#3 Don't Stop Believing in AI: Momentum's Automation Insights, Google's Bias Controversy, Critical Thinking with AI, and more

As a musician since I was a kid, I decided I will start naming each weeks newsletters inspired by songs I enjoy and are connected to the topic at hand.

It is official my friends, the newsletter just crossed 2000 subscribers in 2 weeks which is so exciting! My goal is to always bring you updates, insights, and valuable content to help you in your roles. Give me feedback and let me know what else you want to hear about.

As always, this newsletter and this week's podcast is sponsored by the GTM AI Academy where we focus on teaching individuals, leaders, and teams in Go to Market businesses create true business impact not just learn tools in on demand education courses and live sessions. www.gtmaiacademy.com

Today's newsletter we go over the following:

1. Podcast #3- Interview Overview with Ashley Wilson and Santiago Suarez Ordo?ez of Momentum.io and we dive deep into their passion and tech that is speeding up teams ability to get work done through AI and automation.
2. A case study using Momentum's tech to deliver real time feedback on sales calls.
3. Gemini and Google get blasted by Elon for bias in their AI. What does that mean for GTM teams?
4. Editors Mindset and Critical Thinking with AI
5. Data is your Differentiator
6. AI Tools Demo Library-Free Access for YOU in the GTM AI Academy.

And now the podcast overview:

In this episode we get to talk about the exciting new frontier of AI-powered go-to-market solutions. Ashley and Santi, founders of Momentum, share insights about harnessing AI and automation to transform sales processes. They cover Momentum's capabilities like summarizing multilingual calls and automating Salesforce data entry, along with the upcoming AI-driven coaching module, among the many capabilities of the tool. The discussion highlights the immense potential of AI in GTM for enabling tasks like extracting insights from thousands of calls and delivering more personalized customer interactions.

To listen to this podcast on your favorite places, you can go to Youtube, Apple Podcasts, or Spotify, or streamed live on Linkedin.

Key Takeaways

1. Embrace AI for Efficiency: AI offers productivity wins by streamlining repetitive tasks so teams can focus on higher-value activities. It's a tool to be embraced, not something that replaces human ingenuity.
2. Automation for Scalability: AI-driven automation is essential for scaling sales operations and enablement. It helps teams manage overwhelming volumes of data, coach consistently, and deliver value at scale.
3. Personalized Customer Insights: The future of sales lies in AI's ability to analyze large data sets and uncover deep customer insights. This leads to highly personalized interactions and better outcomes.
4. Training AI for Accuracy: Understanding AI's capabilities and limitations is crucial. Feed it context-specific information through training and coaching frameworks to ensure accurate and actionable output.
5. Adapt or Lag Behind: AI is revolutionizing the GTM landscape. Companies must adopt new AI tools and strategies, or risk being disrupted by those that do.

REAL TIME CASE STUDY OF MOMENTUM AND AI FEEDBACK/COACHING

Momentum has the ability to do many many things with AI and automation such as Slack Deal rooms, automated entry from calls to Salesforce, finding patterns of objections, competition, negotiation tactics, product questions across any calls, and so many more.

Recently, I brought on Momentum in our company for many reasons and use cases which we are still rolling out to the team, but one of the best ones we just started testing is automated feedback/AI Coaching on sales and customer facing calls that are personally and customized to each user on the call so they can have feedback.

So what I have done is:

• Created advanced prompts that include Personas, competencies, sales stage based exit criteria, score card questions.
• Each person will receive a AI feedback review that will give them actionable feedback on 1-Points of Excellence, 2-Points of Improvement, 3-Scoring on Exit criteria by sales stage, 4-Scorecard by sales stage, and 5-Overall score of the call from 1-100%.
• Sales managers or CS managers, receive a weekly review that summarizes strengths, weaknesses, and scores of each team member. It also points out points of feedback that can help in weekly 1 on 1s.
• Enablement receives an automated report already populated in Google Sheets & Slack that can be tracked long term to find patterns.

Since testing this version, I have shown the feedback and worked closely with reps to understand what they think, is the feedback helpful, or is it overwhelming... this is what the team is telling me:

• "It makes each calls like a game where I try to beat my last score by performing better on calls and helps me remember what is important on each call"
• "This is amazing, the scoring is something that triggers my competitive spirit and pushes me to do better"
• "The call that was reviewed, I did not feel like I did that well and the scoring gave a good unbiased review of things I need to do better on and pointed out things I did not think about. I also loved the positive feedback and help me see that I didn't totally fail"

We will continue testing and I will look forward to sharing results from seeing if the feedback is helping performance to improve in KPIs and leading activity metrics, but for now, its positive feedback from the team.

What I love about this is that this feedback is powerful, real time, and helps someone see both strengths and weaknesses.

One of the things I mentioned on the call is that I see Enablement becoming facilitators of the ecosystem using AI that instead of one person or a team of people reviewing calls, (Which let's be real, does not happen as often as everyone would like it to happen) Now AI can review EVERY call, point out areas of weakness or strengths, and also flag any points of interest either good or bad for teams to see real time.

More to come...

And now Elon Musk & Gemini:

Now I refuse to get political in my newsletters, so I will talk about this from an objective point of view as I can, but more importantly, why this matters to GTM professionals when it comes to using these tools and the answers or output you get from ChatGPT, Gemini, or any other AI tool out there.

In summary, here is the story:

Google found itself in hot water with its AI model, Gemini, which was criticized for producing images that inaccurately depicted the racial or gender identities of historical figures. In response to the uproar, Google has paused Gemini's people-image generation feature. The controversy was amplified by figures like Elon Musk, who lambasted Google for what he sees as a "woke" bias in AI and search functionalities. This issue emerged shortly after the launch of an advanced version of Gemini, underscoring its enhanced capabilities, only for Google to retract this feature amid the backlash.

Diving Into the Diversity Debate

At the heart of the storm is a batch of Gemini-created images that misrepresented historical figures, leading to widespread debate. These images quickly caught the attention of social media and news outlets, becoming a focal point for discussions on diversity in AI representations. Critics argue that Gemini's outputs suggest an exaggerated emphasis on diversity. Google has since admitted to shortcomings, stating they are "missing the mark" and are "working to improve these kinds of depictions immediately."

Musk's Critique on Google's Approach

Elon Musk has been outspoken against Google's handling of the situation, sharing his views extensively online. He criticized Gemini's output as "insane racist, anti-civilizational programming," suggesting it reflects Google's broader content moderation strategy. Musk accused Google and its peers of promoting a "woke" agenda, contrasting them with platforms he suggests are more dedicated to unbiased truth-seeking. "The woke mind virus is killing Western Civilization," Musk commented, adding that Google, along with platforms like Facebook and Instagram, are guilty of similar biases in their search results and content moderation.

The Ongoing Representation Challenge

The conversation around diversity in AI and media isn't new but reflects a longstanding debate over representation and bias. Some argue that programming AI to include a diverse range of identities is a step towards inclusivity, while others see it as a distortion of historical accuracy. Google's attempt with Gemini to navigate these complex waters has resulted in significant controversy, highlighting the intricate balance AI developers must maintain between fostering diversity and ensuring accuracy in their creations.

Implications for GTM Professionals in the AI Landscape

The unfolding controversy around Google's Gemini and the ensuing debate underscore a critical juncture for Go-to-Market (GTM) professionals navigating the evolving AI terrain. The incident not only highlights the technical and ethical complexities inherent in deploying AI technologies but also serves as a poignant reminder of the broader implications for brand reputation, market positioning, and customer trust.

Brand Integrity and Public Perception

For GTM professionals, the Gemini controversy illustrates the delicate balance between leveraging AI's innovative capabilities and maintaining a brand's integrity. The backlash against perceived biases in AI-generated content can swiftly alter public perception, affecting brand loyalty and customer engagement. As such, GTM teams must ensure that AI tools align with their brand values and the expectations of their diverse customer base. The swift response by Google to pause certain functionalities of Gemini reflects the necessity of being agile and responsive to public sentiment, a critical lesson for GTM strategies in the AI domain.

Market Positioning and Competitive Advantage

The critique from high-profile figures like Elon Musk brings to light the competitive dynamics of the AI industry, where the approach to diversity and bias can influence market positioning. GTM professionals must recognize that how an AI model handles representation and bias is not just a technical issue but a key differentiator in the market. In an era where consumers are increasingly aware of and concerned with social issues, an AI tool's ability to navigate these complexities can become a competitive advantage or a liability.

Building Trust Through Transparency and Engagement

The controversy also emphasizes the importance of transparency and engagement with stakeholders. GTM professionals should advocate for clear communication about how AI tools work, their limitations, and the steps taken to address biases. Engaging with customers, critics, and the broader community can foster trust and demonstrate a commitment to ethical AI development. As Google's acknowledgment of the issue shows, admitting to and working on shortcomings can be an integral part of maintaining trust and credibility in the market.

Navigating Future AI Challenges

Finally, the Gemini case points to the ongoing challenges GTM professionals will face in the AI space. As AI technologies become more integral to products and services, GTM teams must be prepared to navigate the ethical, social, and technical challenges that arise. This means not only staying abreast of the latest AI developments but also understanding the societal context in which these technologies operate. Balancing innovation with responsibility will be key to successful GTM strategies in the AI-driven future.

One more thought...

Remember, AI in and of itself is not racist or biased, its like saying electricity is biased or racist. Its HOW the AI is "Trained" and/or even the data it is trained on that can cause the bias or racism.

This is why it is CRUCIAL that when using AI tools of any kind or brand, that one of the skills that anyone should work on is critical thinking and putting on the "Editors mindset" As I like to call it in the GTM AI Academy .

What is the Critical Thinking skills and Editors Mindset?

Adopting an "Editor's Mindset" when using AI tools is more than just a good practice—it's essential for making sure the information we get is not just top-notch but also fair, balanced, and inclusive. In a world where AI is popping up everywhere, from how we create content to making big decisions, it's critically important to keep a watchful eye on what it's churning out. Why? Because, let's face it, AI isn't perfect. It can accidentally (or not so accidentally) reinforce stereotypes or leave out important perspectives if we're not careful. Or it could just give us bad sources, bad output, or just incorrect information.

Remember expertise (critically thinking experts)+ AI = SPEED and QUALITY.

Think of it like being the editor of your own newspaper. You wouldn't want to publish stories that only tell one side of the story, right? Same goes for AI. By questioning, tweaking, and sometimes challenging the AI's "first draft," we're not just critics; we're collaborators. It's about rolling up our sleeves, diving in, and helping these smart tools learn to do better. And adding a step where the AI takes a second look at its own work? That's like giving it a chance to learn from its mistakes, making it smarter and more aware with each round.

So, why does this matter? Because by guiding AI with a thoughtful, questioning approach, we're making sure that the technology advancements we're all excited about are actually making the world a better, more understanding place. It's about using our human insight to ensure that AI serves us all, not just a select few.

Here are the steps in order to take on this mindset:

1. Understand the AI's Capabilities and Limitations

• Research: Investigate the functionalities, training data, and known biases of the AI tool before use.
• Stay Informed: Regularly update yourself on any improvements or changes to the tool that might affect output quality.

2. Set Clear and Specific Goals

• Define Objectives: Clearly articulate what you aim to achieve with the AI tool to guide its output more precisely.
• Use Detailed Prompts: Provide the AI with detailed and specific instructions to steer it towards generating the desired output.

3. Critically Evaluate the Output

• Assess for Bias: Look out for any biases in the AI's output, including but not limited to racial, gender, and cultural biases.
• Seek Diverse Perspectives: Compare the AI-generated content against various sources to ensure it encompasses a wide range of viewpoints.

4. Engage in Iterative Feedback

• Provide Feedback: Utilize the tool's feedback mechanisms to highlight any biases or inaccuracies, aiding in its improvement.
• Iterate: Refine your prompts based on initial outcomes and re-run the AI to evaluate different outputs.

5. Employ External Validation

• Consult Subject Matter Experts: For critical tasks, seek validation from experts in the pertinent field.
• Use Multiple Tools: Compare outputs from different AI tools for the same task to detect and address bias.

6. Foster Continuous Learning

• Educate Yourself: Keep abreast of the ethical considerations and debates surrounding AI and bias.
• Participate in Training: Engage in training on ethical AI use and bias mitigation if available.

7. Have the AI Critique Its Own Output

• Self-Assessment: After generating output, use the AI to analyze its own work for potential biases or inaccuracies, if the tool has the capability.
• Prompt for Alternatives: Ask the AI to provide alternative viewpoints or outputs to its original response, helping to uncover and mitigate inherent biases.

8. Advocate for Transparency and Ethical Use

• Demand Transparency: Opt for AI tools that are transparent about their training processes, data sources, and decision-making algorithms.
• Promote Ethical Use: Champion the ethical development and application of AI within your organization and the wider community.

DATA AS A DIFFERENTIATOR

You may or may not have heard about the REDDIT deal where they are selling data aka content or users posts and interactions to a company, which we now know is Google for $60 million a year.

So basically if you posted on Reddit, your posts, info, etc, will be used as part of training data for Googles AI machines.

So what does this mean for you in the GTM world?

AI technology, fueled by unique data, can significantly transform a Go-to-Market (GTM) strategy into a formidable competitive edge. The magic of AI lies in its ability to digest, understand, and act on data in ways that can unlock new levels of personalization, efficiency, and insight. Here’s a general look at how AI can serve as a differentiator, along with examples of data types that GTM teams can leverage:

AI as a Differentiator

1. Personalization: AI can tailor experiences, products, and services to individual customer preferences and behaviors by analyzing data on past interactions, purchases, and engagement. This level of personalization can elevate customer satisfaction and loyalty.
2. Predictive Analytics: By examining trends, purchase history, and customer interactions, AI can forecast future consumer behavior and market trends, allowing companies to stay ahead of demand curves and adapt strategies proactively.
3. Operational Efficiency: AI can streamline operations, from automating customer service through chatbots to optimizing supply chains, reducing costs, and enhancing service delivery.
4. Innovative Product Development: Insights gained from AI analyses can inform the development of new products or services, ensuring they meet a real, identified customer need, often before the customer even recognizes the need themselves.

Examples of Data for GTM Teams

1. Customer Interaction Data: Every touchpoint with a customer, from website visits to customer service calls, is rich with insights. AI can analyze this data to improve user experience, anticipate customer needs, and personalize communication.
2. Purchase History and Preferences: Analyzing past purchases and stated preferences allows AI to make precise recommendations, enhancing cross-selling and upselling opportunities.
3. Social Media and Sentiment Analysis: Data from social media platforms can provide insights into customer sentiments, trends, and brand perception. AI can monitor and analyze this data in real-time, allowing brands to adjust messaging or address issues promptly.
4. Market and Competitor Analysis: Data on market trends and competitor activity can be used by AI to identify opportunities for differentiation or areas where a company can lead the market.
5. Operational Data: This includes data on supply chain logistics, inventory levels, and sales performance. AI can optimize these areas for efficiency, reducing waste and improving profitability.

Real-World Applications

• A retail company uses AI to analyze customer purchase history and online behavior, creating personalized shopping experiences and recommendations for each visitor to their site.
• A tech firm employs sentiment analysis on social media data to gauge customer reactions to a new product launch in real-time, allowing them to quickly address concerns and adjust their marketing strategy.
• A healthcare provider leverages AI to predict patient health trends based on historical health data, improving patient outcomes through personalized care plans.

Last but not least, next week, we will be releasing the GTM AI Tools Demo Library for your use. Over the last few months, I have been collecting a plethora, (Yes a plethora) of AI tools and demos that I want to showcase for you all to view, understand, and see what is possible.

Will be releasing it next week, so you will be the first to know. I believe I am up to 30+ tools of various kinds and use cases across GTM teams.

As always, please give me feedback on what you want to hear more of in the newsletter and podcast, I look forward to more next week!