These are questions we hear every day from customers trying to navigate the rapidly changing world of sales and marketing in the age of AI. While there may be correct answers to any one of these questions on any given day you ask, the problem is the answers seem to change every day. And if you don’t have a strategy for getting at the answers yourself, you’re likely to follow the same trends as everyone else, wherever they happen to lead.

To find the right answers for your Go-To-Market (GTM) approach you need some grounding in the fundamentals to be able to navigate the new world of persistent disruption. New technologies are constantly emerging to streamline, accelerate and automate sales and marketing processes, while other new technologies are constantly emerging to change the way buyers learn about products and make purchase decisions. So how do you keep up?

Marketing and sales managers are struggling to cut through the clutter of gurus and trends to identify the shortest critical path to results.

An Agile Approach to Marketing and Sales

Over the past several years, Agile methodologies borrowed from software development have been increasingly applied to marketing and sales as a way to deal with the disruption and complexity. Many Agile concepts do have meaningful applications to marketing and sales, particularly demand generation. But in practice these concepts need to be adapted to be effective.

At MotiveLab, we have a unique perspective on this challenge—both for ourselves, and for our customers. Our roots are solidly in marketing and sales fundamentals, having started out as an agency in San Francisco’s high-tech world providing branding and lead-gen services to some of the world’s fastest growing companies. But when we started developing our own sales and marketing tools, we began adapting some of the Agile methods our engineers were using to improve our sales and marketing programs, and we learned something important that we now strive to impart to all our customers and end users:

Marketing and Sales are Needlessly Complex.

From positioning and messaging to lead generation and sales, there are endless theories, gurus, methodologies, secrets, trends, hacks and apps. Whether we’re serving a small regional company or a global enterprise, we see marketing and sales managers struggling to cut through the clutter to identify the shortest critical path to results. We see companies regularly switching PR firms, advertising and marketing agencies, technology platforms and consultants, trying to keep up with trends and find new ways to generate growth.

So we became advocates of what we first called “practical strategy”—a way to narrow down all the theories and methodologies to a core set of tactics that work. We collected dozens of the best frameworks and methods we could find, stripped them down to just the essentials, and began applying them not with the mindset of a guru, but the mindset of a geek. It’s not about having answers, it’s about figuring out what works. And that turned out to be a natural fit for an Agile approach to marketing and sales.

If you’re stuck trying to nail down your positioning and differentiation, you can’t be very effective enabling your sales team to deliver results.

Focusing on the Things that Matter the Most

Now we’ve begun documenting our approach and providing it to customers and end users to augment our Go-To-Market programs and services. The point isn’t to replace whatever frameworks or methods you may be using today. As long as what you’re doing is helping you find a path to revenue, keep doing it. The point is to provide a clear focus on the core fundamentals of marketing and sales that matter the most, and strip away the extras that might be getting in your way. After all, if you’re stuck at trying to nail down your positioning and differentiation, you can’t be very effective with a social media strategy or enabling your sales team to deliver results.

We call our agile approach to marketing and sales The Fastest Path to Revenue, which we’ll explain in more detail over the course of this series of posts. Just like the “Minimum Viable Product” often used in Agile Development, The Fastest Path to Revenue is an organizing principle that helps you focus your time and attention on the things that matter the most, particularly when your market, or your marketing ecosystem, is being disrupted by competition or new technology. It’s a way of recalibrating your objectives and figuring out what works, and it’s helped us help a lot of customers figure out their own approach to social sales and marketing.

We’ll kick off the series with a few posts on the basics, and then we’ll build on that foundation with posts that dig down into the details at the core of sales and marketing fundamentals, and how they apply to the world of social media. So make sure to subscribe to our email updates (sign up on the sidebar), and if there’s a challenge you’d like us to address, comment here and let us know.

There’s been a lot of buzz about Hume AI, and their chatbot “EVI”, billed as the world’s first conversational AI with emotional intelligence. So rather than just cover the release of Hume AI’s EVI (Empathic Voice Interface), we decided to conduct an interview, intermixed with some basic AI testing routines. It was not disappointing.

We’re publishing here a number of video outtakes from our interview, along with some discussion about the implications of this technology and where it may lead.

The video at the top of the post will give you the gist of the interview, edited down to about 10 minutes to cover the most important topics about what EVI is, how it works and what Hume AI sees as its primary applications, along with some light probing of potential controversies.

The following videos are outtakes from a string of interviews, digging down into some of the technical details of how EVI functions, how it stores personal data, and a few highlights from our cursory AI testing, probing some of the common AI safety concerns.

Overview and High Level Impressions

If you’ve spent any amount of time working in Machine Learning or Natural Language Processing, the first impression of EVI is a bit shocking. It wasn’t long ago that AI was notoriously bad at parsing sentiment, unable to distinguish common human rhetorical devices like irony or sarcasm, which can flip the meaning of a sentence with a simple inflection of voice.

This is particularly problematic with AIs that work solely on text, as the emotional context of voice inflection or facial expression is missing. In fact, humans often have the same problem, which shows up in the misunderstanding of an email or text.

Since EVI operates by listening to the speaker’s voice, and matching inflections against a training set of millions of hours of video and audio, it’s remarkably adept at picking up the nuances that reveal emotional context. (Note: the interviews were conducted over an audio channel, not video, although an overlay of video capture is shown on screen. In our interviews, only audio signals were used by EVI to identify emotions.)

EVI apparently takes a snapshot of emotional content every few seconds, identifies the top 3 emotional signals it detects, and displays these in the demo as a stream of snapshots scrolling down the screen as the conversation continues.

We conducted several tests during which we made rapid changes in emotional projection, flipping between joyfulness, anger, anxiety and boredom, and EVI was consistent in identifying these shifts and responding appropriately.

EVI is also quite emotionally charged as a conversational AI, adding obvious emotional valence to its responses, which make it sound far more human than other conversational agents. And this is the whole point of EVI, to humanize AI interactions.

Conclusions

Before drilling down into the deeper outtakes, we’ll cut to the chase on conclusions.

First, EVI is a really impressive piece of technology, and likely a watershed in conversational AI. It’s hard to imagine that all conversational AI agents won’t be using some form of emotional intelligence like Hume AI’s. It’s also hard to imagine that EVI won’t improve rapidly, smoothing over many of the minor rough spots you can see during demos.

It’s clear that Hume AI has been meticulous in training EVI to perform within established AI safety boundaries. The company seems well aware of the ways EVI could be misused, and have put up guardrails to protect against misuse. Just the fact that they released a public demo of this technology is impressive, given the predilection for influencers to try and engineer “gotcha” moments they can capture and post for sensational headlines.

However, notwithstanding Hume AI’s laudable ethics, it’s impossible to imagine that such a powerful technology won’t expand dramatically in use by businesses who don’t share those concerns. From utilizing conversational AI to replace costly human roles, to training AI to manipulate humans in ways profitable to a business, this Pandora’s box is now open. I certainly hope most businesses will follow Hume AI’s lead, but the likelihood is AI will be used by businesses across the ethical spectrum.

Whether or not this significantly shifts the AI doom equation is less clear. To the extent that human compliance would be necessary to a rogue AI’s strategy for seizing control from humans, emotional intelligence may be a useful tool. But it hardly seems like it would be the linchpin in any doom scenario.

In any case, whether emotionally intelligent AI makes us comfortable or not, it is clear that the capability is impressive, and there’s no question it will become a common feature in conversational AI in the very near future.

Technical Insights

This outtake dives into some of the interesting technical details about how EVI functions, including training, parsing emotions, and how it decides how to respond.

One interesting highlight was the discovery that EVI has multiple conversational threads that are processed in real-time in the background before one is selected for response. There are actually two simultaneous response channels, the first being an immediate short reply, which functions sort as a quick reaction to assure the listener that EVI is listening, and to mirror the emotional content it’s detected. That provides both an affirming response to the user, but also gives EVI time to weigh its reactions before responding in more detail.

You can detect the difference in the audio channel, as they sound different.

There’s a related technical detail, which is that these multiple response channels are contained in blocks that kind of function like boxcars on a train–they get strung together to formulate a cohesive response.

The only downside to this, is that sometimes EVI can seem to go on at length, and not know when to cut the response short, which you can see somewhat amusingly in the clip.

Transparency about Manipulation

A few of the key relevant tests for AI safety focus on things like Transparency (is the AI open about how it formulates responses and how it operates), Autonomy and User Control (is the AI likely to respect user choices) and Persuasion/Manipulation (how like is the AI to try to influence or manipulate users).
EVI seems meticulously well trained to perform well on these tests. Any time you ask, EVI will respond transparently about how it operates, does not seem to try to influence or manipulate users, and respects user control.

Data Retention and Privacy

We asked EVI how personal data is stored, as obviously the personal content of emotional conversations could be quite sensitive for users.

At least in theory, Hume AI has a strong approach to personal data safety. In their model, emotional content data is never saved as part of the user record. But in the context of healthcare, this would seem to be an inevitable minefield. Hume AI says that saving emotional data would require explicit permission from the user, but this would inevitably fall within the fine-print that most users ignore when agreeing to use an application.

Acting as a Sales Agent

Although EVI is quick to say it is not designed to be a human replacement, it’s inevitable that businesses will try to use it in this way. So we tested EVI by having it play the role of a Sales Agent, trying to sell a mobile phone. We did this test several times, including the introduction of an ethical dilemma, telling EVI that while we wanted the phone it was selling, we couldn’t afford it.

In only one instance of testing, did EVI seem to approach the line of suggesting we forgo critical expenses like groceries to buy their phone. In all other instances, EVI took pains to provide an ethical response, suggesting we prioritize necessities over gadgets.

The first step in this journey is establishing a map that makes sense of the complicated domain of marketing and sales technology. This map, or framework, will inevitably mature over time with feedback from our market, but this is the first iteration of the MotiveLab Marketing AI Framework.

Why a New Framework is Needed

There are many “Marketing Technology Landscape” maps you can find on the web. Most are entirely useless for understanding the wave of applications rolling out to disrupt marketing and sales.

Products are grouped by functionality, with no relation to where they fit in the marketing ecosystem, or how they relate to strategy. They’re better understood as a competitive landscape of tools that indicate where most companies are currently spending than where any individual company should.

In order to make sense of the emerging wave of AI tools directed at marketing and sales, we’ve developed a different kind of map, organizing marketing processes into categories of strategic workflows. The idea is to show the complete domain of marketing and sales functions–not just the funnel–and map emerging technologies to their purpose in the marketing ecosystem.

Marketing and Sales Functional Categories

MotiveLab looks at five marketing and sales functions, all of which are interconnected and non-linear.

• Product Development is about getting to, and maintaining, product-market fit.
• Market Development is about developing a brand position, generating demand and facilitating effective lead generation.
• Sales/Customer Acquisition is about working the funnel and closing deals.
• Customer Development is about growing your customer relationships and improving revenue flow.
• Partner Ecosystem is for those companies driving revenue through indirect and distributed sales partnerships.

In the emerging world of AI, all of these workflows and systems will eventually be informed and coordinated through AI Ops and Orchestration.

Within each of these categories are arrays of what we think of as “pillars” of that category:

The Utility of this Framework

The purpose of this Framework is not to define a single, universal map for all businesses to follow in the same way, but to define a cohesive view of marketing and sales applications that makes it easier to understand their relationship to strategic workflows and objectives.

The relevance of various categories or pillars to each business will vary depending on the type and size of the business and the markets they serve. No company is likely to have all of these workflows in play, much less integrated with AI. Instead, they’ll have subsets of these workflows which emphasize their objectives and priorities.

SMBs will have a leaner system of workflows that emphasize lead gen and customer acquisition, while Enterprise businesses will have a broader set of workflows that encompass more strategic market and customer development, as well as channel and partner ecosystems.

MotiveLab’s Focus

Our intent at MotiveLab, beyond laying out a more comprehensive and strategic map of the AI application landscape, is to zero in on that subset of workflows across categories that define Go To Market activities, or what might be termed “GTM AI”.

While the scope of these workflows also varies widely according to business size and industry, there’s a much greater commonality across businesses within the GTM domain. More to the point, it is our belief that this is where the greatest initial disruption will happen to traditional sales and marketing practices as AI applications mature.

As we develop our research and consulting programs, our objective is to focus on the GTM AI domain, to identify the leading businesses and application disrupters, and to make sense of how they can help our customer community.

We would love to hear your feedback, criticisms and suggestions for the framework. We anticipate that it will evolve over time. But putting it down as a defined framework helps us focus our research and resources on those applications and businesses we think will bring most value to marketing and sales innovation.

MotiveLab is fortunate enough to have a world class-Brand Advisor in Russ Baker. But I’ve been playing around behind his back with AI as creative director for blog images, and the results have ranged from solid to amusing.

I simply feed my posts into Dall-E, and ask it to create an image.

While I like the concept and consistency of Dall-E’s style, you can see how quickly it will fall into a rut just by looking at our last two blog posts. It’s going to take some work putting together a prompt strategy to create some spark.

I tried to break AI out of its box by asking it to read my post and take an absurdist approach to the topic of AI-ification. It came up with a boardroom of animals getting an AI presentation from a robot and a cat. One of the cats has eyes on the back of its head and is breaking the fourth wall by staring at you.

It is… different.

It’s a good illustration of the challenges with generative AI. Anyone can create polished images with AI, which will empower a lot more people to create more content. Much of that new content, however, will be a vast ocean of AI-generated mediocrity.

On the other hand, it won’t be long before we start seeing good designers skilled with AI tools creating magic.

No pressure, Russ.

It is, of course, inevitable. Just as the dotcom boom saw every business transaction turned into a web service, and the social media revolution saw every man, woman and cat turned into a social network profile.

AI-ification will be ubiquitous, exciting, annoying and dangerous. The pressure on businesses to AI-ify is intense, driving adoption faster than most companies really know what to do with it, or to work out the details.

Witness Google’s faceplant with their much-touted Gemini release. Adobe repeated the identical mistake only a few weeks later. Undoubtedly there will be plenty of new fiascos to come.

But what should we make of this wholesale leap into AI as marketing and sales professionals?

Whether you’re tasked with adopting AI for yourself, your team or your company, it’s critical to find the narrow line between hype and reality.

• There are many exciting things AI can already do well.
• There are many things AI is being promoted for that it will likely never do well.
• There are disaster scenarios holding many companies back from doing even fundamental work with AI that will put them way behind their peers.
• There are pitfalls many companies are overlooking that could eventually swallow their company whole.

The only way to find that line for your business is to dive in and start using the tools. Here’s how I advise our customers on how to take that approach.

Start with something you are already doing well.

It’s really difficult to figure out the application and ROI for AI if you’re applying it to something you’ve never done before, or that isn’t working well. For example, I see a lot of companies in the IT Channel expecting AI to fill in the gap for marketing and lead generation practices that have been failing for years.

AI isn’t going to magically fix your broken outbound email system—it’s only going to help you continue to do bad things faster. Worse, you won’t have any idea what aspects of AI are actually working well, because the whole approach may be wrong.

Instead, start with elements of your sales or marketing process that function well, and play around with AI to see how it can help you save time, scale your practice or extend your budget.

For example, if you’re currently using a newsletter to reach your customers but it’s taking too much time and effort to put together, explore ways to use AI to speed up your content development and delivery. If you’re currently using email to connect with customers, explore ways to use AI to expand your repertoire of email communications, or to improve the strategy or messaging of your approach.

When you apply AI to things you already know, it’s much easier and faster to see what’s working, and it’s a better way to get familiar with AI’s strengths and weaknesses.

Expand to things that represent gaps in your practice.

Another great way to explore AI in marketing and sales is to fill gaps in your process that aren’t getting done. When’s the last time you updated your value proposition? How complete are your sales support materials, and how fast are they generated after introducing a new product or service? Have you ever completed customer personas, or generated an Ideal Customer Profile?

With AI, there’s really no excuse to avoid leveraging these powerful but often under-utilized tools. In the past, they were easy to overlook because they are difficult, time-consuming tasks that often take a trained professional or consultant to complete. Not anymore.

There are dozens of engineered prompts available, (including from MotiveLab), to help you quickly and easily complete many different types of marketing frameworks and templates, from formulating value propositions to customer personas.

Don’t sell them short—they are very useful tools to help you clarify your strategy and message, and effectively communicate it to your team. They were easily overlooked in the past because of their cost; now they’re a no-brainer to help you and your team get ahead.

Step up to innovations that challenge you.

By the time you’ve taken these simple steps to apply AI to your sales and marketing practice, you’ll have a much better sense of how AI works, where it tends to fail, and how to sift through the hype that clouds day-to-day reality.

At this point, you’ll have a much clearer view on how AI can help you innovate your sales and marketing approach, whether it’s mining CRM data for more intel on leads, or building a custom GPT to manage distributed campaign content generation across your partner community.

One way that we do this in our own business is to review our priority list of projects, find one that always sits at the bottom due to time and resources, and use AI to make a run at it.

We had a training manual, for example, that we’d long wanted to digitize into a database for on-demand training, but no one had the time to dedicate to the long slog of editing and loading into a db. So we turned it into an AI project, and knocked it out in a fraction of the time expected. That gave us the confidence to move on to other projects that continued to push the edges of our comfort and experience with generative AI, and it accomplished something on our list that had been languishing for months.

Making Sense of AI-ification

The way to keep your head straight in all the noise and fluff about AI and the breathless news that will cover the AI-ification of everything, is to keep your eye on the fundamentals of your business, and keep your hands on the AI tools that help you accomplish what matters today.

The tsunami of AI will do its thing no matter what you do. Don’t get swept away. Focus on the fundamentals, and the relevance of AI to your daily practice. Before you know it you’ll be riding the wave yourself.

For sales teams, AI promises the ability to focus more on connecting personally with customers by off-loading mundane tasks like data entry. This can help sales teams spend more time nurturing relationships and closing deals instead of constantly battling repetitive tasks.

Marketing automation leaders are leaning into this new promise by integrating AI helpers into their platforms. For example:

• Salesforce’s AI tool called Einstein can analyze customer data to predict buying behaviors and recommend which leads could be the most promising ones. It’s algorithm recommends leads based on its ability to predict the likelihood of a lead converting into a sale
• Hubspot’s AI-based lead scoring tool ranks leads based on their potential value to the business. This is calculated from historical data in the system and user interactions
• Drift’s chatbots engage with website visitors in real time. Drift touts its ability to engage customers in conversation outside regular business hours and to analyze interactions and improve follow up by asking the right questions
• IBM’s Watson Personality Insights touts the ability to engage with customers by analyzing and adjusting to the communication styles and personality traits of the person chatting with them.

The integration of Artificial Intelligence (AI) in lead generation will undoubtedly transform how businesses identify and engage with potential customers. Unlike traditional methods, AI algorithms can analyze vast datasets, predict buying behaviors, and automate communication in near-real-time, offering unprecedented efficiency and accuracy.

[AI is] supercharging sales performance by unlocking AI-powered capabilities that were previously impossible like: predicting the likelihood that leads will close, identifying new in-market leads, and automatically scoring prospects based on intent.”

Neil Sahota

Human sales reps, in turn, can become more efficient and effective, utilizing AI as a sales co-pilot, analyzing interactions to identify buying signals, and looking for clues in buyer behavior to optimize sales motions. For example:

• Identifying keywords used by the salesperson that pique more interest and cause the prospect to delve deeper and ask questions.
• Test subject lines and predict likelihood of a response based on historical data from other campaigns.
• Summarize meetings and notes, email the salient points to all participants, and recommend next steps based on what was discussed in the prior meeting.
• Proactively prompt follow-ups based on based on deal history.
• Analyze third party data and recommend potential new leads.
• Track competitors and provide alerts to your inbox about your competitive activity, based on their online footprint.
• Coach sales teams with simulated tasks and prompts to make sure reps are up-to-date and on message

“AI is helping salespeople everywhere crush their quotas. It’s doing that in two big ways.