7/3/25: The End of the Traditional Sales Cycle: Inside the AI Transformation Reshaping How Companies Buy and Sell
This is sponsored by the AI Business Network and GTM AI Academy
Today we get to deep dive into some REALLY good AI and GTM/business topics and specifically what it means for YOU.
We have an amazing guest and some gold nuggets and tools for you to enjoy.
This week we DEEP dive into 10+ articles and an amazing podcast with Brannon CEO of Peel AI
PS here is the Interactive GTM AI Transformation Assessment
Here is 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.
See a test run with one of Peels products here:
"The AI Agent Revolution: How Peel's Voice AI is Killing the Traditional Sales Demo (And Why That's a Good Thing)"
The Genesis Story
Brannon Santos brings a unique perspective as a founder - he's not a technologist who stumbled into sales problems, but a seasoned sales leader who deliberately chose entrepreneurship. His background includes choosing his college specifically for its entrepreneurship program and strategically entering sales to understand how businesses operate from the inside out. This sales-first DNA permeates Peel's entire approach.
The Core Problem Peel Solves
The conversation reveals a painful truth about modern B2B sales: the process is riddled with friction. Santos shares a perfect example - a CMO who hasn't spoken to a salesperson in 15 years because her calendar is perpetually full, yet she researches solutions at 11 PM when sales teams are offline. This temporal mismatch between when buyers want to engage and when sellers are available represents billions in lost opportunity.
The Voice AI Revolution
Peel positions itself as a "voice AI layer" that creates intelligent, conversational agents for brands. But this isn't your typical chatbot - these agents can:
Conduct full discovery calls in 5 minutes instead of 30-45 minutes
Generate detailed "tear sheets" formatted to match specific sales methodologies
Create automated "Peel Rooms" (similar to deal rooms) with all conversation insights
Enable stakeholders to have the same conversation asynchronously
The Data Goldmine
One of the most compelling use cases Santos demonstrates is Peel's ability to conduct mass qualitative research. A marketing agency used Peel to interview 38 sales professionals about lead quality, creating a study called "Do My Leads Really Suck?" What traditionally costs thousands of dollars and takes months can now be done in a day, with results that update in real-time as more participants engage.
The Bow Tie Framework Implementation
Santos reveals how Peel uses the Winning by Design bow tie framework, allowing companies to deploy conversational agents at every stage of the customer journey - from awareness through renewal. This strategic approach ensures conversations are contextually appropriate whether someone is just discovering the brand or negotiating renewal terms.
Best Practices for AI Conversations
The discussion unveils key insights for training conversational AI:
Start with easy, closed-ended questions
Include personal questions early (people enjoy talking about themselves)
Focus on present challenges and near-term goals
Build dynamically based on responses
The Future Vision
Santos predicts that within a year, the entire enterprise sales cycle could theoretically be handled by AI agents. He envisions a world of "agent-to-agent" commerce where your personal AI assistant negotiates with vendor AI assistants on your behalf. While acknowledging human relationship-building will remain important, he sees AI eliminating the mechanical, repetitive aspects of sales.
3. Key Takeaways for Sales & Marketing Leaders:
Immediate Action Items:
Audit Your Friction Points: Map out every step in your buyer's journey and identify where prospects have to wait, repeat information, or jump through hoops. Each friction point is a potential AI automation opportunity.
Rethink Discovery: If you're still doing 30-minute discovery calls for 5 minutes of actual information gathering, you're wasting everyone's time. Consider how AI-driven discovery could pre-qualify and prepare prospects before human interaction.
24/7 Availability: Your buyers are researching at 11 PM. If you're only available 9-5, you're missing significant engagement opportunities. Voice AI doesn't sleep.
Strategic Insights:
Data Collection Reimagined: Every website visitor could be a qualitative research participant. Instead of static forms, conversational AI can gather nuanced insights at scale.
The New Sales Tech Stack: The conversation reveals integration opportunities between conversational AI (Peel), revenue intelligence (Momentum), and other tools. The future isn't about individual tools but orchestrated AI agents working together.
From Surveys to Conversations: Traditional surveys are dead. Dynamic, AI-driven conversations that can dig deeper based on responses provide 10x better insights than static questionnaires.
Competitive Advantages:
Speed to Insight: What takes weeks with traditional research methods can happen in hours with conversational AI. First-mover advantage goes to those who can gather and act on customer insights fastest.
Personalization at Scale: Each conversation can be dynamically personalized while maintaining consistency in data collection - something impossible with human interviewers.
Living Documents: Studies and insights that update in real-time as more data comes in represent a paradigm shift from static reports to dynamic intelligence.
Critical Considerations:
Prepare Your Team: AEs need training for prospects who arrive at stage 4 instead of stage 1. The entire sales motion changes when AI handles early-stage activities.
Quality Over Quantity: While AI can engage thousands simultaneously, the focus should be on meaningful conversations that provide genuine value, not just volume.
The Human Touch: Santos emphasizes AI handles the mechanical aspects while humans focus on relationship-building, deal strategy, and complex problem-solving. The goal is augmentation, not replacement.
Future-Proofing Your Approach:
The conversation makes clear that the question isn't whether AI will transform B2B sales, but how quickly you'll adapt. Companies still forcing buyers through traditional SDR > AE > Demo sequences while competitors offer instant, intelligent engagement will find themselves at a severe disadvantage. The time to experiment with conversational AI isn't next year - it's now.
The 5 AI Disruptions Every GTM Leader Must Understand to Drive Success
Referenced Links Guide:
From Demos to Deals: Insights for Building in Enterprise AI - a16z
The Hidden Scaling Cliff that’s About to Break Your Agent Rollouts
Intro
This week’s articles delve into the rapid evolution and profound shifts occurring in the AI and enterprise sectors. The common thread that ties these pieces together is the increasing integration of AI into the fabric of daily business operations and the ongoing challenges and opportunities it presents for Go-To-Market (GTM) leaders.
AI Adoption in the Enterprise: Accelerating Faster Than Ever
From Genspark’s astounding $36 million ARR in just 45 days to the rapid growth of AI companies discussed in a16z’s insights, AI is accelerating at an unprecedented pace. Genspark’s ability to reach such impressive financial milestones with only a small team and without spending on paid marketing exemplifies the power of word-of-mouth and product innovation in today’s AI landscape.
AI's impact is shifting from a tool for enhancing human productivity to a critical driver of enterprise strategy. Companies are actively seeking AI solutions to streamline operations and reduce labor costs, creating a competitive landscape where speed and efficiency are paramount.
Scaling Challenges: The Need for More Sophisticated AI Agents
However, scaling AI isn’t without its challenges. As discussed in VentureBeat’s article on the “scaling cliff” for AI agents, building AI agents isn’t as straightforward as traditional software development. Agents, by nature, are non-deterministic, adaptive, and outcome-driven, requiring a completely different approach to product development and quality assurance.
AI solutions, particularly in enterprise environments, must not only be reliable but also adaptable to various business contexts and workflows. This is especially true in complex domains like finance and healthcare, where mistakes or inaccuracies can have far-reaching consequences.
AI’s Increasing Role in Knowledge Work and Job Displacement
Additionally, a growing concern around job displacement is echoed throughout the week’s articles. CEOs like Andy Jassy of Amazon and Tobi Lütke of Shopify are openly preparing their workforces for AI-driven transformations, signaling that automation will soon replace many roles traditionally filled by humans. These conversations point to the reality that AI is already being used to replace entire job categories, from financial analysts to customer service representatives.
Despite this, AI adoption at the frontline level remains a challenge. As noted by BCG’s survey, leaders are embracing AI, yet frontline employees are still slow to adopt these tools, leading to a disconnect between executive ambitions and operational reality. This gap must be addressed for AI to reach its full potential.
AI Tools for Collaboration and Productivity: A Step Forward
On a more positive note, ChatGPT’s latest update on “Projects” offers a promising development for enhancing collaboration and productivity. By allowing users to create structured workspaces for research, writing, and planning, this feature moves ChatGPT closer to becoming a comprehensive productivity hub. The integration of voice support, better memory management, and the ability to organize and track multiple related tasks across devices signifies a powerful leap in how AI can support long-term projects.
Simultaneously, Gemini for Education, as highlighted in Google’s announcement, is tailoring its AI capabilities to the educational sector, expanding access to AI-powered tools for both educators and students. This could be a model for AI’s widespread application across various industries, driving the need for tools that are both powerful and user-friendly.
What GTM Leaders AND Professionals Should Know and Do
As AI continues to evolve and expand across industries, GTM leaders and professionals are at a pivotal crossroads. The decisions made now will define how AI integrates into business strategies and operations moving forward. Here’s a breakdown of the most critical insights and actionable steps for GTM leaders:
1. Accelerate AI Integration Across the Business
AI is no longer a luxury or a nice-to-have feature—it is becoming a strategic imperative. The fast adoption by startups like Genspark and enterprise-level decisions from CEOs like Andy Jassy of Amazon make it clear that AI drives growth when adopted quickly and integrated across all business functions.
For GTM professionals, the first action is to prioritize AI in every aspect of your sales, marketing, and customer success strategies. This doesn’t just mean adopting AI tools but weaving AI into the core fabric of the business to ensure that your organization stays ahead of competitors.
Take proactive steps to integrate AI into customer-facing roles like sales and customer service to improve efficiency and personalize interactions.
Automate repetitive tasks: Invest in AI systems that can handle routine sales admin work, allowing your teams to focus on building relationships and solving higher-order problems.
Adopting AI quickly creates a competitive edge—those who move first will benefit from faster go-to-market timelines, improved customer experiences, and a more efficient workflow.
2. Address the Frontline Adoption Gap
While leaders and executives are embracing AI, frontline employees remain behind. The adoption gap between leadership and employees is an urgent issue that needs to be addressed. BCG’s survey and Anthropic’s findings show that while executives are heavily adopting AI, it hasn’t translated to widespread use among those who are the most impacted by these tools—those on the ground level.
To solve this, GTM leaders should:
Invest in comprehensive training programs that empower employees to fully understand and leverage AI tools. Employees who are well-trained in using AI are more likely to see its value and integrate it into their daily work.
SOON TO BE RELEASED IS THE GTM AI ACADEMY LICENSE!
Align AI tools with real-world outcomes, showing employees how AI can help them achieve their goals more efficiently, rather than just automating tasks. Clear value demonstrations can reduce resistance and encourage wider adoption.
Provide leadership support for AI initiatives at all levels of the organization. Leaders who champion AI can help drive its use and create a culture that embraces AI-driven innovation.
By closing this adoption gap, GTM leaders will unlock the full potential of AI, ensuring it has an impact across all teams, not just at the executive level.
3. Prepare for Workforce Changes and AI Displacement
The conversation is shifting. AI is no longer just a tool to assist employees—it is becoming a workforce replacer. As noted by CEOs like Duolingo’s Luis von Ahn and Shopify’s Tobi Lütke, many companies are preparing for downsizing as AI becomes capable of performing tasks once relegated to human workers. This is not just a future concern; it is happening today.
For GTM leaders, the disruption caused by AI requires strategic planning:
Reskill your workforce: As AI replaces jobs, leaders must invest in retraining programs that help employees transition into new roles where their skills are still needed. This proactive approach can mitigate the impact of layoffs while helping employees move into more strategic roles within the company.
AI as an augmentation tool: Rather than seeing AI as a complete replacement for human workers, view it as a way to amplify human capabilities. By positioning AI as a complement to human expertise, companies can create a more collaborative environment that leverages both human intelligence and AI efficiency.
Shift the mindset: As companies adopt AI to replace tasks, GTM leaders need to focus on building human-centered AI systems that support and enhance the employee experience, not replace it entirely.
GTM leaders who embrace this change and position AI as a complement will ensure that their teams remain competitive and adaptable in the AI-driven future.
4. Shift Towards Agile AI Development and Scaling
The integration and scaling of AI tools are fundamentally different from traditional software. As discussed in VentureBeat, scaling AI agents requires constant iteration and a flexible development mindset. Traditional software development cycles simply do not apply to adaptive, outcome-based AI systems.
GTM leaders must:
Adopt agile methodologies for AI development that focus on continuous improvement. Unlike static software systems, AI requires constant tweaking and fine-tuning to stay relevant and effective in real-world applications.
Ensure cross-functional collaboration between IT, product, and business teams to ensure that AI systems are being developed with both business goals and technical feasibility in mind.
Prioritize AI governance: As AI becomes more integrated into the core business, it’s essential to build systems for tracking performance, ensuring quality, and mitigating risks. This includes implementing version control and monitoring systems that can track how AI agents are evolving and performing in live environments.
Agile AI development will ensure that GTM teams remain responsive to market needs, adapting quickly to changes and improving the quality of AI products in real-time.
5. Leverage AI for Collaborative, Long-Term Projects
AI tools like ChatGPT’s Projects feature and Gemini for Education are rapidly transforming how teams collaborate. By creating structured workspaces where AI tracks and builds on long-term projects, these tools are enhancing productivity across the board. For GTM leaders, AI’s role in enhancing collaboration is becoming increasingly clear.
What can GTM leaders do?
Incorporate AI into long-term project management: Using AI tools to track progress, manage milestones, and enhance team collaboration can significantly improve project outcomes and increase efficiency. By creating structured workspaces within AI tools, teams can focus on high-value tasks while AI handles repetitive admin work.
Enhance decision-making with AI: With AI taking over routine tasks, GTM teams will have more time to focus on strategy and creative problem-solving, driving innovation and improving overall decision-making. This shift in focus allows teams to pivot quickly and remain competitive.
By integrating AI into team workflows and collaboration platforms, GTM leaders can foster a more productive, cohesive, and innovative work environment.
To Summarize What GTM Leaders Should Focus On:
Move quickly to integrate AI across teams to capitalize on market momentum.
Empower your workforce by closing the adoption gap with proper training and support.
Prepare for workforce transformations, emphasizing reskilling and AI-enhanced roles.
Adopt agile AI development strategies to ensure continuous improvement and scaling.
Leverage AI tools for collaboration to streamline workflows and drive better decision-making.
These actions will not only help GTM leaders stay ahead of the curve but also position their teams to thrive in an AI-driven future. AI’s potential is vast, but only if it’s approached strategically. Make AI part of your GTM DNA and lead your team through this transformation with confidence.
Key Strategic Insights for GTM Leaders from AI Adoption Trends
Based on the key findings from ICONIQ Capital's 2025 State of AI Report and BCG's 2025 AI at Work Report, there are several strategic insights that GTM leaders should consider in order to capitalize on the evolving AI landscape:
1. Build AI with People in Mind: Training and Adoption are Critical
Both reports emphasize that AI success is not just about the technology but also about human adoption. ICONIQ Capital highlights that AI-native companies are significantly outperforming AI-enabled companies, with 79% of AI-native companies focusing on building agentic workflows. Meanwhile, BCG underscores that AI adoption among frontline employees remains a major bottleneck, with only 51% of them using AI regularly.
What GTM Leaders Should Do: AI adoption requires a top-down commitment to ensuring employees at every level are adequately trained and equipped with the right tools. GTM leaders should prioritize upskilling programs and ensure that training goes beyond superficial tutorials. A key focus should be on regular, in-person training and coaching, as BCG found that 79% of employees who received over 5 hours of training regularly used AI tools.
2. Bridging the Adoption Gap Between Leaders and Frontline Workers
While executives and managers are adopting AI at impressive rates, frontline employees have been slow to follow. BCG reports a stagnation in AI usage among frontline workers, with only 51% adopting AI tools regularly. This is in stark contrast to leaders, where 72% are regularly using AI.
What GTM Leaders Should Do: To close this gap, GTM leaders must focus on enhancing frontline engagement by creating AI tools that directly address the pain points of those employees. This may involve tailoring AI solutions to specific workflows that employees regularly engage with, ensuring the tools are not just high-level solutions but are practical and user-friendly. Furthermore, leadership support is essential—when employees see their leaders actively using and supporting AI, they are more likely to adopt the technology themselves.
3. Reframe the Role of AI: From Tools to Transformative Workflow Engines
The key finding from both reports is that AI tools alone are insufficient—the real value comes from reshaping workflows and operating models. ICONIQ notes that AI-native companies are ahead in developing agentic workflows that truly integrate AI into core business processes, not just adding features to existing products. Similarly, BCG highlights that 50% of companies are already reshaping end-to-end workflows and actively experimenting with new business models enabled by AI.
What GTM Leaders Should Do: GTM leaders must embrace a holistic approach to AI adoption—don’t just bolt AI on top of existing systems, but use AI as a catalyst for rethinking and redesigning core workflows. This means working with product and engineering teams to build solutions that allow AI to continuously evolve and adapt to business needs. This shift requires a fundamental rethinking of processes from the ground up, with AI as the key enabler.
4. Monitor and Control AI Costs: A Growing Challenge
Both reports highlight the significant infrastructure costs associated with scaling AI. ICONIQ points out that API usage fees are a growing concern for many businesses, with 70% of respondents citing this as the most challenging infrastructure cost to manage. BCG adds that inference costs are also rising, especially as AI adoption deepens.
What GTM Leaders Should Do: Cost control in AI adoption is becoming more critical, particularly as AI products scale. GTM leaders should consider implementing hybrid pricing models for AI tools, as noted in the ICONIQ report, and should work closely with product teams to assess the ROI of AI investments. Regularly audit AI infrastructure costs, track API usage, and explore cost-effective AI models to ensure scalability without compromising profitability.
5. Experiment with AI Agents: Unlocking Long-Term Value
Both ICONIQ and BCG emphasize the importance of AI agents in driving the next wave of business value. ICONIQ found that 79% of AI-native companies are exploring agentic workflows, while BCG reports that only 13% of respondents have AI agents fully integrated into their workflows. However, 77% of respondents believe that AI agents will play a major role in the next 3-5 years.
What GTM Leaders Should Do: GTM leaders should invest in AI agent technologies to future-proof their operations. AI agents are poised to become central to the next generation of workflows, from customer service automation to advanced sales operations. Leaders should experiment with small-scale AI agent deployments and use A/B testing to measure their impact on efficiency and productivity. Early experimentation with AI agents will allow organizations to stay ahead of the curve as they evolve from early adopters to industry leaders.
Key Takeaways and Summary
The landscape of AI in Go-To-Market (GTM) operations is evolving rapidly, and this week’s research provides valuable insights that GTM leaders must act on immediately. Here's a comprehensive summary of the most critical findings and what they mean for the future of GTM leadership in AI adoption:
1. AI Adoption is Accelerating at Unprecedented Rates
The AI revolution is no longer a distant future—it's here, and it’s moving fast. Companies like Genspark have proven that AI-native startups can scale at breakneck speeds, achieving $36 million ARR in just 45 days without paid marketing. In parallel, ICONIQ Capital’s report highlights that AI-native companies are the fastest to reach the scaling stage, with 47% already scaling compared to just 13% of AI-enabled companies.
What GTM Leaders Should Do: Speed is now a competitive advantage. GTM leaders must prioritize rapid AI adoption to keep up with market trends, ensuring that their product development cycles are agile and responsive to changing demands.
2. The Frontline Adoption Gap is a Growing Challenge
Despite high adoption rates among executives, there’s a significant lag in AI use at the frontline employee level. BCG's report found that while 72% of leaders use AI regularly, only 51% of frontline employees do. This gap limits the full potential of AI, as adoption at all levels is necessary for AI’s success.
What GTM Leaders Should Do: To close this gap, GTM leaders need to invest in comprehensive training programs and ensure AI tools are integrated into frontline workflows. Hands-on, in-person training and clear value demonstrations are essential to improve adoption and maximize AI’s impact.
3. AI’s Role is Expanding Beyond Tools to Workflow Transformation
The true value of AI is emerging in the form of end-to-end workflow transformation, not just as a set of standalone tools. Both ICONIQ and BCG reports emphasize the importance of reshaping workflows. AI-native companies are ahead in developing agentic workflows, which integrate AI into the very core of business processes. 50% of companies are already working on reshaping workflows, with financial services and tech companies leading the charge.
What GTM Leaders Should Do: To harness AI’s full potential, GTM leaders must focus on reengineering business processes around AI capabilities. Instead of just adding AI on top of existing systems, companies need to embed AI into the heart of their operations to drive real business transformation.
4. The Cost of Scaling AI is Rising
The challenge of managing the costs of AI infrastructure, particularly API fees and inference costs, is becoming increasingly evident. ICONIQ Capital’s report indicates that 70% of companies struggle to manage API usage fees, and BCG highlights the growing concern of AI infrastructure costs as usage scales.
What GTM Leaders Should Do: Managing AI costs is crucial for scaling AI effectively. GTM leaders must explore hybrid pricing models and find ways to optimize infrastructure costs. Developing an in-depth understanding of cost-to-serve models will be key to ensuring that AI investments are sustainable in the long run.
5. AI Agents are a Game-Changer but Require Ongoing Experimentation
AI agents are rapidly becoming a central piece of the future AI landscape. ICONIQ and BCG both emphasize the importance of AI agents in driving business value, though only 13% of companies currently have them fully integrated into their workflows. Despite their early-stage adoption, 77% of companies believe AI agents will play a crucial role in the next 3-5 years.
What GTM Leaders Should Do: Experiment with AI agents now to stay ahead of the curve. Start by integrating agents into small-scale workflows and use A/B testing to track their impact on efficiency and productivity. Over time, refine and scale these agents to unlock their full potential across various business functions.
6. The Importance of Training and Leadership Support Cannot Be Overstated
Both ICONIQ and BCG emphasize the critical role that training and leadership support play in AI adoption. Effective AI tools and infrastructure are only as good as the people using them. The reports reveal that employees with proper training are 79% more likely to use AI tools regularly, while those without training often turn to shadow AI tools.
What GTM Leaders Should Do: Training should be prioritized across the organization, especially for frontline employees. Invest in ongoing education and leadership engagement to ensure that AI adoption isn’t just a top-down mandate but a company-wide movement.
Strategic Implications for GTM Leaders
The key to successfully navigating the AI revolution is a holistic approach—one that balances technology, process, and people. Here are the top actions GTM leaders should take:
Invest in AI integration: Move beyond isolated tools and embed AI into core business processes, ensuring that AI is a driver of workflow transformation.
Close the adoption gap: Ensure that frontline employees are fully trained, equipped, and supported in using AI to maximize its value.
Manage AI costs effectively: Develop a deep understanding of AI infrastructure costs and explore cost-efficient models.
Start experimenting with AI agents: Integrate AI agents into specific business functions, track their performance, and refine over time.
Prioritize training and leadership: Ensure that AI adoption is a company-wide initiative, with proper training, support, and engagement from leadership.
Conclusion: Moving From Adoption to Transformation
AI’s role in GTM strategies is evolving from tool deployment to transformative business models. By focusing on these key trends, GTM leaders can navigate the complexities of AI adoption and positioning their teams for success in an increasingly automated world. AI presents a huge opportunity for growth, but only if it’s managed strategically, with a focus on people, processes, and sustainable scaling.