5/3/25: The Age of Cognitive Infrastructure: Navigating AI's Dual Reality in GTM Strategy
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For the full breakdown of all these articles, you can read for free at the www.gtmaipodcast.com GTM AI Podcast
Welcome my friends! I changed up how we are going to do the newsletter. In the past, I would go into a deep dive of the podcast and then feature 4-5 articles, research, news, and updates. The challenge is, it becomes very long and I want to help keep updated with AI without feeling overwhelmed.
In the Free Newsletter I will cover trending articles and give my thoughts on overall trends as well as links to things you should know about.
For the full breakdown of all these articles, you can read for free at the www.gtmaipodcast.com GTM AI Podcast And if you want to read the full newsletter where I go over more of the overall view this week and updates, you can go to the www.gtmaipodcast.com
We dig into the top articles of the week and the top 7 Trends to be aware of
We get to focus on the talented Tas Hirani going into her journey with putting on the sales hat and experimenting with what happens first hand with AI tech.
Then we dive into TONS of updates that you need to know about in the newsletter itself
And our GTM AI tech of the week is Mica AI
And for the podcast lets go!
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.
Why 90% of Sales AI Tools Fail (and the 3-Step Fix That Changed Everything)
Tas Newsletter:
https://www.linkedin.com/newsletters/7245478675247173632/?displayConfirmation=true
The Experiment That Exposed Everything
When Tas Hirani, a veteran enablement leader with a Six Sigma background from GE, noticed her sales teams struggling despite having access to cutting-edge AI tools, she did something radical. She didn't run another survey or schedule more training sessions. Instead, she went undercover as a sales rep while maintaining her enablement role.
What she discovered explains why companies are spending millions on AI tools that collect dust while reps continue drowning in admin work.
The Brutal Truth About Sales AI Adoption
"Everyone's got LinkedIn, LinkedIn Navigator, ChatGPT, Perplexity... but when I actually sat in the seat and tried to use these tools the way reps do, it was Pandora's box," Hirani reveals. The problem isn't the technology—it's how we're implementing it.
Here's why 90% of sales AI tools fail:
The "Dead Weight" Problem: Traditional tech forced salespeople to adapt their workflow to the tool. As Hirani puts it, "Technology was like dead weight that people were hauling up the hill... trying to get to this sale, but I can't get there because I have to go to 12 different places."
The Generic Solution Trap: Companies throw in Microsoft Copilot or ChatGPT behind a firewall and declare themselves "AI-enabled." Hirani calls this "a recipe for failure" because it ignores business-specific context.
The IT Power Play: When IT departments impose generic AI solutions because they have "those two magic letters," adoption inevitably fails. The tools that work are chosen by the business teams who actually use them.
The Reality Check That Changed Everything
During her time in the sales trenches, Hirani discovered something shocking. When she shared AI tools that worked brilliantly for her, the reactions from her team were mixed:
"Some reps said, 'I don't have any confidence in AI. It doesn't sound like me. My prospect is gonna know that it's not me if I haven't felt the pain and written that email myself.'"
This revelation led to a fundamental insight: Every rep is at a different point in their AI adoption journey, and one-size-fits-all solutions are doomed to fail.
Visual learners needed completely different tools than text-based processors
New reps loved real-time coaching popups; veterans found them distracting
Some thrived with vanilla ChatGPT; others needed specialized solutions
The 3-Step Fix That Actually Works
After months of testing and real-world application, Hirani developed a framework that's transforming how companies implement sales AI:
Step 1: Test With Your Extremes
"Get somebody who's super experienced, been in your company for years, and get them hands-on with the AI. Then take somebody who's fairly new and get them hands-on too."
The litmus test:
Does it improve accuracy for both groups?
Does it increase speed for both groups?
If it only helps newbies, it's not the right solution
Key metrics to measure:
Time saved on admin tasks
Accuracy of data capture
User satisfaction scores from both groups
Step 2: Remove Friction, Don't Add Features
"The AI tools that work are invisible. They're responsive and part of the working pattern, not another portal to log into."
Examples of friction-removing AI:
Automatic CRM updates from conversations (no manual entry)
Push notifications with last conversation context before meetings
One-click data sync that captures the 99% of conversation data currently lost
Red flags to avoid:
Any tool requiring reps to leave their current workflow
Solutions that add new steps to existing processes
AI that requires extensive training to use effectively
Step 3: Let Business Drive, Not IT
"When it comes from the business budget and solves actual business problems, it's singing. When IT imposes a generic solution, I'm seeing it just fail."
The new approach:
Business teams identify the specific pain points
Users get hands-on testing before any commitment
Success is measured by business outcomes, not IT metrics
Questions to ask:
What specific friction does this remove?
Can users see themselves using this naturally?
Does it enhance what they're already doing well?
The Transformation in Action
Companies following this framework are seeing dramatic results. Instead of asking reps to fill in meeting outcomes, AI automatically captures and categorizes them. Rather than requiring manual opportunity updates, conversation intelligence pushes relevant data directly to the CRM.
As Hirani explains, "We've moved from riding horses to building planes. The destination is the same, but how we get there is completely different."
The Bottom Line
The future of sales AI isn't about having the most tools or the latest technology. It's about thoughtful implementation that respects where your team is today while removing the friction that's holding them back.
Your next steps:
Audit your current stack: How many tools require reps to interrupt their workflow?
Run the extreme user test with your most and least experienced reps
Shift budget control from IT to the business teams actually using the tools
Remember: The best AI is the AI your team doesn't even notice they're using—because it's simply making their job easier, not harder.
"Nothing can shortcut effective account research because it's different for every person. What we're talking about here is meeting people where they're at." - Tas Hirani
Title: The Age of Cognitive Infrastructure: Navigating AI's Dual Reality in GTM Strategy
The Collision Course Between Hype and Execution
Over the last 90 days, a clear bifurcation has emerged in how GTM leaders must think about AI. On one side: hyper-acceleration, recursive self-improvement, vibe-coded product launches, and infrastructure-scale investments. On the other: enterprise friction, post-deployment chaos, talent deficits, and agentic AI projects failing quietly behind closed doors.
The flood of recent research, corporate launches, and firsthand case studies reveals the same uncomfortable truth: the AI race is no longer theoretical. It is happening in codebases, consumer behavior, and boardroom mandates right now. But most organizations are structurally unprepared for what is required to build, scale, and sustain in an agent-first economy.
1. We Are Moving From Software to Cognitive Infrastructure
OpenAI’s $6.4B acquisition of Jony Ive’s hardware startup, brain-computer interface breakthroughs published in PNAS, and Microsoft’s multi-agent protocol launch all signal a shift: GTM strategy can no longer be anchored in apps, APIs, or screen-based interfaces alone.
Brain-computer interfaces (PNAS 2025) are enabling persistent control with 96.4% accuracy during motion.
Microsoft Copilot now integrates cross-agent workflows and agent discovery natively across SharePoint, Outlook, and WhatsApp.
OpenAI is placing massive bets on ambient, AI-native hardware with no touchscreen at all (CNBC).
Implication: The user is not your buyer anymore. The agent is. To win in GTM, your product must be callable, observable, and interoperable within protocol-driven ecosystems.
2. The Agent Economy Is Fragmenting Into Three Realities
Sequoia’s AI Ascent keynote reframed the economic opportunity: agents that move from co-pilots to autonomous executors, absorbing labor budgets and capturing SaaS spend. But execution divides into three distinct paths:
Agentic Infrastructure: Mistral, Microsoft, and OpenAI are offering developer primitives—APIs, orchestration engines, and context pipelines. This is table stakes.
Cognitive Workflows: Perplexity Labs and Copilot Studio are reshaping how work gets done—10-minute lab sessions outperforming days of manual effort.
Narrative OS: The OpenAI governance saga and AI 2027 scenario show a complete collapse of epistemic clarity—two worldviews splitting: AI as normal tech vs AI as superintelligence.
Implication: GTM teams must support all three paths simultaneously. Infrastructure-readiness, workflow alignment, and executive narrative framing now matter as much as product-market fit.
3. Vibe Coding and the Collapse of Engineering Scarcity
NPR's story on Chloe Samaha building a product in under 48 hours with AI copilots reveals how software engineering is becoming commoditized. From Figma’s AI design generation to GitHub’s orchestra-of-agents metaphor, the bar for "technical" execution is plummeting.
85% of AI projects still fail due to infrastructure and org debt (Forbes).
Vibe coders, not engineers, are building market-ready products (NPR).
Low-code agents like Perplexity Labs now deliver full apps, not just answers.
Implication: If your GTM strategy assumes engineering effort as a moat or bottleneck, you’re already behind. Start treating software capacity as elastic, and focus instead on trust, interpretation, and orchestration as new value layers.
Scaling AI Agents, Infrastructure, and Human Judgment
In the first part of this deep dive, we explored the volatile duality between AI as an existential threat and AI as a "normal" technology, grounded in industrial pace and human oversight. But beneath those philosophical extremes is a hard operational reality: GTM leaders are now dealing with AI agents in production, facing the day-to-day implications of infrastructure, usability, and human-machine orchestration. In this second part, we shift focus from speculation to execution.
4. AI Agents Will Transform GTM—But Only If You Build for Scale
A recurring pattern across every enterprise case study—from Jotform’s agent-driven remote ops to NTT Data’s agent productivity leap—is that AI agents are not magical out-of-the-box solutions. They are operational platforms. Most agentic AI pilots are failing not because of the models, but because of brittle ecosystems and poor planning.
Three structural patterns emerge across the failures:
Lack of Infrastructure: Enterprises jumped into agentic use cases without scalable pipelines. Forbes reports 85% of agent projects still stall pre-production, largely due to cost overruns and poor ecosystem alignment
AI Without Talent: Accenture found that only 13% of AI projects produce significant value. Most companies spend 3x more on tooling than on upskilling. This results in orphaned agents without owners, proper workflows, or governance frameworks
Post-Deployment Drift: Agents, once live, are often unmanaged. Regular audits, updates, and KPI tracking are rare. Few orgs deploy red-teaming or monitoring agents, even though the tools now exist to do so
GTM Recommendation:
Treat agent deployment like product launch and support:
Assign “Agent Owners” just like product managers.
Build out guardian agents for monitoring security, compliance, and usage trends.
Pair agents with enablement playbooks so users know how to use and course correct.
Align with hyperscaler infrastructure (e.g., Azure, AWS, GCP) to take advantage of integrated scaling.
5. Vibe Coding, Labs, and the New Work Interface
AI is now the interface layer for non-technical teams. Vibe coding—using AI agents to “just build it”—is changing what prototyping means. NPR, Forbes, and Microsoft confirm that non-coders are spinning up applications, internal tools, even production platforms in under 24 hour
Perplexity Labs exemplifies this shift. A Lab can now generate spreadsheets, dashboards, images, summaries, and even simple web apps using self-directed agents with built-in code execution, image generation, and RAG capabilities. It is effectively what a junior team of analysts, designers, and developers would produce in days. Now it’s ten minutes.
GTM Impact:
Product teams can launch campaigns, internal apps, or dashboards faster without backend dependencies.
Enablement leaders must train sellers and marketers not on writing code—but on describing their needs with precision.
Dev leaders are becoming orchestration architects—deciding which parts of a workflow to delegate to agents, which to verify, and which to own.
Strategic Actions:
Start every initiative with a “Prompt > Plan > Prototype” loop using tools like Labs or Microsoft’s multi-agent systems.
Create vibe-to-validate pipelines where non-technical users spin up a tool, then IT formalizes and scales it with security and governance.
6. Model Interoperability and the Rise of MCP as a New AI Stack Layer
As Mistral, OpenAI, Microsoft, and HubSpot converge on the Model Context Protocol (MCP), a new reality is emerging: agents will not live in silos. They’ll need to talk to CRMs, data lakes, task trackers, security models, and each other. And they’re starting to do just that.
Microsoft’s Copilot Studio now supports multi-agent orchestration across its own tools, integrating via the new A2A protocol. OpenAI’s leaked integration of MCP will allow agents to query Gmail, HubSpot, Notion, or any enterprise tool exposed via MCP
Why this matters:
GTM systems are fragmented across marketing, sales, revops, and support. AI agents need cross-platform access to deliver value.
MCP enables agents to perform real work: update deals, fetch docs, check logs, not just summarize.
As Mistral and Perplexity also release orchestration-capable platforms, the pressure is rising on companies to choose open, secure, and governed agent stacks.
Action Plan:
Audit your internal apps for MCP readiness. Expose internal APIs via MCP-style connectors.
Create an agent registry to manage access, permissions, and capabilities. This is your future “AgentOps” dashboard.
Use agent observability tools to measure which workflows drive value vs. noise.
Top 7 Trends to be Aware of:
1. Most AI Projects Fail—But Not Because of the Models
Stat point: 85% of enterprise AI projects fail to scale (Forbes, Gartner)
Cross-article confirmation:
Forbes reported massive drop-offs between AI proof-of-concepts and production deployments due to infrastructure, integration, and change management gaps.
Microsoft's Copilot and NTT Data’s agentic AI stack now include red-teaming and security agents just to keep post-deployment failures in check.
Accenture: Only 13% of AI initiatives generate measurable business value; over 60% of execs underinvest in talent enablement.
Pattern: Most organizations confuse “having AI” with “being AI-capable.” The primary failures lie in orchestration, governance, and skills—not model performance.
2. Agentic AI Is the New Platform—But Without Architecture, It Breaks
Stat point: By 2028, 1 in 3 enterprise tools will have embedded agents; 15% of business decisions will be made by agents (Gartner)
Cross-article confirmation:
Microsoft introduced agent-to-agent protocols, Copilot orchestration, and “computer use” capabilities for agents to interact without APIs.
Mistral’s Agent API coordinates multiple agents and executes Python, web search, and RAG across secure enterprise environments.
HubSpot and OpenAI’s adoption of MCP enables persistent, tool-integrated agent behaviors (not just chatbots).
Pattern: A platform shift is underway—from apps and APIs to agents and protocols. But few organizations have the data access, memory systems, and observability layers to support it.
3. Vibe Coding Is Real—Engineering Moats Are Collapsing
Stat point: AI-generated code now powers production tools built in under 48 hours by non-coders (NPR, GitHub, Figma)
Cross-article confirmation:
NPR’s coverage of vibe coding shows startups launching AI-built products without engineering teams.
GitHub CEO says coders are now conductors managing AI “orchestras” of agents.
Perplexity Labs and Copilot Studio enable 10-minute end-to-end task automation, blurring lines between designer, engineer, and operator.
Pattern: Code scarcity is no longer a bottleneck. GTM, marketing, and ops teams must shift from “request and wait” to “describe and deploy.”
4. Agent Infrastructure Without Guardrails Leads to Drift or Disaster
Stat point: Only 25% of AI projects deliver expected ROI due to lack of post-deployment oversight (IBM)
Cross-article confirmation:
NTT Data deploys “guardian agents” and “red teaming agents” to monitor security, performance, and compliance.
Forbes and Microsoft highlight that most failed agents lacked lifecycle governance (audit logs, updates, system rules).
The “AI 2027” vs. “AI as Normal Tech” debate shows the risks of releasing recursive self-improvement without robust interpretability tools.
Pattern: If you can’t govern the agents you deploy, they either become irrelevant or dangerous. AI governance and AgentOps are now RevOps-level imperatives.
5. Personalization Is Shifting From People to Protocols
Stat point: Over 50% of consumers trust AI for content, recommendations, or purchases (Bain & Company, Perplexity)
Cross-article confirmation:
Google AI Mode performs agentic checkout and personalized visual shopping experiences with real-time updates.
Meta AI uses cross-platform data (Facebook, Instagram) to personalize answers and content feeds.
OpenAI’s CFO emphasized new hardware will be "beyond touch” and context-aware for ambient personalization.
Pattern: Personalization is no longer a UX layer. It’s a protocol-level shift where context is piped from agents across interactions—not authored manually by marketers.
6. The AI Skills Gap Is the Real Bottleneck
Stat point: Companies spend 3x more on AI tools than on training; 87% of employees lack readiness (Accenture)
Cross-article confirmation:
Fewer than 1 in 10 enterprise teams know how to operationalize prompt engineering at scale (Anthropic, OpenAI, Claude Prompt Guide).
NTT Data is retraining 200,000 staff and certifying 15,000 AI practitioners as part of scaling strategy.
Most companies using vibe coding or agents find that human supervision, editing, and escalation paths are still critical.
Pattern: Talent enablement is now strategy. Agent infrastructure without training is shelfware. Agent-enabled onboarding, coaching, and upskilling are where GTM gains compound.
7. AI Is Becoming a Trust Problem, Not Just a Technology One
Stat point: OpenAI’s o4-mini model had a 48% hallucination rate; mistrust is rising across medical, financial, and legal AI use cases (NYT, OpenAI, DeepMind)
Cross-article confirmation:
Perplexity, Claude, Mistral all adding RAG, source linking, and web search to reduce hallucinations—but user verification remains a necessity.
The “AI 2027” report warns about recursive AI decisions without transparency.
OpenAI had to roll back personality tuning in ChatGPT due to sycophancy and loss of trust.
Pattern: Trust must be engineered into AI systems—via visibility, explainability, provenance, and limits. The next competitive advantage is governable intelligence.
GTM AI Tech of the week: Mica AI
Mica AI is an innovative platform designed to transform the way B2B sales teams handle post-call follow-ups. By leveraging advanced AI capabilities, Mica automates the creation of personalized video highlights and comprehensive deal summary pages from sales calls, aiming to enhance sales efficiency and close rates.(ycombinator.com, tryfondo.com)
🔍 What is Mica AI?
Mica AI utilizes conversational AI and automation to convert sales call recordings into concise, shareable video snippets and detailed summaries. This approach addresses the common challenge where key decision-makers, not present during the initial call, need to be brought up to speed efficiently. By providing tailored content, Mica ensures that all stakeholders have access to the essential information needed to make informed decisions.(tryfondo.com)
💡 Key Features
Automated Video Highlights: Transforms lengthy sales calls into short, topic-specific video clips, making it easier for prospects to revisit key points.(linkedin.com)
Comprehensive Deal Summaries: Generates detailed pages summarizing pain points, proposed solutions, pricing, and next steps, facilitating quicker decision-making.(linkedin.com)
Seamless Integration: Works with existing tools like Zoom, Slack, and CRM systems to pull in data and streamline workflows.(linkedin.com)
Real-Time Insights: Provides analytics on prospect engagement with the shared content, allowing sales teams to tailor their follow-up strategies effectively.(linkedin.com)
✅ Strengths
Efficiency: Reduces the time spent on creating follow-up materials, allowing sales reps to focus more on selling.(ycombinator.com)
Consistency: Ensures that all prospects receive high-quality, personalized content, regardless of the sales rep's workload.
Scalability: Enables teams to handle a higher volume of leads without compromising on the quality of follow-up communications.(linkedin.com)
⚠️ Considerations
Learning Curve: Teams may require some time to adapt to the new workflow and fully utilize Mica's features.
Integration Dependencies: The effectiveness of Mica is enhanced when integrated with specific tools; teams not using these tools may need additional setup.(fintechlatvia.eu)
📈 Potential ROI
By automating the creation of follow-up materials, Mica can significantly reduce the time and resources spent on these tasks. This efficiency can lead to faster sales cycles and higher close rates, ultimately contributing to increased revenue. For instance, if a sales team can close deals even a few days faster, the cumulative effect over a quarter or year can be substantial.(ycombinator.com, linkedin.com)
🧠 Final Thoughts
Mica AI offers a compelling solution for B2B sales teams looking to enhance their post-call follow-up processes. By automating the creation of personalized, high-quality content, Mica not only saves time but also ensures that prospects receive consistent and engaging materials. While there may be an initial adjustment period, the potential benefits in efficiency and increased close rates make Mica a valuable tool for modern sales organizations.