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The B2B sales engine is at a tipping point. While revenue leaders have more access to technology and data than ever, the majority of a seller’s time is still lost to non-selling tasks. Even when sellers do engage, traditional training often fails to stick under the pressure of real-world conversations. In response, many teams have deployed siloed AI point solutions, yet these efficiency plays rarely impact the bottom line. To move the needle, leaders must shift from mere activity to true AI seller effectiveness, transforming AI from a basic writing assistant into a strategic co-pilot that expands revenue capacity and win rates.

This critical shift in approach was also identified as a must for leaders to make this year, in Trilliad’s 2026 Growth Imperatives. The traditional sales development playbook isn’t working. Therefore, it’s time to adjust to a strategy that holistically creates a sales performance system.

The State of AI in Sales

The pursuit of AI efficiency in 2025 often led to simply accelerating unchanged, low-yield sales behaviors. To avoid this, organizations must recognize where the true value of intelligence lies:

• Selling vs. Shuffling: Only 29% of a seller’s time is actually spent selling, with the rest lost to administrative tasks, manual data entry, and prospecting (Salesforce).
• The Pilot Problem: A staggering 87% of AI projects fail due to poor data quality (RAND), while 70% fail due to a lack of operational enablement (ADAPTOVATE).
• Systemic Intelligence: When asked where the most untapped ROI for AI exists, 42.4% of leaders pointed to system-level AI, tools built to enhance organizational intelligence, compared to only 5.3% who prioritized seller-level productivity tools (Varicent).

Unlocking true AI seller effectiveness goes beyond singular tools. It requires a holistic view of how intelligence empowers the entire revenue organization.

“Last year was the year of efficiency. This is the year of effectiveness. If sellers can do more of the same bad behaviors faster, that does not drive growth. Effectiveness is what turns efficiency into real results.” 

–  Seth Marrs, Chief Strategy Officer, Sandler, 2026 Growth Imperatives

By shifting the mandate to effectiveness, leaders ensure that every efficiency gain is anchored in better outcomes, not just faster cycles.

Shifting from Episodic Sales Development to Durable Performance Systems

For too long, B2B organizations have treated sales development as a series of episodic events, one-time workshops, or annual resets that decay as soon as the team returns to the field. To drive lasting growth, sales performance must be engineered as an always-on system that operates with the same analytical rigor as forecasting or finance.

The most critical hurdle to this transition is the Ebbinghaus Forgetting Curve. Without intentional reinforcement, humans forget 75% of new information in just six days (Harvard Business Review) and up to 84% within 90 days (Ardent Learning). In the context of 2026, training decay isn’t just an educational hurdle it is a strategic business risk that directly threatens sales revenue stability.

Just as Sercante builds change enablement plans focused on continual reinforcement to ensure technology adoption, sales leaders must move toward a mindset of performance engineering. This ensures that your investment in a sales methodology actually sticks when a seller is facing a high-stakes negotiation.

Unlocking AI Seller Effectiveness

The path to seller excellence is paved with data. By prioritizing an integrated data layer, organizations can identify top-performing behaviors and fuel AI that personalizes reinforcement at scale.

Establishing your data foundation

Modern revenue organizations are often drowning in data but starving for insight. Despite managing an average of over 600 applications (WalkMe Inc.), sellers frequently lack the deep buyer context, such as specific pricing views or topics consumed, needed to lead high-value conversations.

The solution isn’t to connect every disparate system at once. That pursuit of “data perfection” only stalls progress. Instead, focus on untrapping the right data for the right outcome. Start by defining the desired end-experience: What data would empower your sellers to lead with insight tomorrow? This customer-centric lens serves as the ultimate filter for your sales AI roadmap.

Using AI to scale personalized sales development

With a solid data foundation, sales leaders can move from subjective coaching to evidence-based interventions. AI can monitor actual customer interactions in real-time to identify skill gaps and trigger personalized support for sellers. Some examples of what that could look like are: 

• Real-Time Behavioral Monitoring: AI detects the moment a seller stops setting upfront contracts or skips deep pain discovery.
• Triggered “Just-in-Time” Reinforcement: If a seller struggles to articulate value against a specific competitor, the system automatically pushes a relevant AI role-play scenario to their dashboard.
• Exemplar Pattern Matching: Technology identifies the unique behaviors of top-performers and codifies them into the training system for the entire team.
• Evidence-Based Coaching: Managers focus their energy only on the specific areas where data shows a seller is struggling, replacing generic sessions with precision coaching.

This shift turns the sales process into a self-optimizing, sales training reinforcement loop, closing the execution gap in real-time.

(Source: Sandler)

“Technology now allows us to have an always-on view of sales performance. That means we can move from point-in-time training events to sustained sales performance systems that reinforce, measure, and improve performance over time.” 

– David Braun, President, Sandler, 2026 Growth Imperatives

When AI Seller Effectiveness Impacts the Bottom Line

Focusing on AI-powered effectiveness rather than just efficiency creates a 15% growth in revenue capacity per seller (Sandler). By automating non-selling tasks and reinvesting that time into high-yield, reinforced selling behaviors, organizations achieve significant revenue expansion.

Furthermore, systematic reinforcement leads to 10% higher win rates (Sandler). When training moves from an activity checkbox to constant feedback loops, sellers are empowered to handle larger quotas with evidence-based precision.

Shifting your mindset: Critical questions for sales leaders

To guide your transition to a progressive sales performance system, move beyond asking “Did we train them?” and instead ask:

• Behavioral Clues: Which specific selling behaviors correlate with our highest-win-rate deals? 
• Data Visibility: Can our sellers easily access the customer data they need to understand buyer pain points? 
• Risk Identification: Where exactly is training decay occurring before it impacts the quarterly forecast? 
• Resource Allocation: Is our development spend personalized to individual skill gaps or wasted on generalization? 
• Performance Measurement: Can we connect our performance investment to measurable financial outcomes for the CFO?

Answering these questions not only starts to guide the team toward shifting its mindset. The exercise can also help to prioritize the data that will need to be unlocked and the AI initiatives to prioritize first to reach the most impactful sales goals.

Taking your next steps

Unlocking AI seller effectiveness requires a fundamental shift from episodic workshops to durable sales performance systems. It mandates an integrated data layer that provides sellers with context, identifies exemplar behaviors, and proves ROI to the highest levels of the organization. Getting started requires taking a step back to consider: What data can your team access today? Where is training decay hurting your win rates? Then get started by prioritizing the right data to access and the most impactful AI to set up for your sales goals.

If you’d like support with making the transition from siloed AI efficiency to AI seller effectiveness, reach out to the Sercante team. We partner with growth leaders daily to optimize CRM environments and technology stacks that empower sellers to expand their revenue potential. 

The traditional B2B search journey is undergoing a shift, moving away from static search engine results pages toward fluid, conversational AI interfaces. As buyer behavior evolves, AI answer engine optimization (AEO) has emerged as an imperative for brand visibility, serving as the new entry point for the modern buyer. To ensure brands stay top of mind during the critical buyer research, discovery, and evaluation phases, industry leaders in Trilliad’s 2026 Growth Imperatives have identified integrating a Generative Engine Optimization (GEO) strategy into the marketing mix as no longer optional, but a competitive necessity.

AI is the New Entry Point for Modern Buyers

The way buyers discover solutions has been fundamentally reshaped by AI answer engines, with up to 80% of users now relying on AI-generated summaries to distill complex information (Bain & Company). This efficiency has led to a zero-click reality, where 60% of searches end without a single click to a third-party website because the AI provides the answer directly (Search Engine Land). For B2B brands, this creates the danger of “AI Invisibility”. If you aren’t cited in the AI’s synthesis, you can be excluded from the buyer’s journey before human evaluation even begins.

Brands that fail to optimize for generative engine visibility risk a slow decay in market share as AI assistants become the primary tool for synthesizing vendor lists and evaluation criteria. 

“The front door has shifted. Your website is no longer the first place buyers meet your brand. AI search platforms are now the front door, and if you are not showing up there, you are not even being considered.” 

– Marcus Hiles, SVP Strategy, Just Global, 2026 Growth Imperatives

To ensure brand visibility in AI answer engines, marketers must adopt a framework built for Large Language Models (LLMs) in addition to their traditional search engine strategy.

AI Answer Engine Optimization

Just as brands spent decades perfecting SEO strategies to appease Google’s crawlers, they must now develop a GEO strategy to ensure their brand is picked up by AI answer engines. LLMs look for authoritative, structured, and contextually relevant data that can be easily synthesized into a conversational response.

To improve your LLM visibility, focus on how your content is structured to answer the questions that the audience will be asking and the schema of the page.

Content Optimization for LLM Visibility Checklist

• Schema: Use an FAQPage or QAPage to provide clear, structured answers that AI can easily parse.
• Formatting: Use short, declarative, factual paragraphs along with bullets, numbered steps, and tables for clarity. Incorporate statistics, unique insights, and expert quotes that AI engines are likely to reference as primary sources.
• Optimize for Intent: Structure content around “How-to” and “What is” queries that address specific pain points in the buying cycle.

(Source: Trilliad 2026 Growth Imperatives)

By checking these boxes, you ensure your data is machine-readable, which is the first step toward moving from a hidden data point to a featured recommendation.

Getting Started with an AI Answer Engine Assessment

The most effective way to frame your GEO strategy is to understand your current baseline: how is your brand currently showing up (or not) in AI answer engines? An assessment allows you to see the gaps in your visibility and identify which topics your competitors are dominating within AI-generated summaries. By identifying where you are missing from the conversation, you can prioritize your optimization efforts for the greatest immediate impact. This same approach for prioritizing AI initiatives by the most impactful outcome aligns with pillar one of the 4-pillar playbook of The State of AI in Enterprise Report: anchor to the business vision. When teams have a starting point of how their brand is already showing up or not showing up in AI answer engines, it guides them toward establishing a clear goal with measurable outcomes to then guide the rest of their GEO strategy and effectively measure performance.

If you’d like to get started with an AEO Assessment, reach out to the Sercante team. They offer a comprehensive AEO assessment designed to help brands audit their LLM presence and build a roadmap for discovery.

Understanding your current standing allows you to pivot your strategy toward the ultimate goal: securing a spot on the initial vendor shortlist.

Winning with GEO: Brand visibility on the Day One List

Successful GEO can increase LLM visibility for a brand by approximately 40% (Aggarwal et al., 2024). When content is optimized for AI, brands significantly increase their chances of appearing in the crucial “Day One” vendor shortlists, the initial lists formed from AI answer engines that buyers discover and then move forward with for further research and evaluation.

Incorporating a GEO strategy into the overall marketing mix is critical to ensure brand discovery with today’s modern buyers. The landscape has changed, but the goal remains the same: meet the buyers where they are.