7/8/25: The Rise of Agentic AI: Workforce Transformation, Strategic Limits, and Enterprise Reinvention
This is sponsored by the AI Business Network and GTM AI Academy
I literally went through some 15+ articles this week and yes I read them and had a chat with AI about them, because my mind is still boggled.
My goal as always is to cut through the noise and give you some valuable content and read things so you do not have to.
We also have our own Jonathan Moss interviewing Ross LIVE during the Revfest in NYC by Go Nimbly.
Lets get into the podcast!
Why AI Makes Human Sales Skills More Valuable Than Ever
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.
Summary
Ross Rich, CEO and co-founder of Accord, brings a unique perspective to sales technology after scaling Stripe's sales organization from three to 300 people. In this conversation from GoNimbly's RevFest, Rich reveals how execution excellence goes beyond doing more work to creating clarity, consistency, and reinforcing winning behaviors across the customer journey.
The foundation of execution excellence starts with data and knowing exactly who you're talking to. Rich emphasizes that most sales teams fail because they engage with associates and below-the-line people rather than senior stakeholders and decision makers. At Accord, they track both the number of stakeholders and the frequency of engagement as primary indicators of deal health. This focus on stakeholder mapping becomes even more critical as buying committees expand and decisions require broader consensus.
The traditional approach to sales management focuses too heavily on inspection rather than execution. Sales representatives spend excessive time writing deal reviews, updating Salesforce with acronyms, and preparing for internal QBRs instead of working directly with customers. Rich built Accord specifically to address this problem by allowing teams to inspect the actual documents and interactions happening with customers rather than creating separate internal documentation. This shift from internal inspection to customer-facing execution motivates teams and grounds them in real customer outcomes.
The evolution of B2B buying has fundamentally changed what makes a successful seller. Digital native millennials and Gen Z buyers expect experiences similar to consumer platforms like Uber, Netflix, and Amazon. They have infinite knowledge at their fingertips through AI and search tools. This shift means sellers must transition from helping people buy by providing information to offering unique perspectives and industry expertise. The bar continues to rise for what constitutes valuable seller involvement in the buying process.
AI represents both an opportunity and a threat for sales professionals. The technology can handle transactional SMB deals, pushing human sellers toward more complex mid-market and enterprise opportunities. However, this concentration means top performers who previously closed one or two deals annually might now close three to five, potentially increasing the revenue contribution of the top 20% of sellers from 80% to 90% or more. Those at the bottom who rely on lucky breaks or helpful buyers will find their roles increasingly challenged by technology.
The practical application of AI in sales focuses on two key areas. First, deep research that previously required hours of manual work analyzing 10-Ks, financial statements, and stakeholder backgrounds can now happen in seconds. This allows thoughtful sellers to craft more informed outreach and stand out from the noise. Second, AI helps identify and engage the right senior stakeholders with relevant messages. Counterintuitively, Rich finds higher response rates when reaching out to more senior executives because thoughtful, informed messages stand out more in a CEO's inbox than in a mid-level manager's cluttered email.
The implementation of AI tools must meet sellers where they already work rather than asking them to become prompt engineers. Rich points out that if revenue operations leaders struggle to get representatives to input data into Salesforce, expecting them to master AI prompting is unrealistic. Instead, AI should be embedded into existing workflows like account research, stakeholder mapping, and business case development. This approach eliminates the need for additional tabs, tools, or manual updates while enhancing the work sellers already do.
Key Quotes from Ross Rich
"Everything comes down to the customer. One of the biggest things that we look at when it comes to driving execution excellence and operational rigor is are we talking to the right people?"
"The bar is gonna be raised in terms of having that unique point of view. What to say to customers when you get that time. It's harder than ever to break into conversations. When you have that opportunity, what value are you bringing?"
"We're asking people to spend time outside of their day job. 50% of their paycheck if you're in sales is most likely tied to customer outcomes. We're asking them to take time away from that to write down these crazy acronyms and do stuff in Salesforce."
"We're really seeing this transition from helping people buy to selling. I think they're helping themselves buy even more these days."
"Before, to be really informed on a key top account, you'd have to do a ridiculous amount of research. Now, using deep research from Gemini or ChatGPT or any of these other tools and integrating that to Accord in seconds, you can now get all of this info."
"I actually find that we get higher response rates, almost the more senior we get because sometimes the more thoughtful approaches, it seems like a lot to these mid-level people. But when you say something informed and intelligent to a CEO, a board member, someone in the C-suite, I feel like it's actually easier to stand out."
"In this world, you actually need to get even more specific on your ICP and the personas and understand them. Getting deeper on, hey, these are the three people we talked to at this company. We know the ins and outs at every level exactly what they do every day."
"They want to spend time with humans. They want to solve problems. That's what they're excellent at, and that's the craft that they've developed over decades."
Main Takeaways
Measure What Matters Most: Track the number of stakeholders engaged and the frequency of those engagements as primary indicators of execution excellence. Focus particularly on whether you're reaching senior decision makers rather than associates who lack purchasing authority.
Build Culture Before Scale: Hiring people who care about the craft of sales becomes exponentially harder as you grow. Establish a culture that celebrates excellence and holds people accountable through direct feedback. The ability to have tough conversations about performance separates teams that scale successfully from those that don't.
Shift from Inspection to Execution: Eliminate time spent on internal documentation and deal reviews that take sellers away from customer interactions. Instead, inspect the actual work being done with customers through shared workspaces and collaborative documents that serve both internal visibility and customer value.
Prepare for Market Bifurcation: The sales workforce will increasingly split between top performers who leverage AI to close more large deals and bottom performers whose transactional work gets automated away. Invest in developing sellers who can bring unique perspectives and industry expertise rather than just relay information.
Embrace Specificity in Targeting: Success requires knowing exactly which three to five personas you serve at target companies and understanding their daily challenges, tools, and priorities at a granular level. This specificity enables breakthrough messaging in an increasingly noisy market.
Enable AI Within Existing Workflows: Avoid asking sellers to learn new tools or become prompt engineers. Instead, embed AI capabilities directly into the activities they already perform like account planning, stakeholder mapping, and business case development.
Prioritize Senior Stakeholder Engagement: Use AI-powered research to craft thoughtful messages for C-suite executives where your insights can stand out more easily than in cluttered middle-management inboxes. The combination of going higher and wider in organizations with more informed perspectives drives better outcomes.
Focus on Point of View Development: In a world where buyers have infinite information access, sellers must transition from information providers to insight generators. Develop teams that can translate market knowledge into unique perspectives that teach customers something new about their business.
Eliminate Operational Drag: Identify and remove every instance of manual work that takes sellers away from customer conversations. From CRM updates to internal reporting, every minute saved should redirect toward customer engagement and value creation.
Invest in Continuous Skill Development: The craft of sales requires ongoing investment in training, coaching, and development. As AI handles more transactional work, the human skills of relationship building, strategic thinking, and consultative selling become increasingly valuable and must be actively cultivated.
The Rise of Agentic AI: Workforce Transformation, Strategic Limits, and Enterprise Reinvention
References:
Halfway Through 2025, AI Has Already Replaced 94,000 Tech Workers
Goldman Sachs CIO: We must prepare AI natives…
Can Large Language Models Develop Strategic Reasoning? (arXiv preprint)
TheAgentCompany: Benchmarking LLM Agents on Consequential Real‑World Tasks (arXiv)
Introducing Vista’s Agentic AI Factory (Vista Equity Partners)
SaaS Is Dead. Long Live Service‑As‑A‑Service (Forbes)
Gartner Predicts Over 40% of Agentic AI Projects…
AI Voice Startup ElevenLabs Pushes Global Expansion… (CNBC)
Capital One Builds Agentic AI to Supercharge Auto Sales (VentureBeat)
Dust Hits $6M ARR Helping Enterprises Build AI Agents… (VentureBeat)
AI Is Transforming Productivity, but Sales Remains a New Frontier (Bain & Co.)
1. Workforce Disruption vs. Opportunity
The rise of agentic AI is producing a dual phenomenon: rapid displacement of traditional roles and parallel creation of new responsibilities—especially for those who understand, manage, and collaborate with AI systems. On one hand, companies are rapidly offloading repetitive, entry-level functions to AI. These include HR, tech support, content creation, and junior developer tasks. On the other, a new class of “AI natives”—digital-first professionals who intuitively understand how to work alongside AI—is emerging as a competitive workforce advantage.
By mid-2025, over 94,000 tech workers have been replaced or displaced directly due to AI deployments, according to FinalRoundAI. These aren’t traditional cost-cutting layoffs. They're part of a systematic restructuring of workforces to shift spending toward AI infrastructure, research, and automation. Meanwhile, companies like Goldman Sachs have emphasized that AI natives—those entering the workforce with lived experience using generative tools—are uniquely positioned to lead in AI-integrated environments. Unlike previous tech shifts, the velocity of this transition has compressed learning curves and elevated junior employees into supervisory roles, not by tenure but by technological fluency.
The implication is clear: while AI erodes traditional entry paths, it creates unprecedented upside for those fluent in delegation, prompt engineering, and AI oversight. The job of “supervisor of agents” is emerging faster than HR systems can label it.
Supporting Data and Quotes:
✅ “507 tech workers lose their jobs to AI every day in 2025.” – FinalRoundAI
✅ “AI is replacing roles, not just augmenting them—HR, entry-level engineers, and customer support are going first.” – FinalRoundAI analysis
✅ “The generation now entering the workforce has ‘grown up’ alongside generative AI... They are ‘AI natives.’” – Goldman Sachs CIO Marco Argenti
✅ “AI will reduce the size of the corporate workforce over time.” – Andy Jassy, Amazon CEO
2. Agentic AI in Action Settings
Agentic AI has moved from theoretical research and proof-of-concepts into deployed systems that actively execute core business tasks. No longer confined to suggesting actions, these agents perform them—autonomously booking flights, triaging support tickets, updating CRMs, scheduling sales calls, and even running backend code updates. From Vista’s Agentic AI Factory to Dust’s real-time CRM syncing, the shift is material: AI is not a tool; it is now an operational workforce layer.
Goldman Sachs envisions agents as digital coworkers that junior employees must learn to delegate to and supervise. Capital One has already deployed agentic frameworks in its auto sales operations, where AI agents surface vehicle recommendations, complete transactions, and support customers 24/7. Dust, a breakout enterprise agent startup, has AI that turns sales call transcripts into live CRM entries and GitHub issues—cross-functional automation once unimaginable without a team of analysts.
These implementations signal a broader industrial transformation: AI agents are now embedded in the operational core. And businesses are adjusting not just workflows but entire org structures to accommodate this new class of “invisible doers.”
Supporting Data and Quotes:
✅ “AI systems no longer suggest; they act.” – Vista Equity Partners
✅ “Capital One dealership customers reported a 55% improvement in engagement and serious sales leads.” – VentureBeat, Capital One
✅ “AI agents triage transcripts, update battle cards in Salesforce, and open GitHub tickets.” – Dust.ai case study
✅ “AI will power the enterprise systems of the Fifth Industrial Revolution.” – Vista Agentic AI Factory
3. Limits of LLM Reasoning
Despite their natural language fluency and contextual awareness, LLMs still struggle with deep strategic reasoning, particularly in environments that require domain-specific logic, sequential planning, and adversarial anticipation. Research from KAIST and Berkeley explored reinforcement learning in chess—an ideal testbed for strategic cognition—and found that LLMs plateau at ~25–30% puzzle accuracy, far below expert-level thresholds.
Dense rewards and reasoning scaffolds improved performance marginally, but the root cause was clear: foundational strategic knowledge is absent from base pre-training. This insight carries major implications for deploying AI agents in roles that require high-order reasoning: negotiation, complex decision-making, and multi-agent task coordination are still out of reach without rigorous domain-specific augmentation.
The research underscores a critical boundary: LLMs excel at tactical execution, but genuine strategy remains a human advantage. For now.
Supporting Data and Quotes:
✅ “All models plateau far below expert levels—25–30% puzzle accuracy.” – arXiv:2507.00726
✅ “LLMs fail to develop meaningful strategic reasoning despite reinforcement learning.” – Chess RL study
✅ “Current models lack fundamental chess understanding... RL cannot overcome this deficiency alone.” – KAIST/Berkeley researchers
✅ “Strategic reasoning may require more than reinforcement—it requires rich pre-training in domain-specific logic.” – Research findings
4. Enterprise Realignment & Value Model Shift
The AI transition is not just technical—it is economic. As AI agents mature, the enterprise SaaS model is being replaced by outcome-based delivery. What once was a product is now a promise: results, not dashboards. Clients are no longer buying tools; they’re buying completed actions and automated workflows.
Forbes frames this shift as “SaaS is dead. Long live Service-as-a-Service.” Companies like Glide, Corti, and Amplitude no longer just sell software—they deliver business outcomes via AI-powered service layers. This marks a structural realignment in how software is built, sold, and valued.
Yet with this transformation comes risk. Gartner predicts that more than 40% of agentic AI projects will be canceled by 2027 due to escalating costs, unclear business value, and lack of process redesign. Agent-washing—rebranding simple automation as agentic intelligence—is already distorting the market. Enterprise leaders must therefore tie agent deployment to real ROI, with governance, orchestration, and risk controls at the center.
Supporting Data and Quotes:
✅ “Over 40% of agentic AI projects will be canceled by 2027.” – Gartner
✅ “What we’re building is a service nestled inside a product wrapper.” – CEO, Glide
✅ “Clients aren’t buying tools; they’re buying survival.” – Forbes
✅ “You can’t sell point solutions where others are selling outcomes.” – Andreas Cleve, Corti
5. New Agentic Frameworks & Capital Flows
The explosion of agentic AI has catalyzed the formation of new architectural standards, ecosystems, and capital structures. Dust, ElevenLabs, and TheAgentCompany are among a growing wave of agent-native startups building orchestration layers, workflow controllers, and service abstractions on top of foundational models like Claude and Gemini.
Vista Equity Partners, through its Agentic AI Factory, exemplifies the private equity approach to scaling agentic infrastructure across its portfolio. Capital One has created its own internal governance layer, where evaluator agents oversee task agents and enforce compliance with financial regulations—effectively building multi-agent accountability protocols.
The capital is flowing fast. ElevenLabs is IPO-bound and valued at $3.3B. Dust hit $6M ARR and is growing 6x YoY. Anthropic’s Model Context Protocol (MCP) is rapidly becoming the de facto connector between agentic systems and secure enterprise data environments. As Bain notes, the race is no longer about adoption—it’s about building the right scaffolding to manage the agents once deployed.
Supporting Data and Quotes:
✅ “ElevenLabs... valued at $3.3B, IPO in next 5 years.” – CNBC
✅ “Dust hits $6M ARR... AI agents actually doing work, not just talking.” – VentureBeat
✅ “Vista anticipates 5–10 agents per user, deploying 4–8 billion across its portfolio.” – Vista Agentic AI Factory
✅ “Agent that evaluates agents, trained on Capital One policies.” – VB Transform
DEEP DIVE
A. Workforce Transformation
This isn’t just a story of layoffs. What’s happening now is a reallocation of human capital. Roles once protected by specialization—HR generalists, junior devs, legal assistants, support reps—are increasingly considered “AI-operable.” This doesn’t mean they’re unnecessary. It means the tasks that defined them are becoming automatable. That’s a fundamental shift in the labor equation.
What makes this moment unusual is that leadership isn’t hiding it. Executives are openly describing workforce reductions not as downturn-driven, but as AI-fueled evolution. The leaders of Microsoft, Meta, and Amazon are emphasizing efficiency per head, not headcount growth. And internally, headcount is shifting toward infrastructure: AI ops, agent orchestration, data architecture. The role of the “people manager” is quietly being replaced by AI supervisors and junior humans managing the machines.
This is where “AI natives” come in. They're not senior. They’re fluent. They can write, test, and adapt prompts. They know what context an AI needs and when it’s wrong. In a world where most AI is probabilistic and imperfect, these human collaborators are the glue that makes things usable. They're being handed responsibility fast because fluency matters more than rank.
Key Supporting Points:
Microsoft is “flattening layers” and expects fewer support staff as GitHub Copilot handles code generation.
At IBM, 8,000 HR roles were replaced by an AI assistant (“AskHR”) in one wave.
“Even junior hires must now know how to supervise an AI, not just complete a task.” — Marco Argenti, Goldman Sachs CIO
B. Agentic AI at Scale
Agentic AI is no longer a feature inside a product. It is the product. At Dust, multiple agents review call transcripts, pull out product requests, map those to roadmaps, and generate GitHub issues. No human PM in the loop. At Capital One, sales agents don’t just surface leads—they close deals, check loan eligibility, and initiate fulfillment steps.
What enables this is not just capability, but control. Enterprises like Vista and Capital One are wrapping these agents in accountability frameworks. They deploy evaluator agents that audit work, enforce compliance, and flag risk before it escalates. This is how you turn AI from a tool into a team.
MCP (Model Context Protocol) plays a critical role. It allows agents to securely pull enterprise context—like CRM data or policy logic—without leaking or hoarding information. Combined with zero-data retention and permissioning layers, it ensures trust at the core.
What’s emerging is a new design pattern. Not single-agent assistants, but multi-agent stacks with built-in supervision, escalation logic, and role specialization. Like a miniature digital department.
Key Supporting Points:
Dust agents can update Salesforce, generate A/B tests, and file product tickets automatically.
Capital One's evaluators are trained on internal policies and can reject tasks by subordinate agents.
Vista expects “5 to 10 agents per employee” across its portfolio.
C. Limitations in Strategic Reasoning
AI agents today can follow instructions and automate workflows. What they can't do reliably is reason over time. The chess study from KAIST revealed that even with dense reward signals and advanced reinforcement techniques, models plateaued far below expert levels. They often failed to understand consequences beyond the immediate move.
That limitation matters in business. Strategic reasoning means anticipating downstream effects, recognizing trade-offs, adjusting under ambiguity. It’s how sales managers plan pipelines, how marketers adjust campaigns, how executives make budget calls. AI can’t do that yet—not without being force-fed the rules and goals in extreme detail.
What we’re seeing instead is a division of labor. Agents are great at “do this next” tasks. But they still need humans to answer “why” and “what now.” This constraint is why companies are staffing AI-literate roles faster than ever. The strategy stays human. The execution goes agentic.
Key Supporting Points:
Even large LLMs fine-tuned on chess strategies hit a ceiling of ~25–30% accuracy on tactical puzzles.
Researchers found models lacked an internal simulator for board state—couldn’t even follow simple SAN moves reliably.
“LLMs can’t develop strategic depth without pretraining on domain-specific logic. RL alone is not enough.” — arXiv preprint
D. Enterprise Transformation, Not Just Tech
Companies are discovering that agentic AI is not a plug-in. It’s a structural change. To unlock ROI, it must be integrated across processes, managed by teams, measured by new KPIs, and governed at the enterprise level. The Bain research is clear: companies that pilot agents in isolation rarely succeed. The winners start with outcome mapping and process redesign.
The problem? Most companies aren’t ready. They don’t have clean data. Their workflows are fragmented. They’re still experimenting with AI on the side while running the core business in old systems. This is why Gartner projects that 40% of current agentic deployments will be shelved by 2027.
Agentic AI requires alignment between the business, the data, and the tech. You can’t automate what you don’t understand. And you can’t govern what you can’t track. That’s the transformation—it’s less about AI models and more about how work gets done.
Key Supporting Points:
Bain: “One use case rarely moves the needle. Without full process mapping, results are minimal.”
Gartner: 40% of agentic AI projects will be canceled before production due to lack of ROI and readiness.
Only 130 of the thousands of “agentic AI” vendors have meaningful capabilities beyond basic automation.
E. Business Model Reinvention
AI is pushing companies away from feature delivery and toward outcome delivery. The software itself is vanishing behind the scenes. Interfaces are shrinking, workflows are being triggered via Slack messages or APIs, and the buyer is no longer choosing tools—they're choosing results.
Service-as-a-Service is taking hold. Companies like Glide and Corti no longer pitch dashboards. They pitch results: retention improvements, better diagnoses, or faster loan onboarding. The product is there, but it's silent. Customers don’t care what model you used. They care what got done.
This shift is influencing how founders pitch, how VCs invest, and how companies build. Enterprise buyers are learning to expect co-pilots, not toolkits. Sellers are bundling service delivery into their pricing and packaging. And startups like Dust and ElevenLabs are riding this wave toward IPO-level valuations.
The future isn't SaaS with AI on top. It’s outcome delivery with AI inside.
Key Supporting Points:
“We’re not selling a dashboard. We’re selling the outcome that the dashboard used to help you find.” — Glide CEO
Amplitude, ElevenLabs, and Glean are all shifting to outcome-centric offerings with embedded agent automation.
“Don’t just build scalable point solutions. Build services that make the product disappear.” — Forbes
Final Takeaways for GTM Leaders and Professionals
1. Rethink Roles, Rethink Teams
GTM leaders must confront the new reality of team design. Entry-level sales reps, BDRs, customer success managers, and enablement roles are no longer task-heavy functions. With AI agents now handling research, email drafting, call summaries, and CRM updates, these roles shift from doing the work to managing the outcomes. Teams that used to scale by headcount now scale by AI fluency and orchestration.
The most valuable reps are becoming orchestrators. They do not spend their time building decks or logging notes, they prompt the system, review its work, and refine the message. This isn’t theory. Companies deploying agentic systems are hiring fewer entry-level roles and elevating AI-savvy contributors who can delegate and audit effectively. Leadership must invest in new role definitions, competencies, and onboarding frameworks that train people to manage machines, not replace them.
Supporting Bullets:
Microsoft cut 40 percent of legal, product, and dev roles in 2025, while GitHub Copilot wrote nearly 30 percent of the new code.
Goldman Sachs CIO: “Even junior hires must know how to supervise an AI, not just complete a task.”
At IBM, 8,000 HR roles were removed when AskHR, an AI assistant, replaced routine workflows.
2. Focus Your GTM Messaging on Outcomes, Not Features
Selling AI today is not about what your product does, it is about what it finishes. Enterprise buyers no longer want dashboards, they want decisions. They do not want data, they want revenue impact. The shift from product-led growth to outcome-led delivery means GTM teams need to rewrite their messaging. Features are optional, outcomes are mandatory.
Winning AI companies are designing offers that guarantee results. Whether that means automated onboarding, pre-qualified leads, or pipeline expansion, buyers expect automation to show up as execution, not tooling. GTM leaders should reposition their narrative around completion, not functionality, and move from “we help you” to “this gets done.”
Supporting Bullets:
“What we’re building is a service inside a product wrapper,” Glide CEO told Forbes.
Glean transitioned from enterprise search to “knowledge operations,” delivering answers, not documents.
Zendesk now pitches “automated customer service,” with AI agents resolving tickets without human escalation.
3. Treat AI Adoption as a Business System, Not a Plugin
Agentic AI doesn’t fit neatly into a feature update or a new campaign. Deploying it requires coordination across operations, sales processes, enablement, product, and legal. Without aligning across these domains, the result is chaos: partial automation, broken workflows, missed SLAs. The difference between a successful agent deployment and a canceled pilot is whether the business is willing to treat the change as systemic, not tactical.
GTM leaders should partner closely with operations and data teams to clean data pipelines, define high-value workflows, and install monitoring on agent activity. It is also crucial to create incentive structures that reward human-machine collaboration rather than outdated activity metrics. Done right, AI should reduce work, not shift it sideways.
Supporting Bullets:
Bain: “One use case rarely moves the needle. Most companies automate inefficiencies instead of removing them.”
Gartner predicts over 40 percent of agentic AI deployments will fail before production due to lack of ROI or governance alignment.
Capital One built a governance layer with evaluator agents that can stop tasks if policies are violated.
4. Redesign GTM Enablement Around Agent Fluency
Most sales enablement programs today train reps on products, competitors, personas, and objection handling. Few train them on AI prompting, agent orchestration, delegation logic, or error supervision. That is a blind spot. The reps of the future are not just closers or storytellers. They are part-time engineers, part-time product managers, and full-time AI conductors.
Enablement teams should build new competency frameworks that include how to prompt for pipeline inspection, how to run playbooks with AI agents, and how to monitor for hallucination or drift in outputs. GTM onboarding must now teach reps how to “talk to” their assistants, set up guardrails, and adapt based on context. These are coachable skills and will become deal-critical fast.
Supporting Bullets:
Dust agents write product summaries, update CRMs, and open GitHub issues directly from sales calls.
Vista expects 5 to 10 AI agents per user across its portfolio, performing execution tasks traditionally done by people.
“The individual contributor is shifting into a player-coach. Supervising machines, not just peers.” – Goldman Sachs CIO
5. Align Pricing and Packaging With Automation Value
When your product completes tasks that used to require a person, your pricing should reflect the business value of that automation. Many GTM teams are still quoting based on seats or usage, not outcomes. This disconnect erodes credibility and slows adoption. Buyers are used to measuring impact in time saved, dollars earned, or pipeline grown. Match your commercial model to those expectations.
Offer automation tiers that map to complexity. Consider bundling services, support, and AI orchestration into one offer. Provide guaranteed outputs where possible, and charge for what gets delivered, not what gets clicked. This approach simplifies the buying process and builds trust faster. It also forces internal alignment around delivering results, not dashboards.
Supporting Bullets:
ElevenLabs is exploring voice-as-a-service models with enterprise integrations rather than licensing APIs alone.
Bain found that AI-led selling motions that aligned pricing with impact saw >30 percent improvement in conversion rates.
“Service-as-a-service is the new SaaS. You are not selling software, you’re selling completion.” – Forbes
6. Know Where to Start, and Where Not To
Not every process is ready for automation, and not every team will benefit from AI immediately. The best AI GTM strategies begin with high-impact, repetitive workflows that lack strategic ambiguity. Prospecting, email generation, call follow-ups, and initial demo prep are excellent starting points. These are measurable, testable, and low risk.
Avoid overcomplicating your rollout by starting with multi-agent chains or niche use cases that depend on human nuance. Your first few wins need to be simple, visible, and undeniable. From there, build confidence, clean up the adjacent processes, and scale outward. Think in layers, not leaps.
Supporting Bullets:
Dust began by automating CRM updates and battle card summaries before expanding to full call analysis and ticket generation.
Capital One started with auto dealerships where tasks had structured, bounded workflows and a clear success metric.
Gartner advises starting with workflows where “decisions are needed, automation for routine tasks, and assistants for information retrieval.”
GTM AI TECH OF THE WEEK: WISPR FLOW
What Is Wispr Flow?
Wispr Flow is a seamless, AI‑powered voice dictation assistant that transforms spoken language into polished, formatted text across any desktop app (Windows, macOS) or iOS. Unlike built-in dictation features, Flow:
Transcribes in real-time with high accuracy—removing filler words, correcting context mid-sentence, and formatting as you speak
Supports 100+ languages, adapts tone per app, and offers features like whisper mode for on-the-fly typing
Incorporates Command Mode: let you edit tone, structure, or summarize spoken drafts without leaving your existing workflow
Enables vibe-coding by integrating with developer tools (e.g., Warp terminal, Cursor, Replit), translating speech into code commands
Testing It in Real Time
I’m currently evaluating Flow in a variety of GTM contexts:
Email & Slack drafting: Instead of typing follow-ups post-meetings, I hold the hotkey, speak updates, and release—the result is clean, structured text with no filler. It’s consistently 3× faster than typing.
Meeting summarization: Speaking out live recaps while working yields formatted bullet summaries instantly. Command Mode helps me refine tone or compress points on the fly.
Cross-platform flow: I can start notes via iPhone shortcut, then continue editing on desktop—Flow syncs context and custom dictionary
Developer support: I’ve experimented with speaking Jira ticket descriptions and commit messages directly using Warp—Flow handled domain-specific terms and punctuation flawlessly .
Strengths & GTM Impact
🚀 Faster Content Production & Response Times
GTM professionals churn through emails, proposals, launch docs—and Flow dramatically speeds this up. Reports suggest users save ~1.5 hours weekly and type 2–3× faster✨ Cleaner, More Polished Communication
With auto-punctuation, filler removal, and context-aware tone, your outbound messaging looks consistently polished—critical for brand voice in customer and partner communications.🌍 Language & Multilingual Edge
GTM teams in global roles benefit from fluent dictation across 100+ languages, switching between them mid-sentence without needing reconfiguration👥 Team Coordination & Onboarding
Spoken hand-off notes, live partner updates, and quick recaps in team channels become accurate and effective—easing collaboration friction.🤖 Code & Technical Integration
For those that straddle marketing, sales, and product ops, the ability to insert commands or draft light code via voice is a productivity booster—and a differentiator in technical GTM roles.
Weaknesses & Considerations
Privacy & system access
Flow requires broad permissions—clipboard, keyboard injections, screen context—which raised concerns among privacy-focused users. Teams must assess compliance and data policies before full deployment.Context limitation
In noisy environments, whisper mode helps, but background speech can still creep in. It’s not ideal in open offices; privacy or shared desk settings remain tricky .Hotkey learning curve
Effective use requires adopting hotkeys and pausing between speaking and pasting—new users may fumble until it becomes muscle memory.
Potential ROI & Market Fit
Time saved vs cost: With Flow Pro at $12–15/mo and enterprise teams at $10+/seat, the ROI shows up quickly if you save even an hour per week.
Higher-quality outputs: Cleaner first drafts lead to fewer revisions. For tech GTM roles, coding support adds disproportionate ROI.
Onboarding multiplier: Sales reps and marketers quickly trained on dictation can focus more on strategy and less on drafting content.
Overall Take
Flow stands out in the crowded dictation landscape. It tackles the GTM pain points—speed, clarity, personalization—without disrupting existing tools. Based on real-world testing, I see it as a powerful assistant for:
Accelerating proposal and content creation
Enabling clear voice-first communication internally and externally
Supporting multilingual and developer-adjacent workflows
If your team drafts frequently, meets often, or straddles technical content ops, Flow is worth piloting. The key is balancing its speed and polish against privacy and environmental context.