3/12/25: Reuben Bailon AI Adoption Interview, Manus AI, OpenAI Agents, AI Dulls the Brain? And Sesame AI Voice
Another week my friends and as per usual, SO MUCH is going on. I had the pleasure of attending HumanX and will have a more thorough review next week after the event is done, but am so excited to dive into today.
As per usual, a little bit about us:
AI Business Network Our new community for Business leaders from all walks of business, HR, GTM, Finance, Legal, Revenue, etc. We focus on community, live features and demos, special guest trainers and speakers. This community is for you if you want to know how to use AI as a business leader, both large Fortune 1000 strategies and small businesses.
Apply as a member at AI Business Network
GTM AI Academy Which is on demand online content and community for individuals and teams of how to use AI to make real impact. You have options of attending live events or just accessing the hundreds of modules as a member.
Become a member of the GTM AI Academy
This week we have the following:
Let’s get into the podcast!
You can go to Youtube, Apple, Spotify as well as a whole other host of locations to hear the podcast or see the video interview.
Art & Science of AI IMPLEMENTATION with Daniel Bailon
In the ever-evolving landscape of artificial intelligence, it’s crucial to navigate how its integration can enhance business operations. During a fascinating episode of the GTM AI Podcast, I had the pleasure of conversing with Reuben Bailon, an expert in the AI field. Our discussion delved into the world of AI implementation, highlighting the indispensable steps necessary for leveraging AI successfully.
Understanding the Key to Successful AI Implementation
When it comes to incorporating AI into your business, success starts with defining the problem. As Reuben emphasized, the journey begins by identifying the 'why' behind the issue at hand. The first step entails really comprehending the core of the problem, including the underlying factors contributing to it. This understanding paved the way for the rest of the process.
Step 1: Clearly Define the Problem
"The right problem definition is crucial," Reuben noted. “Understanding the why behind the problem helps in designing an effective solution.”
Step 2: Align with Business Goals and Objectives
Alignment is key. It's vital to ensure that any AI project aligns seamlessly with overarching business goals and objectives. Without this alignment, the likelihood of project failure increases significantly.
Step 3: Define Success Criteria and ROI
Having a clear success criterion is essential. “Defining what success looks like and understanding how to measure the current state against future goals is crucial,” Reuben explained. It’s about setting realistic benchmarks against which progress and ROI can be measured.
Step 4: Create Stakeholder Alignment
It’s imperative to create stakeholder alignment intentionally. This requires effort to ensure everyone involved understands the project’s value and the role they play. “Stakeholder alignment won't happen organically; it needs to be intentional,” Reuben stressed.
Step 5: Set Phases and Milestones
Implementing phased plans allows for more effective project execution. By setting clear phases and milestones, organizations can track progress and ensure successful implementation over time.
Adopting a Strategic Mindset
One aspect of AI integration Reuben and I found fascinating is the shift in mindset required. Adopting AI isn’t just about the technology itself but also about embracing change. It's important to focus on shifting roles within the organization and ensuring each team member understands the benefits of adoption on a personal level.
“We find it critical to involve users in the adoption plan and empower them to help guide the process,” Reuben shared. It's not just a top-down approach but an inclusive one that ensures longevity and success.
Future Projections: AI's Transformative Power
Looking ahead, the potential of AI to transform business operations is immense. Reuben predicts a future where AI doesn’t merely supplement human efforts but becomes an integral part of the business strategy, working hand-in-hand with teams to augment productivity and efficiency.
AI is progressively moving from augmenting repetitive tasks to reshaping roles entirely. This transformative power allows individuals to focus more on creative and strategic endeavors, bringing greater fulfillment and efficiency to their work.
Emphasizing the Human Experience
What stood out to me was the human-centric approach Reuben advocated for. He underscored the significance of improving the employee experience through AI, emphasizing that enhancing workplace satisfaction leads to reduced attrition, increased productivity, and a thriving organizational culture.
As we wrapped up our conversation, I couldn’t help but reflect on the boundless possibilities AI presents when integrated thoughtfully into businesses. The future is not just about surviving with AI – it's about thriving and embracing the art of work.
Stay tuned for more episodes as we continue exploring the intersection of AI and business. If you’re looking to keep the dialogue going or have any questions, feel free to connect with Reuben on LinkedIn.
Incorporating AI is an exhilarating venture, transforming not only how businesses operate but also how individuals experience their work. Let's embrace the exciting opportunities together.
Manus AI: The Future of Autonomous AI Agents and Its Impact on GTM Teams
Artificial Intelligence is moving beyond simple chatbots and assistants—we are entering the era of fully autonomous AI agents. Manus AI, an advanced autonomous agent developed by the Chinese AI startup Monica, is one of the most promising advancements in this space. Unlike traditional AI models that require human prompts, Manus operates independently, initiating and executing complex tasks across industries without ongoing human supervision.
For Go-To-Market (GTM) teams, this represents a massive shift in automation, intelligence, and decision-making. AI is no longer just a support tool—it’s becoming an active team member, capable of analyzing data, making strategic recommendations, and even autonomously executing workflows.
What Is Manus AI?
Manus AI is a next-generation autonomous AI agent that is designed to:
✅ Analyze, decide, and execute without constant human guidance.
✅ Personalize insights and automate workflows based on user behavior.
✅ Work in real-time and in the background, continuously improving its efficiency.
Unlike traditional AI copilots like OpenAI’s Operator or Claude Code, which require continuous user interaction, Manus proactively identifies tasks, gathers necessary information, and executes solutions autonomously.
🚀 For GTM teams, this means AI that doesn’t just suggest actions—it takes actions.
1. Key Features of Manus AI and Why It Matters
🔹 Full Autonomy: AI That Works Without Human Prompts
One of the most revolutionary aspects of Manus AI is its ability to function independently. Traditional AI assistants react to human prompts—Manus anticipates needs, identifies tasks, and executes solutions without explicit commands.
📌 Example:
In a sales environment, Manus could:
Analyze CRM data to find dormant leads.
Generate personalized follow-up emails.
Schedule meetings automatically if engagement is detected.
Run predictive analytics to determine which deals are most likely to close.
🚀 GTM Impact:
Eliminates manual lead prioritization, follow-ups, and scheduling.
Enables sales teams to focus on closing deals rather than operational tasks.
Improves efficiency by running in the background without human intervention.
🔹 Intelligent Decision-Making and Personalization
Manus AI is not just about automation—it’s contextually aware and makes intelligent decisions based on real-time data, user behavior, and company-specific goals.
📌 Example:
In a marketing campaign, Manus could:
Analyze past campaign performance and A/B test results.
Identify the best-performing audience segments.
Automatically adjust ad spending across platforms based on real-time engagement.
Generate personalized content variations for different personas.
🚀 GTM Impact:
Removes the need for manual campaign adjustments.
Automatically optimizes ROI by reallocating marketing spend in real-time.
Ensures hyper-personalized messaging without human oversight.
🔹 Multi-Domain Performance: Sales, Marketing, and Customer Success
Manus AI is designed to operate across multiple GTM functions, making it one of the first truly cross-functional AI agents.
📌 Example Use Cases for GTM Teams:
1️⃣ Sales:
Auto-generates competitive battlecards in real time based on sales calls.
Monitors pipeline health and flags at-risk deals before they stall.
Conducts sentiment analysis on prospect interactions to improve win rates.
2️⃣ Marketing:
Conducts real-time competitive analysis and adjusts SEO and content strategies.
Automates LinkedIn and email outreach based on prospect engagement.
Creates dynamic customer segments based on behavioral data.
3️⃣ Customer Success:
Identifies churn risks early and automatically triggers proactive engagement.
Monitors support ticket trends and suggests self-service solutions.
Improves onboarding workflows by adapting learning materials based on user engagement.
🚀 GTM Impact:
AI is no longer limited to a single function—it integrates across the entire GTM motion.
Removes friction between sales, marketing, and customer success.
Ensures data consistency and optimizes GTM workflows at scale.
2. How GTM Leaders Should Prepare for Autonomous AI Agents
🔹 Rethinking GTM Strategy: AI as a Core Growth Engine
With Manus AI and similar autonomous agents, AI is evolving from being a support tool to an active GTM contributor.
📌 What GTM Leaders Should Do:
✅ Shift from AI as a task assistant to AI as an autonomous strategist.
✅ Integrate AI directly into pipeline management, marketing execution, and retention workflows.
✅ Train teams to collaborate with AI, ensuring human-AI partnership rather than competition.
🚀 Strategic Impact:
GTM teams that adapt to AI-driven decision-making will outcompete those stuck in manual execution.
AI-powered workflows will increase deal velocity, reduce churn, and improve forecasting accuracy.
🔹 Measuring ROI: How to Quantify AI-Driven GTM Impact
Adopting autonomous AI agents like Manus AI requires a new approach to measuring GTM success.
📌 How to Measure AI’s Impact:
✅ Lead Conversion Lift: Compare AI-optimized sales outreach vs. human-managed pipelines.
✅ Marketing ROI: Track campaign performance with and without AI-driven optimizations.
✅ Customer Retention: Measure churn rates before and after AI-driven customer success workflows.
✅ Revenue Acceleration: Compare deal velocity in AI-assisted sales vs. traditional workflows.
🚀 GTM Impact:
AI is not just about automation—it’s about revenue acceleration, efficiency, and improved decision-making.
Teams that track AI performance effectively will refine and scale AI-driven GTM motions faster.
3. The Future of AI in GTM: Where Manus AI Leads the Market
🔹 The Next Evolution: AI That Sells, Markets, and Supports Autonomously
Manus AI represents the next evolution of AI adoption, moving beyond prompt-driven AI to fully autonomous AI teams.
📌 Where AI is Headed Next:
✅ AI agents that autonomously book meetings, generate contracts, and close deals.
✅ AI-driven customer engagement that replaces traditional customer success teams.
✅ End-to-end AI-powered GTM workflows with minimal human intervention.
🚀 Why This Matters:
GTM professionals who embrace AI as a full-fledged team member will outperform those who see it as a tool.
Companies investing in AI-driven automation will scale faster with leaner teams.
Autonomous AI agents will soon become standard in RevOps, Sales, and Marketing.
Final Thoughts: Why GTM Leaders Should Pay Attention to Manus AI
🔹 The Shift from AI Assistants to Autonomous AI
Manus AI represents a paradigm shift—it’s not just an AI that helps, but an AI that executes independently. This is a massive development for GTM teams that rely on automation, data-driven decision-making, and efficiency at scale.
📌 What GTM Teams Should Do Now:
✅ Assess which GTM workflows can be fully automated by AI.
✅ Train teams to collaborate with AI agents rather than seeing AI as a competitor.
✅ Experiment with AI-driven revenue growth strategies to stay ahead of the market.
🚀 The Bottom Line:
Manus AI is not just another AI assistant—it’s an autonomous revenue-driving force that reshapes how GTM teams sell, market, and engage customers. The companies that adopt AI-first GTM strategies will dominate, while those who wait risk being left behind in an AI-driven economy. 🚀
OpenAI’s Next Big Bet: High-Priced AI Agents and Enterprise Adoption – What It Means for GTM Leaders
OpenAI is making a massive shift toward AI agents, positioning them as the next phase of AI-driven productivity for businesses. With its agent-building platform and its plans to charge up to $20,000 per month for PhD-level AI agents, OpenAI is signaling that 2025 will be the year of AI agents for enterprises.
For Go-To-Market (GTM) professionals, this marks a fundamental shift in AI adoption, pricing models, and enterprise AI integration strategies. AI is no longer just an augmentation tool—it is becoming a fully autonomous worker, capable of performing specialized, high-value tasks at scale.
This article explores:
✅ What OpenAI’s AI agent strategy means for enterprises.
✅ The implications of AI pricing models for businesses and GTM teams.
✅ How businesses should rethink AI-driven GTM workflows and adoption.
1. OpenAI’s AI Agent Strategy: The Next Wave of Enterprise AI
OpenAI’s latest announcements reveal a clear enterprise AI roadmap, focused on making autonomous AI agents the new productivity standard.
AI Agents for Businesses – OpenAI’s new agent-building platform allows companies to develop their own AI agents for tasks like financial analysis, legal research, customer service, and workflow automation.
Tiered AI Pricing Models – OpenAI plans to sell high-end AI agents at:
$2,000 per month for knowledge work.
$10,000 per month for software development.
$20,000 per month for advanced AI capable of PhD-level research.
AI Revenue Expansion – OpenAI expects 20-25% of its revenue to come from AI agents, signaling that this is a core growth area for the company.
🚀 For enterprises, this means AI is moving from being an “assistive tool” to a “full-time autonomous employee.”
Brad Lightcap, OpenAI’s Chief Operating Officer, put it plainly:
“Now, people can engage with agents. Those agents can go off and actually reference files, they can search the web, they can use computers.”
With enterprise AI adoption accelerating, OpenAI is positioning itself as the go-to provider for autonomous AI workforce solutions.
2. AI Pricing Strategy: Justifying $20,000 Per Month Agents
🔹 Why Is OpenAI Charging So Much for AI Agents?
AI pricing models have traditionally been based on per-user subscriptions, API usage, or bundled SaaS packages (like OpenAI’s integration into Microsoft’s Office 365). OpenAI’s new high-priced AI agent model represents a shift to AI as a full-service worker rather than a simple software tool.
📌 How OpenAI Justifies the Cost:
Enterprise-Grade Performance: AI agents are expected to replace high-cost knowledge workers—for example, a $200K-per-year software engineer or a PhD-level research analyst.
AI That Performs Specialized Work: OpenAI claims its high-end AI agents will be able to run advanced research, analyze legal documents, and even help solve scientific problems.
Agent-Led Business Workflows: AI agents will be able to execute complex business processes without direct human supervision, automating tasks like financial modeling, sales lead ranking, and even market research.
📌 Examples of AI Agent Use Cases
Lawrence Livermore National Laboratory is already using OpenAI’s AI models to assist with nuclear fusion research.
Stripe built an AI agent to read sales-tracking spreadsheets, generate invoices, and send them to customers.
Box integrated OpenAI’s agents to help businesses build AI-powered workflows on top of their document storage.
🚀 What This Means for GTM Teams:
High-priced AI agents will drive enterprise AI adoption, but only if they deliver clear business value.
GTM leaders must understand how AI can replace or enhance knowledge work across sales, marketing, and customer success.
The cost of AI will be measured against business outcomes—companies will need clear AI ROI frameworks.
3. AI Agents as a GTM Disruptor: What’s Next for Enterprise AI Adoption?
🔹 The Shift to Custom AI Agents
AI adoption has moved from general-purpose chatbots (like ChatGPT) to industry-specific AI solutions. OpenAI’s agent-building platform enables companies to:
✅ Develop AI agents customized to their business processes.
✅ Deploy AI workers that automate high-value tasks.
✅ Integrate AI-driven decision-making into GTM workflows.
According to Aaron Levie, CEO of Box:
“Every single enterprise conversation I’m on, they’re now talking about agents. Twelve months ago, only about three to five percent of our customers could even process what we were talking about.”
This shift suggests that AI agents will become as standard as CRM or ERP systems in GTM operations.
🔹 The AI Agent Economy is Here
AI agents will:
✅ Run entire marketing campaigns autonomously.
✅ Handle sales prospecting, lead qualification, and follow-ups.
✅ Execute real-time competitive intelligence analysis.
✅ Manage customer onboarding, engagement, and renewals.
🚀 What This Means for GTM Teams:
GTM professionals must rethink AI integration—AI agents will replace some traditional roles while augmenting others.
Sales and marketing automation will be AI-driven, reducing the need for manual data entry, reporting, and pipeline analysis.
The ability to train and manage AI agents will become a critical GTM skill.
4. GTM Strategy: How to Leverage OpenAI’s AI Agents for Competitive Advantage
🔹 How GTM Teams Should Prepare for AI Agents
📌 Sales Teams
Implement AI-driven sales automation for lead qualification and pipeline forecasting.
Train AI agents to handle follow-ups, emails, and initial prospect interactions.
📌 Marketing Teams
Use AI agents for real-time content optimization and multi-channel campaign execution.
Automate A/B testing, audience segmentation, and ad targeting with AI workflows.
📌 Customer Success & RevOps
Deploy AI agents for customer onboarding, proactive retention, and churn analysis.
Automate customer feedback analysis and support ticket resolution.
5. Final Thoughts: The AI Agent Revolution is Here—Are GTM Teams Ready?
OpenAI is fundamentally reshaping how AI is deployed in the enterprise. With custom AI agents, enterprise-grade automation, and high-end pricing models, OpenAI is moving beyond simple AI applications into full-scale AI workforce solutions.
🔹 What GTM Leaders Need to Do Next
1️⃣ Evaluate AI Agent Use Cases – Identify which GTM workflows can be fully automated with AI.
2️⃣ Assess ROI for AI Investments – Determine if AI-driven sales, marketing, and customer success automation justifies the cost.
3️⃣ Train Teams on AI Management – Build expertise in managing and integrating AI-driven decision-making.
4️⃣ Stay Ahead of AI Cost Trends – Watch for price declines in AI adoption to scale AI-driven GTM operations.
🚀 The Bottom Line:
The AI agent economy is here, and GTM teams must adapt quickly to stay ahead. AI isn’t just an efficiency tool—it’s becoming a core business driver, capable of replacing traditional workflows and accelerating revenue growth at unprecedented speeds.
GTM leaders who embrace AI agents early will dominate, while those who hesitate risk being left behind in the AI-first enterprise era. 🚀
Microsoft Study Finds AI Use May Dull the brain
A new Microsoft and Carnegie Mellon University study suggests that over-reliance on generative AI may dull cognitive abilities, particularly in critical thinking and problem-solving. The research, set to be presented at the 2025 ACM CHI Conference, analyzed 319 knowledge workers across industries, including teachers, forex traders, marketers, and analysts, who regularly use AI tools like ChatGPT and DALL-E.
The key takeaway? Workers engaged in critical thinking only about 60% of the time when using AI-assisted workflows. The more confidence they had in AI’s ability to complete a task, the less effort they put into evaluating or refining the AI-generated results.
This raises an important question for GTM professionals who rely on AI-driven automation:
Are AI-powered efficiencies coming at the cost of deep strategic thinking?
Is AI replacing, rather than augmenting, human expertise?
How can leaders balance AI adoption while maintaining critical skills within their teams?
The Risk of Cognitive Atrophy in AI-Driven Workflows
The study found that workers often defaulted to AI for tasks they deemed “trivial” or outside their core expertise. In some cases, they turned to AI because they weren’t given enough time to complete tasks manually—a clear signal that AI is being used to patch operational inefficiencies rather than enhance strategic execution.
Most strikingly, a nurse used generative AI to create an educational pamphlet for newly diagnosed diabetics, raising concerns about blind trust in AI-generated content. While verification is possible, a lack of deep engagement with AI’s outputs could introduce risks in high-stakes industries.
What This Means for GTM Leaders and AI-Driven Teams
For sales, marketing, and revenue operations, AI is a powerful enabler, but this study suggests that:
AI should be used to enhance—not replace—critical GTM decision-making. Teams that rely too heavily on AI for strategy, messaging, and customer interactions risk losing creative problem-solving capabilities.
Blind trust in AI-driven insights could lead to bad decision-making. AI-powered sales forecasting, lead scoring, and customer segmentation are helpful, but they must be continuously evaluated against real-world performance.
Leaders must encourage active reflection on AI-generated outputs. If teams are simply accepting AI’s recommendations without scrutiny, they could be misled by hallucinations, outdated data, or irrelevant insights.
Balancing AI Efficiency with Human Expertise
The irony, as the researchers noted, is that automation often removes routine opportunities to practice judgment, leaving workers less prepared when critical exceptions arise.
To prevent this, GTM leaders should:
✅ Encourage teams to validate AI-generated insights rather than accepting them at face value.
✅ Train employees to use AI as a co-pilot, not an autopilot, ensuring that AI enhances strategic thinking rather than replacing it.
✅ Balance AI-driven automation with human creativity, particularly in content generation, messaging, and sales engagement.
Final Thoughts: AI Is a Tool, Not a Replacement for Strategic Thinking
AI is reshaping knowledge work, but this study highlights the potential downside of unchecked automation. For GTM professionals, the key is to embrace AI’s efficiency while maintaining human oversight and strategic judgment. AI should be a partner in decision-making, not the decision-maker itself. 🚀
GTM AI Tool of the Week: Sesame AI Voice
Introduction: Breaking the Emotional Barrier in AI Voice
For years, AI-driven voice assistants have failed to capture the emotional depth and conversational flow that make human interactions feel real. Sesame AI is aiming to change that with its new Conversational Speech Model (CSM)—a breakthrough in voice presence that enhances AI’s ability to engage, understand, and respond with natural expressivity.
For Go-To-Market (GTM) professionals, this development could redefine customer interactions, sales enablement, and AI-driven engagement strategies. With voice AI becoming a critical touchpoint in customer experience (CX), sales, and retention, GTM leaders must understand how advanced AI speech technology can drive real business outcomes.
1. Why This Matters: The Uncanny Valley Problem in AI Voice
Traditional AI voice assistants (think Alexa, Siri, and Google Assistant) lack true emotional intelligence—their flat tone, robotic pacing, and limited conversational awareness create frustrating user experiences that don’t translate into long-term engagement.
Sesame’s CSM aims to solve this by introducing:
Emotional intelligence – AI voices that adapt to user sentiment.
Conversational dynamics – Realistic pauses, interruptions, and emphasis.
Contextual awareness – Adjusting tone based on conversation history.
Consistent personality – AI that maintains a coherent speaking style.
For customer-facing GTM teams, this is a critical evolution. Customers are becoming less willing to engage with lifeless, transactional AI interactions—they expect AI to feel human, provide real value, and integrate seamlessly into their workflows.
🚀 What This Means for GTM:
AI-powered sales assistants will sound and feel more authentic in prospect interactions.
AI-driven customer service will be more engaging, reducing churn and increasing satisfaction.
AI in marketing personalization will improve response rates and brand affinity.
2. The Conversational Speech Model (CSM): How It Works
🔹 The Core Breakthrough: Real-Time Contextual Speech Generation
CSM is a multimodal model that processes both text and speech, allowing AI to generate context-aware, emotionally nuanced responses. Traditional text-to-speech (TTS) models fail because they generate audio directly from text, missing the broader conversation dynamics.
CSM fixes this by leveraging:
Semantic Tokens: Encoding meaning, phonetics, and linguistic structures.
Acoustic Tokens: Capturing speaker identity, intonation, and timbre.
Autoregressive Transformers: Generating speech that adapts based on prior conversation context.
🔹 Real-Time Conversational Awareness
Unlike previous models, CSM doesn’t just read a script—it actually “thinks” about the best way to express something before speaking. This is critical for business applications where voice AI needs to adjust based on customer sentiment, urgency, and context.
📌 Example:
In customer service, a frustrated user calling about a billing issue would hear a calm, reassuring voice rather than a neutral, robotic response.
In sales, an AI assistant following up on a demo could sound confident and enthusiastic, reinforcing the product’s value.
🚀 What This Means for GTM:
Better AI-driven customer conversations → Increased trust and loyalty.
More natural AI sales assistants → Higher engagement and conversion rates.
AI that understands context in CX → More accurate problem resolution.
3. GTM Use Cases: How CSM Can Revolutionize AI-Powered Customer Interaction
🔹 1. AI-Powered Sales Assistants with Human-Like Presence
AI-driven sales engagement tools like Conversica, Exceed.ai, and Empler AI are already automating lead qualification and follow-ups. But the missing piece has been natural voice interactions.
📌 With CSM, AI sales assistants can:
✅ Engage prospects with warmth and confidence, improving conversion rates.
✅ Use real-time sentiment analysis to adapt pitch, tone, and urgency.
✅ Personalize follow-ups with contextual recall, reducing drop-off.
🚀 GTM Impact:
AI-driven outbound sales calls will feel human, leading to higher appointment rates.
Conversational AI will be more effective in handling objections, improving pipeline velocity.
🔹 2. AI in Customer Support: Moving Beyond Chatbots
AI voice assistants in customer support have often failed due to rigid, transactional dialogue that leaves customers frustrated. With CSM-powered AI, voice bots can handle complex inquiries with empathy and adaptability.
📌 With CSM, AI-driven support can:
✅ Reduce frustration by responding naturally to customer tone and pacing.
✅ Handle escalations smoothly, knowing when to transfer to a human rep.
✅ Provide personalized, context-aware responses instead of scripted ones.
🚀 GTM Impact:
Higher customer satisfaction (CSAT) scores and lower churn.
Reduced call center costs by improving AI-first resolution rates.
Stronger brand reputation as AI voice support feels genuinely helpful.
🔹 3. AI-Powered Marketing & Personalized Brand Voice
Marketers are investing heavily in AI-driven personalization, but most AI-generated content still lacks the emotional nuance that builds real connections.
📌 With CSM, AI-powered marketing can:
✅ Deliver brand-aligned voiceovers for video content that sound human.
✅ Create emotionally engaging ad scripts that match audience sentiment.
✅ Enhance AI-generated social media responses with dynamic, context-aware tone shifts.
🚀 GTM Impact:
More engaging AI-powered content = higher conversion rates.
AI marketing chatbots with voice capabilities will feel more human.
Dynamic customer interactions will lead to stronger brand affinity.
4. Measuring ROI: How GTM Teams Can Track AI Voice Success
For AI-powered voice solutions to prove business value, GTM teams must track key performance indicators (KPIs) related to customer engagement, sales performance, and operational efficiency.
📌 Key AI Voice Metrics to Track:
✅ Sales Impact: Increased meeting bookings & pipeline velocity from AI-driven outbound calls.
✅ Customer Retention: Higher CSAT & Net Promoter Scores (NPS) from AI-powered support.
✅ Marketing Engagement: Improved ad performance & personalization response rates from voice-driven AI marketing.
✅ Operational Efficiency: Reduced human agent workload & call center costs via AI automation.
🚀 GTM Leaders Should Ask:
How is AI voice impacting customer sentiment and engagement?
Are AI-driven interactions improving conversion rates?
What are the measurable cost savings from AI-enhanced automation?
5. The Future of AI Voice in GTM: Beyond the Uncanny Valley
Sesame’s Conversational Speech Model (CSM) is a major breakthrough, but this is just the beginning. Future AI voice models will:
✅ Support multilingual, culturally adaptive conversations.
✅ Integrate with AI-driven video content creation.
✅ Enable real-time voice assistants for complex, high-stakes GTM use cases.
As AI voice technology continues to cross the uncanny valley, companies that invest early in AI-driven conversational engagement will have a huge competitive advantage in sales, marketing, and customer experience.
🚀 Final Takeaway for GTM Leaders:
AI voice is becoming a critical business tool—early adopters will dominate.
GTM teams must integrate AI-powered conversational speech into sales, marketing, and customer support.
The next generation of AI-driven customer interaction will be voice-first, not text-first.
💡 The companies that master AI voice engagement will define the future of GTM success. 🚀