marketers to consider during manual decision making. The remaining 26% are using autonomous AI, which can act on its own insights and work collaboratively with marketers.

Marketers using AI in an assistive capacity are experiencing similar complexities in their processes and operations as they were before AI adoption. While an assistive approach may speed up certain campaign-oriented tasks, it’s limited by its reliance on humans to make decisions and manually complete tasks.

Where they have potential to be: AI has far more to contribute if we start thinking about it as a collaborative and autonomous system for scaling marketing campaigns rather than cool technology that exists solely to help brands make semi-informed decisions. Even equipped with machine-generated insights, marketers don’t have all the information they need to make the same number of decisions with the same level of clarity and act on them at the same scale.

Marketers must redefine the division of labor between humans and intelligent technology. Humans will tackle all things creative, strategic, intuitive and emotional. AI will take on all things data gathering and analysis and then act autonomously on the insights it surfaces.

This shift is inevitable. According to a global study by Pegasystems and Marketforce, “Sixty-nine percent [of marketers] said they expect the term ‘workforce’ to eventually encapsulate both human employees and intelligent machines. “

2. Treat AI as a fundamentally different kind of technology

Today’s direct-to-consumer marketing is all about creating personalized, high-touch experiences for consumers. As the lines between our digital and physical worlds blur, brands need to work at the speed required to provide consumers with the experiences they want, at the cadence they need, across online and offline channels, wherever they are in their journeys.

Brands need to understand where they are in their own AI journey as well. Based on our report, only 43% of marketers said that their technology was helping improve customer experience. Thirty-nine percent said it’s helping increase customer retention, and only 33% credited it with increasing customer acquisition.

These are not just nice-to-haves; they’re make-or-break brand and business objectives. If brands are using AI but not winning in these places, it’s often because they’re hanging onto manual approaches or are struggling with the idea of relinquishing control to a “robot.”

Though some marketers fear working with an autonomous machine, AI can’t do its job on its own. A machine will never have a marketer’s deep knowledge of a particular brand, nor understand the subjective factors that influence a customer. Its job is to translate customer insights into actionable marketing outcomes; collect, integrate and manage data; and operate fast enough to keep up with the rapid pace of interactions.

This shifts the role of humans so that we do what we do best while pulling us out of the data weeds once and for all.

3. Consider whether it makes sense for a brand to ‘become an agency’

Marketers’ relationships with their agencies are changing. In 2016, 37% of marketers reported feeling overly reliant on their agencies for driving marketing strategy. This year we’re seeing a complete shift, with 42% of marketers saying that they’re exploring the potential of “in-sourcing,” or taking part of their digital media and/or creative in-house, and 24% already intending to do so.

Brands like Unilever are leading this new trend toward in-sourcing select marketing functions and making it look very appealing by sharing numbers such as the €500 million it saved in 2018. As Unilever discovered, there are, of course, challenges that come along with replicating agency teams — namely, hiring, retaining and organizing staff, and scaling operations.

AI is one of the strategies brands are deploying to make in-sourcing possible. Insourcing is not just about adopting AI, though; it’s about restructuring internal and external teams, bringing in an AI operator to act as a go-between between the machine and creative teams, and collaborating with the technology in an autonomous capacity rather than simply using it.

Moving from an assistive solution to an autonomous one is the first step in bridging the AI opportunity gap. From there, it’s about rethinking the division of responsibilities between human and machine, adopting a forward-thinking approach to working with this new technology, and deciding which capabilities are absolutely strategic to in-source and which are better left outsourced.

Article previously featured in Forbes, Three Ways Brands Can Bridge The Opportunity Gap, June 24, 2019

 A new study, conducted by Forrester Consulting on behalf of Albert, revealed that 88% of marketers today have adopted – or are in the planning stages of adopting—artificial intelligence, and that the benefits vary according to both the type of AI they have adopted and their application of AI in their marketing programs.

Of those that have adopted an AI-driven marketing solution, 74% of respondents reported using assisted AI technology, which surfaces insights for marketers to consider during manual decision making. Only 26% of marketers reported using autonomous AI, which can act on its own insights and work collaboratively with marketers.

Check out the infographic for more findings about marketers’ journey with AI.

The once-clear boundaries between brands and retailers have faded. Brands have learned how to be retailers, and retailers find themselves in competition with the very brands they carry, which are now selling directly to consumers.
This has resulted in an e-commerce marketplace with twice as many sellers as there were just 10 years ago. Consumers are overwhelmed with choice. The price of customer acquisition has skyrocketed. And with so many touch points across devices and channels, brands and retailers can barely keep up.

This complexity and the resulting deluge of data is too daunting for humans to handle alone, but it presents an ideal environment for machines. Having worked with pretty much every kind of direct-to-consumer (D2C) brand as they turn to artificial intelligence (AI) to handle different aspects of their customer acquisition and paid digital marketing efforts, I’ve identified three distinct learning curves that marketers inevitably encounter along the way.

1. AI thinks in terms of micro-personas, not buyer personas

Not all AIs are created equal, nor do they all do the same thing. Some AIs surface insights for humans to act on, while some act on those insights autonomously. But none of them act like a know-it-all. Successful D2C brands and e-commerce marketers have often done a ton of work to identify and get to know their audiences, which AI will likely confirm, but in seeking growth, they shouldn’t limit AI to their knowledge alone.

Given the chance, an autonomous marketing AI will explore every nook and cranny of the channels it’s working in to uncover opportunities and audiences in its own way. While brands might define their audiences in the form of several distinct buyer personas, complete with psychographics, AIs generally creates thousands of micro-personas. Examples of this might include “people who search for diamonds online respond to motorcycle ads at a disproportionately high rate,” “men who identify as engineers on Facebook are engaging 300% more with jeans ads than those in other professions,” or “men over the age 65 in Sydney, Australia, who like to go flying are likely to buy experiences as gifts for friends and family.”

Often times, these micro-personas don’t fall neatly into existing buyer personas, which presents brands with an opportunity to gain an understanding of the long tail of their customers. While it can be tempting to reject these new audience types as inherently “wrong,” AI reaches statistical confidence much quicker than a human does.

Marketers can use these insights in many ways. The most immediate would be to introduce micro-campaigns for high-potential micro-personas. Such a campaign might consist of highly targeted visuals and copy tailored to resonate with these niche audiences on a granular level. Upon clicking on a promotion, these consumers could be taken to a dedicated landing page, designed and populated with content specifically for them, resulting in a consistent experience and narrative, from initial ad engagement to the brand’s site.

2. Different rates of creative fatigue mean that testing and learning take on new importance

Direct-to-consumer marketers are constantly surprised to learn how quickly consumers suffer from ad fatigue. In other cases, they’re surprised to discover creative exceptions that continue to work profitably long after brand managers think the ads should be retired.

If you’re a part of a retailer or brand that’s using AI in digital advertising, then you need to make sure you have strategists on the team who can understand what’s working, what’s not, and why, and provide appropriate direction to creative resources who can then produce content (images, videos, headlines and captions). And since AI targets hundreds to thousands of micro-personas, rather than only a handful of buyer personas, brands are able to test every idea they can come up with to personalize their efforts to a far more varied consumer mix.

3. Autonomous doesn’t mean … autonomous. Don’t forget to implement your humans

Left to its own devices, AI will focus purely on making data and technology decisions that lead it to the goals it’s given. Brands are great at setting revenue or customer acquisition targets, but it is important to have an AI that can accept various kinds of guardrails. Without established boundaries, some of the decisions an AI will make in pursuit of meeting its goals might be off-brand, appear ill-informed or just seem odd to the consumer on the receiving end.

Take, for instance, brands targeting very specific markets (such as those selling warm-weather clothing during the winter), buyer types (e.g., luxury shoppers) or demographics (baby products or men’s shoes). In the case of a luxury marketer, the AI might discover that a discount offer is performing particularly well and therefore scale the use of the promotion to meet its acquisition goals even quicker. But the brand, not wanting to be viewed as a discounter, will likely see this as a brand positioning fail.

AI also doesn’t know it should be showing a dedicated creative set on holidays; it only knows what creative is converting. It doesn’t know which topics are sensitive; it only knows that some topics attract more visitors than others.

There are also more nuanced kinds of considerations that human strategists must share with AI. For example, a customer of ours, a direct-to-consumer sustainable meat and fish grocer, finds that when there is mention of a food recall in the news, consumers become more aware of their food choices. AI isn’t tuned into the news and wouldn’t know about a salmonella outbreak, so it relies on its human colleagues to equip it with specific messages that cater to consumers looking for information about what they’re putting in their bodies.

Long story short, even artificial intelligence needs human insights and strategic guidance to be successful. And while AI might discover new high-potential audiences, it’s up to the brand to determine its approach to interacting with and engaging those audiences.

Brands that set up guardrails and master the art of collaborating with a machine rather than simply operating it can then let the machine run free to do its job.

Article previously featured in Forbes, What D2C Retail Brands Need to Know Before Using AI For Customer Acquisition, April 17, 2019

To address the complex challenges created by today’s digital marketing landscape, marketers are looking to AI technologies to analyze and understand huge amounts of data so they can develop strategies to optimize customer engagement. They need deeper data analysis, the ability to understand campaign performance, faster decision-making, and personalized and optimized campaigns coordinated and executed across multiple channels.

According to a recent commissioned study conducted by Forrester Consulting on behalf of Albert, the number of marketers leveraging AI technologies has increased from a mere 43% in 2016 to an overwhelming 88% in 2019. Yet, in the same survey, only 50% of marketers said that their current marketing technology was able to provide marketers the ability “to gain more direct control over digital media buying”. The percent of marketers who reported that their current marketing stack was able to deliver on other key marketing objectives was as little as 33%.

Why the paradox between the high adoption rate for AI and the underwhelming impact marketers have seen? Is there a misalignment between the expectations for AI and its ability to deliver on marketers’ needs? In short, the answer is no. The limited impact of AI technologies today is the result of how it is being implemented by technology providers and used by marketers.

In fact, research shows that the way in which companies adopt and implement AI technologies has a huge impact on its ability to deliver on key marketing KPIs, such as improved campaign performance and ROI. Specifically, the research compares the impact of AI-assisted marketing to that of AI-autonomous marketing.

Marketers using an AI-autonomous approach adopt AI more holistically. Autonomous AI marketing employs the technology to move beyond the operational tasks of data analysis to decision-making and execution within as well as across channels.

By leveraging AI to drive the process from data collection and analysis through to strategy and execution, it allows the technology to measure engagement data, make recommendations, and execute in near real-time. It also enables the ability for true customization and personalization as AI has the capability to analyze and manage vast amounts of data and to act on a scale that even a small army of marketers couldn’t match. With this approach, AI-autonomous marketers are better able to harness the full potential of AI technologies.

To find out more about the challenges marketers face in adopting AI technologies and how marketers are successfully implementing AI, view our recent webcast with Forrester.

When the concept of digital transformation in marketing moved beyond early adoption and hit critical mass earlier this decade, marketers shared a vision of seamless digital technologies that would replace their cumbersome manual processes. Their thinking was simple: “What I’m handling manually right now, technology will digitize for me.”

So they reviewed their channels and tactics from top to bottom to determine where technologies could create efficiencies, and what it would look like operationally and culturally to get their teams to adopt these new systems. Because hindsight is 20/20, we now know that they ultimately swapped their cumbersome manual processes for a series of cumbersome digital systems.

Marketers didn’t know it then, but we’ve since learned that digital marketing technology should not be organized into channel silos the way we’ve traditionally organized teams. We now know that data collected from one channel needs to inform efforts in every other channel and that technologies that were introduced as channel-specific tools now need to work across entire organizations — something even the marketing clouds have trouble with.

Because of the way marketing technology has evolved, marketers are left managing very complicated tech stacks comprised of multiple technologies, stitched together to complete what should be seamless and interconnected marketing processes. It’s no wonder that even though companies have more technology at their disposal than at any other point in history, only 39% of executives today say they feel they have the digital capabilities they need to compete.

As someone who has spent the last decade reimagining how to process, analyze and act on audience, channel and tactic data at scale, I believe the introduction of artificial intelligence (AI) will be the final tipping point for marketing’s digital transformation — despite challenges that remain. Here’s how. 

1. Digitization Will Become Intelligent

One fatal obstacle along digital transformation’s journey to uncomplicate interconnected business processes like marketing is the idea that simply digitizing systems or going electronic would be transformational. The goal post has since shifted to reflect reality: Transformation doesn’t result from merely digitizing manual tasks; it comes from automating entire processes and leaving humans to guide strategy rather than execution.

This requires handing over data processing, analysis and pattern discovery to intelligent machines that can autonomously and instantly act on insights. At the same time, organizations must recognize that these remarkable digital systems can only go so far without talented human guidance.

2. Humans Will Apply More Of Their Intelligence

The introduction of artificial intelligence to digital transformation initiatives will result in a dynamic where employees no longer “use” technology, but collaborate with it. Successful AI transformation will be characterized by a symbiosis between man and machine, where each does what they do best and uses their individual strengths to heighten overall levels of performance. Humans, for example, are needed to guide the AI on matters of strategy, brand and customer experience. By doing this, these systems function as true collaborative partners, amplifying their capabilities beyond the limits of a single human or simple automation. When these hybrid human-machine teams learn to interact and experiment, they create entirely new possibilities and outcomes for companies.

3. Technology Will Become Cross-Linked Into Full Processes

Digital transformation efforts have resulted in massive amounts of valuable data, and there are now technologies smart enough to take this data, learn from it and orchestrate campaigns across channels using existing technology stacks. I believe we’ll see many companies fully realize digital transformation by doing exactly this.

The AI will enable marketers to bring together disparate technologies using the ability to ingest and process massive amounts of data and find patterns in the noise that yield unexpected insights and results at lightning speed.

Challenges Ahead

Each of these three steps is, of course, a massive undertaking, and AI transformation certainly won’t happen overnight.

There are a few common challenges we’re seeing that will inevitably delay companies, across all industries and use cases, from realizing full AI transformation.

One such challenge is the seemingly innate human need to tightly control artificial intelligence systems while getting comfortable with them. Giving only half of the control to an AI, however, will only yield half of the learnings. Any AI system requires a certain amount of testing and data accumulation in order to learn and ultimately perform. Though this period of learning can be relatively short, humans tend to become impatient and step in to manipulate the process, rather than letting the machine run. Other times, this is less a matter of lack of patience and more a response to seeing the machine tackle problems in a different way than the human would. That tends to make humans want to control its process.

AI developers must bear some of the responsibility for both of these responses by guiding users through the upfront adoption process. We learned fairly on, for instance, that we cannot be passive technologists — we must be educators, too. Companies’ reaction to this upfront learning curve will set the tone for the rest of their experience, so they must be coached to let the AI go through its process without interruption no matter how tempting it is to step in and guide it.

On the complete opposite side of the spectrum are companies that give the AI too much control without giving it a strategy. When companies view AI as a magic bullet, they often make the mistake of sitting back and not setting parameters that will guide it toward their desired outcomes. Make no mistake: Artificial intelligence is dependent on — and better because of — human intelligence.

Both of these scenarios point to issues that arise when companies don’t recognize that there’s a clear division of labor between man and machine. The first example illustrates a situation where humans are too involved, and the second illustrates a situation where humans aren’t involved at all.

Mastering the fine line of giving up control on everyday execution and retaining control of strategy will be critical to organization-wide AI transformation.

Article previously featured in Forbes,Why AI Transformation Is Digital Transformation, Fully Realized, February 11, 2019

Today we’re introducing Albert’s new logo and brand identity. This next step marks a significant milestone in our journey, reflecting an evolution of everything that the original Albert brand stood for and all that we aspire to become.

Over the past few years, Albert has experienced tremendous growth. We’re grateful to have become a trusted partner to brands and agencies around the world who agree that there’s greater value in human imagination than the rote processing of information.

Digital marketing teams who adopt Albert discover that the collaboration between man and machine achieves results neither could operating alone. Albert is the leading edge of an entirely new kind of workplace experience. Our customers are pioneers who discover that their partnership with an AI can only be described as having Unprecedented Impact.

That was the inspiration for the new Albert logo and our entirely new look. Inspired by our mission to help digital marketers keep up and win with today’s consumers, this next phase in our journey acknowledges that the AI transformation we are seeing is rapidly moving beyond the early adopters, and that the mainstream is now ready to open their minds to a radically more effective way to plan, allocate, optimize and attribute their digital campaigns.

Our new graphic system is a depiction of impact visualized. Albert is a collection of over 200 skills, all evolving, working in concert, enabling a machine that analyzes the unanalyzable to take purposeful action. We chose a design with elements that permit us to represent this complex collective intelligence and what it can do for marketers.

Behind the new look we’re still the same rapidly growing company and team (of mostly R&D, data scientists and engineers), dedicated to providing our clients with the autonomous AI that becomes their digital marketing ally.

There are seemingly thousands of artificial intelligence solutions for marketers — but only four questions marketers need to ask to differentiate between them.

Does this problem really require AI?
Brands should have a defined problem set or desired outcome in mind before considering AI. Their challenge or objective should guide their AI journey — and reveal whether they actually need AI or not.

For example, if creative optimization is the goal, consider whether adding an AI vendor into the mix will change results significantly. For brands with a strict, relatively small creative set, no AI system will help them better understand if changing “click now” to “buy now” will have impact.

On the other hand, for brands with hundreds of thousands of creatives, AI can help sort the performers from non-performers by revealing the signal in the noise.

Vendors should also be able to explain why traditional methods have failed until now, and why AI is necessary to succeed.

What does the machine do — versus what does your marketing team do?
Do you want the AI to step-analyze data and provide you with recommendations you can execute on your own? Or do you want it to take actions on your behalf in pursuit of KPIs?

An AI that helps with decisioning vs. an AI that makes and acts on the decisions in real time operates at two very different altitudes. For organizations that aren’t particularly wedded to one option, an alternative question to ask is, “What level of automation is sufficient to solve our workflow and scaling challenges?”

Will it play well with other systems and data?
AI requires massive data sets to perform best, so marketers will want to be able to integrate it with other systems, giving it access to many datasets created by their efforts. The two questions to ask here are: Can the AI at hand be integrated with your brand’s other systems? And, straight to the point, can this AI be enriched with external data sources?

If the AI can be integrated with existing systems, how long does the vendor’s typical integration take?

Weigh time to market and ability to integrate large datasets against the end benefits among the vendors under consideration.

Who’s building it?
AI is very challenging to build, so it’s important to understand the DNA of the vendor’s team. How much of the team is dedicated to research and development? If, for instance, they have 100 people but only five of them are in R&D, it’s likely they have relatively simple technology.

Also consider who is on the R&D team. Do they have backgrounds from high-profile research universities and have on-the-ground practical AI experience? Or, are even the most senior members of the team learning AI on the job?

AI comes with a price tag, so it’s important for marketers to know exactly what they’re paying for.

Article previously featured in MediaPost’s Marketing Insider, Do You Really Need Artificial Intelligence? How To Decide December 31, 2018

As ads are bought and sold on an increasingly minute-to-minute basis in an effort to create more personalized experiences for more users, the line dividing programmatic from other types of advertising is blurring. Some companies are even erasing it completely.

This December, the New York Times collapsed its programmatic sales department into the larger sales team as one part of a broader reorganization, Digiday reports. While their team had traditionally consisted of two parts — direct and programmatic sales — the new arrangement renders this distinction completely moot. The Times is only one of the many publishers, agencies, and brands who have begun to recognize programmatic as the de facto channel for the buying and selling of ad inventory.

Buzzfeed is another notable example of this trend. Earlier this year, the content aggregator announced that not only would their programmatic team be joining the larger sales department, but also that their programmatic inventory would be opened to the entire sales team. A long-time programmatic holdout, BuzzFeed’s move into automated ad purchasing indicates the skyrocketing importance of digital media sales. It’s also creating urgency within the industry to break down operational silos that might hinder programmatic performance.

The Factors at Play

While several factors are at play in the push to push to merge programmatic and direct sales teams, the most clear-cut reasons lie in the numbers: over 80% of digital display ads are now being bought and sold programmatically. That’s why the Times’ entire sales team received in-depth training on programmatic advertising after the recent reorganization: specialized expertise in other kinds of ad sales is no longer valued the way it once was.

However, the numbers also hint that the industry’s increasing reliance on programmatic is creating some discord. According to the World Federation of Advertisers, less than half of marketers believe their programmatic partners are sufficiently transparent. Many programmatic buyers offer their services as walled gardens, meaning that many of their methods and decision-making practices are hidden from their clients. Distrust over transparency is likely responsible for the recent shift from agency trading desks to media agencies for programmatic buys.

This new focus on transparency extends beyond the buyer-publisher relationship — eMarketer points to discrepancies in knowledge and planning between different sales teams in the same company as one of the primary culprits behind the programmatic silo breakdown. Dan Davies, Senior Vice President and Director of Media Sciences at Mediahub, says he’s seen programmatic and direct sales teams compete with each other over sales opportunities.

“In some cases, the direct salesperson intentionally left the programmatic salesperson out of the situation,” Davies recalled to Digiday. “I’ve seen it be that internally contentious.” Publishers have thus worked to stem infighting and improve lines of communication by merging the two sides of sales into one cohesive team.

On the buyer side, brands and agencies are seeing frustration in their efforts to amp up their programmatic capabilities, with just 18% of brands and agencies reportedly satisfied with their programmatic training. Clearly, brands need help bridging the gap between programmatic and non-programmatic — and AI may be just the thing.

Streamlining the Process

Trying to match your team’s capabilities to an increasingly automated media buying landscape can seem impossible, but it doesn’t have to be. A tool like Albert™ — the first fully autonomous AI marketing platform — can be an invaluable addition to your brand or agency in the digital age.

Fully equipped to handle media buying and campaign optimization autonomously, Albert is already a programmatic expert — and he’s learning more every day. By partnering with Albert, marketers and publishers can ensure that their media buying teams are ready to face the shifting digital landscape.