Your best copy might sound like AI to clients. See why creative value is under pressure and what it means for agencies.

AI is changing how clients view creative work. Even the best human efforts are starting to be questioned — not for quality, but for authenticity. I recently saw this firsthand.

The feedback I was not expecting

Below is recent feedback from a client on some content we created.

“I do have a piece of feedback for them. I’m not sure which AI writing tool they’re using to create these, but they may want to take a second pass… a lot of these pieces of copy are clearly first pass AI generations…”

The problem is that our copywriter didn’t use AI and would be offended by the feedback. He’s an award-winning creative with a big agency background. As you might expect, he’s vehemently opposed to using generative AI.

Where is the feedback coming from? It’s from a company whose marketing department strongly embraces generative AI for content creation. I guess they assume their agency is using it as well.

That will be a significant issue if you are on the agency side. Perhaps it already is, and I’m just now experiencing it. Our copywriter is very talented and has a lot of great experience. As a result, he is not inexpensive.

He warrants the rate he receives, now threatened by clients who will discount the value, assuming a tool is responsible for his output.

As more clients adopt generative AI tools for marketing, questions will arise regarding the outputs and cost of agency services, particularly creative agencies.

In this example, it’s content. However, the same scenario could apply to any creative service: creative concepting, AI image generation, media planning, AI audience segmentation, etc. AI is everywhere.

The real problem behind the perception

The reality of AI tools is that they are handy and efficient. As a result, we face the challenge of protecting the craftsmanship of our creative resources while leveraging the tools’ value.

I’ve always believed that good copy or content is an art. But what happens if no one appreciates the art? What will be its value?

Many creatives use AI tools for ideation, content refinement, editing, etc. However, to date, I’ve not seen any evidence that AI-generated or modified outputs perform any better than what humans have created in the past.

Dig deeper: What AI means for the future of agency-brand partnerships

AI vs. human-generated content

Maybe it’s not a choice of “either-or,” but knowing the advantages and disadvantages of each approach (see below) and when to use them situationally to our advantage.

AI-generated content

Human-generated content

Dig deeper: https://martech.org/riding-the-ai-tsunami-harnessing-creativity-and-efficiency-in-the-digital-age/

The blended future of creativity

After pulling this together, I realized where the client feedback came from in our latest content round. Over the last few months, we have been hand-crafting emails for announcements, follow-up emails to events and other marketing outreach activities.

This most recent round, and what generated the feedback, was a change in our approach. We profiled the personalities of the target audience and found that the most dominant personality type was one that prefers concise, to-the-point, no-nonsense content.

We removed the “emotional depth and empathy” mentioned as a strength of human-generated content. As a result, the content had an “emotional flatness,” making it sound like a machine wrote it.  The blending of machine and man is not going away. The only real question is, will it improve our results?

Dig deeper:  The AI-powered marketer’s roadmap to the future of content

The post When clients assume your best work was done by AI appeared first on Carbon Design.

5 Ways to Protect Against an AI Bot Attack

Businessman and former Presidential candidate Andrew Yang once said, “Automation is no longer just a problem for those working in manufacturing. Physical labor was replaced by robots; mental labor is going to be replaced by AI and software.”

AI bots are starting to deliver on that promise in the market research arena, to the detriment of research practitioners and their clients. Recently, we deployed an online survey for a client, dangling a financial incentive for completing the twenty minute questionnaire. As soon as the link hit social media with the financial incentive the AI bot sharks smelled blood in the water.

Within a few hours we had over 400 completed forms, and over 1,500 (!) within a day. And their completion rate was much higher than among our real human survey takers. Yes, AI bots are now trained to be able to navigate a 48-question survey with multiple choices, including intelligence designed to eliminate responses that don’t fit our profile.

If that isn’t shocking enough, no two survey responses were alike. Bots responded as if they were in different industries, big and small companies. And presumably by gauging the length and difficulty of the survey, bots seemed to learn to slow their response pace down to more closely mirror the typical speed if taken by human beings, typically completing at similar lengths of time as our population of human participants.

Before we shut things down after discovering what was happening, the bots completed the survey 1600 times out of 2100 starts.

How did we detect them?

The most obvious giveaway was the sheer volume of responses received within a very short time. We had been sending out the survey via email and had anticipated a response rate, based on past experience which was much slower.
Additionally, we could tell by the fake emails they created. The bot submissions ended with false gmail accounts (an email was required in order to receive the gift card award – easily spotted as garbage.

How to prevent this in the future?

According to our head of research, Steve Wolf, recommends five things to consider in order to prevent bots from hijacking your survey:

• Gauge expected response metrics – starting with a trusted, proprietary sample of responders (e.g., a client’s customer base) can provide a baseline of start and completion rates, and time per survey, when taken from living breathing humans.
• Create a unique link – don’t rely on one master weblink to the survey, which would make it very difficult to identify which respondent is coming from where. Instead, assign a unique URL per channel (such as email, client website, social media). This way, one can isolate bot submissions to the channel which they took over.
• Add open ended questions – bots have gotten smarter, but they still are unable to answer open ended questions…yet. Sprinkle in 2 or 3 open ended questions throughout the survey.
  Use a “trap question” – trap questions ask the survey taker to take an action proving they’re a human – similar to a CAPTCHA. For example, a survey asks “please enter the number 32”. Bots are best at reading multiple choice surveys and picking an option.
• Try adding an invisible question – using a hidden question is another way to trick a bot. Use white font on a white background. Since humans can’t see the question it will always be skipped, but bots will answer it.

And unfortunately, the best option would be to avoid tempting bots in the first place – especially as AI bots become more and more sophisticated. Just avoid “open” surveys – such as those promoted on social media channels or advertised via banner ads – and recruit target participants from other “closed” sources such as email, or at least “quasi-closed” sources such as company newsletters or blog posts.

Lastly, today’s researchers must be especially diligent on reviewing the completed surveys. If you are using a financial incentive to drive survey completions, know that there are bad actors out there looking to cash in.

More Bot Bad News

More and more people are using AI assistants to read and respond to emails. As a result, it is impacting your campaign success rates.

We just switched email platforms. Our company newsletter was the first item to be sent and the performance dropped significantly. Open and click thru rates were half of what they historically had been. The reason? The new platform can filter out bot/agent responses.

For the most part, marketers have been focused on the upside of AI, in particular, generative. What has been missed for the most part is the impact of AI on our results. As I just illustrated, AI bots are disrupting our marketing research efforts, and AI assistants are distorting email response rates. Expect more disruption as AI agents become better at understanding workflows.

AI is turning out to be a double-edged sword. The focus of last year was mostly application and production. This year, we will need to make the time to start considering the impact it will have on our efforts. Or, maybe just program your own bot to figure it out.

The post When Bots Attack! appeared first on Carbon Design.

Sales may be a “numbers” game but selling is not.

We often confuse the two. Treating prospects as a “number” that we need to reach more broadly, and more frequently.

I had the opportunity this year to evaluate a half a dozen new AI enable sale and marketing tools on the market. New tools that promise the world but at the end of the day they deliver basically the same thing we are using tools for today.

• Volume – aka scale, scratching the “reach more’ prospect itch
• Speed – scratching the “more frequently’ itch
• Efficiency – an output of speed and some interesting capabilities to improve productivity

If the goal is greater efficiency you’re in luck, but if it’s efficiency AND effectiveness you’ll be disappointed.

Why? Because performance is not a scale or speed issue, in fact, they make it worse. To get to the root of the performance problem, you have to do post modem on stalled or lost deals.

Here’s what we’ve seen based on our assessments:

• Corporate Priorities – as in you’re no longer on it, priorities shift all the time as well as budgets, this one is interesting because companies often come back to solving the issue at some point
• ROE – the costs, the solution, resources investment, timeline, roi, etc. and/or some combination of those kills the decision
• Motivation – loss of sponsorship, other priorities, effort level, elements of ROE slow the progress
• Inside Job – they did it themselves, gave it to an existing vendor, picked someone they knew/trusted, etc.

Can you think of any sales or marketing tool that can fix the bullets above? If you answered no, you’re right, because they are “selling problems.”

Keep that in mind as you’re watching demos of the latest and greatest.

The post What Value Should You Expect with New AI Tools? appeared first on Carbon Design.

As previously published on 1/16/25 in MarTech

An interesting piece of research was released last week but may have been lost in the busy holiday season.  

Previsible, an SEO consultancy, announced that traditional Google search has “basically plateaued and has begun to have its search dominance degraded.”  Why? People are using AI assisted search because it has gotten more capable and accessible.  

ChatGPT, Claude, Co-pilot, and even Google, offer an AI search version available to most users. Compared to the traditional search, which relies mostly on keyword matching, AI search uses advanced algorithms to understand the context and intent behind the query. As a result, at least in theory, it should provide more relevant and personalized results.  

The new capabilities and changing user behaviors are creating a potential warning about the risk of relying on AI. Because of AI’s ability to draw upon vast amounts of information, users often default to trusting that the query output is most likely to be the right answer, solution, recommendation, etc. 

As opposed to the traditional search which returns the links to what are the most likely options to answering the query, the user has to make the effort of analyzing the results, reading and filtering information, and drawing conclusions. 

And here lies the potential problem.

Nvidia’s CEO, Jensen Huang, stated on the company’s most recent earnings call that we are “in the beginning of a new generation of foundation models that are able to do reasoning and long term thinking.”  

Cognitive scientist Gary Marcus says the AI  we are currently building is basically like “System 1 thinking,” a reference to Noble Prize winning psychologist, Daniel Kahneman’s, “Thinking Fast and Slow” book. 

In his book, Kahneman explains the dichotomy of human thought. System 1 being intuitive and fast with no voluntary control. This being one of the reasons he concluded that humans are bad at making decisions. System 2 thinking allocates attention to the effort that demands focus, oftentimes because of its complexity and/or need for computations. Think of the two systems as instinct or “gut feel” and critical thinking. 

If, as the AI experts state, we are building System 1 AI models then users are at risk of making the same mistakes using AI as they might make in day-to-day decision making. And, as an observer of younger generations of marketers using AI, they may be particularly vulnerable.

My son, home for the holidays from grad school, mentioned that classmates are not only using ChatGPT to summarize course work, but also to write their presentations. And…they’re not questioning it, they follow the recommendation completely because it “saves time.” 

B2B marketers are using AI tools profusely for research, writing, and recommending actions because they are “quick and accurate.” They have grown up in an environment that has emphasized scale and speed, and they lack the experience or interest to question the accuracy of what is being outputted by AI tools. 

Where is this headed? Combine all of these factors and it could point to a massive wave of “group thinking” marketers that either lose the ability to think creatively and/or strategically, or  eliminate it completely because they are wired to trust AI. 

Generative AI has already come for the creative department as witnessed by Omnicom’s recent acquisition of IPG. If marketing executives don’t act now to create a plan to manage AI,  “Hal” could become your CMO in a few years.   

 How should marketing executives respond to this threat? Daniel Kahneman might suggest focusing on skill development that emphasizes System 2 thinking. Teach your team how  to do long term,  critical and strategic thinking. 

Combine the strength of using AI System 1 thinking to enable your staff with training on higher level System 2 type efforts like competitive intelligence (which I rarely see any more), market intelligence and strategy. 

There is good reason to get back to these core strategic marketing building blocks. Marketing performance in 2024 was significantly down across channels and activities. It’s time to dig in on strategy. There are significant challenges to address. Going faster and creating more noise in the market is not a strategy that will win.

In 2017, I wrote an article on how Amazon had become the default search engine for buyers who knew what they wanted based on our research on buying behavior. In that post, I predicted that because of that trend, Amazon would soon eat away at Google’s advertising monopoly. At that time, Amazon only had 1% of the global advertising market. By 2020, it had grown to over 10%. This year it will be 14%, and by 2026, it’s estimated to become over 17%. 

I see a similar trend with AI eating away at the marketing department, not because of the tools themselves, but by how behaviors are changing because of them (similar to what I observed with consumers and Amazon). To be clear, it’s not necessarily the technology that is the threat, but rather the behavior change caused by it. 

If marketers want to remain valuable inside their organizations they will have to learn how to use AI tools to enable better decision making and not default to them as the decision maker. Or as my son’s professor said; “use them to become a better student, not to be the student” and remember, they’re only System 1 thinkers 😉.

The post Could This Be the Biggest Threat of AI to Marketers? appeared first on Carbon Design.

As previously published on 11/27/24 in MarTech

Artificial Intelligence search company, Perplexity, has raised three rounds of funding this past year. They have now begun their fourth round of funding which will value them at more than $8 billion. 

The startup seeks to challenge Google’s supremacy in search by combining the best of traditional search functionality with providing answers to questions similar to ChatCGT. The company currently has annualized revenue of  slightly over $10 million. It recently launched an enterprise version for corporate customers that will search internal files. 

Last year, we signed an agreement with an AI startup who provided that same enterprise functionality. This year, we didn’t renew the agreement because it’s now built into our Google Workspace platform, Gemini. We went from paying $150 per month, to $10 per month, for the same functionality. See what Google did there? 

Guess who else is playing this same game? Hmmm…Have you heard of Copilot, Microsoft’s new “AI Companion.” The partnership and investment with Open AI has all the bells and whistles as Chat GPT 4.0 (see below).

There is a free version, a Pro version (priced similar to Google Gemini for $30 a month), that will integrate into your Microsoft 365 suite. 

With a voice interactive interface ( four voice options), you can now delete your Amazon Alexa, Calm, Apple News, and many other apps, if you choose. See what Microsoft is doing? 

Apple has quietly acquired more AI companies over the last three years than any other company in the world. Last year, they acquired 32 companies in the AI and machine learning space, almost twice as many as Microsoft. The new IPhone 16 with Apple intelligence is just the beginning. 

Oh, and let’s not forget Facebook, who is in the process of launching new tools via Meta’s Ads Manager now, and throughout next year. Ad creatives will get new background, image and text generators.

All of this is happening at a time when marketers are looking to consolidate and/or reduce costs related to their martech stack. According to MarTech’s latest Replace Research, 61% of respondents said the number one factor for a replacement solution was cost savings.  

Every technology wave brings about winners and losers, but it also creates an evolution, a better way to accomplish something. The dot com bust gave us Amazon, Ebay, Coupon.com and new ways to buy traditional products more efficiently.  AirBnB, Uber, and Venmo emerged from the ashes of the “Great Recession” of 2008, providing us with more efficient ways to buy and pay for services.  

The AI bubble will produce a similar set of winners and losers. It has already provided us with new ways to create code, images and content. But, outside of Open AI and Antrophic, it’s  not clear who else in the AI generative space will be a winner. 

One thing is sure. We are only at the beginning of the wave of AI solutions. According to CB Insights, AI startups are currently getting one  third of all investment dollars with B2B startups getting $10 to every $1 invested in B2C applications. As a result, we know we are going to see more and more AI applications aimed at B2B marketers. 

For Perplexity, will it emerge as an AI winner or will Google put it out of business by building its unique functionality into search? Marketers are facing a similar question, which may come down to opportunity cost. Is it worth the expense and time of learning a new tool, or do we play the waiting game to see if our current platforms integrate the functionality? 

Perhaps I am drawing the circle too small by just focusing on integration and costs.  Carrie Mahon, CMO at Unanet point of view is “Embracing new AI tools early not only provides marketers with a strategic advantage in creativity and efficiency but also fosters a mindset shift that speeds up AI integration and unlocks greater benefits. Delaying could mean missing out on these initial advantages and innovation opportunities in a rapidly evolving tech landscape.”

Could the real value and strategy be experimenting with new AI technologies, although perhaps costly, and then moving to more efficient platforms as they become available. Capture the opportunity to learn and experiment, then become efficient. 

As an old IBM client once said about new technologies “Let a thousand flowers bloom then cut them all down except for the tallest few.” He would then follow up with “make sure that you tend to your garden!” 

Maybe the AI wave is not a bubble to burst, but rather a garden to nurture. 

The post Burst the AI Bubble appeared first on Carbon Design.

As previously published on 3/7/24 in MarTech

Every year, B2B marketers gather in Scottsdale, Ariz., to mingle and talk shop. This year, they talked a lot about AI.

The B2B Marketing Exchange (B2BMX) conference takes place annually in late February or early March at The Phoenician hotel in Scottsdale, Ariz. I was in attendance, along with a good 250 to 300 marketers. It was my first event since before the COVID-19 pandemic. Toss in representatives from about 75 vendors and the event was thick with pitches, presentations and promotions.

My goal for the three-day event was to check out and learn more about new technologies from the sponsors and gain insight into the top issues that practitioners are wrestling with day-to-day.

Here are my takeaways:

The major themes of B2BMX 2024

Artificial intelligence was everywhere

AI was everywhere — the vendors in the exhibit areas, the presentations and in conversation. That said, I didn’t really see anything that knocked my socks off. A bunch of generative AI content tools were on the vendor floor and featured in presentations, but nothing that was game changing.

Vendor consolidation

Vendor consolidation was not necessarily an official event theme, but martech consolidation was a definite vibe. From what I heard from vendors and marketers, the day of reckoning is upon us. Marketers are drilling into ROI, contract terms, spend and functionality.  Ironically, Scott Brinker’s Chiefmartec newsletter came out during the event and announced that SaaS martech stacks shrunk 8% from 2023 to 2024. I expect that number to be much higher next year.

My favorite sessions

Keynote

Dan Gingiss, author of “The Experience Maker,” presented a lively and entertaining discussion of his WISE framework for creating notable customer experiences. Here’s the key point from his presentation: If something is expected or “normal” do the opposite. But to do that, you have to make time for it. Too many people are just going through the motions. To create truly memorable experiences, you have to take the time to think about it. It doesn’t have to be complicated. It can be as simple as how you respond to a customer on the phone. In fact, one of his examples was hold music.

Favorite workshop session

My favorite workshop was called “From Strategy to Tech Stack.” I actually never got to see the presentation — they couldn’t get the projector to work. So it became a “fireside chat” with Megan Crone from Palo Alto Networks and Amy Holtzman from CHEQ. The topic centered around cybersecurity for marketers, specifically protecting pay-per-click campaigns from bots. We’ve long known that bots are a nuisance on blogs, etc., but AI bots have become much more sophisticated in evading standard bot protection mechanism like CAPTCHA.

Most interesting vendors

• Writer. Writer is probably the best LLM (generative AI content) provider of the bunch with an impressive client list to boot. The template-driven approach was smart and well thought out.
• CHEQ. CHEQ is the vendor I didn’t know I needed. It’s ugly out there, and getting more dangerous every month. This is the tool you need to protect your marketing investments.
• The B2BMX event app. To me, the most impressive technology I experienced was the event app itself. The app allows you to customize your agenda, reach out and connect with others, download the presentations, track your points for visiting with vendors and apply the points you earned to SWAG.

Best vendor SWAG

Speaking of SWAG…

I don’t know if it was the “best,” but I will say it was the most usual giveaway I’ve seen at an event: an eye mask. But here’s the funny thing, there is no company branding on it so I don’t remember the vendor. A stand out giveaway with no branding… hello, marketing?!

Final word

B2BMX is an event for practitioners: managers, senior managers and director level attendees. I was surprised that many of the sessions didn’t reveal new insights (particularly relating to ABM) despite the fact that I hadn’t been to a conference in nearly five years.

It left me with the impression that we are still chasing the shining new technology instead of performance improvement. As an example, the sessions with the highest attendance (from what I observed) all had AI somewhere in the title or description.

The post Here’s what happened at B2BMX 2024 appeared first on Carbon Design.

As previously published on 6/28/23 in MarTech

By Scott Gillum
Estimated read time: 5 Minutes

What if they are wrong? 

When responding to questions about AI replacing humans in certain roles, most ‘experts’ claim that AI will replace some jobs, but will be a much more valuable tool for augmenting human intelligence and ability. 

In all of the hype associated with this latest technology wave, an important trend is occurring across industries that could significantly change the impact of AI – the retirement of the knowledge worker.  

We need to look no further than the last wave of intelligent technology – the “internet of things” (IoT) to see the impact. 

The term ‘Internet of Things’ was coined in 1999 by  computer scientist, Kevin Ashton. While working at Procter & Gamble, Ashton proposed putting radio-frequency identification (RFID) chips on products to track them through a supply chain.

“Machines talking to machines” started rolling out in early/ mid 2010 making their way into manufacturing, precision agriculture, complex information networks, and for consumers in a new wave of wearables. 

Now, having about a decade of experience of how IoT has impacted certain industries and markets, perhaps it can give us some interesting insights on the future of AI. 

In 2010, Cisco launched the “Tomorrow Starts Here” IoT campaign at the time when communication networks were transitioning from hardware “stacks” to software development networks (SDN). 

The change meant that in order for carriers to expand their bandwidth, they no longer needed to “rip and replace ” hardware. They only needed to upgrade the software. This transition began the era of machines monitoring their performance and communicating with each other, with the promise of one day producing self healing networks.

Over this same period, network engineers who ushered in the transition from an analog to digital began retiring. These experienced knowledge workers are often being replaced by technicians who understand the monitoring tools, but not necessarily, how the network works.  

Over the last dozen years networks have grown in complexity to include cellular, and the number of connections has grown exponentially. To help manage this complexity, numerous monitoring tools have been developed and implemented. 

The people on the other end reading the alerts see the obvious, but have a difficult time interpreting the issue, or what to prioritize. The reason is, the tool knows there is an issue but is not smart enough yet to know how to fix it or if it will take care of itself. Technicians end up chasing “ghost tickets,” alerts that have resolved themselves, resulting in lost productivity. 

The same thing is repeating itself in marketing today. As one CMO told me; “I can find people who know the technologies all day long, but what I can’t find is someone who thinks strategically. Ask a marketing manager to set up the tools and run a campaign and they have no problem, but ask them to write a compelling value proposition or offer for the campaign, and they will struggle.” 

It’s easy to get sucked into the tools. AI generators are really intriguing and can do some amazing things. But based on what we have seen, the tools are not smart enough to fully deliver on their promise…yet. 

Here’s the warning from IoT – as tools become more knowledgeable, the workforce operating them is becoming less. It is leaving a knowledge gap. As that knowledge is transferred from worker to machine, we need to ask ourselves what we’ll be left with. Will there be enough experience and expertise in our workers to know if what comes out of the machine is accurate, factitious, or even dangerous. 

In a recent WSJ article, Melissa Beebe, an oncology nurse, commented on how she relies on her observation skills to make life-or-death decisions. When an alert said her patient in the oncology unit of UC Davis Medical Center had sepsis, she was sure the AI tool monitoring the patient was wrong. 

“I’ve been working with cancer patients for 15 years so I know a septic patient when I see one,” she said. “I knew this patient wasn’t septic.”

The alert correlates elevated white blood cell count with septic infection. It didn’t take into account that this particular patient had leukemia, which can cause similar blood counts. The algorithm, which was based on artificial intelligence, triggers the alert when it detects patterns that match previous patients with sepsis. 

Unfortunately, hospital rules require nurses to follow protocols when a patient is flagged for sepsis. Beebe could override the AI model, if she gets doctor approval, but faces disciplinary action if she’s wrong. It’s easy to see the danger of removing human intelligence in this case, it also illustrates the risk associated with over relying on artificial intelligence. 

AI will free us from low value tasks, and that is a good thing, but we need to redistribute that time to better developing our people, and our teams. The greatest benefit from these game changing technologies in the business to business environment will be realized when we combine equal amounts of human intelligence with machine intelligence.

The post Artificial Intelligence + Human Intelligence = Success, or is it Artificial Intelligence – Human Intelligence = Failure? appeared first on Carbon Design.

by Glen Drummond
Estimated read time: 6 minutes

Part Two in a two part series

Recently, I published an article with a provocative observation.  While much attention has been devoted to the need for organizations to adopt Artificial Intelligence as a core capability, we should consider an even-more-pressing need for “artificial empathy.”

If you did not read part-one, I’ll retrace some footsteps here. The corporation is a creature of human invention. But the creature has grown so enormously in its size, capabilities ,and power, that we the people now encounter a diminishing sense of agency for ourselves and an increasing sense of agency for corporations to shape our future on issues including privacy, equality, safety, the environment, and the behavior of public institutions that once governed these things. Not to mention the stuff of everyday experience: stupid IVRs, impenetrable clam-shell packaging, and infuriating password implementations, just to name a few.  

The ramifications of this observation extend beyond marketing strategy. But still, people who think deeply about the relationship between people and brands will play a role in how this narrative unfolds.  

And here’s why: In our fast-thinking minds, we perceive the brands that stand for corporations as if they were other people.  

Now, people – except for sociopaths – are naturally empathetic. And moreover, we expect them to be so.  When we sense a sociopath, the hair on our neck springs, and adrenalin shocks our bloodstream.  

As social creatures, we are born pre-wired with miraculously-adapted endocrine and neurological systems that reinforce our empathy in a positive feedback system known as friends and family, community and kin. But corporations are not born with anything of the sort.

Do you see the problem?   

At least in our hearts, we have an expectation for brands to behave in a way that they are poorly equipped to fulfill.  Expectations disappointed are brands diminished. 

Organizational scale amplifies this problem. (We all know what “faceless corporation” means.)   So does the doctrine of maximizing shareholder profits. Are there signs that both society and corporate leaders are beginning to discern that the corporation has gained such power, that the power needs to be matched with greater empathy? The recent “statement of social purpose” by 181 corporate leaders suggest this might be so.       

The question is how?  Some people who read my first post may have been under the impression that I had a plan for  how “artificial empathy” could be created. Rest assured this was far from the case. I’m sympathetic to the aspirations of the customer experience movement, but I’m skeptical those aspirations are advanced by continuing to ask socially clueless questions that amount to: “How do you like me now?”    

Still, having once stumbled upon the problem of  artificial empathy, it’s tempting to speculate. So, with apologies for pairing a ten dollar question with nickel and dime answers, here are some preliminary thoughts.

Biomimicry    

If you’re familiar with the literature on biomimicry – you will know that many industrial inventions  begin with the observation of patterns in nature. Could we re-conceive the information systems used by corporations through this lens?  

In that case,  the challenge of  “artificial empathy” would cause us to think about a system involving a sensory apparatus, a cortex that integrates the signals from the senses, real-time feedback,  amplifier mechanisms and so on.   

It does not take long to see that analogues for each of these things already exist within the information systems of corporations – but what’s lacking is an architecture marshalled by the imperative of empathy.  

For humans as social creatures – empathy is essential for survival.  Embracing the biomimicry idea in an IT architecture geared to artificial empathy would mean  that the selfish subjectivity of the corporation would need to be subjugated to human experience and dignity.   Do we have engineers this creative and leaders this courageous? 

Philosophy

There is a branch of philosophy, “epistemology,” that deals with the question of how we know what we know.  Historically, for corporations, and indeed any large organization, to operate at scale has required that an internal representation of customers and prospects is shared across the organization. Sometimes this internal representation goes out of date. Sometimes it is simply wrong-headed from the start.  Invariably this internal representation is reductive. 

Done well, the disciplines of customer segmentation and personas offer steps in a journey away from the most reductive internal representations of the corporation’s publics. But too often in practice,  people mistake the map for the territory. In a product-centric world-view with no imperative for empathy, mistaking the customer map for the territory is standard operating procedure – “best practice” even.  In a corporation seeking to attain the capacity of artificial empathy these old habits must die.       

While corporations have raced to hire data scientists and put them to work on the analysis of customer behavior and customer responses to various stimuli, they have not been as quick or adept at hiring and training people in the discipline of keeping separate the map from the territory while the study of people is underway.  

The pairing of these disciplines feels important going forward. Data scientists are in demand now.  Data scientists with a flair for philosophy will be the rarest and most valuable of all.   

Artificial Intelligence

Setting aside the semantic arguments about the existence of AI,  we now can access algorithmic tools that can explore data-sets to find multiple features of interest about people, and discover patterns of difference, similarity and prediction that are more subtle than those derived from averages, demographic co-variates, single-touch attributions, and the other mainstays of traditional customer analytics. 

Indeed, if we are going to operate with less reductive representations for people, and if we are going to simulate the biological mechanisms of empathy within a corporation, artificial intelligence may be the disruptive game-changing technology that finally enables meaningful progress against a problem that has been building for some time.   

Final Thoughts

None of these answers by themselves is a prescription for artificial empathy. The confluence of all three may point in a worthy direction.  Still, some journeys are worth taking, even when the destination is distant and the route uncertain.

This might be one.

Get more thoughtful content on how to ‘think differently’ on marketing, business, and work by subscribing to our newsletter.

The post Artificial Empathy, Part Two appeared first on Carbon Design.

by Glen Drummond
Estimated reading time: 7 minutes

Part One in a two-part series

Empathy.  It’s such a defining human quality, you could say it’s in our bones. For sure, it’s in our brains. Neuroscience reveals that we have “mirror neurons” that cause other people’s emotional experiences to become our own. That concept would be astonishing if it were not so familiar.   Empathy runs in our veins. The hormone oxytocin – makes us closer to those we’re close with.  

Beyond this, there are the mental gadgets that history has draped on our biology. For instance, our fine-tuned sense of justice, fairness, and balance.  These qualities also incline us to prosocial behavior, such as helping a stranger on the street, supporting a local non-profit,  separating our recycling…

So if empathy comes naturally, why call for “Artificial Empathy?”  (Presuming, of course, that such a thing could even be possible?)  The answer begins with an observation about a trend in scale. Human nature developed over a long period in which there were rewards for co-operation within groups and competition between groups.  But compared to today, the groups were small. It’s not clear that biologically-rooted empathy equips us adequately for the scale-change.    

It’s not merely that there are more of us, although the human population has tripled since 1945.  It’s that the nature of connectivity between us is transformed.   As members of media-fueled electorates, our mood-swings are damaging institutions that took centuries to build.   As members of a global economy, our collective emissions are generating planet-scale impacts on the environment.  

There are broad conversations underway about these forms of our connectivity. Less so about our participation in corporations.   Arguably, no prior form of connectivity rivals the modern corporation’s capacity to pursue its objectives with such speed, scale and precision

And big corporations are getting bigger.  The World Bank reported in 2016 that among the 100 largest revenue-collecting entities in the world, 69 are corporations; 31 are nation-states.  A decade ago, the US Supreme Court awarded corporations a human right: freedom of speech.  The Danish government has appointed an Ambassador to liaise between the midsized nation and giant tech corporations.

If you have spent your career inside corporations, you know there are instances where scale acts as a liability as much as a strength.  The world knows that something went wrong at Volkswagen, at Facebook, at United, at Boeing.   And while the particulars are different, the circumstances rhyme.  A group of people sincerely felt it was their job to do something that the public would come to hate and the owners would come to regret.   What corporation is free from this risk?  

So why does business need “Artificial Empathy?”  It’s partly because natural empathy is poorly matched to the scale of the modern corporation.  And it’s partly because the consumer and the public are not going to let corporations off the hook for un-empathetic behavior.    

Here’s the basis for my confidence in that second observation.  People imagine brands as if they were other people. The marketing practice of managing brands using a system of archetypal characters speaks to this fact.  So does the blow-back that follows when corporations act in notably inhuman ways. There’s even neuro-imaging research that shows we look at logos and faces in surprisingly similar ways.      

So here, in a nutshell, is why brands need artificial empathy:  

1. Because we imagine brands as if they were other people,  and 
2. Because we expect other people to be inherently empathetic, so 
3. We also expect brands to be inherently empathetic too, and
4. Brands have no natural capacity to fulfill this expectation

This fabric of observations explains a lot. Corporations,  pursuing their interests without paying attention to this prevalent expectation, violate customer trust. And sometimes, public trust too. 

Only on the rare occasion does this violation happen in the dramatic ways cited in the cases of Volkswagen’s emissions masking or Cambridge Analytica’s democracy hacks.   

Far more common are violations so banal they barely register. Robotic voice response systems that remind you: “please continue to hold,  your call is important to us.” Departure lounges that add acoustic assault to the list of insults suffered by air passengers. Manipulative marketing and sales tactics like the email that arrived this morning in my inbox, by no coincidence, at 9:18 AM with the subject header, “9:00 AM Meeting.”   

Viewed through the lens of empathy, (and the lack thereof)  the distinction between the dramatic and undramatic instances becomes only a distinction of degree, not kind. And that observation is potentially helpful because it offers some guidance on what needs to be done.  

Now, you might say, “Ah, you’re talking about customer experience,” and yes, in a way that’s true.  But insofar as the term “customer experience” stands for a department, a performance measure or one in a set of parallel business disciplines,  a “customer experience” capability will only act on symptoms while failing to address the root cause. (Sociopaths are known, after all, for their ability to charm.)

Or, you might say, “Ah, so you’re talking about corporate governance.”  And yes, again in a way that’s true. But how much real capacity do the people charged with such weighty responsibilities have to intervene in the minor daily violations of the customer’s expectation of empathy?  It’s been observed for some time, that “The road to hell is paved with good intentions.”   

Since empathy violations appear to take place despite the ubiquity of “customer experience” and “corporate governance” functions the empathy gap – the delta between customer expectations of empathy and the level of empathy corporations are presently organized to muster – is a real business problem.

It seems like a problem that would be worth taking risks to explore, based on the value of the potential outcome if it could be solved.  

To summarize, let’s retrace our steps.   

• Corporations are large, powerful, engines of collective influence and action.   
• They are growing increasingly large, powerful, and influential in the lives of people.
• People expect them to act empathetically, but corporations have no natural inherent capacity, like people do, to fulfill that expectation.
• So, we should expect the empathy gap will grow with the power and reach of corporations, until such time as either corporations design a technology of empathy – “artificial empathy” if you will – or face a more concerted backlash directed at individual brands (“United breaks guitars”), at industry sectors (say, “big tech,”) and at corporations in general.   

Despite all the technical progress, investment and hype devoted to it, there remains a debate over whether “artificial intelligence” (AI) actually exists.  The concept of “artificial empathy,” if it were to enter the public discussion, would be subject to a similar philosophical challenge.  

So why talk about it at all? 

Because corporations have plenty of resources for tackling challenges once they can be identified. This one is staring us in the face. 

Since the processes, which we call “artificial intelligence” will inevitably shape more of the experiences that corporations project and customers and the public will absorb, is there any question that the need for artificial empathy will grow with each passing day? 

The conjunction of “artificial” and “empathy” is a provoking framing of a problem that exists. It matters greatly to a corporation’s stakeholders and deserves far more rigorous thinking and effort than has been devoted to it thus far. Rather than being a zero-sum game, “artificial empathy” will be a project that aligns the interests of shareholders, employees, customers, and the public.  Rather than being a departmental problem, “artificial empathy” will require a systems-level response.  

I’ll leave for a subsequent article the questions of how “artificial empathy” might work and what resources it might draw upon.   For now, suffice it to say if corporations need empathy and don’t have it as a natural quality, then the commercial incentive is there to synthesize it. 

The ingenuity and organized effort that has made predictive science – machine learning, deep learning, expert systems, big data, or more generally, “artificial intelligence” –  such an important component of corporate strategy today, provides at least a framing metaphor for this initiative – and maybe some important tools too.   

But intelligence (natural or artificial)  is no substitute for empathy. No matter what strides we make in AI, brands need to make progress now on Artificial Empathy. And if AI begins to make strides on its own, there’s a good chance brands will need to pick up the pace.   

Don’t miss out on what’s new at Carbon Design Co., join our email list here!

The post Brands Need Artificial Empathy. Here’s Why. appeared first on Carbon Design.

There is a grocery store a few miles from my house. It’s small and older, at least thirty years in its current location. Usually, the shelves are poorly stocked with a limited selection compared to the newer stores surrounding it. Despite these facts, the store manages to stay in business which is somewhat hard to comprehend given the cut throat, low margin nature of the industry. It survives because it has a secret weapon.

His name is Andres. He’s a cashier and has been at the store for twenty plus years. Andres speaks five languages and knows most of the customers by name, typically, greeting them in their native language. He knows where everything is, or isn’t, and if it’s not there he knows when it will arrive. He is the store.

While some customers, like my wife, frequent the store because it’s convenient, and quick, as long as the item is on the shelve. The majority of the customers go because of Andres. The store is in an affluent international neighborhood with many retirees. These core customers have time to shop and chat with Andres. For them, a trip to the store is an experience, not an errand. I haven’t seen the numbers, but I would guess that the revenue per square foot is why it survives.

The interesting thing, having worked with B2B companies for the past twenty years, is that many of my past clients also have an “Andres.” His, or her name may be different, but their role inside their organizations are not unlike Andres. They know the customers, how to get things done, where the “dead bodies” are buried, and how to navigate the complexity of the organization. They are the company.

As organizations rapidly move to “digitalization” and look for AI to play a larger role in customer interactions, they need to consider the importance of these essential employees. Like the grocery store, there are customers who may be highly profitable that aren’t doing business with your company because it’s convenient or fast. They are and have been customers, because of the experience. And a good portion of that experience is shaped by the “Andres” of the organization.

As other grocery stores move quickly to eliminate cashiers, Andres’s store has no self-checkout or online store pickup. Management seems to recognize the importance of the shopping experience, which seems to make up for the lack of selection and inventory. As your organization moves toward the future, does the management team fully understand that not all customers are the same, or want the same things. They may also speak separate languages and while self-service may work well for some, others want the full experience, which may include a personal conversation with their “Andres.”

The post Do You Know Your Andres? appeared first on Carbon Design.