A Conversation With Boathouse’s John Connors On AI As A Horizontal Game Changer + Reach As A Growth Driver

Originally published in Forbes

As 2023 unfolds, AI continues to be the bright shiny object that everyone is buzzing about, yet few really know what it is beyond a surface level. There is even wide spread discussion of how ChatGPT will suddenly make humans obsolete in every industry from technology to journalism. However, what too many people are overlooking is the slippery slope that exists when AI is used in isolation without any type of ongoing direct human input. As all organizations continue to struggle to acquire and retain customers, being as human led in their approach to operationalizing AI as possible will be critical.

For my latest column, I wanted to speak to someone agency side who is at the forefront of leveraging AI in ways that enhance human intelligence, not replace it. With notable stints at iconic agencies such as Hill Holiday and McCann World Group, John Connors, currently Founder and CEO of Boathouse, is an industry visionary helping to imagine how the best of humanity and technology will enable the world’s leading marketers to best future proof their brands. Following is a recap of our conversation:

Billee Howard: Tell me about the vision of Boathouse and why you think old brand infrastructure is broken?

John Connors: We believe that the traditional brand management model is no longer viable. It is like an old car with too many miles that you simply can’t trust to get you to your destination. And yet, marketers have a fondness, a nostalgia that makes them unable to abandon the old rust bucket.

When the idea of brand management was introduced in a three-page memo by Neil H. McElroy at P&G in 1931, it was visionary. Mr. McElroy ran advertising for Camay soap, and he needed to differentiate Camay from Ivory. The solution matched the problem and brand management proved an exceptional model for almost 100 years.

But today, the problems companies face have changed, the scope has changed and the speed has changed. Leadership teams must manage across multiple stakeholders ranging from employees to investors, media, consumers, regulators and community. They also have to contend with multiple issues that cut across the spheres of the economy, politics, the environment and business, in a marketplace where brand and reputation are converging. Yet interestingly, many marketers still seek inspiration from the increasingly dated brand management playbook.

Howard: You mentioned you view AI as a horizontal game change. Tell me more.

Connors: As you know, marketing has reinvented itself numerous times since brand management was introduced in 1931. Marketing has evolved as the dominant channels dictated. Throughout these cycles of change, marketing departments have continued to add on to their org charts, building new departments to manage new channels. Today, most marketing org charts have some combination of brand, digital, social, analytics and communication specialties. Each specialty has a team, a budget and an agency partner and the unintended consequence is that we have created silos that, along with antiquated strategy models, are creating drag on speed and performance.

Now you add A.I. to the mix - the latest market shift - and it gets interesting. A.I. does not respect silos. A.I. wants to work across brand, acquisition, news, digital, social and data. We are starting to face a new “manager vs. machine” battle. A.I. will challenge the control and the sprawl. CMOs will be forced to deconstruct their org charts to drive performance. But it is not just the org charts that will be challenged. A.I. will also challenge the brand management model and will force marketers to look for new strategic frameworks that are built for this era. A.I can handle multiple stakeholders, multiple issues, constant change and speed. Marketing departments, as currently constructed, cannot.

Howard: We spoke about the economics of narrative. Can you share more and why this is such an important emerging way to look at brand?

Connors: In our search for new, more relevant models we started inside the marketing thought leadership circles. Unfortunately, there are simply not many new strategic models in marketing. Today a search of “marketing thought leaders” reveals a collection of CEO stories, media personalities and academics riffing about brand management. The leading marketing thinking of today seems more focused on selling books and building followers more than truly original models.

This led us to search outside of marketing. In our research, we uncovered a model called Narrative Economics from Nobel Prize winner Robert Shiller. His premise was simple - narratives drive economic impact. Shiller’s case was based on the data that emerged when Google indexed every book and newspaper in history. By combining historical and economic data, Shiller was able to go back in time and highlight the relationship between market narratives and economic cycles. We believe Narrative Economics in combination with more sophisticated A.I.-driven tools is the beginning of a new strategic foundation for modern brand management.

Howard: You are building a next Gen AI stack designed to do many things…treating reach as a variable and exponential growth driver is something you mentioned when we first spoke and it’s quite intriguing. Can you share more?

Connors: As we progressed in our thinking, we started to focus on how we leverage A.I. and narrative into a clear benefit for our clients. We started to align our tech investments, our strategy and our delivery on a simple and bold goal: increase client reach 10x over three years. The reality is that most CMOs are given irrational goals and no additional money to achieve those goals. In practice CMOs then sprinkle the dollars equally across all their silos and little changes. We aimed to upend that cycle of failure.

To help make 10x a reality, we are building an “A.I. Stack'' out of best of breed technologies for strategy, media, creative and analytics. Like the MarTech stacks of the last decade, this collection of technologies allows us to mix and match best in category solutions. By connecting the A.I. stack. + narrative framework we can seek out relevancy, insert ourselves into the conversation and expand our client reach and impact. We have a relentless focus on what performs rather than what is sexy. We use AI in the places where it will help us to help our clients to out-think, out-execute and outperform the competition. Everything we do is centered on our goal of helping the CMO find new models and new tools to be successful.

Follow Billee on Twitter or LinkedIn. Check out my website or some of my other work here. 

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Confront the fear and embrace the power of control of AI

Originally published in ClickZ

What’s the fear?

Since its release in November of last year, ChatGPT has taken the world by storm. Now in its 4th iteration, a mere 5 months later, the buzz in only growing, as people talk about its uses in healthcare, science, research, education, marketing, advertising and PR, among other things. ChatGPT has launched a vigorous discussion about Generative AI – AI that can produce things – and its role in our lives.

We know from Pew Center research that people fear it in healthcare, yet also believe it can help in surgery and pain management and much, much more. They believe it will play a critical role in discoveries and education. But many are wary despite the fact that AI already is in our lives with things like Siri and Alexa.

Quizzed on the role of ChatGPT in transforming journalism, only 16 percent of Pew respondents see it as a major advance. More than 25 percent see it as a minor advance, with the rest somewhere in a squishy middle. I raise this because these attitudes probably reflect the same kind of uncertainty among marketers. Some believe ChatGPT and Generative AI can fundamentally replace us. Others probably see it as no big deal and a majority are somewhere between.

And through it all, we are seeing discussions on things like the inherent bias of machine learning, the ethics of the platform, how we will govern its growth and other questions – some serious, some not – on its implications on our lives. In fact, as I write this, I am listening to a Squawk Box discussion on CNBC extolling the virtues of AI to radically transform the microchip industry, fostering new hope, but also creating fear as the hosts wonder what it means for humanity.

Where does that leave our industry? Somewhere on the spectrum of inquisitive to hysterical. My first experience with fear-based predictions resulting from technology displacement was the advent of the PC, which promised to lead to a paperless society. In the intervening period, truly disruptive technologies, like the Internet, completely transformed things like retail, banking and finance, and the media industries. Blockchain is seen as a promise – a promise to give us a decentralized currency regime, seen as a panacea to some. Never mind that crypto currencies are prone to 90 percent value swings and that displacing the dollar as the global reserve currency might crater the US economy, rendering our life savings worthless. Because these things aren’t perceived as an immediate threat to our livelihoods, we largely cheer them on.

But Generative AI presents a real and present danger in the thinking of some people in marketing. And pundits like Jim Cramer contribute to this when they claim that it will “level” – as in wipe out – advertising employment, while extolling the virtues for companies like Nvidia, which make the underlying chip technology. This isn’t to argue that Generative AI might eventually replace us and what we do. But is it going to happen soon? Unlikely. While it is impressive that Generative AI can access and organize ideas into coherent output, on closer inspection these outputs lack the heart, soul and overall value of human produced thinking, content, collateral, and written assets.

Where is the opportunity for marketers?

Rather than focusing on the potential of being replaced by Generative AI, we should instead be focused on Performance AI – the kind of science that allows us to access and organize massive amounts of data into actionable insights to underpin better marketing strategies and execution. Performance AI is the near-term future – one we should embrace and nurture to our collective advantage.

Marketers need to avoid getting wowed by what the technology can do and instead focus on how it can help their business. For example, it’s neat that a piece of AI can write a press release, but PR practitioners are far too focused on whether that will replace them, as evidenced by the myriad coverage of it. Same thing for creatives thinking they are going to be displaced. Or clients who are thinking of these capabilities in terms of efficiency rather than business outcome. So, the number one thing is, don’t get caught in the noise.

Instead, marketers need to be evaluating and using AI from the perspective of how it informs business strategy and, by extension, marketing and communications engagement. They need to identify platforms and technology solutions that can give them a view of their key constituents and conversations relative to their businesses and business goals. Preferably solutions that are dynamically processing information across wide geographies, topics and constituent groups. They need solutions that can easily collate and organize data so that it can be molded into actionable insights – that is, executive summary-type analyses with accompanying points of views and action plans rooted in the data. Finally, they need flexible solutions with adaptable dashboards that allow the insights to be customized to specific and changing requirements.

Based on these insights, organizations are more empowered to develop the right mix of marketing solutions and a strategic ability to address key constituent groups via the right strategic mix and channel. The result is a far clearer path to achieving a 10x multiple in impressions at a reasonable investment level.

Ultimately, these are the key differences between Generative AI and Performance AI. The first focuses on outputs and is being leveraged as a way to cut manpower and cost (at least for the moment). The second focuses on a holistic view of business, marketing and communications strategy for agile implementation and lasting impact (with a focus on augmenting growth as opposed to replacing human capital to cut cost).

Generative AI, should it reach its promise, has the potential to be a disruptive technology as defined by the late Harvard Professor Clay Christensen (as he articulates in this 2015 HBR article ). But Performance AI, while not necessarily disruptive by that definition, already has the potential to radically transform the way that we do business with clients and, done right, not displace people in our profession, but instead enhance their performance.

Where do we go from here?

My past year has been spent with colleagues assessing, evaluating, and experimenting with a wide range of solutions that use natural language and machine learning to access an unimaginably large amount of data on key topics relevant to our clients and organize it into bite size, actionable data sets. Based on this, we have developed a point of view and a solution set that harnesses the power of AI to make us smarter and deliver better results. We have coined the phrase Performance AI because we see it as AI that can drive better overall performance.

What we have learned, in this process, is that nearly everyone is attaching the moniker AI to their technology – just as everyone applied the term Web 3 to technology last year and the year before. Just because it is called AI doesn’t mean it is AI. Sometimes a social listening tool is just that. That’s not to impugn the value of social listening tools. But merely to point out that for AI to be AI it needs to have certain technology-enabled characteristics that allow it to process with logic and scale an output.

What we have also learned is that there are certain redundancies and gaps within the technologies specific to our industry. In our view, no single solution can deliver a holistic solution. We started with wanting to answer the following question: What kind of insights do clients need to take measurable action to drive marketing programs that support real business results? Through this lens, we were able to identify a suite of solutions that could be fashioned into a high-performance dashboard with outputs that answer key questions across constituent groups ranging from investors to customers, to employees, to thought leaders. Every consulting group needs to find its own right solution specific to the client problem it is trying to solve. We recommend starting with the question.

Another key learning is focused on build versus buy. The first impulse it to develop a homegrown AI solution. We curbed that impulse early, recognizing that technologists are better at technology. We should focus on what we do well – marketing – and leverage solutions from a range of partners to develop best-of-breed solutions specific to making us better marketing partners for clients.

We are in an invigorating time, when the potential for AI to revolutionize our lives is great. As marketing professionals, we have the opportunity to approach this in fear or with an attitude of discovery and innovation. My preference is the latter. Will there be unanticipated obstacles and pitfalls along the way? For sure. But the ability to give our clients better service while engaged in great discoveries is too much to pass up.

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Ethical AI: How to disrupt data injustice worldwide

By Karen Baker

Originally published in Smart Brief

Artificial intelligence is experiencing exponential growth, which generates excitement and fear, especially as it relates to the future of work. Generative AI for creative jobs is the biggest fear amongst content creators, journalists and writers — potentially exposing a new forage to hiring disruption. 

The Brookings Institution’s Alex Engler calls this hiring trend “algorithmic creep,” which is the combination of increased algorithm use within different hiring stages and more firms using algorithms at each stage.

AI will lead to redesigning workplace business models and changing office culture and how we hire. To build ethical AI solutions, the tech sector needs a wider range of perspectives and diversity of thought, particularly to gain awareness of all the potential forces contributing to the (often unwarranted) success of the elite. We need practices and governance to ensure these changes are at the forefront; thereby, avoiding data bias and unethical practices.

Most technology companies need to become more familiar with the practice of design justice to disrupt data injustice. Practices such as design justice work to demonstrate how universalist design principles and practices erase certain groups of people. Incorporating this practice into the development and execution of AI could prevent negative stereotypes and bring ethics into the conversation. 

Ethical AI is not just a tech issue

The benefits of AI are exciting, not only for technology but those in media and marketing. Yet, left unchecked, technology indulges in unethical outcomes, bias and cultural appropriation without regard. University of Virginia Professor of Practice in Data Science and Nonresident Senior Fellow at The Brookings Institution, Renée Cummings, talks about algorithms that are not accountable, transparent, explaining, or auditable, and explains how these algorithms undermine “the extraordinary possibilities of ethical AI” in her lectures. As marketers specializing in human-centered interaction design, we aim to create equitable user experiences. 

Utilizing AI that weaves AI + HI (human intelligence) to move toward resolving ethical and cultural dilemmas in research is something we should consider. This process collates massive amounts of data and organizes it in logical pieces, allowing people to interpret it in specific contexts and enabling better directions and outcomes.

However, accessing and logically organizing massive data via AI that incorporates human intelligence requires more than just typing in words. It involves training in the language and literacy of AI — something for the media to consider when using ChatGPT. 

AI governance will require multidisciplinary teams — tech can’t be the only one at the table. 

In 2021, the Federal Trade Commission wrote a blog post about the benefits of AI while looking at the governance of bias. This post discusses and recaps the three laws, data analytics, algorithms and AI expertise that lead to seven approaches to ensure equity and inclusion. Experts should review these before launching AI into the marketplace, in addition to applying the White House Blueprint for an AI Bill of Rights:  “The Blueprint for an AI Bill of Rights is a guide for a society that protects all people from these threats — and uses technologies in ways that reinforce our highest values.”

The issue of ethical AI will not go away on its own. Do we need to build a slow movement, per the efforts of Dr. Timnit Gebru, and get a sense of responsibility and balance to create technologies that work for everyone? Or do we continue to fast-track without looking back, making it an experience for some, not all?

Viewing AI as a social problem – not just a technological issue – can build more ethical practices.

Karen Baker is a founder and the president of Boathouse, an independent, full-service integrated marketing and communications agency.

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No Turning Back: AI Is the Future of Social Media

By Geoff Gates

Originally published in ANA

Just as we've witnessed massive shifts in social media since its early days, we find ourselves at another pivotal moment in the still nascent existence of social media — one that I believe has the potential to completely disrupt what we consider social media to be. Artificial Intelligence (AI) is fundamentally changing the way we interact with the internet, and in the coming years it's going to shape social media, and the way people use it, more than any other variable.

Facebook, Instagram, and Twitter all have leveraged algorithms, or sets of instructions, to serve users content. These platforms continuously analyze their algorithmic performance and optimize. But with AI, machine learning happens within the system itself. Instead of continuously telling the program what to do, the AI itself determines the best course of action and acts.

Here's what marketers must understand to succeed in the new social landscape.

AI will make follower counts obsolete. 

With AI powering what content gets seen, the number of users following a brand or creator won't have as large a performance impact as it has in the past. We've seen this effect, before Tik Tok mastered it, with YouTube. Subscribers to a YouTube channel tend to make up a very small portion of total views of any given video because YouTube's algorithms are very good at serving users relevant content.

To put it plainly, legacy accounts will see impressions plummet if they aren't producing compelling, timely content. Marketers will have to update their KPIs and communicate upwards to leadership that followers are no longer a measurement of success.

Competition for eyeballs will skyrocket.

Content performance on social media will be more democratized than ever. I don't stand a shot at mass exposure on a platform like Instagram that prioritizes influencers and celebrities with large followings. But on Tik Tok, with AI that grades content individually considering advanced audience insights, everyone has a chance to take off. In fact, we see it happen all the time on the platform. Because of this, we're going to see a large rise in the number of micro-creators, or creators who don't have large audiences but still gain large reach.

In order to keep up, marketers are going to have to increase spending on social and content resources. Platform experts will be needed, paired with content specialists who know what content works, where. Remember, quality content is what drives results. Platforms are simply the delivery method.

Speed and risk will become huge factors in achieving success.

The window to catch attention is getting smaller and smaller, and in the future, it will be more important than ever to go where the eyeballs already are instead of trying to get eyeballs onto your owned channels. Trends used to last years, now they last weeks, sometimes days. If a brand or creator is late to the party, they're going to miss it completely.

This approach comes with inherent risk. Accounts that participate will have to be diligent in doing their research before commenting on or participating in conversations. It takes years to build trust and only one mistake to lose it. Put a timely process in place for moments such as this, one that goes through multiple people and that takes a critical look at the context of the moment, but also one that allows for enough speed to capitalize on the moment.

Content production quality will decrease.

Because of the new demands for speed, marketers will be forced to capture content for social media in scrappier ways. Cell phone footage and less-produced videos are going to be the new norm due to speed and access. It's difficult and costly to mobilize a production crew. The most successful marketers will be the ones who understand the data, act quickly, and prioritize story over production. And to clarify, production quality decreasing does not mean that the quality of the content decreases. You can have a fantastic piece of content filmed on a cell phone. Marketers will need to embrace this approach to stay relevant and continue to get results.

We're going to continue to see the evolution of AI's involvement in social media, and I for one am very excited to see where it goes. Unfortunately, the platforms who don't keep up will become obsolete. After all, we only have so many hours in the day to scroll.

The views and opinions expressed are solely those of the contributor and do not necessarily reflect the official position of the ANA or imply endorsement from the ANA.

Geoff Gates is creative director of social strategy and content at Boathouse.

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Why the Fear That AI Could Replace Comms Professionals Is Overblown

Originally published in O’Dwyer’s

November 1, 2022—a day that lives in infamy in the PR and marketing professions, among others. In the before-time, we never worried about machines taking our jobs. But with the launch of ChatGPT by Microsoft—and with three subsequent upgrades—we have been treated to an endless stream of fear and despair about its potential to replace skill positions in our professions.

When ChatGPT was launched, I was pejorative in my remarks, calling it a search engine on steroids and a parlor trick. Since then, Microsoft has launched new generations with improvements and Google has launched its Bard chatbot. Both are more than a parlor trick, but I stand by my initial claim that it will be a while before we see these innovations replacing humans in mission critical content creation roles.

It is true that generative AI can write a cogent press release and reasonably decent byline articles. But a cursory review reveals the output for what it is—gathering a series of facts, a la a search engine—and organizing it into cogent paragraphs. The pieces are reasonably good, but lack the nuance and understanding that a human can apply to writing. In my younger days, I had a business partner who was a trained musician. After meetings, we might gather up in a hotel lobby for lunch or a drink. In the course of it, I would comment that the player piano sounded good. He would always correct me. His trained ear could hear the errors. The same applies to current generative AI—it lacks a certain nuance to replace human intelligence and interpretation.

Setting aside the style of writing, there are also questions about accuracy and fact checking. Right now, as we test these technologies, we are closely qualifying outputs to ensure they match the facts. Consider this: are we prepared to simply put these outputs into the wild without quality assurance and fact checking? If the answer is “no,” how then can we be confident about replacing humans with machines. Bard, in particular, is illustrating qualities of machine learning which, over time, may result in total confidence in outputs absent any human interaction and review. But, for the time being, the fact we are bothering to QC the work means we don’t fully trust it.

What I have heard, in travel, speaking engagements and conversations, is that practitioners see the current generation of tools as enhancing productivity through faster, smarter research. Rather than having to rely on conventional search, many are turning to generative AI to aggregate large amounts of data in answer to complex questions that might previously have required several people, different resources and more time to acquire.

There is little doubt the technology is evolving rapidly. I’ve already been solicited by one tech entrepreneur wanting me to beta test his company’s platform. But the reality is, this kind of wholesale replacement is a ways off—if it is to ever occur. Consider the complexities of the clients we work with—healthcare companies, highly regulated financial services groups, complex technology, and more. All require a degree of nuanced thinking and human intelligence as core to the work.

Meanwhile, there are a number of performance-focused AI solutions that don’t pose a threat to our industry, but instead can be leveraged to drive better, faster, more precise results. When we speak of Performance AI, we are thinking in terms of machine learning and language-enabled solutions that can gather massive amounts of data and feed it to us in specific actionable insights. For example: The kinds of conversations occurring around a political issue. The hashtags powering a social discussion about customer satisfaction for a particular company or product. The intensity of passion of conversations around a company’s policies. Employee discussions about issues.

We see the proper focus of AI being on quickly collating and analyzing these terabytes of data and delivering them to humans in small, mission-specific categories that focus on our clients’ business opportunities, marketing and communications goals. Armed with this, we can more easily interpret the data, develop strategies and execute campaigns in the right channels for faster, more precise delivery. The ability to deliver more and better results is the kind of competitive advantage we should be identifying every day in our industry.

I have been in this industry for more than 25 years. Too often we are focused on leveraging new technologies—not on creating value, but on slashing costs. Generative AI and the related buzz is the most recent example of our collective approach to driving down the value of what we deliver by leveraging a technology to replace human beings. It’s a zero sum game that does little to enhance client results and very much to destroy morale and work product.

I am not arguing that we can blindly stand against technology and its inevitability. Rather, I am advising that we, as an industry, focus on things like Performance AI as a means of driving better value through more targeted results. In this context, we should not fear AI but, instead, embrace it, turning the machines to our advantage.

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What’s Next for AI and Marketing?

Originally published in AdWeek

Since its launch in November 2022, ChatGPT, the chatbot developed by OpenAI, has been inescapable. Across the business world and society as a whole, people have been equal parts amused by its capabilities, excited for its potential and impressed by its iterations like GPT-4. They’ve also been fearful, with many speaking up about the technology’s safety, carbon footprint and ethical implications.

The fervor surrounding the new AI tool culminated in an open letter, signed by over 10,000 people at the time of writing, calling for a moratorium on AI development. Though the letter is unlikely to result in a full-scale shutdown of AI projects, it underscores the divide between proponents and skeptics—and at a time when geopolitical and economic uncertainties are injecting more caution into the next phase of AI’s evolution.

Given these realities, we’ve asked industry experts to weigh in on the use cases, possibilities and limitations of generative AI across their sectors of marketing and offer recommendations on how to proceed. The consensus among them, of course, is that along with the advantages come significant risks, and use of the technology without human oversight is irresponsible. It’s clear, though, that the industry is on the precipice of something big, not to be ignored and it’s best, for now, to tread carefully.

Media buying

From expediting research to streamlining ad buys, AI-based solutions can help marketers make the most of their valuable time and resources in day-to-day tasks. As with all AI, finding the right input to ensure a particular outcome is paramount. When conducting research through tools such as ChatGPT, marketers need to set clear parameters: Define how technical the responses should be, a clear subject matter and what the research will be used for. And don’t forget to fact-check.

More sophisticated AI solutions for ad campaigns, meanwhile, can assess which ad formats and mediums are best for achieving business objectives and optimize media buying accordingly. Marketers can specify that they want to purchase inventory that boosts engagement or conversions, for instance, and AI can use previous successes to inform future buys. —Sam Matharu, director of analytics, marketing science EMEA, Xaxis

Processes and workflows

We’re all being challenged to do more with less. But recession or not, success in media is all about the execution of the task at hand. Implementing automation and AI into existing processes and workflows should be seen as an opportunity for growth and expansion of skills.

One area we’re focused on for integrating AI is AdOps, which traditionally is a highly manual and repetitive process and a perfect candidate for automation. Automation enables a reduced order-to-cash process time, allowing for quick invoicing and smoother cashflow; faster revenue reconciliation means a more cash-positive business and a faster ability to reinvest in new business initiatives or new hires. —Jay Kulkarni, CEO and founder, Theorem

Branding and packaging

Using OpenAI’s GPT-3 and DALL-E 2, we let AI create the packaging and product descriptions for seven trendy new products from scratch. From wagyu to oat milk, each product was tested with a nationally representative sample of 300 U.S. consumers to gauge the believability and effectiveness of concept and branding. Each product was graded on a 1-10 scale in seven key measurement areas: overall appeal, likelihood to purchase and others. Results are measured against Zappi’s established norms for product performance, which we’ve gathered by testing over 100,000 products to date.

In short, the AI-created products greatly underperformed with consumers, ranking in the bottom 33% of every product ever tested on our system. Despite aesthetic designs and descriptive language, consumers found the products disjointed and, in some cases, potentially dangerous or irresponsible. It wasn’t all bad, however: The AI-designed products performed well for uniqueness, signaling AI’s potential as an idea generator. —Steve Phillips, CEO and co-founder, Zappi

Thought leadership

As more people start leaning on ChatGPT, we expect to see a lot of subpar thought leadership getting pushed out. This presents a real opportunity for those with genuine points of view to stand out and build credibility for themselves and their companies, and to use AI as a supporting tool to do it faster than ever before.

What makes great thought leaders compelling is the unique perspective they’ve built from years of distinct experiences and a willingness to share it at moments when their business, industry trends and customer needs intersect. An overreliance on AI for thought leadership content creation and contributed commentary robs it of the thing it needs to stand out—originality. AI should absolutely be incorporated as a tool for creating efficiencies, but there are still real limitations in what it can deliver and risks in trusting what it generates, especially because OpenAI hasn’t provided much transparency into the data set the chatbot was trained on. —Brendan Shea, svp, content, INK Communications Co.

Cultural bias

With 1 out of 4 Gen Zers in the U.S. being Latinx, a lack of representation and expertise across general marketing teams and the democratized power of AI at our fingertips, brands and marketers will be inclined to leverage this technology to find efficiencies to engage with Latinx audiences. I cringe at the idea of any marketer crafting multicultural marketing strategy around AI-generated content and justifying it as a cultural source of truth.

When it comes to the multiculturalism of Latines, AI is unable to reflect the diversity of our ambicultural, language-fluid experiences—but more dangerously, its outputs can become an enabler of stereotypes and cultural and linguistic insensitivities. Marketers need to be aware of the technology’s elevated bias when it comes to the Latinx audience, and they need to think of culture-filtering workflows and strategies as they navigate AI adoptions. —David Velez Mejia, executive strategy director, Remezcla

Video and audio

On the marketing side, AI-assisted video creation is helping brands customize content to specific viewers and capture attention in cluttered media environments where visuals and sound trump text. For example, sports publishers are producing videos that integrate player and team stats updated in real time. Similarly, finance publishers like Bloomberg are using AI to showcase the latest market moves. When AI-driven video is implemented, the scope of what’s possible in the creative process expands to what creators can conceptualize and describe rather than what is most practical or cost-effective.

While many took notice of generative AI’s text-to-image capabilities, text-to-3D and text-to-audio have pushed the technology into a new phase. Text-to-video is a major development as well, with companies like Meta and Google developing software in the space. Some capabilities of the tech are creating live portraits from photos converted into realistic talking head models and creating stylistically different versions of original footage for publishers to iterate on successful content. —Dor Leitman, svp, product and R&D, Connatix

Social media

AI is fundamentally changing the way we interact with the internet and on social media. In the coming years, we’re going to see social following become obsolete. With AI powering what content gets seen, the number of users following a brand or creator will have a fraction of the weight on performance it has had in the past. Competition for eyeballs will skyrocket, even for legacy accounts. Speed and risk will become huge factors in achieving success, and because of this, content production quality will decrease. Cell phone footage and less-produced videos will be the new norm due to accessibility—great content is agnostic to the quality in which it’s captured. —Geoff Gates, former head of social media, LA Lakers; current creative director, social strategy and content, Boathouse

Influencer marketing

AI is already helping brands discover creators as well as analyze content to identify good fits for partnerships and how well the content would perform. It can also analyze audiences and recognize fake followers that will not result in quality engagement for brands. Once content is live, AI tools can evaluate campaign metrics and ROI or assist with the campaign itself, automating tasks throughout the process including contract negotiation. There’s also the case of AI-generated influencers themselves, which can potentially give brands more control over campaigns—as long as the partnership lands appropriately with the target audience.

While AI can do great things for influencer campaigns, at what point does the use of AI tools affect the authenticity a creator’s audience relies on? Creators build their followings, big or small, based on trust. Brands looking to partner with creators depend on that same level of sincerity, since it provides credibility to product reviews and recommendations. —Ali Fazal, vice president of marketing, GRIN

Personalization

Over the past few months, there’s been a significant shift in mar tech from marketing automation to marketing autonomy. An autonomous marketing framework has three essential elements—data, decision-making and delivery—and artificial intelligence is the main player in each. Personalized data triggers AI to initiate actions. Decision-making engines handle segmentation and forecast behaviors, ultimately delivering more constant and customized communications to customers.

We’ll see an even deeper integration of OpenAI into creative processes. However, I still see OpenAI more as a productivity tool than a content creation engine. According to Accenture, AI can increase productivity by up to 40%—though tests on our AI email generator proved to be six times faster at creating newsletters, slashing the average time from 19 to 3 minutes. The productivity potential of AI tools may be underestimated, and we are yet to be impressed by it.

What’s left to marketers in the era of autonomous marketing, then? Strategy ideation and creative choices. —Aleksandra Korczynska, CMO, GetResponse

Programmatic

For our AdOps team and possibly others across the ad-tech industry who use it, ChatGPT has been a key resource in generating code for Meta/Facebook pixels using a developer tool link and analyzing competitors’ websites to help create new marketing strategies with a competitive edge. In terms of ChatGPT’s impact within programmatic, that could be more challenging to predict due to the nascency of the tool. Audience and channel planning will still require human insight and optimization. However, there’s potential for a programmatic application to provide optimization recommendations within the demand-side platform during a campaign’s flight. —Liza Bortnikova, COO and co-founder, AI Digital

Search

We don’t use Google search the same way we used to. The average attention span of internet users is 2.5 seconds: When looking for a quick result, many of us scan Instant Answers, the zero-click summary that Google generates, rather than clicking the links provided. Now, ChatGPT is offering an even simpler solution; users can ask the AI chatbot questions and receive detailed, tailored information that directly answers their query.

Microsoft has invested $10 billion in ChatGPT and plans to integrate the AI software into its existing search engine, Bing, whose app saw a tenfold increase in downloads after this announcement. Google has now opened the waitlist for its own AI chatbot Bard. As more users take advantage of the benefits of AI-integrated search, brands stand to gain less traction from SEO and paid Google ads as traditional search engine usage decreases.

Brands must overcome the migration away from Google search due to AI software with a social-first advertising strategy that includes brand campaigns on TikTok. Creating searchable videos that inspire user-generated content will spread awareness of your brand among a Gen Z audience, even as fewer users turn to Google to discover new brands. —Chris Kastenholz, CEO and co-founder, Pulse Advertising

Ecommerce

Retailers can use AI to help answer customer FAQs or accelerate customer support responses. They can also use AI to conduct sentiment analysis on reviews to gain better insights into their products, allowing them to pivot business strategies and approaches to increase sales. While the impact of AI technology is evident, retailers must be highly attentive when checking responses to verify accuracy. This is especially crucial when working with global customer bases and across different languages.

It will be interesting to see the percentage of consumers that turn to AI for product searches and recommendations compared to search engines and ecommerce sites. Retailers should prepare for changes in consumer behavior and the rise of a new channel to add to their marketing mix. There will likely be new industries created around organic and paid placement in the results of these models, much the same way the advertising and search engine optimization industry evolved with the evolution of search engines. —Stephen Curial, CTO, Jungle Scout

Research

I was looking for some textbooks on international advertising and asked ChatGPT if there were any new books that had been published recently. It gave me a list of titles, complete with author and publisher, that got me excited. They were perfect for my class and, surprisingly, I had never heard of any of them. Better yet, they had all been published in the last year. I was thrilled…that is, until I tried to find the books. They literally did not exist. I kept asking ChatGPT how to find the books; finally, it told me: “I apologize for the mistake in my previous answer. It seems that the books I mentioned may not have been published or may not be currently available. As a language model AI, I rely on the information that I was trained on and there may be instances where that information is not up-to-date or may have changed.”

This is not an isolated incident, but rather a deeper problem with AI as it stands today. A recent New York Times article confirmed that AI models “are prone to what AI researchers call ‘hallucination,’ making up facts that have no tether to reality.” That’s not always a big deal for me, but it’s a huge deal if you’re making recommendations to a client or brand based on information that does not exist or has been warped by the software. Long and short, unless you know the data it has been trained on, the data it has access to and how it is organizing that data—and you don’t—you should look at the results with some skepticism and double-check their correctness. —Brian Sheehan, professor of advertising, Syracuse University

Data privacy

The use of AI to create personalized content through third-party platforms raises concerns about privacy. The algorithms need data, either from the brand or the consumer. Are consumers ready for an AI to use their personal images and videos to create branded content? Until we have more transparency on data practices, as well as the storage and sharing practices of these platforms, brands should consider the risks before incorporating them into their content creation strategies. —James Turner, founder and president, Delineate

Copyright infringement

Regulators have yet to make any decisions on whether AI-generated images infringe on copyright rules—but this decision will come soon enough. Cryptocurrency offers a relevant comparison: After the technology had been around for nearly a decade, key players were suddenly being fined tens of millions of dollars by the SEC. While a creative may want to leverage an AI-generated image in a social campaign on a whim, it isn’t worth the potential future risk. Brands must protect themselves from costly, lengthy litigation and the reputational risks that might come with jumping the gun.

The root of the issue comes down to the creative, whose work enters the algorithm and becomes public domain without their consent. In the 1990s and early 2000s, tools like LimeWire and Napster allowed individuals to pirate music with a few clicks and some patience. By the 2010s, Pandora and Spotify delivered an experience people were willing to pay for, so the market returned to equilibrium. While it may take time, the same will occur with AI.

Most brands are willing to pay for work, but technology companies must make it possible to do so. It’s crucial that these companies do their part to provide transparency and compensation around AI-generated images. Regulators must partner with industry experts to develop clear guidelines for their creation and use, as well as rules for ensuring that artists are fairly compensated for their work. —Analisa Goodin, founder and CEO, Catch+Release

Education

We’re at a tipping point in education, requiring us to think differently about what and how students learn. Enter generative AI and GPT. They have a lot more processing power than our students’ brains. They can recognize and recall for us, without much humanity. And that’s where our students can meet the technology. In a marketing course, AI can collect data while students find insights. ChatGPT can write segments while students map them to a customer journey. AI can measure marketing strategy while students interpret the impact of the strategy.

The mar-comms professional of the future needs a relentlessly curious mind, which is an inherently human attribute. We can free students from the cycle of memorization and regurgitation to solve problems and allow them to be what they are best. —Jacqueline Babb, senior lecturer and director of the Integrated Marketing Communications program, Northwestern University

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Weighing AI Hype Vs. Reality With ‘AI Marketing Agency’ Boathouse

Originally published in AdExchanger

Does AI present “profound risks to society and humanity”?

Yes, according to a group of more than 1,400 tech leaders, academics and AI researchers (including Elon Musk and Apple co-founder Steve Wozniak) who signed an open letter last week calling for a temporary pause on AI development until there are better safety standards in place and more official oversight.

The letter sparked a debate between those who accuse the signatories of public fearmongering and those who believe that training AI models without guardrails is an invitation to disaster.

But there is a moderate point of view here, which involves recognizing that we’re on the precipice of massive change, acknowledging there are practical uses for AI (including to support marketing) and devising rules of the road and regulations concurrent with development.

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Listen to the Podcast:

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“In the case of AI, we have a chance, I think, to start fresh and learn from the mistakes we’ve made [in the past],” says Peter Prodromou, president of Boathouse Group, an independent AI-focused marketing and communications agency, speaking on this week’s episode of AdExchanger Talks.

Take social media.

“I’ve been in technology and marketing for 25 years and I remember touting the notion of social media as democratizing the internet in such a way that everybody would have a voice,” Prodromou says.

But before anyone had a chance to seriously question the potentially negative consequences of social media, it had already become a staple in people’s lives.

Considering the speed at which AI is evolving, caution is warranted.

“But [AI] is here to stay,” Prodromou says, “and we need to learn to live with it.”

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Indie Agency Boathouse Taps AI For Data-Driven Brand Monitoring

Originally published in Ad Exchanger

Image source: Unsplash.com

Brands and their agencies are experimenting with artificial intelligence for all manner of applications, from generative art to generative chat.

Boathouse Group, an independent marketing agency outside Boston, is using AI for a new form of social listening, web monitoring and reputation management.

On Tuesday, Boathouse launched a product called Narrative Transformation through partnerships with consumer and market intelligence platforms Signal AI and NetBase Quid.

The tool gathers data from tens of thousands of publicly available online sources, including tweets, Glassdoor reviews, Reddit posts, blogs, news stories, regulatory documents and a multitude of other English language conversations happening around the world. That data is then placed into categories, such as source, sentiment, passion, theme and conversational hashtags.

The purpose is “to monitor and understand evolving conversations at a micro level,” said Boathouse President Peter Prodromou, and “to shape recommendations for how our clients should be engaging the narrative.”

To win in today’s climate, he said, companies need to get ahead of what’s “gurgling around the internet” so they can “anticipate a potential crisis or opportunity.” This precept is true, but, with current technology, predictions will not be accurate 100% of the time.

A proactive approach to messaging isn’t optional, Prodromou said.

“[But] the question becomes … how do you understand the conversations that are happening?” he said. “That, for us, is where AI comes in.”

Boathouse’s dashboard allows companies to tap into the hashtags, topics and terms that are defining conversations about them. For instance, CEOs can see when employees, customers or investors are unhappy with their organization, which allows them “not only to set a narrative but make a business decision,” Prodromou said.

Brands need to “cut through the clutter, get to the 10 or 15 most important conversations and give actionable insights,” he said.

Although Narrative Transformation is still in beta, a few clients have started testing it, including the University of Massachusetts Global (UMass Global).

UMass Global is using the tool to get a better real-time understanding of how people perceive the university and how it compares to other online universities “relative to mindshare, passion and sentiment,” said Terri Carbaugh, vice chancellor of public affairs.

For example, through searches for terms related to institutions, corporate and nonprofit partner C-suites, think tanks, trade outlets and student and alumni experiences, UMass Global has uncovered local, state and national trend lines.

These insights “underpin our strategic communications campaign,” Carbaugh said.

“We’ve identified specific areas where our communications can, and must, be bolstered if we are to achieve our reputational goals,” she said.

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How Marketing Agencies Can Develop Partnerships with AI Companies

By Wyatt Ferber

Originally published in ANA 

Image Source: unsplash.com

For marketing agencies interested in using the latest technology to drive growth, empower the workforce, and expand capabilities for clients, artificial intelligence (AI) offers the opportunity for greater executional efficiency, better distribution of valued resources, and more insightful data to deliver desired outcomes.

However, as more and more AI solutions become available, how do agency leaders identify, evaluate, and partner with the best AI companies? What are the benefits of a partnership with AI companies over a standard client relationship? How can a marketing agency maximize the value of using AI technology?

Below are key steps to ensure your agency secures a relationship with the best possible AI partner.

Partnering with AI Companies

While the offerings of AI are infinitely expanding, the cost of adding such tools to an agency's repertoire can be daunting. It is this financial burden that makes the development of partnerships so attractive to agencies. Partnerships provide opportunities to grow revenue and clientele in new markets both domestically and globally, access to cutting edge technology and experienced engineers without adding to an agency's workforce, as well as reliable sources of data for strategic planning purposes for both clients and the agency itself.

On the other side, partnerships offer the AI company incentives, in prospective clients and market-informed feedback, that allow them to lower the price tag for the client agency. Developing a win-win between the marketing agency and AI company is crucial to ensure the benefit-cost ratio is favorable to both parties.

Pre-Search Agency Self-Evaluation

For the process to be successful in finding the best AI technology, agency leadership must first evaluate the strengths, weaknesses, needs, and goals of their own company to determine which areas of the organization require the greatest assistance. While conducting this "agency self-evaluation," make note of which agency capabilities are high margin versus low margin. Also take note of which capabilities benefit from artificial intelligence versus augmented intelligence.

When thinking about the operations of the marketing agency, augmented intelligence tools help with scaling, empowering the workforce to produce more; artificial intelligence tools offer data-informed insights and processing that is a product of machine learning that exceeds what the average human hire could deliver. As an exercise, plot the agency's high margin and low margin capabilities against the need for an augmented intelligence or an artificial intelligence tool.

How leadership slots the agency's capabilities will help define the AI company search to follow, with the obvious focus on attacking high margin areas and determining whether to address the capability with an augmented or artificial intelligence tool.

Defining the Field and Conducting Due Diligence

With the prioritized capability identified, the initial stage of the search should focus on defining the field of AI companies to include in a preliminary round of discovery calls. Due to the constantly evolving technology of AI, finding the best AI company for partnership requires due diligence to review an AI company "under the hood" including its technology and engineers; the likelihood the company is acquired during the time your agency is using the tool, and the long-term vision and direction of the company (including how they are investing in R&D).

Marketing agencies should consider their relationship with any AI company as an investment – funded regularly but managed carefully with yearly ROI goals in mind. Rather than designating an AI tool as merely an expense, it should be promoted as a differentiator for the agency and its' end user clients. Therefore, under such a classification, it is the responsibility of the agency to conduct a comprehensive review of each AI company to ensure the investment being made is the best use of valued resources and is in an AI company with shared values and visions for the future.

One suggestion is to develop a checklist and grading system to make sure all questions are asked, all desired information is gathered, and all companies being evaluated are measured against one another. Among the notes on the checklist should be information about company leadership, location of headquarters, size of company, notable partners and investors, recent rounds of funding, references as well as noteworthy clients and projects, the ratio of engineers to sales members, and details about the team that will be handling the account.

These are just a few of the many questions that need to be answered – especially since the AI company will acquire a similar amount of information about the marketing agency prior to and during a discovery call. For the agency to be on equal footing as the AI company in partnership negotiations, having an equal amount of data is crucial.

Handling Discovery Calls and Demos

Next in the process of securing a partnership with an AI company for a designated capability are discovery calls and demos. To put in bluntly, discovery calls can often be a waste of time for a marketing agency. They are designed to provide the A.I. company with information to help their selling process – learning about the agency to promote their product efficiently and effectively.

To make sure these discovery calls don't become a burden to the agency, the agency should appoint a single individual to handle all discovery calls. This ensures that the information being distributed is consistent, that time of agency leadership isn't wasted on discovery calls, and this point person can direct the demo process to follow by determining which agency leaders should attend the demo.

Unlike discovery calls, demos are immensely important in the partnership process. Forgetting the traditional slide deck to open the demo, the opportunity to see how an AI company's platform operates in real time, how to navigate the tool, and how the tool creates value for the agency and end user client is the most important part of the partnership process up to this point. Agencies that will use the tool for multiple capabilities or have several departments using the tool should feel comfortable requesting multiple demos, thus allowing each department leader to ask questions specific to their needs without feeling rushed or limited by a single demo.

Negotiating a Partnership

If after a comprehensive demonstration, the agency's leadership doesn't recognize the value of the AI tool or the AI company fails against competitors in the grading system, quickly move on in the search process. However, if the AI company demonstrates value, successfully advances through an evaluation and comparison against peers, the marketing agency should communicate the interest of forming a partnership.

Rather than merely acknowledging the AI company as the automatic tool to purchase, agencies should approach the AI company with an offer focused on joint selling opportunities, long-term projects with current or prospective clients, and a unified effort to generate revenue and clientele for one another.

For marketing agencies, the partnership "give" is payment to be a client, the proposal of referring current clients to the AI company, and feedback on the AI company's technology after receiving intel from end user clients.

For AI companies, the "give" is greater access to the AI tool, access to their clients who could use marketing assistance, and possibly a reduction of fee depending on how well the agency-AI company relationships become co-selling ventures. The negotiation process should reflect both companies' interest in "expansion by association" – marketing agencies and AI companies benefit the most from having a trusted ally validate their abilities to potential clients. Partnerships that tie networks together and develop coordinated selling plans are the relationships that both the marketing agency and AI company should be seeking.

To review the process, marketing agencies should be open minded about the possibilities of AI, perform a self-evaluation to determine how AI could be of greatest assistance, conduct comprehensive due diligence to identify the best AI companies in a designated area, manage discovery calls and demos to maximize the value of information acquired — and then promote a relationship focused on partnership instead of a limited buyer-and-seller arrangement.

If a marketing agency handles the process correctly, it will be rewarded with advanced technology at its disposal, novel resources to deliver for end user clients, and an additional team of motivated selling partners to contact and court prospective business. Partnerships keep agencies' costs down or counterbalanced by revenue opportunities and business development possibilities. Marketing agencies should invest the resources, time, and commitment to identifying, evaluating, and partnering with the best AI companies possible.

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Podcast: B2B Marketing and AI for Streamlined and Strategic Communications

Interview with Peter Prodromou

Founder/President Boathouse Palo Alto

Originally published in MaretingProf

Image source: Unsplash.com

Passion, for one thing, says Peter Prodromou of Boathouse.

"If you're in the upper right-hand corner with passion, chances are people are going to want to work with you or buy your product," he says on the latest episode of Marketing Smarts. "Think about Apple and Tesla; those are two brands that are very much about passion. Your ability to convey that is critically important."

AI is just an algorithm, after all. "Everybody is going to shop at Amazon because they have the best algorithm, and there may or may not be passion for it," Peter says. "If we can...understand things like passion and sentiment, then we're better positioned to help our clients establish narrative structures that are going to help them to create value."

Peter also emphasizes the importance of AI in social listening and sentiment, which is vital to marketing strategy: "What I look at is AI tools that can help me to gather scads of information from virtually everywhere...and then being able to collate that data into something that makes sense in a snap of a finger."

For more on how AI can streamline communication and strategy, check out Episode 527 of Marketing Smarts. You can listen to the entire show from the link above, or download the mp3 and listen at your convenience. Of course, you can also subscribe to the Marketing Smarts podcast in iTunes or via RSS and never miss an episode.

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Full Transcript: B2B Marketing and AI for Streamlined and Strategic Communication

George B. Thomas: Are you ready for something that is zero artificial but completely intelligent? That's a bad dad joke. We're talking about B2B marketers and AI for streamlined and strategic communication. That's right, today we're going to talk about what the heck do we even mean when we're talking about AI for streamlined and strategic communication. We're going to talk about what keeps the president of Boathouse (Peter Prodromou) up at night around this conversation, we're going to talk about communication, the good, the bad, the ugly, the hurdles, words of wisdom, you know all the things that we're going to talk about.

Before we get into that, Peter Prodromou spent more than 15 years helping to build and lead Racepoint, a global digital marketing, strategic communications, and PR firm. He held several roles there, including head of global accounts, head of public affairs, and president and CEO of a wholly-owned network with offices on four continents. Over his career, he has worked with some of the world's best-known brands, like Samsung, AT&T, Dassault Systems, IBM, Kaiser Permanente, and Glidden Paints, hot startups defining the next generation of innovation, and government leaders in the US, Indonesia, the Middle East, and Africa. Peter has been around, he knows some things, and, as you can tell, has probably been a super busy guy trying to streamline his day and have strategic communications.

Marketing Smarts listeners, today is going to be interesting. I love the topic. I think I'm going to actually really enjoy Peter along the way. We're talking about B2B marketers and AI for streamlined and strategic communications with Peter Prodromou. Today, it's going to be really exciting. We have a journey. Of course, you know where I start with this conversation always, with what keeps Peter up at night.

As the president of Boathouse, Peter, what keeps you up at night?

Peter Prodromou: These days, worrying about whether or not we're going to find our way through the mess that I think social media has created from a political policy standpoint. I go back to the earliest days of social media and preached for the longest time that it was an opportunity to democratize access to information and give everybody an equal opportunity to have a voice. I used to joke when I was in Washington that what was great for marketing might not be awesome for democracy. Now I look at the quality of dialogue, and that was a decade ago, and it really worries me. We'll see where that goes.

George: That's definitely some interesting stuff right there, for sure. Let's dive into this AI conversation. When we talk about AI for marketing, this could mean a bazillion different things. I always love to do, here's the road that we're traveling down today for the listeners. When we talk about AI for marketing, what do you think this really means, where does your mind go, where should the listener be directing their attention to for the rest of this conversation?

Peter: I think about it in terms of media and access to media, understanding and getting closer to media and influencers. My background is in PR and communications, so I've always come at it from that perspective, as opposed to people thinking about it as purely behavioral or replacing people in the creative sphere, for example.

George: Because with marketers, that is a large portion of the conversation, "Am I going to lose my job? Will AI be able to replace me as a designer or writer?" Things like that. Let's go a level deeper, though. Let's go off the beaten path for a second. When we're talking about this for streamlining or being strategic, or what you've said as far as connecting, what are some things that you've seen, or how do we paint the picture of what we really mean by that?

Peter: We at Boathouse, and I in particular, think about marketing through the lens of how we communicate in narrative. The economist Robert Shiller published a book a few years ago that basically used Google Ngrams to go back and evaluate conversations and data over the last 200 years across various industries, movements, and so forth. What he found was remarkable consistency with respect to how if you can control a narrative, you can create value for your organization, either perceptual or economic value, or both.

Thinking about that as an underpinning, what you really need to know is what people are thinking and what they're saying, how they think about things, not just from a sentiment perspective but a passion perspective. What are the individual remarks they're making at any given time that could hurt or help your reputation? What do key stakeholder groups think about? Like your employee, not just your investors and customers. It's a massive amount of data to collect, if you think about the way that we are communicating using tools like Twitter and Facebook every day.

From my perspective, what I look at is AI tools that can help me to gather scads of information from virtually everywhere using things like Twitter as a clearinghouse, because I think Twitter is very much like a modern media clearinghouse, and then other sources as well, of course, and then being able to collate that data into something that makes sense in a snap of a finger. If we can get to the bottom of that and understand things like passion and sentiment, then we're better positioned to help our clients establish narrative structures that are going to help them to create value.

There were two fascinating things from my perspective with respect to the AI that we're putting together. The first was this passion indicator. If you're in the upper right-hand corner with passion, chances are people are going to want to work with you or buy your product. Think about Apple and Tesla, those are two brands that are very much about passion. Your ability to convey that is critically important.

The second thing is our AI looks at employee engagement. During the pandemic, post-pandemic, and we're seeing it right now with how Twitter employees are behaving and taking the power over. What your employees have to say about your brand completely defines it. The tools that we're looking at factor that in as well.

George: It's interesting. For me, I'm like there's the narrative that you want to kind of control, we talked about that. I heard you there and my brain went to internal narrative and external narrative as far as potential prospects and things like that. Then you really got me when you started to talk about passion and this passion index, because at the end of the day, we're talking about AI, but we're all dealing with humans. As marketers, we're dealing with humans.

Right now, where we sit, I know there's probably two groups. Maybe more, but I'll break it down into two groups. There's the group that is heck yes, give me more, I need to leverage AI, I want to know this passion index, I want to know what the next steps I should do to get started to make this happen. Then there's the other group that might be sitting here going no, it's not for me. I want to circle back around and just ask the question, is it all marketers, is it some marketers, should marketers be focused on leveraging AI right now in 2023 and beyond? Give me who you think a good fit might be, maybe what a bad fit is, and why they need to lean into this modern technology for today's marketing efforts.

Peter: It's a great question. I honestly think it has to be everybody. To your point, the uptake is going to be variable across organizations. The thing is marketers are not trained as technologists, so number one, we're really bad at selling technology, and number two, we're technology resistant. We got into this profession because we can do things like take a story and humanize it and get a journalist to talk to us, or we can do tremendous creative and create emotive stories that way.

Those are very much human things, they are not about the machine, but the reality is the machine is informing everything at this point. If we don't understand it, if we're not ready to embrace it, then we're going to lose because somebody out there is going to be doing that. I think the perfect combination is the ability to find the cross-section around AI and human intelligence and human emotion as well.

Everybody talks about the algorithm and its ability to identify behaviors and move transactions based upon that or move relationships based upon that. The problem with that is this; by stripping out all of the humanity, we turn every relationship into something that is purely transactional. Ultimately, everybody is going to shop at Amazon because they have the best algorithm, and there may or may not be passion for it. What we have to do is basically find out how to take that information and apply a human overlay to it so that we can create a piece of marketing or communications that is going to speak to people based on that information.

George: I love this so much. One of the things that I like to focus on is that with our marketing it should be less transactional, more transformational. When you do that, you are tying into those emotions, you are tying into being human, you are tying into that passion index. It's interesting because we're talking about strategic communications with AI, but as I listen to you, it's the same conversation back on the creative of AI is a great way to get to a good starting point that then you layer on a human element over it to take it to the touchdown zone, or to hit the homerun, or to use whatever sports analogy you want to use as you're listening to the podcast.

Where I go back to on this then is if we're talking about strategic communications in the understanding that the AI machine is giving us all of this information, it's helping us understand it at the click of a finger, when we talk about strategic communication, what the heck do we mean? We get the information. Now what do we do with it? What is that human layer or do we mean by having a strategic conversation or communication? Because, by the way, it's going to be completely different that what we once would have called a strategic level of communication as just a pure human to now what that means with human and AI working together.

Peter: That's a great question. There's a tremendous episode of The Office, the old television show, where Michael and Dwight are out driving and they're using their nav system, and they drive into a lake. Michael is like, "It says to turn," and they literally drive into a lake. I think there's a danger of infecting what we do from a marketing and communications perspective.

Step back a minute and you had two great questions in there. One was about creative and how things are affected from a creative perspective. I was having a conversation with an analyst at Forrester a couple of years back, and he asked me what I saw as the next generation of great advertising. I said it's the return of creative. He agreed with me, which I love because even this far into my career, I'm looking for validation and affirmation from other people, that's the client service business. The reason we both agreed on it was this notion of so much AI has completely transactionalized relationships, there is no way to stand out, so the ability to stand out is the re-establishment of narrative or brand visually. I think that's going to be increasingly important in social and digital marketing this year, next year, and the year after.

The second thing is with respect to strategic communications, the ability to figure out very quickly what the key messages are and what the key passion points are has to be done by an individual or a group of individuals pulling that data. When you get that a-ha moment, you can then figure out what to do with respect to your messaging and very quickly get it out to market.

The things I'm looking for in that context are what are the channels that my audiences are focused on and what are the ways they like to consume information, because ultimately what I want to do is use AI to find the 10, 15, 20 most important distributed influencers around a subject or a topic matter, the channels that they prefer to engage in, the channels that my clients are most able to communicate in, and then match those things up. If I can do that based on the AI, I'm going to deliver more value on every piece of information that I'm engaging on and every account is going to get more value.

George: It's interesting. My brain goes to this thing of time is money. As I heard you talking about that last segment, it really came down to, you even said it in a couple of different words and ways of speed, the speed at which you can complete the things or understand the things that you need to know and can complete. If you can do it faster, then there is a better return on the investment for the things that you're about to do. I think that's interesting for us to sit back and think about. The bonus to us as humans is the rate in which we can understand and complete the tasks, which if you're a human, that makes you realize you can golf more or spend more time with your family or do the things that you love because you've embraced that AI machine.

Let's get down to brass tacks. If somebody has gotten to this portion of the podcast episode and they're like, "How do I get started? What do I need to know?" I guess that's the question. How in the world, when it comes to this AI for streamlined and strategic communication, do we get started?

Peter: I would say take a step back and, this is going to sound very cliché, but talk about the three to five things that the CEO, the CMO, and CCO want to accomplish from a business perspective. From there, there are a host of fantastic tools out there that you can either use alone or in a best-of-breed mix that identify things like natural language, machine-learning, the ability to match key phrases and really source information from all different kinds of places.

That's going to inform the terms that you're going to want to feed into your AI, the things you're going to want to be searching for. Ultimately, what you need to do is identify the four to five outputs from the AI that are going to tie back to those business objectives. Then you have a campaign that you can get up and running fast.

I'll give you a quick example. I was doing work for a large healthcare services company back in 2014, and they were getting a ton of information from a social learning tool that they were feeding up to their CEO. The CEO was trying to figure out how to move into different states after the passage of the Affordable Care Act, and the assignment was this: "Our CEO cannot, using this data, figure out where we need to be, it's just too much." The client literally said, "Can you just give me the four screens I need that I can show him every day that are going to tell him what he needs to know."

We walked in, spent a few minutes with the CEO, understood better the business objectives, "We want to XYZ in Maryland, ABC in Florida," and we were able to identify where we wanted to pull data from, what key searches we wanted to do, and then based on that data we put together a meaningful dashboard and a report to the CEO so that he could make informed decisions.

Fast-forward eight years, and now the tools are so much more sophisticated and the ability to get that information is so much better that if you take that approach every time, you're going to be locked into a client relationship forever, or you're going to be a CMO or CCO who is not going to worry much about your job security.

George: God knows that there's a lot of us out there that don't want to worry about our job security right now, for sure. It's funny though, because when I'm listening to you talk about this, I was like not only are we talking about streamlined communication, but we're talking about simplifying the ability to measure and report on the success or even measure, report, and show the things that those above us in the C-suite might need to know to make decisions as we move forward. It's very interesting how this all kind of collides together in a little bit of an AI spiderweb, if you will.

One of the things that I like to do on the podcast is to get everybody aware of the potential potholes or hurdles that they might run into. Goodness gracious, you know and I know that when it comes to AI we can jack it up real quick. What are some of the potholes or hurdles that you've seen marketers fall prey to that you would want to be like pay attention to these things, don't do these things?

Peter: It's two things. One is we fall in love with the toy and there are just way too many screens. As marketers, our new competition is these people, these technologists who are creating these tools. They're coming in and saying to CEOs, "We can completely automate the process, you don't even need humans anymore." Our reaction can't be they don't need humans anymore and we have to match them. Instead, we have to go in and say you need the human interaction to understand and interpret this data and then put out something off the back of it that is valuable to you.

The second thing is don't get completely mired in the data just for data's sake. I'm a huge sports fan, and even to this day I'll spend tie on my phone looking at somebody's scoring average, somebody's batting average, and all the advanced data that half of it I can't process or understand what it means anymore. That's the point, you can't process or understand what it means. Everybody is throwing these statistics at you, and in the end, you want to go back to batting average, homeruns, and RBIs. What's the equivalent of it in our business?

George: I love that. So good. I don't know why, maybe because it's around statistics and sports, but Moneyball came up. The way that they hacked a way to figure things out, most humans can't do that. Almost every human could probably do that with a little help from their friend the AI machine, on the conversation that we're having today.

You shared an example, which then led my brain to it's great that we have a good example, but one of the things that I love to paint on the podcast episodes, too, is what is the Zen mountain moment or if you're on the Olympics number one podium with the gold medal around your neck. Talking about streamlined and strategic communication and using AI to be a large portion of that, and the things we've talked about as far as emotions and passion and the human layer, what the heck does AI success look like when we're talking about this conversation?

Peter: More generally, for me, it means I've found the ten most important influencers in a category and I'm talking to them over and over again, and they're talking about my client over and over again, so I'm capitalizing on their distributed influence and the power of them as a masthead, as opposed to worrying about The Wall Street Journal and TechCrunch or whatever.

There are other instances that are even cooler than that. I was working with a large company in Washington on trying to affect a piece of legislation, and they really needed to get to the chairman of the FCC at that point, and they had no way in. We were able to use our AI to identify which channels he was engaged in on social media and then map relationships in our client organization back to it so that they were able to make contact with him around a key piece of information, and that very much helped with their point of view being factored into consideration.

To me, that's the Holy Grail of AI. If you can scale things down to a single individual relationship and get that close to a key decision-maker, then you're really delivering.

George: I love that. Before I ask the final question, I do want to go off the beaten path one more time. Through this interview, you've actually mentioned three or four times the specificity around what you do as far as influencers and finding those influencers and talking to those influencers. I fully know in asking this question that it could probably be a completely other podcast episode, but I feel like because we're talking about communication and streamlining, and some of this communication will be trying to bring in external people into your marketing efforts or whatnot. When you think of the power of connecting and empowering influencers around your brand, where does your brain go?

Peter: That's such a great question. A lot of times, it goes to how we can communicate social good. Some of the most fun that I've had in my career is building influencer networks to help with things like regional economic development in impoverished countries or communicating the power of something that somebody is doing in a developing nation and helping them bring that product to market in the West. Those were instances of creating networks of influencers, but also bringing people together in communities and in social media communities. When I can do that, that's when I feel at my best.

I think as we're thinking about where things are going with respect to influence and the power that the individual has, and the fact that Gen X values very much social good ahead of economic gain, our ability to construct those networks and use AI in social and digital to bring people together could be critical. That goes back to the first question you asked me about what keeps me up at night. This is where my mind goes when I think about the good of what social media and AI can do, so that's what keeps me optimistic.

George: I like that, what keeps you up at night and what keeps you optimistic. That's a good tie-in. Speaking of that optimism and looking in the future, one of the things that I realize about myself and many of the guests that we have on the Marketing Smarts Podcast is that we've all been through a journey. Most of us aren't spring chickens, we've probably accumulated some wisdom along the way.

As we end this podcast episode, what are some words of wisdom that you would want to share with the Marketing Smarts audience? This can be on the conversation and topic that we've had today, or this might be just some guru Zen moment for you. What are your final words of wisdom?

Peter: Be a critical thinker, read a lot, and don't be afraid to challenge the status quo. I was educated in communications and marketing, and I didn't really get smart until I started to read a lot and be a critical thinker. I'm always impressed by people who can come straight at me with a great idea, regardless of their age.

George: Marketing Smarts listeners, did you take lots of notes? I sure hope so. I have to ask, what is your one thing, your number one execution opportunity after this podcast episode? Make sure you reach out and let us know in my inbox or on Twitter using the hashtag #MPB2B.

I also have to ask are you a free member of the MarketingProfs community yet? If not, head over to Mprofs.com/mptoday. You won't regret the additional B2B marketing education that you'll be adding to your life.

We'd like it if you could leave us a rating or review on your favorite podcast app, but we'd love it if you would share this episode with a coworker or friend. Until we meet in the next episode of the Marketing Smarts Podcast where we talk to Sydni Craig-Hart about inclusive B2B marketing strategy techniques you can't afford to ignore, I hope you do just a couple of things. One, reach out and let us know what conversation you'd like to listen in on next. Two, focus on getting 1% better at your craft each and every day. Finally, remember to be a happy, helpful, humble B2B marketing human. We'll see you in the next episode of the Marketing Smarts Podcast.

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Podcast: AI & Avoiding Data Overload

Originally published in Provoke Media

Image source: Unsplash.com

A longtime industry veteran, Boathouse President Peter Prodromou is a big believer in the benefits of AI in informing business decisions — on the condition that it’s subject to human editing and interpretation along the way. In this episode of the PRovoke Media Podcast, Prodromou joins Diana Marszalek to discuss the importance of managing AI so that it doesn’t lead to data overload.

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How Brands Can Use AI For Strategy Work

By John Connors

Boathouse Founder/CEO

Originally published in Marketing Insider

Image source: Unsplash.com

Very few things live up to the hype, but AI just might. If the Internet drove the last 25 years of marketing and branding, AI will likely drive the next 25. It will underpin most everything, including marketing and the internet. With that in mind, here are five steps to take to use AI for marketing or brand strategy:

Step #1: Get in the game.

The first piece of advice is to simply use AI: Test it, try different tools, fail, try again, and try not being a perfectionist, a critic, or a cynic. The first websites built by marketers and brands don’t look so great in hindsight, and a lot of people in marketing discounted the internet. Don’t be one of those. Pick a few tools and get the ball rolling. The people that lead early, will lead for a long time.

Step #2: Visualize your company or brand strategy.

Before you go down a rabbit hole with the tools, try and capture your company strategy on a single page, whiteboard, or screen. The key is to capture all the bold ambitions and strategies that are discussed in marketing meetings, board meetings and investor presentations so you can later use AI to find out if they are getting to the market.

Step #3: Find a good AI partner.

There is a new category of software companies all competing to be your AI strategy tool of choice. Each of them has attracted hundreds of millions of investment dollars and are racing to index the world’s news and social content. Some use natural language AI, others are topic-based AI, one is ESG-based. Companies like Signal A.I., NetBase Quid and Polecat are good places to start your tool search.

You should expect to spend around $50K to get in the game, but you will get demos and maybe trials with some. Beware the shiny objects. I was hypnotized by one for six months until I realized it was useless.

Step #4: Test the tools.

The first thing we will typically do is to analyze three companies across news and social. For example, we recently used one of our tools to analyze Delta vs. United vs. American Airlines. In less than 30 minutes time the tools will index every piece of news media, a majority of social, all employee comments on Glassdoor and CEO sentiment.

In less than an hour you can see if team perception matches market reality. It is like having a map for the first time. You see your business from 35,000 feet in a way you cannot imagine until you try.

Step #5: Make AI actionable.

We all know that C-suite executives need data presented in actionable ways and with recommendations on how to leverage it. AI will help you with the C suite, help you create better strategies, improve your execution and help your teams be less siloed. The magic of AI is that it will help you see things as they are, not as you wish them to be.

Marketing has not been easy for a while now, but AI shifts the playing field. It puts marketers back in the driver’s seat in management meetings and board rooms. The data it creates allows marketers to create better strategies, to execute better and to hold your C-Suite colleagues more accountable. The next 25 years will once again change everything -- and AI will be at the core.

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How PR and Marketing Can Ensure AI is Useful and Avoid Data Overload

By Peter Prodromou

Founder/President Boathouse Palo Alto

Originally published in PRNEWS

When used effectively, AI can transform how PR and marketing target, engage, activate and measure campaigns.

The evolution of AI platforms comes as executives increasingly ask that PR and marketing help solve business problems. AI can help whether a business seeks a more informed way of penetrating markets, addressing customer satisfaction issues or anything in between.

However, in spite of AI’s improvements, issues remain.

For example, without human intervention in the form of editing and interpretation, AI outputs can overwhelm business leaders.

As such, absent clear linkage with a company’s goals, AI’s voluminous outputs are fun and interesting perhaps, but they are not helpful answering C-suite business problems. What company leaders want are easily processed insights that can help their business.

For example, say a national healthcare provider is seeking a larger footprint. It wants an assessment of media coverage so it can better understand potential new markets.

Each day its CEO receives a large dossier of AI-generated media data. It's overwhelming.

Instead, the company’s PR team should have:

• Ascertained the company’s business objectives

• Aligned them with search and

• Embedded these criteria into natural language and trend analysis AI, adding social conversation as part of the search criteria

The result: easily digestible data sorted into tabs: business objectives, attitudes and geography.

Bearing this example in mind, below is a prescription for leveraging AI’s power and potential in service of business outcomes:

Identify Business Goals

Before setting output parameters, interview the C-suite. Identify business problem(s) it wants to solve through marketing and communication. Identify 1-3 key business issues.

Align Content with Goals

Determine source material that will inform engagement around the above issues. Set not just search and output, but also an action plan for engagement. This lets you link engagement and outcomes directly to business goals as informed by these outputs. In addition, it enables constant evaluation and adjustment, if necessary. Limiting output to business goals will help you manage the flow of useful data.

Narrow the Universe

Tech-first solutions providers enjoy showing off the muscularity of their technology. They’ll say their technology’s output volume will amaze you. Probably will. Yet, as we saw above, disciplined communication and marketing relies on useful insights not volume. Again, thinking through linkage to business goals, you can make specific choices about the universe of outputs, resulting in better outcomes.

Apply Human Intelligence

This is critical. While output is impressive, the ability to interpret it is the real area of separation. An algorithm will surface precision content, but it can’t interpret its application. This requires human intelligence, blended with the power of AI.

As an industry, communication and marketing belong in the C-suite. Unlike business strategy consultants, our work is tied to engagement every…single…day.  AI has the potential to make our already strategic position even more so.

But to reach that potential we must harness AI and use it in smart, programmatic ways. Otherwise, it’s just another tool of confusion for executives seeking clarity.

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