Be Found, Be Ready:  Winning the AI-Informed Buyer

Your buyer has probably asked AI about you before your sales team ever gets on a call.

Gartner’s 2026 buyers research found that 45% of B2B buyers used generative AI during recent purchases. Yet 69% still preferred to validate what AI told them with a salesperson. 

That creates two moments that matter. First, AI has to surface your company and describe it accurately. Then, when the buyer reaches out, your seller has to be ready to confirm, challenge, or build on what that buyer already believes. 

We call it Be Found. Be Ready. We use tools for GEO marketing and AI sales coaching to achieve these two goals.

Most companies aren’t fully set up for either yet. BCG’s 2026 survey of 300 CMOs shows how much of this work is still underway. Ninety-six percent said AI was driving significant end-to-end transformation in marketing. Forty-two percent were still using generative AI mainly as an assistant for individual tasks in a limited number of workflows. 

At TechCXO, we’re seeing the same shift in our own work and in client engagements. We use a simple way to frame where AI can support the revenue process: Be Found. Be Ready.

Be Found is about understanding how your company appears in AI-assisted discovery. Be Ready is about helping sellers bring better context into the sales process and improve as opportunities move forward. 

Focus Generative Engine Optimization on the Conversations Buyers Care About 

Search visibility has traditionally been measured through rankings, traffic, and the keywords that bring people to a website. Generative engine optimization (GEO) adds another view. It looks at how a company appears when buyers use AI tools to research a problem or evaluate potential solutions, including which brands are mentioned, cited, or associated with a topic. That matters because AI tools don’t simply list results. As Leah Nurik, co-founder of Brandi AI, put it at our recent client GTM roundtable:

“AI doesn’t return facts. AI constructs narratives.”

Every AI answer tells a story about who a company is, what it’s known for, whether it can be trusted, and how it compares. GEO shows you which story is being told.

There are a growing number of GEO tools in the market with widely varying levels of true innovation and value. At TechCXO, we use Brandi AI to monitor the prompts and questions that matter to our audience and understand where those conversations sit in the funnel. We use that intelligence in our own marketing and with clients to see where a company is showing up, where it is missing, and which conversations deserve more attention.

We have seen that shift in our own work. Early analysis showed that TechCXO was not appearing as consistently as we wanted around some of the conversations tied to our areas of expertise. That analysis gave us our first, clear view of what AI search questions were popular and where our content and points of view needed more support. We then reinforced those topics through thought leadership, LinkedIn, PR, podcasts, video, and other channels. Over time, we saw a 282% increase in domain citations and significantly expanded our presence in AI-generated results. Our GEO Share of Voice surpassed 12% in a highly competitive marketplace. 

That is where the work becomes practical. Prompt patterns can shape which content gets developed, where an existing point of view needs more evidence, and which channels can help reinforce it. GEO also gives us a way to track whether that work is beginning to influence how the company is represented in AI-assisted discovery.

SEO continues alongside that effort. As we often remind clients,

“Don’t walk away from SEO. Augment it with GEO so you can see the conversations shaping how buyers find and evaluate you.”

When one client came to us frustrated that its SEO agency was producing content across roughly 20 topics, we went back to the ICP and narrowed the focus to the four AI conversations the company actually needed to have with its buyers. Those were the issues most likely to shape how the right audience understood the company and where its expertise needed to appear more consistently.

GEO becomes useful market intelligence at that point. It helps experienced marketing leaders decide where to invest attention, which conversations matter most, and where the company needs a stronger point of view.

That work is part of fractional CMO leadership because the technology only becomes useful when it informs positioning, content priorities, and decisions about where the company should compete for attention and online authority.

Use AI Sales Enablement to Prepare for a Better Buyer Conversation 

A buyer may have already researched the company, competitors, pricing, category, and likely solutions before the first conversation. And as Gartner research shows, most still want a salesperson to validate what AI told them.  By the time a meeting appears on the calendar, the buyer may already have formed a view of the company and its alternatives. Preparation therefore matters more, not less.

Revenue Growth Agent, an AI-powered sales preparation and coaching tool, helps us shorten the preparation work without stripping out judgment. We can enter what we already know about the contact and company, including the person’s role, LinkedIn profile, and existing context. Initial research comes back in less than two minutes. Comparable preparation once took hours. 

We still review the output and add what we know. The research gives us a stronger starting point for understanding the business and preparing questions that will fully resonate with buyers by predicting their situation.

The context also carries forward. In one case, we returned to an opportunity after a large number of other prospect conversations. Revenue Growth Agent immediately brought the earlier context and next questions back into view, so we did not have to reconstruct the opportunity from memory.

For a fractional chief revenue officer, that continuity matters as much as the time savings. Better preparation makes it easier to pick up where the buyer left off and spend the conversation on what actually needs to move forward. 

Turn AI Sales Coaching Into a Continuous Feedback Loop  

Sales coaching is most useful when it reflects what is actually happening in real customer conversations. In the last 20 years, TechCXO Partners have trained roughly 10,000 sellers in complex selling skills, and we have seen how easily even well-established disciplines can become inconsistent over time.

Revenue Growth Agent gives us a way to analyze prospect-call transcripts immediately after a conversation and look for patterns that matter. The analysis can surface gaps against MEDDIC, including whether we learned enough about the buyer’s decision criteria, identified the person with final economic authority, or fully explored the cost and consequences of doing nothing.

After one sales call, the Revenue Growth Agent identified five questions to carry into the next conversation. They were based on gaps in the prior discussion, including areas where the buyer’s decision process or the consequences of inaction needed more depth.

A seller should not have to wait two weeks for a manager’s one-on-one or depend on that manager finding 30 minutes to review every recording. AI can analyze each transcript within minutes and surface patterns across conversations. The manager can then spend coaching time on the behaviors that need attention and how to improve them.

“AI makes sales coaching constant instead of one and done.”

Even experienced sellers can drift from established disciplines. Continuous transcript analysis gives managers a stronger starting point for coaching and helps reinforce the behaviors and build the strategic selling skills that maximize buyer conversion. 

Build AI Workflow Automation the Team Can Actually Use

These workflows become more valuable when they can be repeated across a team.

In a typical company’s AI journey, a small group of people will invest the time to become highly skilled with ChatGPT, Claude, or other general-purpose AI tools. A much larger group needs the useful parts of AI built into the way they already work.

That makes adoption a change-management issue as much as a technology decision. The software has to make the workflow easy enough that people can get useful results without becoming prompt engineers.

The examples above show what that looks like in practice. With Brandi AI, prompt intelligence can shape content priorities and then feed back into measurement as visibility improves. Revenue Growth Agent carries context from research into the sales conversation, then into coaching and later opportunity work.

The business case becomes clearer when AI improves enough of a connected workflow to affect how work moves through the organization. 

“You have to have AI working across enough of the workflow to reach the critical mass you need for ROI.” 

A faster email or first draft may create useful efficiency. The bigger question is what happens when that efficiency compounds across a process and across entire teams.

Gartner found that AI was saving sellers an average of 4.8 hours per week, while 72% of sales organizations reported low reinvestment of that time into higher-value activities.

Leaders need to decide how much of the revenue workflow AI can meaningfully improve and where the resulting capacity should go. Making those choices deliberately creates a clearer path from AI adoption to measurable business value. 

Measure AI Against the Revenue Work It Supports 

AI is useful when it improves a part of the revenue process the business already cares about.

For GEO, that may mean stronger visibility around the conversations that matter to the ICP, followed by downstream measures such as qualified traffic, engagement, and demand. For sales preparation, the question is whether better context improves opportunity quality and progression. For coaching, the early signals may be stronger discovery, better qualification, more consistent execution, and shorter sales cycles across deals.

The same principle applies to workflow automation. Saving time matters, but the more important question is what happens to the work because that time was saved.

We are leveraging these capabilities directly inside client engagements. With Brandi AI, that has included giving clients a firsthand view of what the analysis reveals before deciding how the capability should fit into broader go-to-market planning and future budgets. The tool becomes part of the work, helping leadership understand where the company stands, where attention should go, and what may need to change in the go-to-market process.

This is also how we think about Revenue & Customer Growth at TechCXO. The question isn’t whether a team is using AI. It’s whether the work is improving. Are the right buyers finding you? Are sellers better prepared? Are opportunities accelerating? Is coaching and selling skills getting better? Those are the results that matter. 

How to Make AI Pay Off Across the Revenue Workflow 

As AI moves deeper into revenue work, leaders have to make choices about ownership, adoption, workflow design, and how the capacity it creates gets used.

Five decisions matter most: 

• Decide what should stay human. Research, analysis, and preparation can move faster, but someone still needs to interpret what matters, make tradeoffs, and decide what happens next.
• Design for the people who will actually use it. A workflow cannot depend on everyone becoming an expert prompt writer. The useful context, instructions, and guardrails need to be built into the process so a broader team can use them consistently.
• Put the saved capacity somewhere valuable. If AI gives a seller several hours back each week, leadership needs to decide where those hours should go. More customer conversations? Better account planning? Coaching? Pipeline development? Time saved is only the beginning of the ROI question.
• Know when a pilot becomes an operating change. A successful demo proves that something is possible. Building it into budgets, responsibilities, processes, and expectations turns an experiment into part of the business.
• Give the work an owner. Someone has to decide which capabilities are worth adopting, how they connect with existing systems, what the team is expected to change, and whether the investment is producing a result.

Most of these decisions come down to ownership, and that doesn’t always require another full-time executive. TechCXO fractional CMOs and CROs work alongside existing teams to assess the opportunity, strengthen the revenue process, and lead implementation. We already bring Brandi.AI and Revenue Growth Agent into a client engagement, use them to inform the work, and help determine where they belong in the broader revenue process.

Being found puts your company in the conversations shaping how buyers discover and evaluate you. Being ready means your team walks into those conversations with better context and keeps getting better from there.

“The goal is to move from a few smart people using AI well to a revenue organization that can perform better together.” 

Need help moving AI from experimentation into the revenue work itself? Talk with TechCXO’s Revenue & Customer Growth leaders about where AI can make the biggest difference in your revenue workflow.