Where are all the AI converations headed?

Currently, like cars on an unmarked highway five lanes wide, conversations involving AI tend to drift between lanes. At any point, the conversational thread weaves into the various lanes: scientific, technical, #AI_Ethics, political, and the aspirational futures of humankind.

At this point, technologists’ claim the wheel. They steer the conversation because, well “It’s my car!”

With one hand rewiring the navigation system and the other thumbing through a college prep handbook on utilitarianism, technologists own all five lanes, even those where they have no specialized expertise in traversing.

Meandering out of your lane while driving is… fine-ish. If people know what they’re doing and the speeds and stakes aren’t too high. Live and let live I say. Afterall, a front left panel with scraped paint isn’t going to kill anybody.

Speeds and stakes: Accelerating and existential

In the case of AI, however, few are arguing the speeds and stakes of AI are low. To the contrary, the operative watchwords in that context are acceleration and existential. At their nexus is the fuzzy, magical idea of Accelerationism, a grouping of intense phenomena – technological change, peak capitalism, racial conflict, and the singularity – not simply destabilizing the world-as-we-know-it, but deconstructing it and wiping the slate clean. With that in mind, the use of the word existential as table stakes to enter the AI conversation clearly makes sense:

“Howdy pardner, we’re talkin’ AI? Deal me in, here’s my table stakes …AI is existential.”

The word existential is always tossed onto the table in conversations about AI’s stakes. From what I can recall, the word has gotten more mileage in the past 18 months than it did even in the bong-fueled dorm room sessions about nuclear annihilation cascading from 1983 to late 1984, during the height of the Cold War. (But don’t hold me to it. Knowledge and memory tend to get anchored to a bonged state of mind, aka ‘state-bound knowledge’. But, I digress.)

Back to our analogy about AI conversations as careening cars, which we can now clearly see are accelerating wildly in a life-or-death race driven by the tech giants. The tech giants who say “Obviously, we’re in a life or death race with each other, and btw with China and Russia. But, no worries, you’re spectators. You are exempt from the existential stakes (especially if we win the race).”

The tech giants then point to regulators who are frantically building a chain link crash fence meant for race cars even as they notice the racing vehicles look increasingly like an Air Force general’s wet dream. These all purpose jets have horizontal lift, cruise at  Mach 3 (2151 mph),  and are covered in racks for attaching specialized weapons and gear. No worries, the tech giants whisper, sit back, grab a beer and enjoy the race!”

Everything is not fine.

The lanes are there, but aren’t well marked. Moreover, the drivers who originated in the  technical lane are supported by drivers from the scientific lane and are speeding up quickly.

Getting home to the spouse and younguns, no doubt. Isn’t that nice? Yes, but.. they are weaving into others where they have no experience or expertise.

In fact, they’re acting like they own the road – they’re hogging it. They’re veering into conversational lanes originally devoted to drivers whose dispositions – expertise, experience, worldview etc. –  primarily stem from ethical, political, and aspirational knowledge and expertise. Lawyers, doctors, priests, shamans, church volunteers, truck drivers, caddies…everyone.

Not because they should – clearly the technologists’ interest in the other lanes is based on their technologist-based dispositions and in service to the interests ($$$) of their own lane – but because they’re being allowed to.

In essence, the current AI conversational corollary to a rule-of-the-road looks like ‘He who builds the vehicle owns the lanes in the road also’.

The AI conversation is disguised as a vehicle in a shared conversation on a shared road.

Whether they know it or not, it is actually more of a monologue and a declaration by technological triumphalists building the accelerationists’ Mad Max-ian jet. Like the chrome paint huffing from the Mad Max movies, the news about Nvidia computational increases and scaling of LLMs adds further to AI technologists’ disassociation from fellow travelers (cue the dysopian heavy metal music).

Beep! Beep! (Honda 2005 CRV honking noise).

The AI conversation, in short, needs other voices from other lanes.

Unfortunately, AI technological triumphalists don’t have chrome painted teeth and aren’t identifiable in that way. However, when the AI pontification floats into lanes in which the speakers have scant dispositional expertise consider it a sign – the paint fumes are rising and its time to get off at the next exit.

At minimum, AI panel discussions about AI should include experts from at least one of the other lanes.

Why is trust in AI decreasing even as the technology of AI advances?

A peer-reviewed study in the June 2024 Journal of Hospitality Marketing and Management the trust factor and #AI has garnered headline articles in CNN and other media outlets. Entitled ‘Adverse impacts of revealing the presence of “Artificial Intelligence (AI)” technology in product and service descriptions on purchase intentions: the mediating role of emotional trust and the moderating role of perceived risk’ (Cicek, Gursoy, & Lu, 2024) the research captures the fear and anxiety involving AI made manifest in the marketplace and consumer choices.

In many ways the consumers’ lack of trust in products labeled as “AI” is predictable as argued in “Shifting from Trusted AI to Constructing Trustworthy AI” and Taking Steps to Constructing Trustworthy AI. The mistrust stems from the tendency of AI and other technologies to privilege certain types of knowledge, specifically technical craft knowledge (techne) and scientific principles (episteme), over tacit, embodied types of knowledge humans employ in their day-to-day lives. The privileging of scientitic and technical expertise creates a disconnect between AI’s power, the technologists who develop it, and its marketplace promise based on whether people trust or mistrust it..

Dunning-Kruger knowledge-type effect

Technologists, moreover, often confuse a technology’s power with its market potential. Their focus on techne and episteme creates a knowledge-type variation of the Dunning-Kruger effect, i.e., Dunning-Kruger knowledge-type effect. The original Dunning-Kruger effect is an increasingly well-known cognitive bias that causes people to overestimate their abilities or underestimate the abilities of others. It occurs when someone lacks knowledge or skill in a particular area, yet they perceive a concept as simple because of their own knowledge is limited. This leads them to believe they are smarter than they actually are. As a result, they’re less inclined to explore the concept further.

In the Dunning-Kruger knowledge-type effect, for example, technologists’ high expertise in technical and scientific types of knowledge and low knowledge involving the other types of knowledge (practical wisdom, phronesis; cunning intelligence, metis; and aspirational striving, arete, etc.) needed to construct market adoption, for example, leads technologists to believe they do not need to understand these other types of knowledge, nor consider the knowledge of people who possess these other types of knowledge.

The Dunning-Kruger knowledge-type effect creates a bias. The bias questions the legitimacy and value of other types of knowledge. This may especially be true in high-stakes technical settings where episteme and techne are essential to the products driving the organiztion.

And yet, individuals and organizations want to succeed. So why don’t individuals in organizations prioritise the most relevant types of knowledge to achieve their goals?

AI mistrust: Power and Privileging

The answer lies in power and human nature. The instinct to maintain power is a deep and ancient root in human social structures. This ancient genetic factor often overshadows the more recently evolved capacity to envision promises about a future. Consequently, immediate power based on position, title, etc. takes precedence over future potential and individuals in organizations privilege certain types of knowledge. This dynamic explains why a pure meritocracy, based on the full spectrum of knowledge possessed by individuals and groups, is never the organizing structure of an organization.

In fact, managing the tension between the selfishness of individual power and the more altruistic nature of promise for the wider group is arguably the core challenge facing most companies. Moreover, while traditional hierarchies may appear outdated in Silicon Valley’s startup culture, yet the power structures within these organizations express a change in form, not in their intrinsic nature. The power-effects privileging techne and episteme remains firmly in place.

The technologists who have expertise in those types of knowledge tie it to power as an inseperable duality. To paraphrase Foucault, “It is not possible for power to be exercised without knowledge, it is impossible for knowledge not to engender power,” (Foucault, 1980).

Other types of knowledge, such as action-based cunning intelligence used in sales and politcal manuevering (metis), ethical decision-making and practical wisdom used in navigating social realities (phronesis), and the aspirational logic of humanity’s incessant strivings, visions, and goals towards excellence (arete) have not disappeared but instead are pushed further off stage.

In the case of consumers, the lack trust in AI is not merely because they don’t trust technologists and Silicon Valley tech firms. Nor is it solely becasue they lack an understanding of what AI is or the positive and negative things AI can do.

It is because consumers recognize the immense power of AI. They sense AI will impact their lives, but also that its is built on only a subset of the types of knowledge they value. Moreover, the technologists employing the subset of knowledge insist that they can be trusted without demonstrating trustworthiness which requires the use of the types of knowledge valued by consumers.

As noted earlier, the privileging of technical and scientific knowledge is pervasive among technologists. Indeed, these types of knowledge are embedded in the fundamental structuring of AI as a technology. Because the dispositions of technologists are deeply informed by these types of knowledge and the basis of AI is formed by these types of knowledge, the Dunning-Kruger knowledge-type effect is often invisible to those inside these companies.

The bias towards these types of knowledge is doxa, taken for granted, just the way things are, and goes unnoticed. However, this is not true for consumers or others outside the AI ecosystem. The study makes clear that anxiety over AI is evident and has noticeable impacts on the marketplace.

And yet, at events and conferences, technologists continue to claim the entire road involving the path to the future of AI. The deeply woven relationship between techne and episteme types of knowledge and power is at the bedrock of Silicon Valley culture, whether a company is located there or transposed to somewhere else in the world. Building the path to trust in AI will involve constructing trustworthy AI. It will include raising the profile of other types of knowledge consumers value and wedding an understanding of these other types of knowledge (phronesis, metis, arete) into the fabric of AI.

References

Cicek, M., Gursoy, D., & Lu, L. (2024). Adverse impacts of revealing the presence of “Artificial Intelligence (AI)” technology in product and service descriptions on purchase intentions: the mediating role of emotional trust and the moderating role of perceived risk. Journal of Hospitality Marketing & Management, 1–23. https://doi.org/10.1080/19368623.2024.2368040

Foucault, Michel. 1980. Power/Knowledge: Selected Interviews and Other Writings, 1972–1977. Translated by Colin Gordon, Leo Marshall, John Mepham, and Kate Soper. New York: Pantheon.

AI has been around for decades. The following describes why AI has received a surge of attention recently. It also describes the forms of knowledge that AI is impacting and the limits of AI’s capabilities within various forms of knowledge. Increasingly, expertise involves the coordination of systems as well as multiple forms of knowledge. Several areas of knowledge are accessible to people and not accessible to AI. Also, there are areas of knowledge that require human expertise in order for AI to increase the expertise sought. It concludes with recommendations to salespeople interacting with AI including areas of focus, authenticity, trust clusters, and social networks, as they seek to use AI and best position themselves in fields with prospects.

Multiple forms of knowledge

Techne (craft/art knowledge) is a form of knowledge recognized as work expertise in organizations. It includes the recognized skills, technical, and managerial used to create and do things. Techne is transferable “how to” knowledge. Recognized for their techne a person can “hang a shingle” labeling themselves as an attorney, marketer, or salesperson and claiming expertise in a profession.

The shingle, so to speak, is a social signal that a person is claiming specialized expertise in a specific category of techne. The social claim can be discounted or disproven by other social actors, institutions, professional organizations, etc. based on their judgment of the results or expertise delivered.

Two types of techne

Techne1 involves mathematical-based expertise, such as measuring and constructing a boat that floats or completing the math of financial statements that balance. If the end result of a techne1-based claim is failure – a sinking boat, or unbalanced financial statements – the person cannot claim they have expertise.

Techne2 recognizes a process of improving end results “better than chance” is a form of expertise. For example, even though doctors practicing medicine will lose patients to disease they can claim expertise. Also, sales professionals complete deals with only a fraction of total prospects, yet they can also claim expertise.

Techne2: Passing the law school exam

Interest in AI surged when ChatGPT ventured from the techne1 to the techne2 area of knowledge (see above graphic). The category of generative AI made this possible. AI began participating in professional techne2 areas, such as a law school exam, where expertise didn’t need to result in a mathematically correct end result. The result simply needed to demonstrate expertise “better than chance.” Generative AI did so, for example, by first passing the law school exam at the bottom of the class rankings and then at the top of the law school class ranking.

Opening areas of techne2 expertise to AI is a massive expansion. The expansion touches on areas of craft and artistic expertise previously held by professionals working with text, images, coding, etc. However, techne2 expertise often also requires contextualized knowledge that AI cannot access.

Expertise as disposition

AI is taking in inputs provided by people in the form of text and data, which are representations of specific situations. Further, these situations are interpreted by people and then reinterpreted and transcribed back as representations outputted by the AI. In effect, AI outputs are based on the inputs it receives. If the inputted representations are based on low levels of techne2 expertise then the corresponding output will exhibit low expertise.

Techne2 expertise is especially sensitive to the nuances of specific situations. For example, an expert salesperson knows when a prospect is able to buy based on a host of factors, including history, power dynamics, tone, mood, and ability to “read the room” in social situations. A “feel for the game” by an expert salesperson involves their disposition.

Dispositions are informed by many lived experiences specific to the area of expertise, often in highly complex and shifting situations, as well as the areas of expertise adjacent to and around the specific area of techne2 expertise, such as sales. A salesperson, for example, may warm the environment of a sales interaction by referring to a sporting event, children, the weather, etc.

Limits on AI trust

The sales expert knows how and when to do so in a manner that builds trust. The manner in which it is done is socially authentic within the specific context and feel of the situation.

Even though the prospect in the social situation fully knows the self-interest of the sales expert is involved in the conversation about a sports team, the sales expert is able to navigate the interaction to a successful transaction. There is a difference in the trust generated between two transacting people and a person interacting with technology, even AI with the capacity to act with techne2 expertise.

The trust and authenticity generated by AI receiving representations as inputs and then delivering them as outputs are not the same as that a salesperson generates in a relationship with a prospect. Even if the AI trust signals are identical, the fact that AI is not embodied means the signals will not be interpreted similarly by the person receiving them.

Conclusions: Authenticity, trust clusters, and social networks

In an era of AI-based techne2 expertise, authenticity derived from a relationship-based form of embodied trust will increase in value. Also, technical trust clusters informed by social networks will serve as places where prospects vet sales propositions. These trust clusters will parse techne2 sales artifacts (narratives, material, etc) and weigh their authenticity based on their AI and human characteristics. Salespeople attempting to navigate the fields where trust clusters are occurring and where authenticity is judged will rely on their social capital. They will use their social capital to build on claims of personal authenticity in order to further establish relational trust because they (their narrative, reputation) embodies the products they are selling.

With Marketvines as your trusted partner, we leverage advanced algorithms and state-of-the-art AI tools to precisely identify and engage with decision-makers who are actively seeking technology solutions. We quickly and meticulously analyze data and industry insights to generate a steady stream of qualified leads aligned with your target market.

Unlike generic marketing services, Marketvines is specifically tailored for higher-value B2B technology services. You are looking for meaningful conversions and cultivating long-term partnerships and that is what we deliver.

By seamlessly integrating AI automation and the savoir-faire of the human touch, Marketvines ensures that you benefit from a personalized, results-oriented approach that consistently exceeds your expectations. Experience the transformative power of Marketvines as we unlock new avenues for your business growth and propel you ahead of the competition.

Marketvines: Fueling B2B Success through Intelligent Connections.

Experts in demand generation take risks. Like the best person at a wedding, they tell jokes on the edge of inappropriateness, hoist glasses and chairs, and smash plates on the dance floor. 

They also slide their fingers across their throats to stop the kids from whacking glassware with a spoon to get the bride and groom to kiss – again. All while telling a few, carefully chosen, details about the soon-to-be hitched.

You get the point.

Demand generation gets the party going. They don’t do it by following the crowd. They do it by getting out in front of the crowd in the right way, with the right stuff, and at the right moment.

Demand generation, to paraphrase billionaire investor Ray Dalio has to ‘Go against the consensus and be right.’ 

Too often demand generation is discouraged from taking risks or applying expertise. Cue the “We already tried that” response to ideas from outside demand generation. 

Maybe you’ve heard? Markets are moving faster than ever. Applying whatever you tried last year or last quarter, is happening in a market that has moved on. AI marketplaces are sliding around like otters on mud banks – or something like that.

A wedding party does not stand still.

Demand generation is a series of strategic moves. Demand generation is not a tactic. It, like enterprise sales, is a process and system of strategic moves. 

Weirdly, this is Big News for many. (Not so weirdly really. Self-defeating leadership goes hand in hand with companies where demand generation fails.)

If demand generation’s strategic value is, shall we say, immature, then you’re better off making the person in RevOps pulling lists your next CEO. The rapid curation of targeted lists is core and insanely revealing. After a few months, the junior RevOps list puller is closer to the market movements than many a CEO. 

Who are we talking to, why, and who is listening is the strategic marketing “channel” everyone should be dialed into.

Is engagement high, but then dropping off? If you don’t understand basic market factors you’ll catch prospects on the first pass, but they see through you quickly.

How to fix that? Understand basic marketing factors about your target verticals: what does everyone need to understand to do business in a specific market? 

Here’s hint one: in the education vertical, the basic audiences are called students, faculty, staff, and administration. Stop referring to them all as “your employees.” Hint two: The budget cycle for public educational institutions ends in June. Ends. Not begins. 

Hint three: targeting hospital systems? EPIC, and Health Information Privacy and Portability Act (HIPPA). You better know something about how these factors relate to your product. Hint four: They do, even if you think they don’t. Enough said. 

A quickie on demand generation messaging: Demand generation messaging driven by egos in the C-suite or the latest “Hey this is cool” message that a board member suggests to a CEO. You and your demand generation are screwed. You’re the fall guy for somebody else’s guesses. Call your network with a quick “The direction they’re taking is self-defeating” and position to Get Out. 

Getting your demand generation messaging from product management? Forget about it. They speak a different love language. Demand generation loves engagement. Sales loves the deal. Product loves technology.

Hey Product, don’t kid yourself. Even if you wrote your own wedding vows it is always better to have someone else – the best person or priest – read them out loud. The same goes for demand generation. Product, write whatever you want, and then focus on your beloved bride, the technology. That is what the groom – your market – wants you to do! They want to sense that unwavering gaze and devotion to absolute technical perfection. (Whew. Is it getting hot in here?)

Let demand generation take what you wrote and address the crowd. 

Demand generation gets the party (and the product) rocking. Let them tell the jokes, and make the toasts, while sharing just enough personal details to keep things going. Product, if you do that you’ll get your chance to make wishes come true – later. At this point in the engagement process, you’re still a perfect and alluring new partner. Sit back and enjoy the party. 

Engagement’s a demand generation thing.

McKinsey & Company, a global management consulting firm, recently published a report on the future of B2B sales. It explores the shifting landscape and offering valuable insights for businesses to stay ahead of the curve. The report covers key aspects of the B2B sales process, including the evolving role of sales representatives, the increasing importance of digital channels, and the need for a customer-centric approach.

Key Takeaways

Shifting Roles of Sales Representatives

The McKinsey report highlights that the role of sales representatives is changing significantly. Traditional sales tactics like cold calling and face-to-face meetings are becoming less effective, while digital channels are gaining prominence. Sales reps must now focus on providing value-added services, such as insights and expertise, to differentiate themselves from competitors.

At Marketvines the go-to-market (GTM) approach and the SDR team are empowered to bring their expertise to bear in the form of story based on their experience and authenticity based on how they interpret and find meaning.

Rise of Digital Channels

The report emphasizes the importance of digital channels in the B2B sales process. Companies that have embraced digital tools and platforms have seen significant improvements in their sales performance. Digital channels enable businesses to reach a wider audience, streamline the sales process, and make data-driven decisions.

At Marketvines the digital channel is aligned with the goals of the client. For example, in some cases a laning page is a good opeion and in other cases a presentation deck provided in the initial email or a follow-up LinkedIn communication is more appropriate. IN the end, the prospect is selecting a product and a relationship with the vendor. Knowing how that relationship is best curated via a channel is a unique for each partner.

Customer-Centricity

The McKinsey report underscores the need for a customer-centric approach in B2B sales. Businesses must prioritize customer needs and preferences, offering tailored solutions and personalized experiences. This approach helps build long-term relationships and fosters customer loyalty, driving sustainable growth.

As in any relationship, at Marketvines it is important to pay attention to the details and listen to how a vendor’s prospects prefer communication to occur and the pace of interactions.

Implications

The findings of the McKinsey report present both challenges and opportunities for businesses in the B2B sales landscape. Companies must adapt to the changing roles of sales reps, invest in digital channels, and embrace a customer-centric mindset to stay competitive.

Failure to adapt to these changes may result in lost market share and decreased sales performance. On the other hand, businesses that proactively embrace these shifts can capitalize on new opportunities and drive growth.

Actionable Steps

To leverage the insights from the McKinsey report and stay competitive in the future, businesses should consider the following strategies:

Invest in Digital Capabilities: Develop and implement digital tools and platforms to enhance sales processes, improve customer engagement, and enable data-driven decision-making. Companies that partner with Marketvines to grow their business see this digital push forward firsthand.

Redefine Sales Team Structures: Restructure sales teams to focus on value-added services and expertise, rather than traditional sales tactics. This may involve retraining sales reps, hiring new talent with specialized skills, or collaborating with external partners.

Focus on Customer Experience: Prioritize customer needs and preferences, offering tailored solutions and personalized experiences. This can be achieved through in-depth customer research, data analytics, and continuous improvement of products and services.

Collaborate Across Departments: Foster cross-functional collaboration between sales, marketing, and product development teams to ensure a cohesive and customer-centric approach. Marketvines uniquely embodies this cross functional integration between sales and marketing with a feedback loop to product development.

Conclusions

The future of B2B sales is marked by significant shifts in the roles of sales reps, the adoption of digital channels, and the need for a customer-centric approach. Businesses that embrace these changes and implement the actionable steps outlined above will be well-positioned to stay competitive and drive growth in the evolving B2B sales landscape. Embracing change and innovation is crucial for success in this dynamic environment.