AI has moved from tool to terrain. It shapes how buyers discover brands and how revenue teams execute. Withย 88% of peopleย having engaged with a chatbot in the past year, the way buyers find, evaluate, and engage with brands has fundamentally changed. Your old GTM playbook cannot handle this reality.
Revenue teams struggle to adapt because disjointed systems and manual processes create friction. Many still rely on legacy SEO for external visibility while internal workflows like lead management and sales tasks remain slow and error-prone. The gap between AI-driven buyers and manually operated teams stalls growth.
Use this two-part audit to modernize your GTM. First, assess your external brand visibility for Generative Engine Optimization. Then, apply the same audit lens to your MQL-to-SQL handoff and daily sales workflows to find automation wins.
Auditing your brand’s visibility for generative engine optimization (GEO)
Generative Engine Optimization is the successor to traditional SEO. The landscape has shifted from simple keyword rankings to conversational, AI-synthesized answers. Success now means the AI cites your brand as an authority in its response, not just listing you on a page of blue links.
This shift is already here. In a recent episode ofย The Go-to-Market Podcast, host Dr. Amy Cook and guest Saul Marquez discussed this transformation. As Marquez put it, “It’s now GEO.”
Step 1: Identify and test your core queries
Start with conversational, high-intent queries that mirror how buyers actually ask for help. Think in questions and comparisons, such as “What is the best revenue operations platform for a mid-market tech company?” or “Compare Fullcast vs. a homegrown solution.”
Test these queries across ChatGPT, Gemini, and Perplexity. Document where your brand shows up, how the models describe you, and which competitors they include. This process is the foundation of modernย Answer Engine Optimization.
Step 2: Measure key visibility metrics
A GEO audit needs KPIs that go beyond rank and traffic. Focus on four areas to understand your visibility in AI-driven search:
- Citation frequency:ย How often does the model mention your brand, product, or content for core queries?
- Accuracy and sentiment:ย Are the descriptions correct and positive? Incorrect summaries can hurt more than silence.
- Prominence:ย Do you appear first as the recommended solution or at the bottom of a long list?
- Competitor share of voice:ย How do your citations and prominence compare with direct competitors for the same queries?
Step 3: Analyze gaps and optimize your content engine
Review your findings to identify where you fall short. You may see thin citation rates for key use cases, outdated product facts, or competitors who dominate the conversation.
Fixing these gaps requires a shift from content for clicks to building an engine thatย informs AI platforms. Publish authoritative, well-structured answers to your buyersโ core questions so models can easily cite you as a trusted source.
Auditing internal workflows for AI automation wins
A strong GEO presence can drive demand, but you lose momentum if your internal processes slow follow-up and routing. Apply the same audit discipline to your revenue engine to find automation opportunities that accelerate pipeline.
Audit your MQL-to-SQL handoff
The handoff between marketing and sales leaks revenue. One industry snapshot puts theย average MQL-to-SQL conversionย at 13%, which signals misalignment and inefficient process. An internal audit shows you exactly where the flow breaks.
- Map the workflow.ย Document every step, tool, and stakeholder in the handoff. Flag manual bottlenecks, such as spreadsheet-based routing or slow lead assignment.
- Measure what matters.ย Track MQL-to-SQL conversion rate, lead velocity from stage to stage, and sales acceptance rate for marketing-generated leads.
- Audit data quality.ย Incomplete or inaccurate CRM data causes dropped leads and weak follow-up. Strongย data hygieneย is not a chore. It is the foundation for reliable scoring, routing, and reporting.
The AI solution is to automate the friction away with intelligent workflows. AI can score leads using behavioral and firmographic signals, route them to the right rep in real time, and enrich records automatically so sellers have context on day one.
Identify and automate non-selling tasks
Sales reps lose hours to admin work instead of revenue-generating activity. Studies show teamsย save 2-3 hours dailyย by automating non-selling tasks.
Find the biggest wins by reviewing CRM activity logs, surveying reps on tedious tasks, and mapping the workflow from prospecting to close. High-impact examples include:
- Automated data entry and call logging into the CRM
- AI-powered email personalization and follow-up sequences
- Automated meeting scheduling and calendar coordination
As companies scale, manual processes break. For a company likeย Degreed, critical processes like lead routing were managed across spreadsheets and four separate tools. Nate Kimmons, their former CRO, noted, “Fullcast solved the lead routing and territory management problems no one talks about until they become a drag on the entire sales org.”
By automating non-selling tasks, you free your sales team to spend more time selling and less time on busywork.ย For leaders who want a structured framework, learn how toย find and fix your GTMย gaps with a targeted AI audit.
Build your AI-first revenue command center
Your audit delivers the blueprint. The transformation happens when you turn findings into automation. Replace fragmented tools and manual spreadsheets with a connected system that gives you end-to-end control of your revenue engine.
This is where an end-to-end Revenue Command Center helps. Instead of stitching together planning, enablement, and reporting, a unified platform likeย Fullcast Copy.aiย connects marketing, sales, and RevOps workflows in a single, AI-powered environment. It provides one system to help your team plan confidently, perform well, and get paid accurately.
Stop operating in silos and start building a connected, AI-first GTM motion. For marketers ready to adapt, this is theย new playbookย for succeeding in todayโs landscape. Build a revenue engine that compounds results over time.
FAQ
1. What is Generative Engine Optimization (GEO) and how is it different from SEO?
Generative Engine Optimization (GEO) is the evolution of traditional SEO for AI-driven search environments. Instead of ranking on a list of links, success in GEO means being cited as an authoritative source within AI-generated answers. For example, when a user asks an AI assistant, “What is the best CRM for a small business?” GEO aims to have your product and its key benefits featured directly in the AI’s summarized response, which requires optimizing for conversational, high-intent queries rather than just keywords.
2. Why are traditional Go-To-Market playbooks becoming obsolete?
Traditional GTM playbooks, which often rely on linear funnels and keyword-based content, were not designed for the modern AI-driven buyer. Today’s buyers use AI assistants to research, compare, and get recommendations, effectively bypassing many traditional marketing and sales touchpoints. This creates a fundamental gap, as playbooks built around MQLs and cold outreach fail to engage a customer who has already made it 80% of the way through their decision process within an AI environment.
3. What is the main problem with the MQL-to-SQL handoff process?
The handoff between marketing (MQL) and sales (SQL) creates significant operational friction that directly impacts revenue potential. This friction stems from manual processes, which cause delays in lead follow-up, and poor data quality, which forces sales reps to waste time re-qualifying prospects. When a sales rep receives a lead with incomplete or inaccurate information, they cannot engage effectively, leading to a poor customer experience and a high probability of the lead going cold.
4. Why is data hygiene important for AI-driven GTM strategies?
Good data hygiene is a strategic imperative because AI systems are only as effective as the data they are trained on. In an AI-driven GTM motion, clean and accurate data is crucial for everything from lead scoring to personalization. For example, if an AI-powered sales tool is fed outdated contact information, it will waste resources on bounced emails and failed calls. This inefficiency cascades, leading to flawed analytics, unreliable forecasts, and a general lack of trust in the technology across the revenue organization.
5. How does poor ICP definition impact revenue teams?
When revenue leaders lack a clear, data-backed Ideal Customer Profile (ICP), it creates strategic misalignment across the entire go-to-market motion. Marketing teams end up targeting too broad an audience, which floods the pipeline with low-quality leads that do not convert. Consequently, the sales team wastes valuable time disqualifying these leads instead of focusing on high-potential opportunities. This inefficiency not only inflates customer acquisition costs but also leads to higher churn, as the company ends up selling to customers who are not a good long-term fit.
6. How much time can sales teams save by automating non-selling tasks?
Industry data suggests sales representatives often spend over half their day on non-selling administrative tasks. By automating this work, teams can reclaim a substantial portion of their week to focus on what matters: building relationships and closing deals. Automating tasks like CRM data entry, scheduling meetings, and sending follow-up emails can free up several hours per week for each representative, directly translating into more time for revenue-generating activities.
7. What does it mean that AI is now the environment for buyers?
AI is no longer just a tool that teams use; it has become the primary environment where buyers discover products, conduct research, and make purchasing decisions. Previously, a buyer might use a search engine to find a blog post. Today, they ask an AI assistant for a direct comparison of three vendors. This fundamental shift means that simply using AI tools to support old strategies is not enough. Revenue teams must now build their presence and authorityย withinย these AI environments to ensure their brand is visible and influential at the point of decision.
8. What types of tasks should sales teams automate first?
Sales teams should prioritize automating the most time-consuming, repetitive administrative tasks that do not require strategic thinking or human interaction. This allows sales professionals to focus their energy on building relationships and closing deals. Key tasks to automate first include:
- CRM data entry and updates
- Meeting scheduling and calendar management
- Standard follow-up email sequences
- Lead routing and assignment
9. How does territory management affect sales productivity?
Poor lead routing and outdated territory management create significant operational drag that slows down the entire sales cycle. For example, when a high-value inbound lead is manually routed to the wrong representative, the resulting delay in follow-up can be the difference between winning and losing the deal to a faster competitor. This also creates internal confusion and disputes over lead ownership. Properly automated and optimized systems ensure the right lead is assigned to the right person instantly, increasing response times and improving overall sales efficiency.
10. What strategic shift is required to succeed with AI-driven buyers?
To succeed, revenue teams must shift from a funnel-based mindset to an ecosystem-based one. Instead of just optimizing for keywords to capture leads, the new strategy is to become the most cited and authoritative source of information across the entire digital ecosystem that AI models learn from. This involves creating high-quality thought leadership, technical documentation, and customer case studies designed to answer high-intent, conversational queries. The goal is no longer just to rank on a results page but to be the definitive answer provided by the AI itself.
