Tim Brunelle is
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Tim Brunelle

Useful Lunacy:
Thinking about thinking, creativity and the power of ideas.

Reigniting the Future of Advertising

“If you want to learn something, teach it” is probably my favorite adage. 

And so, once again, I’m returning to teaching this fall—renovating and reinvigorating the Future of Advertising course I first taught at MCAD back in 2008 (after ruminating on it the year before). Almost a decade later and the future is still out there, and it starts in about a week. I’m grateful to MCAD’s Entrepreneurial Studies program for asking me back. 

I wrote about the joy of teaching in 2009. It is a pleasure to stew in the subject material and collaborate with students to figure out what resonates and why. It’s also a great excuse to connect with the people much smarter than I and elicit their perspectives. I am forever grateful to everyone who reacted to the syllabus below, asking important questions, suggesting refinements and additional content. Kudos to Rosie and Faris Yakob, Chris Bradley, Edward Boches, Kevin Swanepoel, Zach Pentel, Paisley Schade, Rick Webb, Sung Chang, Julie Scheife, Jim Splinter and Lori Yeager Davis.  

The subjects will evolve, of course, especially as a slew of awesome guest speakers influence things. Here are the building blocks of the official syllabus. 

Course Description:

The marketing and advertising industry is grounded in the supremacy of ideas and is constantly adjusting to emerging communication platforms. This class examines those adjustments and emergence in depth to understand how effective ideas continue to come to life across digital and social media. We’ll explore and define how consumer, category, and platform insights matter. We’ll consider and develop strategic foundations that support ideas across multiple media. We’ll dive into the tools marketers and agencies use to power ideas today. Assignments throughout the semester will introduce students to exercises in developing content for existing and emerging digital platforms while exploring the relationship between humans, and existing and emerging technologies to create innovative campaigns. Marketing and advertising guest speakers will join the faculty in reviewing assignments and offering feedback on assignments. Prerequisite: Introduction to Advertising or Copywriting.


Students completing this course will be able to do the following and more:

  • Understand context — how and why the marketing/communications/advertising/PR industry has evolved since the Internet (and why that matters)
  • Describe the forces—new processes, platforms, technologies, roles and deliverables that solve modern branding and advertising problems
  • Conceive, research, refine and pitch integrated ad campaigns that utilize longstanding as well as modern (i.e. programmatic, dark social, VR/AR, voice activation, etc.) components
  • Develop work for a portfolio that demonstrates fluency relevant to the future of advertising


Various—lecture, guest speakers, online research, reading assignments, course assignments, etc.

Required Textbooks, Readings and Websites:

The course will utilize two books—one first printed in 1965 and the other in 2015. They are both remarkably useful when plotting the future of advertising. Both books should be available in the MCAD Book Store. You can also find new and used print copies and Kindle editions via Amazon.

  1. Paid Attention – Innovative Advertising for a Digital World by Faris Yakob
  2. A Technique for Producing Ideas by James Webb Young

In addition to the books, we will also reference various blogs, news articles and opinion pieces found online. I’ll share links with you when appropriate.

Please download and look through Mary Meeker’s 2017 Internet Trends Report (http://www.kpcb.com/internet-trends).

And please bookmark the One Club’s online archive of over 250k iconic ads (since 1991) http://www.oneclub.org/awards/theoneshow/.

Finally, please subscribe to the following six email newsletters. Each will provide useful and frequent insights and information relevant to the course.

CB Insights (https://www.cbinsights.com/)

Launch Ticker (https://www.launchticker.com/)

Scott Galloway’s No Mercy/No Malice via L2 (https://www.l2inc.com/email-signup)

Bob Hoffman – Type A (http://www.typeagroup.com/)

The Prepared – curated by Spencer Wright (https://theprepared.org/newsletter)

Neil Perkin’s Only Dead Fish (http://www.onlydeadfish.co.uk/)



Class #1 – Course Overview / Welcome to the Future

Meet each other, set expectations, review course calendar, guest lecture schedule, textbooks and resources. Let’s also review the world we live and work in, as a means of clarifying the role of marketing and advertising—i.e. The way we own and buy products and services has changed, which means why and how we market and advertise changes, too. And what about the purpose of brands? Nevermind globalization and the transparent flow of diverse and niche cultures (Despacito, anyone?) and their impact on the business of ideas. Finally, let’s discuss the core skills advertising agencies (and their clients) need in 2017 and beyond.


Class #2 – The New Infrastructure — Data, Location Awareness/Connectivity, Artificial Intelligence & Automation

Let’s dig into everything Don Draper and Peggy Olson never imagined. We work now in a world of ideas where data informs everything. A world that is “always on,” connecting faster and contextually aware. A world that automates and optimizes more and more of marketing. And a world where AI threatens the role of Idea Person. How do all these forces impact advertising and the people who work in it?


Class #3 – Social Media Platforms, Circa 2017 & the Rise of Dark Social

In less than a decade we’ve witnessed a global communications transformation that’s profoundly affected advertising: the rise of empowered individuals and digital communities. Let’s review how the evolving world of social—sharing, influence, ratings, creation tools—impact brand building and modern advertising strategy and ideas. And we’ll review the wealth of platform best practices to understand how they seek to engage and assist marketers and agencies.

Class #4 – Storytelling in Multiple Dimensions — How Ideas Come to Life across Virtual Reality, Augmented Reality and 3D Holograms

Let’s dig into all of the ways in which brands and advertising ideas are moving off the printed page and untouchable screen and into our lives to provide a digital lens on the physical world.


Class #5 – Conversation Design—Advertising Through Voice Activation, Chatbots & Podcasting

Let’s talk about how we research for, strategize/architect and develop brand strategies and ideas when the medium is conversation. Let’s also talk about the recent rise of audio storytelling as a means of brand building via podcasting.


Class #6 – Influence & Truth & Everything is PR

Is Word of Mouth the most authentic form of advertising? Let’s discuss how branding and modern advertising works with influencers and across shifting contexts (i.e. within social networks, next to fake news and extremist publications, etc). We’ll also review how public relations and social sharing have transformed the way we strategize, define and produce advertising ideas so they generate even more attention.


Class #7 – Course Assignment Briefing



Class #8 – Agencies of All Shapes and Sizes, Circa 2017

Let’s compare and contrast how ad agencies are evolving and differentiating beyond Independent, Network and In-House (i.e. 3M, Hogarth, Best Buy), to include Consultant agencies (i.e. Deloitte+Heat), Social Platform “in-house” agencies (i.e. Facebook, Google), and Publisher agencies (i.e. Buzzfeed, Brainjolt).


Class #9 – Campaign Architecture – Business Definition and Marketing Strategy, Circa 2017

Let’s dig into all the brand work that must occur before modern creative briefs are written and ideas are developed. What critical analysis must occur to define and transform business challenges into opportunities which marketing and advertising can affect? Some say, “everything (in business) is (now) marketing.” If this is true, how does technology, data, digital targeting and marketing automation influence business operations, logistics, pricing, governance, etc.? In addition, let’s bring our own digital lives into consideration. How do our own biases and digital consumption affect how we perceive target audiences as well as marketing challenges and opportunities?


Class #10 – Media Buying & Technology & Measurement, Circa 2017

How are advertising ideas distributed, and how do we know if they work? Let’s focus on client-side and third-party marcom stacks (DMP, databases, etc.), advertising measurement as well as social listening, site and influencer measurement, always-on dashboards, etc. How are brands measured in this evolving age of digital? If you’re agency-side, how do you explain all of this to your boss/client?


Class #11 – Creative Briefing & Selling Ideas, Circa 2017

Let’s discuss the business and process of developing and selling modern ideas. How has the briefing process evolved since A Technique for Producing Ideas, and later, “A Fisherman’s Guide” (inside Truth Lies and Advertising) by Jon Steel? How do we effectively differentiate briefing for conversation platform ideas versus traditional one-way media ideas? What methodologies and tools have evolved to enable the briefing process?


Class #12 – Course Assignment Check In + The Modern Making-Of, i.e. Rapid Prototyping

First, let’s review your progress on the Course Assignment. Then, let’s get pragmatic and discuss the apps, tools and processes which enable modern strategic development, creative ideation, production and distribution.


Class #13 – The Business of Digital & the Future of Advertising

Let’s take a deeper dive into how Google, Amazon, Apple, Facebook (Galloway’s “the Four Horsemen”) have become gigantic corporations and brands themselves. What does all this mean for the business of brands and advertising ideas?


Class #14 – Pitch Day: Course Assignment Due

Present your work to the class and potential visiting guests.


Class #15 – Wrap Up: Living in the Future of Advertising

Recap the semester’s topics, assignments and guest speakers. Make up assignment presentations as needed. Let’s focus on your path forward—what will you do next to prepare for and/or engage in the future of advertising?


The End.  

(For those keeping score at home, you might question the delay in discussing strategy (#9) and briefing (#11). I believe it’s better to review the myriad forces impacting advertising (Infrastructure to VR to podcasting, et al) first, then dive into the agency world and their processes.)

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How idea people thrive in an age of artificial intelligence and automation

This post is an edit of a luncheon keynote given to the MMPA’s 2017 Summit on April 26, parts of which were also included in a May 2 address to the graduating students in the MCTC Design Program. It’s focus is on the role of the idea person in marketing, advertising and publishing, which has never been more challenging. Originally published on Medium


I’m keen on the idea of Idea People; and especially the Business of Ideas. I believe that idea discovery, idea articulation, idea presentation, idea optimization and idea distribution are all scientific and artful, in equal measures.

As Idea People, we are also agitators. I’ll paraphrase Robert Grudin, who describes us in his book The Grace of Great Things, “Many [Idea People] initially are seen as troublemakers simply because their vigorous and uncompromising analysis exposes problems that previously had been ignored.” Grudin warns that, “Creativity is dangerous. We cannot open ourselves to new insight without endangering the security of prior assumptions. Creative achievement”—and that’s what I believe all of us Idea People are all about — “Creative achievement is… an adventure. It’s pleasure is not the comfort of the safe harbor, but the thrill of the reaching sail.”

So onward we sail.

Now, here’s the thing: We’ve been here before.

Every innovation, ever, offers threats and opportunities to job roles, to the kind of work we do, to industries, to culture. And in each case, innovations (like artificial intelligence) offer a sense of what the author Neil Postman might refer to as “magic.” But I prefer Arthur C. Clarke’s Third Law definition.

“Any sufficiently advanced technology is indistinguishable from magic.”

If we work backwards from today, consider…

First, the magic of Desktop publishing.

— Which threatened the idea of how we print, who prints, when and where we print, the notion of control of image and design

— Simultaneously, desktop publishing created more content by specific authors and publishers for more specific audiences. The magic of desktop publishing created new ways for more people to be idea people.

Then consider the magic of Photography.

— Which threatened the idea of visual expression and the recording of images and likenesses as defined by the hand and eye through painting and drawing

— And yet Photography created a new way of seeing, a new Art. Photography expanded our understanding of the world around us and helped deliver a boom in publishing .

Going further back, consider the magic of Printing.

— Which threatened oral traditions and the power of those few who could speak and tell stories

— Meanwhile, printing created a need for literacy, a need for teachers, the expansion of nations and belief systems.

So we’ve been here before, over the millennia. New innovations arrive and the residents panic. And sometimes rightly so.

Let’s begin by talking about the idea of Automation, of robots and scale; of simple, repetitive work once done by humans then handled by machines and now handled by software. The stats can look grim.

“83% of US jobs paying less than $20 per hour will be subject to automation or replacement. While up to 47% of all US jobs are in danger of being made irrelevant due to technological advancements, with most job losses due to occur amongst the undereducated.” So says a January 2017 report authored by The Obama White House titled, “Preparing for the Future of Artificial Intelligence.” [Source via Scott Abel @ The Content Wrangler]

In a “Robot Proof Jobs” report from the consultants at McKinsey, we hear, “Across all occupations in the US economy, one-third of the time spent in the workplace involves collecting and processing data. Both activities (collecting and processing) have a technical potential for automation exceeding 60 percent.” The report continues, “And it’s not just entry-level workers or low-wage clerks who collect and process data; people whose annual incomes exceed $200,000 spend some 31 percent of their time doing those things, as well.”

Bringing things closer to home, James Somers writes recently in The Atlanticthat, “Newspapers and magazines used to have a rather coarse model of their audience. It used to be that they couldn’t be sure how many people read each of their articles; they couldn’t see on a dashboard how much social traction one piece got as against the others. They were more free to experiment, because it was never clear ex-ante what kind of article was likely to fail. This could, of course, lead to deeply indulgent work that no one would read; but it could also lead to unexpected magic.”

There’s the crux of it. Can automation help scale our labors in the continuous search for unexpected magic?

As Idea People, we ought to look at Automation for its ability to serve our readers, to enable the audience instead of to deceive them.

So, yes, please, Automate processes that make reading and enjoying your product easier. Automate the means for your audience to engage, on their terms, versus yours. Just don’t try to automate unexpected magic.

Not when you could have an artificial intelligence create it for you. Right?

It’s abundantly clear that “Artificial Intelligence” is the buzzword du jour. And not without merit.

Stanford organizational sociologist, R. David Dixon Jr., writes, “We humans are largely only still involved in the process because we’re still the cheapest option for whatever task we’re doing. Cheaper because the technology is currently too expensive or non-existent, and cheaper because wages can always be lowered. As technology advances, however, humans are increasingly less effective and more expensive than good machines. This is true not just for those working at the ground floor, but also for the managers above them.”

Wait, it gets better!

Dixon continues, “As artificial intelligence and machine learning develops, particularly in their ability to understand and contribute in natural human conversation, humans will reach the end of their usefulness in an increasing number of industries systems entirely.”

How’s everyone feeling? Who’s excited to return to work tomorrow?

We’re already seeing this story evolve within the financial services industry. Paraphrasing from The New York Times in March of this year… “The investment firm BlackRock laid out an ambitious plan to consolidate 11% of its actively managed mutual funds ($30 billion in assets) with peers that rely more on algorithms and models to pick stocks. As part of the restructuring, seven of BlackRock’s 53 stock pickers are expected to step down from their funds. At least 36 employees connected to the funds are leaving the firm.”

The researchers at Forrester posit that today, 38% of enterprises are already using artificial intelligence (AI), growing to 62% by 2018. Forrester is predicting a 300% increase in AI investments in 2017 compared to 2016 and IDC believes AI will be a $47 billion market by 2020. [Source]

Oh, and some of the Idea People at Coca Cola have announced they want to use AI to facilitate making advertising.

Well, let’s not cower under our afghans just yet.

At this point, it’s worth asking the question, what, exactly, is Artificial Intelligence? Or as Neil Postman reminded us back in 1985, “…in every tool we create, an idea is embedded that goes beyond the function of the thing itself.” So, what’s the idea embedded behind Artificial Intelligence?

The concept was first coined by Stanford professor John McCarthy in the 1950s. And we know that intelligence, artificial or not, is rooted — as AdAgeeditor Kate Kaye writes, “in the tsunami of data generated by digitized systems, and the availability of relatively inexpensive and fast cloud computing.”

So, AI, in short is predicated upon Data. And lots of it. Data easily connected, easily parsed, and inexpensively processed — to generate what looks like and smells like and wiggles and wobbles like—thinking.

The Defense Advanced Research Projects Agency’s Information Innovation Office has weighed in via YouTube, and suggested we distinguish between three different waves of AI. (A big hat tip to Roey Tzezana at Futurism.com for summarizing DARPA’s lengthy video.)

Summarizing Tzezana’s summary:

“First Wave artificial intelligence systems are capable of implementing simple logical rules for well-defined problems, but are incapable of learning, and have a hard time dealing with uncertainty.” “With first wave AI, parameters for each type of situation are identified in advance by human experts. As a result, first wave systems find it difficult to tackle new kinds of situations. They also have a hard time abstracting — taking knowledge and insights derived from certain situations, and applying them to new problems.”

In other words, first wave AI only knows what it knows. Take voice activation. As examples of first wave artificial intelligence, Alexa or Google Home can only give you answers they have access to, for questions they comprehend.

Summarizing Tzezana again: In Second Wave AI systems…

“Engineers and programmers don’t bother with teaching precise and exact rules for the systems to follow. Instead, they develop statistical models for certain types of problems, and then ‘train’ these models on many various samples to make them more precise and efficient.”

For example, consider how we’re training AIs to recognize images of cats or faces, or the recent advancements in both accuracy and speed of Google Translate. These second wave AIs are using complex models to compare and hypothesize the accuracy of a response. It’s closer to what you and I do when thinking, but it’s not yet human thinking.

Finally, Tzezana summarizes, Third Wave artificial intelligence will go beyond leveraging models we humans create, to “discover by themselves the logical rules which shape their decision-making process.” Sounds almost human, doesn’t it? But let’s be clear that third wave AI is — at least according to DARPA — decades away from reality.

But why? The answer is Data.

As Joe Lonsdale, a co-founder of Palantir and general partner at investment firm 8VC, noted recently, “Before artificial intelligence can tackle some of the harder problems — it will take years if not decades to [figure out how to] structure the data these systems will ingest.”

Ah, data. The soil upon which intelligence takes root. Currently our data is messy, dissimilar, inconsistent. Julie Fleischer at Neustar, calls it, “a swamp: an opaque, poorly understood mess.” Which is why Lonsdale and others claim, “AI is decades away from matching human creativity.”

If the data is a mess, so too is the intelligence.

Despite Move 37.

It’s true AlphaGo’s historic win against Lee Sedol, the world’s best Go player in Match 2 was unexpected. But, we haven’t seen evidence Google’s artificial intelligence understands its own achievement. Yes, the artificial intelligence won, but did it even know it won — or what winning means? As technology pundit Shelly Palmer puts it, “AlphaGo is dangerous to 9-dan [level] Go masters, but harmless to people who optimize media purchases.”

By way of another example, take Minnesota’s own Lucy, an artificial intelligence focused on marketing services from the team at Equals 3 Media. Lucy is powered by IBM’s Watson. Lucy’s intelligence can certainly help you get closer, help you focus, help you distill insights to fuel an idea. But Lucy isn’t going to suggest you Think Small. Or suggest you put Andy Warhol in a soup can on your magazine cover.

It still takes the brainpower of Idea People to connect the dots.

And it also takes thoughtful UX and UI to benefit from artificial intelligence. It takes amazing Design. Remember, artificial intelligence can’t yet organize and design itself. How we humans experience AI — how we interact with it, how we query, how results or actions are delivered, how confusion is resolved — oftentimes matters much more than the intelligence itself.

But the clock is ticking.

Seth Godin says, “The question each of us has to ask is simple (but difficult): What can I become quite good at that’s really difficult for a computer to do one day soon? How can I become so resilient, so human and such a linchpin that shifts in technology won’t be able to catch up?”

Indeed, how?

It’s not clear yet whether we are headed down Orwell’s dark or Huxley’s bright, yet dark path. Because the artificial has not yet learned to be curious the way Idea People like you and me are curious.

So I believe the one word answer to Godin’s question, and to the threat of both automation and artificial intelligence is Curiosity.

Curiosity demands we seek a further, less obvious, less assured horizon. As Grudin puts it in The Grace of Great Things, “One must cultivate a leaning for the problematic, a chronic attraction to things that do not totally fit, agree or make sense. …To think creatively is to walk at the edge of chaos. In thinking the original, we risk thinking the ridiculous.”

Now, I don’t believe Curiosity is ridiculous. Perhaps Niccolò Machiavelli put it best…

“And one ought to consider that there is nothing more difficult to pull off, more chancy to succeed in, or more dangerous to manage, than the introduction of a new order of things.”

Now, I’m not saying Curiosity gets us out of harm’s way. Far from it.

This new order of things — a world increasingly run via Automation and AI — is unavoidable. And it is driven by sharp, extremely curious minds.

What matters most now is our reaction to these developments. Don’t throw up your hands. Don’t fold. Instead, be even more curious. Can our thinking, our ideas outpace technologies which might appear to threaten our existence?

And the thing I’m most curious about is how Idea People like you can enhance our publications, our content, our engagement through automation and/or artificial intelligence.

As VC Joe Lonsdale put it, “There’s just a huge gap between how the biggest industries in America currently run, and how they will run with the best IT and with the best computer science.” So I’m curious — what if you editors, you publishers, writers and designers thought of yourselves as technologists? How might your product evolve, what new products would emerge — from curious Idea People seeking to apply the benefits of AI to the sustained, periodic shipment of words, images and motion to subscribers?

Thomas Hayes Davenport and Julia Kirby, authors of Only Humans Need Apply put it this way, “Instead of viewing these machines as competitive interlopers, we see them as partners and collaborators in creative problem solving.”

We should explore. We should embrace and prototype. What kind of two-week sprint will your team run, starting tomorrow, to understand and leverage artificial intelligence or automation inside your organization? What new experience of your publication is waiting to be revealed as a result?

Jonas Prising, the CEO of Manpowergroup — the multinational human resource consulting firm, says, “In an environment where new skills emerge as fast as others become extinct, employability is less about what you already know and more about your capacity to learn.”

So thank you for this opportunity to talk today.

I must admit I am not a scientist. I am not a software developer. I can’t spool up an artificial intelligence on Amazon Web Services. But I can ask questions and I can learn. In learning about AI and automation I’ve found I am not afraid of the future of Idea People. I’m bullish on our abilities to derive opportunity from the evolution of technology.

I believe the long term, passionate, purposeful thinkers in this room will discover unique, robust and profitable ways to benefit from automation and artificial intelligence. If we remain curious.

I’ll leave you with a last, favorite quote, from Boston Symphony conductor Ben Zander and his wife Rosamund, from their book, The Art of Possibility.

“Grace comes from owning the risks we take in a world by and large immune to our control.”

Thank you very much.


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The future of where ideas come from

Things ain’t what they used to be. We used to write ads with pens and sketchbooks. 
I had a dream last week where I proposed three headlines to an artificial intelligence (AI), which promptly:
  1. Displayed them within a carousel of ad templates, indicating which—because of sentence length, use of verb, tense and “Prospect Relate-Ability™” were most likely to engage our target audience
  2. The display also formatted the headlines for all manner of social and gave a score showing which would perform best on Facebook versus Twitter, etc. also versus programmatic banners, depending on time of day and audience segmentation
  3. Flagged which of my ideas were similar to headlines already out in the world
  4. Ranked the headlines for degree of positive search-engine appeal
  5. Ranked the headlines for “Award Show Worthiness™” based on a 10-year analysis of winning headlines from Cannes, the One Show, the Andys and D&AD
  6. Offered 1,600 new headline options to consider that the AI thought might be “better” 
Then I woke up.
Yes, “Ads is (still) Art.” Mark Fenske had that right (additional word mine); even as our robot overlords sneak up on all the white collar jobs and intellectual leisure time pursuits. Wired’s recent guest editing of Digg offers a slew of useful AI links. 
But the business of being an ideas person is shifting dramatically, especially in advertising and marketing, whether you’re a writer, art director, designer or strategist. The way things used to be pitted the idea person alone against the world, or maybe just two of you — feet up on the table. The act of idea conception was the idea person’s solitary pursuit. The headlines didn’t write themselves. Now they might. 
Consider today’s HTML5 programmatic banner ads. A human designer and copywriter assemble an initial template and list of headlines for a 728×90 animated banner ad. They may even supply a list of phrases and words. It’s up to the code to assemble those ingredients to find the best-performing combination. While not “intelligent,” you’ve got code doing a lot of iteration and decision making that was once the provence of Creative Directors and Clients. 
Or consider the path almost any designer takes today when beginning a project. Pinterest, Google Image search, CompFight et al enable visual thinkers to quickly gather then curate potential visual direction. Again, it’s not (yet) “intelligent,” but there’s certainly artificial (i.e. code) help. 
We live in a realm of enabled idea development. 
(Need a primer? Two long-reads warrant your time: Paul Ford’s treatise “What is Code” in Bloomberg Businessweek is a great place to start. His treatise will help you grasp computational thinking—the bedrock of our age. Second, read The Great AI Awakening in The New York Times. You’ll get the basics and context for what it means for an intelligence to be created artificially, and how the leap towards automation of cognitive processing – e.g. thinking – can impact professional services jobs like those in marketing and advertising.)
To understand this shift I’m talking about, let’s understand (or maybe it’s better to say “clarify”) the creative process. What happens when an ideas person comes up with an idea? And how might that human process be augmented or replaced by technology? When I’ve taught the process of developing advertising ideas, I explain it this way:
We begin with constraints—in the form of an assignment (or creative brief). So it’s not any idea imaginable we’re after, but an idea specifically purpose-oriented. We need to understand who we’re writing headlines for, in order to influence their behavior. What topics or conditions or emotions might influence a change? Under what circumstances (media location, timing and frequency) would we best influence them? Second, how is what we’re selling—product, service, etc.—relevant, distinct, or necessary enough to evoke a behavior change in that specific audience? 
Well, we’ve already got AIs helping us in this first phase of idea development. Consider Lucy, the Watson-fueled AI for market research and customer segmentation. Lucy can tell you, from across your entire addressable audience, who best to focus on. And “she” doesn’t need healthcare or take vacation days. Sorry, Planners. Or, as Godin put it recently, “The current era of on-demand, widespread looking things up offers a whole new level of insight for those that care enough to take advantage of it.” An AI such as Lucy helps you take advantage, and clarify the kind of idea you need, and where and when you need it. Granted, the impetus is still human. Someone has to start the process rolling. But it’s not hard to imagine thresholds and timers for optimal marketing circumstances, which automatically trigger the need for an advertising idea complete with bespoke creative brief.
So why not have the AI create the ideas, too? This process evolves, in my experience, across three stages: Themes, Hypothesis, Optimization. How would an AI proceed?
Let’s use the structure of headlines as our example in this scenario, since headlines aren’t highly subjective imagery and we’ve already seen how computational thinking impacts paid search writing. It’s easiest to spell out this theory if we keep it constrained to text only. 
And let’s assume Lucy has informed us our best vehicle in which to influence is some kind of static advertisement (e.g. out-of-home billboard or online banner). For the sake of this discussion, let’s assume we’re advertising an update of an existing automobile, a new Volkswagen Jetta. It’s got a more powerful engine, stronger standard brakes and brighter exterior lighting, again, standard. 
So, Themes.
I usually start with general topics under which an idea could sprout or inspire more general topics. In other words, let’s not try to solve the headline all at once. So, in this case, our themes might be: German engineering (it’s a VW); Power (engine, brakes); Speed; Easier to See (brighter); Greater Value (since the improvements are now standard features), Urban Life; Four (seating); and Nimble (VW reputation). That’s where I start for now. But I’d prefer to have 12-15 themes. So imagine an AI taking all it can know about the VW Jetta via every car magazine issue ever and all it can know about advertising cars via every automotive advertising solution that’s ever won an award. We get #ThemesForDays. An ideas person can already do this, of course, with assistance via search. But I don’t think it’s a stretch to imagine an AI such as Lucy offering “potent advertising themes” based on readily accessible data. We’re talking about keywords, after all. 
This is the earliest stage of idea-making, the rough sorting of generalities. We’re not worried about idea quality yet. We’ll get there. At this point, we’re only worried about potential. The point in the Theme stage is quantity, and computational assistance is fantastic for volume parsing. 
Hypothesis is the quintessential stage of idea-making. 
It is the writing down, the stating of, a raw concept. It’s what they dramatize in Mad Men. Inspiration strikes! But ideas do not come from thin air. To continue that metaphor, the “air” from which ideas emerge is richly woven with data. So let’s take our Themes, and take them apart. For example, underneath the subject of Easier to See, we might start to gather and assemble words and phrases (data) such as: Blind, Aware, Glasses, Superpowers, Turn on the lights, Lights Camera Action, Illumination, Flashlight, Searchlight, Lifting the veil, Fog clearing away, See further, etc. We’re moving from abstract to concrete. It’s a process of reference and pattern-matching to loose assembly, vetting and editing. All in fractions of a second. So, maybe Easier to See leads to Superpowers leads to flying leads to that scene in The Incredibles where Edna Mode tells Mr. Incredible “No capes!” leads to Cyclops in the X-Men leads to that lyric “The future’s so bright I gotta wear shades.” Do we have a coherent headline yet? No, but that’s not the point. The point is hypothesizing enough—churning through enough shards of data to get to coherent-enough assembly—and get to what look like fragments of ideas. And “hypothesizing enough” is potentially what an AI could be doing for you. Call it ‘cognitive flexibility.’ This ability is at the core of how idea people work, which explains why some at Facebook are apparently “not” <cough> working on a General AI about, “taking ideas learned in one scenario and applying them in another.” In other words, Facebook says it is not outlining the architecture for code that can hypothesize enough, which in a general sense, humans would recognize as “thinking.” But why not?
Imagine an AI rattling of a thousand, “Hey, what about ________?” loose assemblies of phrases that might lead to a great headline—predicated on our inputs above. It’s my dream from earlier, minus the part where I wrote the first three headlines. So the AI suggests headline hypothesis culled from Themes supplied by market, product and audience data points. Then you, the writer, vet the tonnage and edit your favorites into actual headlines. Better yet, you instruct the AI to vet the tonnage for you, based on additional inputs. “Hey Alexa, tell Copywriter to write these in the style of Mark Fenske.” (And let’s just note it would take a programmer a lot of blood, sweat and tears to define the protocols for headline writing in the style of Mark Fenske.)
So AIs could be helping us with the initial, broad due diligence of idea creation by generating and assessing themes. In fact, they already kind of are: witness Meta, “a company that created an artificially intelligent system that reads and analyzes scientific literature, then connects insights across millions of papers,” that was acquired by the Chan Zuckerberg Initiative. The next step would be for an AI to assist in the loose knitting together, the hypothesizing, the tendering of rough phrases and sentences. (Assuming we’re still on our headline writing assignment.) Or let me put it to you this way… One of my favorite assignments to give when teaching advertising copywriting is called “Write 100 Headlines, Due Tomorrow.” And let’s assume it’s for the same VW Jetta task we’ve been talking about. In this assignment, we put the pressure on and see the idea-generating brain in action. Maybe you just start writing complete headlines. If you do, what process do you imagine your brain going through to assemble those initial headlines? I bet you’re following a Theme-to-Hypothesis operation, even if it feels split-second intuitive. Point is, to write 100 headlines by tomorrow you’re going to need to go wide first, generate quantity then hone quality. You will humor all corners of possibility, writing down any word, fragment or phrase that might lead to gold. I argue that all that gathering and hypothesizing could be done by a well-trained AI. Because what matters isn’t the tonnage. Walk into any ad agency during crunch time and you’ll see that tonnage on the floor. What matters isn’t the brute force processing. What matters are great ideas—which you don’t have quite yet. Because “great” ideas are the result of optimization. 
Optimization is Creative Direction.
Optimization is the removal of the extraneous, the prioritization of the most compelling. It’s killing your darlings. Some people call this Taste. And right now it appears to be highly subjective, a skill even. How does an artificial intelligence replicate all that? How could an AI tell the difference between what could be a D&AD Gold-winning headline and any other headline? Let’s be clear, I’m talking about undermining my own job here, so please understand I theorize with the greatest respect for all the creative directors I’ve ever worked with. The business of taste is not simple or obvious or oftentimes clear. It’s about feeling as much as rational analysis. Can an AI be taught to “feel” the subjective differences between Headline A and Headline B and surmise—again, based on inputs we humans teach it—which headline is “better?” 
I’m of two minds on the answer. First, we work in an era of almost unlimited distribution for ideas, abetted by vast and swift processing. Why not put as many headlines out into the world that you or your AI thinks are great, and test them against each other? In other words, why gate-keep anything; why not test it all? Why not let your audience’s reactions serve as the Creative Director? It’s a math equation, isn’t it? Time + Money = # of Ideas We’ll Run. So run ‘em all. Let the robots sort out which is “best.” Why waste the human hours on optimizing? 
On the other hand, as the aforementioned Fenske mentioned, “ads is art.” Today I suspect we’d likely feel amused and cheated if celebrated artwork in a museum, or a film in the theater, or a book, or maybe even the ad on that billboard turned out not to be created by an artistic (human) visionary. Fair enough, but is it okay to use an AI to write your newsletter? We’ve been taught the provence of Art is populated only by humans. We enjoy a Pixar film for its humanity, despite all the artificial intelligence that went into making it. How do we react when an AI enters this realm, and subjects us to its taste—even (and especially) if the end result is more effective than one derived by humans? 
If you’re on the side of the human idea people, cheer up. Godin, in his article linked earlier, also notes, “It still takes talent and time to find the right thing in the right place at the right time.” That talent is you, an ideas person, generating themes, hypothesizing coherent concepts, and optimizing what your taste tells you is the best solution. And yet, we are moving ever faster towards defining and replicating the synaptic events that “create” taste, and giving those skills and subjectivity to artificial intelligence. 
Time to wake up.
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28 ideas about ideas

Forget that the article linked below is about Trump. It’s really about communicating. It’s about the ways in which design and messaging prioritization and insights and measurement make our ideas more or less effective. While these ideas are pointed at reporters, they could just as easily be pointed at copywriters, designers, creative directors and marketing leaders. 


A few of my favorites: 

#8. Write more than one headline. Publish all of them. [i.e. Let the audience decide which headline/’interpretation of the idea’ works best.]

#14. Think about new ways to measure success. How much does our [idea] connect to a real problem our [brand’s customers] have? Did our [ideas] change someone’s attitude? Did our [ideas] change someone’s life?

#26. There is no such thing as an audience anymore. We’re all amplifiers and sharers and content creators and analysts. [Head over to Dan Hon’s recent newsletter (scroll to section “1.2 Recreational Whiteboarding”) to wrap your head around a deeper dive on all this.]

Big kudos to Melody Kramer at Poynter for synthesizing all this utility. 

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