5/21/2025: Gartner CSO Event Special, trends and patterns from Enterprise and AI, Loyee.ai
If you are reading on email, you will want to read the full newsletter, I deep dive into Gartner insights.
This is sponsored by the AI Business Network and GTM AI Academy
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.
So after today, here is what I will do on Linkedin:
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.
This week I am literally attending the Gartner CSO event and wanted to give some highlights from the Gartner research team on AI and revenue or enterprise teams for you.
If you prefer to listen instead of read, our AI Friends are here for you:
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
Also today is the Domo Agentic AI Free event, come hang! https://bit.ly/4j2mX14 or on Linkedin:
https://www.linkedin.com/events/agenticaiinnovationsummit7315150571710136320/theater/
Live Session Agenda:
9:00 AM-9:05 AM Host Welcomes
Mark Boothe – CMO, Domo
9:05-9:30 AM Jim Fairweather – Head of AI GTM, Google
9:30-10:00 AM Kevin Petrie – VP, Research, BARC
10:00-10:15 AM Awards and On-Demand Session Release
10:15-10:35 AM Juan Sequeda – Head of AI Lab, data.world Mohammed Aaser – CDO, Domo
10:35-10:55 AM Danielle Barnes – Managing Director, AI, OneMagnify Tom Thomas– VP, Data Strategy & AI, FordDirect
11:15-11:20 AM On-Demand Session Release
11:20-11:35 AM ME! Jonathan M K. – Head of GTM Growth, Momentum.io
11:35- 11:55 AM Frederick DeWorken – CEO, BlueYeti
11:55 AM-12:15 PMSteve Harlow– VP, Software Development, OPUS
12:15-12:20 PM Closing Remarks
Lets dig in to 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.
This episode I get to dive in with Lauren Morgenstein Schiavone with Wonder Consulting LLC talking about her 5 steps into AI Transformation.
Introducing Lauren's AI Journey
Lauren kicked off by giving us a little background about herself. She spent 16 years with P&G before making the bold decision to step away to focus more on her family and her new interest—AI. Lauren described how she quickly identified AI as a transformative force, much like the early days of e-commerce. Her leap into AI was about reshaping business strategies and enhancing flexibility in life.
Lauren's 5-Step Framework for AI Transformation
Lauren introduced a powerful 5-step framework. Unlike many who dabble without direction, she offered a structured roadmap to integrate AI into business processes. Let's break it down:
Assess: Know your starting point. It's important to understand your organization's current capabilities and technological landscape. Lauren suggested using a scorecard not just to get a score, but to drive conversations that push boundaries.
Establish: Once you're aware of where you stand, form your AI council. Make AI integration a priority linked to business goals, not just an extracurricular activity.
Experiment: Identify tasks that are critical yet repetitive and candidates for AI. Test the impact of AI on these areas based on business needs and employee enthusiasm, combining that with a pragmatic approach to experimentation.
Scale: When you find what works, expand it. Continuous iteration is key, just as strategic planning for what's next.
Adopt: Don't wait. Adoption is about preparing your organization for the smarter AI-driven future. Let AI do the heavy lifting in repetitive tasks so that human creativity and innovation can flourish.
AI Transformation: A Cultural Shift
Lauren spoke on the necessity of change management. She emphasized the importance of a culture that supports experimentation, transparency, and accountability. Empower your people to challenge the status quo and facilitate continuous learning. AI is about collaboration, not competition.
Our talk also touched on the concept of AI-native companies—businesses fundamentally built on AI principles. These entities could very well leapfrog traditional competitors by fully harnessing AI’s potential from day one.
Final Thoughts on Tools and Adoption
Lauren wasn't shy about sharing her toolkit, although she stressed the power of mastering just one—like ChatGPT. However, she also highlighted up-and-coming tools like Google's Deep Research for enhancing productivity. The lesson here? Leverage what you already have to the fullest before adding more complexity.
Lauren and I ended the conversation buzzing about the potential of AI to fundamentally reshape strategies, processes, and cultures. She emphasized that the journey is just beginning, and the need to stay open, educated, and collaborative is more critical than ever.
In the end, AI isn't just a tool. It's an opportunity—a way to rethink and reinvent our ways of working for growth and balance. Stay tuned because we’ll keep having these conversations, and I promise you won't want to miss them.
GTM AI TECH OF THE WEEK: Loyee.ai
Loyee.ai is an AI-driven sales intelligence platform designed to enhance B2B sales and marketing efforts by providing real-time, granular insights into potential leads. Unlike traditional databases, Loyee.ai leverages machine learning to analyze external data sources such as websites, news articles, LinkedIn profiles, job descriptions, and financial reports, ensuring that the information is current and actionable. (loyee.ai)
I can tell you from personally being involved with the founders and also seeing the output, its INSANELY Valuable, lets dig in.
🔍 Key Features
Granular ICP Analysis: Loyee.ai identifies high-value leads by analyzing detailed data points, including company initiatives, strategies, tools, and decision-makers. This allows for a more precise targeting approach. (loyee.ai)
Real-Time Data Enrichment: The platform enriches company and persona data in real-time, pulling information from various external sources to provide up-to-date insights. (G2)
Automated Lead Prioritization: Loyee.ai automatically tiers, segments, and pre-qualifies leads based on the enriched data, streamlining the lead qualification process. (G2)
Personalized Messaging: The platform can pre-write personalized messages based on different attributes and signals, facilitating scalable and targeted outbound campaigns. (G2)
CRM Integration: Loyee.ai seamlessly integrates with popular CRMs like Salesforce and HubSpot, allowing for easy data synchronization and workflow integration. (momentum.io)
✅ Strengths
High Accuracy and Transparency: Users have praised Loyee.ai for its accuracy and transparency in signal intelligence, noting its flexibility in setting up granular queries. (G2)
Up-to-Date Data: By incorporating LinkedIn data and other real-time sources, Loyee.ai ensures that the information provided is current, enhancing the relevance of outreach efforts.
Enhanced Response Rates: Companies have reported significant increases in email response rates, attributing this to the platform's ability to identify and target leads based on timely and relevant signals.
📊 Pricing
Starter Plan: $830/month, suitable for small teams, includes research and enrichment of up to 5,000 accounts per year, AI-driven customer profiling, dynamic account prioritization, and HubSpot integration.(OMR)
Pro Plan: $2,500/month, designed for growing companies, offers research and enrichment of up to 30,000 accounts per year, personalized messaging with AI-driven insights, AI-driven contact search agent, and advanced integrations (Apollo, Outreach, Salesforce).(OMR)
Enterprise Plan: Custom pricing, tailored for large enterprises with complex sales and marketing needs, includes all Pro features, custom account research and enrichment, AI-driven market mapping, AI-driven lookalike account lists, and a dedicated customer success manager. (OMR)
🔄 Comparison with Clay and ZoomInfo
Loyee.ai vs. Clay
Data Sources: Loyee.ai focuses on real-time external data sources for lead enrichment, while Clay integrates with over 50 data providers, using a waterfall enrichment method. (Clay)
Customization: Clay offers a spreadsheet-first approach with extensive customization options, whereas Loyee.ai provides a more guided experience with its AI-driven insights.
Use Case: Clay is ideal for teams needing advanced data enrichment capabilities and custom workflows, while Loyee.ai excels in providing timely, actionable insights for targeted outreach.(stackfix.com)
Loyee.ai vs. ZoomInfo
Data Freshness: Loyee.ai provides real-time data by analyzing external sources, whereas ZoomInfo relies on a static database that may not always reflect the most current information.
Granularity: Loyee.ai offers deep insights into company initiatives and decision-makers, while ZoomInfo provides broad contact and firmographic data.(AppSumo)
Pricing: ZoomInfo is generally more expensive, making Loyee.ai a cost-effective alternative for teams seeking real-time, granular insights.(G2)
💡 Potential ROI
By leveraging real-time data and AI-driven insights, Loyee.ai enables sales and marketing teams to:
Increase Efficiency: Automating lead enrichment and prioritization reduces manual research time, allowing teams to focus on high-value activities.(momentum.io)
Enhance Targeting: Granular insights into company initiatives and decision-makers improve the precision of outreach efforts, leading to higher engagement rates.
Improve Conversion Rates: Personalized messaging based on timely signals increases the likelihood of converting leads into customers.(G2)
🧠 Conclusion
Loyee.ai stands out as a powerful tool for B2B sales and marketing teams seeking to enhance their outreach efforts with real-time, AI-driven insights. Its ability to analyze external data sources and provide granular information on potential leads enables more targeted and effective campaigns. While it may not offer the extensive data provider integrations of Clay or the broad contact database of ZoomInfo, Loyee.ai's focus on current, actionable intelligence makes it a valuable asset for teams aiming to improve their lead generation and conversion strategies.
I attended 10+ speakers and analysts from Gartner this last week, I wanted to share some highlights and trends I am seeing as a result
Section 1: The Strategic Imperative—Reinventing Sales at the Pace of Transformation
1.1 The New Productivity Mandate
The keynote and supporting presentations from the 2025 Gartner CSO event converge on one central thesis: sales is undergoing an industrial revolution at the speed of COVID. Sales cycles are extending—rising from 83 days in 2022 to 123 days in 2024 according to Gartner data. The legacy approach of marginal gains is no longer sufficient. Leaders are now forced to chase a new revenue productivity frontier under conditions of increased scrutiny, decreased budgets, and executive pressure for AI-led transformation.
Three major expectations are reshaping the role of CSOs:
Increasing quotas without increasing headcount.
Doubling management ratios (6 reps per manager to 12).
Driving transformation with agentic AI, not just automation.
This shift is reinforced by executive-level pressure: CEOs now hold CSOs personally accountable for AI transformation—not just company-wide, but as an individual leadership competency.
1.2 Agentic AI: The Core Technology Catalyst
Gartner's keynote redefined AI through the lens of agentic systems, which they describe as software with discretionary decision-making power:
Goal-driven: Agents are activated based on specific sales objectives (e.g., creating an account plan).
Contextual intelligence: They reason with historical data, CRM signals, and deal activity.
Executional autonomy: Agents pull together 80–90% of a deliverable like a key account dossier in seconds, based on learned sales tradecraft.
This technology is already showing results:
Companies deploying agentic AI for solution configuration are 2.1x more likely to acquire customers.
Accenture’s use of 12+ AI agents reduced proposal time by 50%, showing measurable productivity impact.
Gartner emphasizes this is not “if-this-then-that” automation. It’s “if this, then decide what to do.” This fundamental change to how work is executed is likened to deconstructing the craftsmanship of sales and rebuilding it into a scalable, AI-augmented system.
1.3 Transformation at Velocity: Days and Weeks, Not Years
The keynote repeats the idea that sales leaders must now lead transformation in days and weeks, not years. This framing introduces Gartner’s RXT Matrix (Revenue x Transformation):
Defend: Time-saving use cases; protect current market position.
Extend: Growth-focused AI initiatives that optimize cost of sale.
Upend: Self-disruptive bets that reconstruct go-to-market execution.
CSOs must lead structured AI portfolios across these three vectors, with clear communication around investment level, transformation scope, and expected return. Gartner warns against falling into the “FOMO Portfolio” trap—where 80% of AI work is time-saving automation with little top-line impact.
1.4 The Leadership Lens: Beyond Tech to Human Capital
CSO accountability is shifting from pipeline forecasting and quota enforcement to talent orchestration and AI operationalization. Two insights stand out:
Only 25% of sales leaders currently feel personally accountable for AI transformation—even though this is now a top expectation from boards and CEOs.
Upside talent, defined by flexibility, growth mindset, and AI partnership ability, will outperform legacy “experienced” reps when supported by agentic AI.
Future-fit sales leadership demands the ability to operationalize AI strategy, deconstruct sales workflows, and simplify sales roles to reduce cognitive and admin load, unlocking time for strategic work. Cisco and Lenovo were highlighted as exemplars for deploying agents to drive significant operational savings and seller capacity gains.
Section 2: AI Economics—From Skepticism to Strategic Value
2.1 Widening Gap: Confidence vs. Value Realization
Wendy Butler-Mafuz’s session, "The True Economics of AI", confronts a critical contradiction in the market:
47% of CSOs cite a lack of clarity on ROI-driving use cases as their top barrier to value realization.
>50% report underutilization of GenAI across sales stages.
50% say GenAI produced far fewer benefits than expected.
Despite the market’s clear commitment to AI (94% of sales orgs have at least one AI initiative in motion), leaders are stuck between uncertainty and action paralysis.
Butler-Mafuz presents a framework that breaks the bottleneck: AI Investment Portfolios, modeled like financial assets—balancing risk, ROI timing, and transformation impact.
2.2 The Revenue-Transformation Matrix: A Strategic Lens
Building on the RXT Matrix from the keynote, this session breaks AI use cases into three investment categories with clear business case modeling:
Portfolio Type Focus Characteristics Example Use Cases Defend Efficiency Low risk, short-term ROI, time-saving AI meeting summaries, CRM automation Extend Growth Medium risk, measurable top-line value Deal scoring, pipeline acceleration Upend Disruption High risk, high reward, market reinvention Full-cycle sales agents, AI-first buyer journeys
Most CSOs are stuck in the Defend quadrant, where AI is used as a digital assistant to reduce administrative load—but not yet to drive revenue transformation.
2.3 Strategic Recommendation: Treat AI Like a Portfolio
To move past paralysis, CSOs must treat AI initiatives like they do product launches or market expansions—with portfolio management discipline:
Map initiatives by ROI vs. transformation difficulty
Measure time to value (TTV) as a core economic input
Balance quick wins (Defend) with 2–3 Extend bets
Model cost of inaction—quantify the revenue lost by not automating or improving key workflows
This approach has been adopted by leaders like EmployBridge and Accenture:
EmployBridge saw 4x win rate improvements and a 2.6x increase in gross margin per rep by combining AI accelerators with a disciplined execution rhythm.
Accenture manages 12+ agents with individual roadmaps and biweekly executive reviews to double the ROI on proposal creation alone.
2.4 AI’s Strategic Value = Productivity Delta × Scale
A new formula emerged during the session to help CSOs frame ROI expectations with precision:
AI ROI = (Δ in Seller Productivity) × (Org Scale) – Deployment Cost
For example:
A 20% improvement in win rate from AI-driven proposal generation across 1,000 sellers = substantial impact on revenue.
Deployment cost amortized over time becomes negligible compared to productivity delta multiplied by opportunity size.
CSOs are encouraged to stop thinking in terms of “cool tools” and start thinking like CFOs—treating AI as a capital allocation decision backed by:
Comparative performance baselines
Sales role–specific productivity metrics
Workflow-level action mapping
Closing Message from Butler-Mafuz
“If you wait for certainty, you will be outpaced. AI investment is not a leap of faith—it’s a strategic experiment portfolio. Manage it like you manage revenue.”
Section 3: Revenue Action Orchestration—The Operational Backbone of AI-Led Sales
FYI to reader, RAO is literally what My company Momentum.io is and does
3.1 The Rise of Revenue Action Orchestration (RAO)
In Steve Rietberg’s session, "New Market Spotlight: Revenue Action Orchestration", Gartner introduces RAO as the necessary infrastructure to operationalize AI strategy across GTM teams. RAO is not another category of sales tech—it’s a unification layer.
According to Rietberg:
“RAO is where AI stops being a tool and becomes the system of action for your revenue engine.”
RAO enables:
Frontline execution: AI-guided next best actions
Managerial control: Live visibility into deal momentum, skill gaps, and rep prioritization
Leadership decisioning: A single source of structured commercial truth for forecasting, coaching, and planning
3.2 What RAO Replaces: The Legacy Sales Tech Stack
The diagram on page 6 in Rietberg’s slides shows the before-and-after view of RAO:
Old Stack:
CRM
Sales engagement platforms
Sales automation tools
Revenue intelligence vendors
→ Result: Fragmented data, overlapping functionality, bloated costs, seller overload
RAO Stack:
Single vendor platform
Unified workflows
Deep integrations across tools
AI-first interaction design
→ Result: Operational consolidation and 45% higher productivity likelihood due to reduced tech overwhelm
3.3 RAO Architecture = AI + Data + Action
The architecture domino model on page 7 outlines the layers required to achieve RAO outcomes:
AI-Captured Activity
Email, calendar, call summaries, CRM updates
Unified into one commercial dataset
AI Capabilities Layer
Deal scoring
AI coaching
Forecasting
Guided playbooks
Buyer insights
Pipeline alerts
Operational Output
Rep prioritization
Forecast accuracy
Opportunity progression
Coaching moments
This cascading structure enables organizations to go beyond activity logging to AI-led judgment, reducing noise and increasing signal fidelity.
3.4 RAO Use Cases: Cross-Functional Execution
RAO isn’t limited to sales managers—it stretches across roles:
Sellers: Execute AI-informed micro-actions across their book
Managers: Run comparative performance diagnostics and behavior-based coaching
Ops/Enablement: Monitor workflows, flag deal risk, orchestrate rep and manager nudges
RevOps: Automate insights into dashboards, revenue signals, and roll-ups
Highlighted use cases:
Pipeline forecasting: AI interprets data patterns for more reliable commits
Talent development: Dynamic coaching aligned to deal progression signals
Customer retention: Action triggers to drive post-sale engagement and upsell plays
3.5 Why RAO Matters: It Solves the AI Execution Gap
In Butler-Mafuz’s AI economics session, the gap was clear: the tech exists, but execution is inconsistent. RAO solves this by:
Providing the governance layer to align AI with revenue strategy
Enabling a closed feedback loop between execution, analysis, and optimization
Eliminating what Gartner calls “tech stack bloat,” which contributes to rep burnout and missed targets
The average B2B sales team uses 9+ sales tools across stages. RAO consolidates this down to an AI-native execution environment.
Closing Message from Rietberg
“Revenue orchestration is no longer a metaphor. It’s a system design. RAO is the new GTM operating model—where data flows to the places decisions are made, and AI acts as the conductor.”
Section 4: Value Realization—The Next Competitive Battleground
4.1 The Value Gap Is the Hidden Threat
In Daniel Hawkyard’s session “Value Realization: The Key to Unlocking Customer Retention and Growth”, Gartner exposes a fundamental failure in modern GTM strategy: most teams stop at the sale. The result? A dangerous gap between what was promised and what customers perceive as delivered.
This is the Value Gap—the delta between:
The supplier’s value proposition (what was sold)
And the customer’s value realization (what was experienced)
Consequences of this gap:
Lower retention
Declining advocacy
Slower expansion
Higher acquisition pressure
Compounding churn costs (3 new customers are needed to replace 1 lost customer)
4.2 Customer Value ≠ Customer Satisfaction
Hawkyard makes a clear distinction: customer value is not about sentiment. It’s about measurable outcomes achieved after the purchase.
The Value Pyramid on page 16 breaks this into four levels:
Project outcomes – Did we implement correctly?
Adoption outcomes – Are users engaging?
Technical/functional outcomes – Is the product working?
Business outcomes – Are we solving real business problems?
Gartner data shows that 71% of B2B buyers say they are more likely to stay with and expand with a supplier that proactively helps them extract value—even if it requires changing what they originally purchased.
4.3 Realization Is a Team Sport: Enter the Commercial Coalition
One of the strongest cross-presentation patterns from Hawkyard, Sandhya Mahadevan (RevOps), and Wendy Butler-Mafuz (AI ROI) is this:
GTM teams must evolve from “pipeline machines” into value realization engines.
This means tearing down post-sale silos. A siloed CS, Marketing, and Product model exacerbates the value gap. Gartner calls for a unified commercial coalition, where RevOps, sales, marketing, customer success, and product share:
A common value framework
A unified customer journey map
Shared success metrics
On page 22 of Hawkyard’s presentation, Gartner reports only 21% of GTM teams collaborate on buyer journey mapping. This failure creates blind spots in post-sale experience and limits visibility into value delivery.
4.4 The CSO’s Role: Anchor the Business Around Value
Gartner urges CSOs to take an active role post-sale. This includes:
Owning the value realization narrative
Embedding it into renewal and upsell playbooks
Using RAO signals to orchestrate post-sale action triggers (e.g., expansion nudges, churn risk, and success signals)
Aligning performance reviews and coaching around customer outcomes, not just closed-won deals
Enablement and frontline teams must now be equipped to answer:
“What does success look like for this customer?”
“What business problems have we solved?”
“Can I prove it with data?”
4.5 Value Realization Is the Bridge to Retention and Expansion
Retention is now a strategic growth lever, not just a defensive KPI:
73% of CSOs in 2025 prioritize growth from existing customers over net new logos
Companies that master post-sale value orchestration report:
2.3x higher expansion rates
40–60% lower churn in strategic accounts
Consistent net revenue retention (NRR) above 120%
This strategic shift aligns perfectly with the earlier theme of agentic AI and RAO—both of which provide the infrastructure to detect account health, trigger actions, and support CSMs with insights that matter.
Summary of Strategic Implications for Post-Sale GTM Execution
Function New Responsibility Required Capability Sales Own value realization metrics Post-sale alignment, renewal playbooks Customer Success Prove ROI, not just satisfaction Success plans, outcome reporting RevOps Enable value signal tracking Instrumentation of RAO + CRM Enablement Train for customer outcomes Post-sale use case alignment Leadership Track NRR as primary growth metric Revenue portfolio balancing
Final Recommendations Across All 4 Sections
Reframe your GTM model around the productivity frontier.
Use agentic AI to deconstruct and reconstruct workflows from seller onboarding to key account management.Run your AI strategy like a capital investment portfolio.
Balance Defend, Extend, and Upend use cases. Monitor ROI with hard metrics like time savings, revenue impact, and TTV.Adopt RAO as your operational GTM OS.
RAO brings structure, action, and AI together in one execution layer that cuts through the sales tech chaos.Anchor GTM execution in post-sale value.
Redefine success as realization—not just close rate. Embed that thinking across all customer-facing functions.
Gartner CSO 2025 Conference Synthesis Report
Final Section: Executive Conclusion & Cross-Functional Strategic Patterns
Section 5: Executive Conclusion – Converging Signals, Strategic Imperatives
Going to all these 10+ key sessions and synthesizing quantitative and qualitative insights from the 2025 Gartner CSO event, a clear picture emerges:
The 2025 GTM mandate is not optimization. It’s reconstruction.
This is not incremental change. This is a structural realignment of how revenue is created, delivered, and measured—powered by agentic AI, governed by strategic ROI modeling, executed through orchestrated systems, and sustained by value realization.
Gartner frames this shift as “The Great Sales Awakening.” And it’s happening across all three major dimensions of GTM execution:
5.1 Patterns Across Presentations
Pattern #1: AI Disappointment ≠ AI Failure. It’s Misalignment.
50% of CSOs report underutilization of GenAI
1 in 2 say it hasn’t delivered the expected value
But companies using agentic AI in workflows (e.g., proposal generation, account planning) are 2x more likely to acquire customers
➡️ Conclusion: AI fails when used for novelty. It succeeds when aligned to revenue-critical workflows and tracked with outcome metrics.
Pattern #2: Sales Cycles and Buyer Preferences Are Outpacing Sales Teams.
Sales cycle duration: +48% growth in 2 years (from 83 to 123 days)
61% of B2B buyers prefer a rep-free experience
Only 49% of sales and marketing orgs agree on lead definitions
➡️ Conclusion: Old GTM cadences are misaligned with modern buying behavior. AI and RevOps must re-architect the buying experience with insight orchestration and aligned engagement.
Pattern #3: Growth Is Coming from Existing Customers—But Few Are Ready for Value Realization.
73% of CSOs prioritize account expansion over net-new acquisition
But only 21% of GTM teams collaborate on buyer journey mapping
And the Value Gap—between promise and post-sale outcome—is shrinking retention, expansion, and advocacy
➡️ Conclusion: The shift from CAC to NRR must be matched with an end-to-end customer value strategy led by CSOs and powered by data.
Pattern #4: RevOps and Enablement Are Becoming Strategic Orchestration Layers.
Enablement is moving from “training and content” to workflow-integrated performance coaching via RAO
RevOps is evolving into a portfolio governance office for AI and productivity strategy
RAO becomes the system of action—not just the system of record or insight
➡️ Conclusion: Execution has to move beyond insight and training—it now requires orchestrated action, structured enablement, and shared accountability across commercial functions.
Pattern #5: Future-Fit Talent Is Built Around Upside, Not Tenure.
Only 6% of job descriptions include AI-related skills
CSOs who simplify roles and empower reps with AI are 4.5x more likely to hit goals
Flexibility, AI dexterity, and customer-focused problem-solving are now the core competencies—not years in industry
➡️ Conclusion: Talent strategies must prioritize upside potential and AI enablement over rigid experience filters. AI can now derisk ramp time, freeing orgs to hire for mindset over CV.
5.2 Cross-Functional Mandates for 2025 GTM Strategy
Function Strategic Shift Operational Mandate Sales From quota pursuit to orchestrated execution Deploy AI agents across deal cycles; coach on RAO insights Marketing From top-of-funnel volume to pipeline velocity Align with sales on value prop, journey triggers, and buyer enablement Customer Success From CSAT to ROI realization Create value playbooks and post-sale success plans with RevOps RevOps From reporting to orchestration Operationalize AI ROI, lead RXT portfolio tracking, unify signal capture Enablement From training to performance acceleration Embed learning into RAO workflows, support upside talent activation Leadership (CSO/CRO) From inspection to transformation leadership Sponsor agentic AI strategy, value realization, and RAO implementation
Final Takeaways: What GTM Leaders Must Do Now
✅ Codify Your RXT Matrix
Map all AI and process investments into Defend, Extend, and Upend categories. Use this to allocate budget, focus effort, and report to the board.
✅ Rebuild Sales Execution Through RAO like Momentum.io
Think beyond CRM. Orchestrate frontline, manager, and RevOps workflows using real-time AI signals. Replace fragmented tooling with unified action systems.
✅ Instrument Value Realization Across the Journey
Set NRR as your north star. Build a commercial coalition to measure, prove, and act on realized customer outcomes—pre- and post-sale.
✅ Simplify, Specialize, and Scale Talent
Deconstruct complex roles. Use AI to scale knowledge and focus human talent on moments that require judgment, influence, or creativity.
✅ Operate in Days and Weeks, Not Quarters and Years
Adopt a 12-quarter vision, 12-week execution cadence. Tie every cycle back to ROI acceleration, value realization, and competitive advantage.
Final Statement
The 2025 GTM transformation is not a technology problem. It's an operating system rewrite.
Agentic AI is the new steam engine. RAO is the assembly line. The CSO is no longer just the Head of Sales—they are the Chief Architect of Revenue Execution.And time is running out. Three years from now, the winners won’t be the ones with the most data or the best CRM—they’ll be the ones who rebuilt faster.