Strategy

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.

Area	Key action
Meta Data	Answer the buyer’s question in your meta description	✔
URLs	Use a natural prompt-style slug	✔
H1/H2	Write as questions or declarative answers	✔
Intro	Begin each section with a 30-80 word “answer block”	✔
Schema	Use FAQPage or QAPage + Article schema	✔
Content	Short, declarative, factual paragraphs	✔
Lists	Add bullets, numbered steps, or tables for clarity	✔
Internal Links	Descriptive anchors between topical pages	✔
Visuals	Alt text answers a sub-question	✔
Recency	Display and maintain “last updated” dates	✔

(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.

The promise of artificial intelligence has sparked a gold rush, yet many organizations find themselves starving for insight despite being drowning in data. While the potential is vast, the reality is stark: 56% of executives have yet to see a true impact on their bottom lines from AI investments (Oxygen Staff). The bottleneck is rarely the technology. More often, it’s a strategy problem. Organizations invest in AI capabilities without connecting them to measurable business outcomes. An effective AI roadmap for enterprise initiatives fixes this by anchoring use cases to concrete results.

Why Don’t the Majority of Organizations Have Operational AI?

Across the enterprise landscape, activity hasn’t translated into traction. Pilots are multiplying across departments, yet 92% of companies have yet to operationalize AI in any meaningful way (McKinsey & Company). Instead of a unified force, AI initiatives are often stuck in siloed efforts where marketing, sales, and customer success are running separate tests without a connective tissue to bind them.

These teams are set up to fail when they’re answering to too many stakeholders instead of working as a cohesive unit. Without an overarching vision, organizations focus on AI point solutions to maximize output, yet they neglect the core process alignment required to see a return on investment.

Transitioning from these isolated experiments to a state of systematic effectiveness requires a strategic shift in how teams define and sequence their goals.

The AI Roadmap for Enterprise

The foundation of operational success is shifting the question from “What can AI do?” to “What value is AI going to bring to the company?” An AI roadmap brings structure to ambition,  connecting what organizations want from AI to how they’ll actually get there. Unlike a simple list of cool features, a roadmap is highly sequenced, helping organizations understand which capabilities to build first and how each piece creates a foundation for the next.

By tying every integration and deployment to a clear end goal, leaders can cut through the noise and move forward with a prioritized plan. This is the foundation of the first pillar in the playbook for scaling AI for success in the State of AI in Enterprise report. This roadmap ensures that AI is not just a line-item overhead but the essential machinery that enhances internal capabilities for high-resolution customer experiences.

A successful roadmap relies on the ability to turn these high-level visions into measurable, tangible targets.

Getting Specific with Your Metrics for Value-Driven AI Use Cases

One of the primary reasons AI projects fail to scale is a lack of measurable outcomes. For example, many teams may have efficiency as one of their goals, but efficiency can mean a lot of different things. To find real value, you must define exactly what you are measuring, whether it is revenue per customer, faster conversion behavior, or the accuracy of RFP turnaround times.

• Establish a Baseline: You cannot prove impact without knowing your starting point.
• Define Success Early: Connect every AI use case to clear KPIs, remember to get specific!
• Avoid the Capability Trap: Don’t lead with the technology. Lead with the specific business outcomes you intend to solve.

Once these outcomes are defined, the focus must shift to the fuel that powers them: your data.

Establishing Your Data Foundation

A significant barrier to scaling AI is the illusion of data perfection. Shattering this preconceived notion was one of the core disciplines that industry experts across the Trilliad organization emphasized as being critical for operationalizing the data layer for impactful AI in the 2026 Growth Imperatives.

Many leaders feel pressure to connect every application before they can begin—a mindset that stalls progress before it starts. An AI roadmap cuts through this by sequencing which data to unlock first and how each step builds toward the next, keeping focus on the data that actually moves the needle. 

Distrust in AI outputs usually traces back to the data layer. A standardized, well-governed data foundation is what transforms AI from a source of noise into a reliable driver of decisions.

Equally important is designing for adoption and ensuring change enablement is built into each phase for the people who will actually be using the solution.

Addressing the Human Factor in AI

70% of AI project failures are organizational (ADAPTOVATE). When teams only focus on the technical aspects and do not consider the human factor, it results in low AI adoption and limited results. Change is inherently hard, and people are bringing varying levels of uncertainty and AI mindsets to an AI rollout. Therefore, to sustain adoption, you must incorporate impactful change enablement strategies that include: 

• Transparent Communication: Address your team’s fears and questions, such as “Am I being replaced?” early and honestly.
• Persona-Based Enablement: Tailor training to the specific journey of each role to ensure they are set up for success.
• Continued Reinforcement: Enablement should not be treated as a one-and-done engagement. How will you continually reinforce the best practices for using the AI solution to maximize results?

By engineering sustained execution, you ensure that your AI investment sticks and reflects in real-world situations across marketing, sales, and customer success.

“Recognizing the need for humans in the process design is key to AI success… including pinpointing where human expertise and intervention are essential.”

– Debra Engles, Change Enablement Director, Sercante

Your Path to Delivering Real Value with AI

When you organize your people, processes, and data around a cohesive, sequential AI roadmap for enterprise initiatives, you move past the pilot phase into a state of competitive advantage. By grounding your strategy in measurable outcomes and the human factor, you transform AI from a buzzword into a high-performance growth engine.

If you’d like support with creating your AI roadmap, reach out to the Sercante team. We partner with go-to-market leaders daily to help them bring their AI vision to reality and achieve their business goals.

The traditional B2B growth engine is reaching a breaking point because it remains disconnected from the modern buyer’s journey. While self-guided discovery and AI-driven entry points become the norm, buyer expectations for a seamless, personalized experience have reached an all-time high. When departments operate in silos and fail to pass sufficient context, the experience becomes fragmented, and the growth engine stalls. To overcome these challenges, leaders must adopt a go-to-market (GTM) data strategy that establishes a foundational data layer as the connective tissue between marketing, sales, and customer success.

To get the complete expert insights for approaching an integrated data layer for your organization, download the Trilliad 2026 Growth Imperatives, The Era of Precision Growth in B2B: A GTM Motion Powered by Data. 

Market Trends Calling for a Shift in GTM Data Strategy

The mandate for a data-powered approach is driven by a widening gap between internal processes and the actual buyer’s experience:

• 88% of buyers state that the experience a brand provides is as important as the product itself (Salesforce).
• Yet GTM teams are failing to provide the seamless experience they expect, as 77% of buyers shared that their last purchase was very complex or difficult (Advertising Week).
• Despite record access to tools, 62% of leaders report that growth is getting harder (Trilliad 2025 Sustainable Growth Study).
• In an attempt to gain some efficiency, many organizations have deployed AI point solutions, yet 56% of executives have yet to see a true impact on the bottom line (Oxygen Staff).
• While, 87% of AI project failures point back to poor data quality (RAND).

“Data continues to be the foundation that powers experience, but it has a newfound importance with the era of AI.” 

– Austin Frink, Director, Data Technologies, Sercante

Today’s trends demonstrate that marketing, sales, and customer success do not have access to the data they need to effectively power AI and provide the tailored, smooth experiences that today’s modern buyer expects. There is a lack of buyer context being passed from one department to the next, and data is locked up across a tangled tech stack of disparate systems. 

Today’s Challenges of Establishing a Solid Data Foundation

Creating a data foundation is often hindered by legacy habits and technical complexity:

• Tech Sprawl: The average large enterprise manages a technology stack of over 600 applications, leading to unparalleled volumes of fragmented data (WalkMe Inc.)
• Short-term Fixes: Prioritizing quick-fix point solutions over core process alignment is a habit that has led to the tech sprawl that creates disparate silos, preventing a cohesive view of the customer.
• An Unclear Path Forward: Leaders often feel overwhelmed at the thought of trying to connect all their data. Sorting through questions of: where do we start? Will we ever get to a point where our data will drive the value from AI that we need? Will we ever be able to be fully confident in our data and easily access actionable insights?

As Andrea Tarrell, Founder & CEO of Sercante, shared in the 2026 Growth Imperatives, it’s not about gathering more data or connecting it all at once. It’s about the right data for the right outcome. Understanding the results that can be achieved when an integrated data layer is established across marketing, sales, and customer success is one of the first steps to approaching your foundation. Knowing the impact that’s possible helps you to establish a vision that grounds your data initiatives in measurable business outcomes.

“More data does not make you better at anything. You need the right data, the right activation layer, and a team and process that knows what to do with what they are seeing.”

– Andrea Tarrell, Founder & CEO, Sercante

The Impacts of Integrated Customer Lifecycle Data in B2B

By architecting data as the connective tissue across the customer lifecycle, growth teams can deliver truly personalized experiences at scale and make smarter data-informed decisions that enhance brand engagements, maximize sales growth, and expand customer relationships. Unlocking this data also provides the Chief Revenue Officer and GTM leaders with the visibility needed to optimize the entire revenue cycle and prove the definitive financial impact of every initiative.

Marketing connects brand experiences to demand impact

Marketing shifts from disconnected lead lists to a cohesive target account approach. This ensures consistent, emotionally engaging storytelling that connects early brand interactions across the entire buying group to demand impact, proving measurable account-based ROI. The data not only allows marketing to enhance the level of personalization they deliver to buyers, it positions them as a value-driving engine that impacts the bottom line. Furthermore, when data is connected end-to-end, studies show that organizations are 50% more likely to achieve high revenue growth (Trilliad, 2025).

The data unlocked for marketing is then passed through to sales, creating a beneficial ripple effect for the buyer’s journey and the organization’s performance.

Sales creates a durable sales performance system that drives revenue

The customer lifecycle data gives sales access to insights about the buyer’s interests, potential goals, and what products and pricing they may have already viewed. It allows them to be a strategic guide to the buyer, to lead with meaningful conversations that are relevant to their needs. During these active deal conversations, top-selling behaviors can be reinforced and personalized for sellers with a progressive sales performance system that allows them to apply the skills that progress opportunities forward in the pipeline. All because they finally have access to the data they need to connect top-performing sales behaviors to financial outcomes and fuel AI with the right information to tailor impactful sales development for each seller at scale. Leading to 10% higher win rates and a 15% increase in revenue capacity per seller (Sandler, 2025). 

It’s the GTM data strategy that powers a more effective, data-driven sales organization and fuels a growth-obsessed customer success team that can finally take a proactive approach to account expansion.  

Customer success guides proactive account growth

Customer success transitions from reactive troubleshooting to proactive engagement. By using shared data to identify at-risk accounts and expansion opportunities before they arise, teams can make key enhancements that create lasting loyalty and increase customer lifetime value. 

Imagine a customer success team that anticipates the buyer’s needs before they raise a hand, positioning them as strategic advisors who build sustainable account growth.

“A strong data foundation transforms Customer Success teams from reactive support into a proactive, outcome-driven function, driving real, measurable results for customers and innovating the end-user experience with agentic reporting.”

– Behrang Asadi, Director, AI & Analytics, Sercante

A GTM Data Strategy that Drives Sustainable Growth

In today’s market, data must be reimagined as the foundational competitive advantage of the B2B growth engine. Organizations that successfully operationalize their data layer will be able to fuel AI that drives real results, advances GTM, connects initiatives to measurable financial outcomes, and delivers the seamless, individualized experiences the modern buyer expects. Creating deeper customer relationships that result in lasting growth for the business.

If you’d like support with designing your GTM data strategy or building impactful integrations that unlock meaningful data activation and actionable insights, reach out to the Sercante team. They partner with marketing, sales, and customer success leaders daily to help them achieve their goals with their data.

Many organizations today are discovering that one of the most significant barriers to AI success isn’t the technology itself, but the human factor in AI adoption. To move from siloed experimentation to operationalization that drives real impact, leaders must shift their focus from purely technical requirements to the organizational confidence of their people. This human-centric shift in mindset allows companies to convert technological capability into a strategic advantage by prioritizing the unique journey of every individual involved in the rollout.

The urgency for this human-centric approach is underscored by the current state of AI trends:

• A staggering 92% of organizations admit they do not yet have operational AI (McKinsey & Company).
• Research indicates that 70% of AI project failures are attributed to organizational and human factors rather than technical flaws (Adaptovate).
• Between 70-90% of AI projects fail to scale beyond the initial pilot phase (Forbes).
• Approximately 95% of generative AI pilots fail to achieve their intended revenue acceleration (Fortune, MIT Report).

When these initiatives stall, leaders often mistakenly blame the technology or the lack of data cleanliness. However, realizing true impact requires addressing the human and cultural gaps that keep teams fragmented and unsure, limiting their adoption. Which is why addressing the human factor of AI adoption is the fourth pillar in Sercante’s playbook for scaling AI for success in the report, The State of AI in Enterprise: Closing the Gap Between Investment and Impact. By understanding existing AI mindsets and observing how people actually operate, leaders can apply effective change enablement strategies, built on trust, clear guidelines, and role-based support, to finally unlock the confident adoption necessary for measurable AI impact.

The Human Factor of AI: Three Pillars of Uncertainty

Before your team can master a new tool, they must feel secure using it. AI rollouts can trigger unique psychological barriers that act as inhibitors for AI adoption. There is uncertainty that can swirl around in the thoughts of people, such as being replaced, mistrusting AI outputs, and not knowing what the true end goal is. To ensure a successful implementation, the change enablement approach must confront the three pillars of uncertainty:

• “Am I being replaced?” With major corporations reducing staff, anxiety is high across all sectors as people wonder if it’s due to AI efficiency replacement. The most successful implementations treat AI as a capability amplifier rather than a replacement, focusing on moving humans from transactional work to high-value validation and oversight.
• “Can I trust this data?” Hallucinations and AI-driven misinformation have eroded the fundamental concept of digital truth. Building trust requires formal processes, such as a cross-functional Data Trust Committee, to demonstrate a commitment to data integrity and output auditing.
• “What is our long-term goal?” Initial AI pilots and early experimentations are often implemented without a clear AI roadmap. Without transparency regarding the long-term plan and what the end goal is that is trying to be achieved with the AI solution, teams often become disengaged or fearful of the “next shoe to drop”.

Addressing the thoughts and feelings of uncertainty that people may be having around AI starts to meet people where they are, building trust that supports the human factor of AI adoption. In addition to uncertainty, there are also different mindsets that people can bring to the workplace toward AI that can either strengthen rollout success or limit it.

What is Your Team’s AI Mindset?

Understanding where people stand today around how they view and use AI is another step in meeting them where they are. There are five common AI mindsets, ranging from low to high openness and usage: Skeptic, Quiet Adopter, Evaluator, Enthusiast, and Trailblazer. How they fall on the scale is illustrated below. 

Mindset	Openness & Comfort	Usage Level	Common Behaviors	Change Enablement Strategies
Skeptic	Lowest	Lowest	– Hesitant or resistant to AI. – Relies on traditional methods. – Questions AI’s value or accuracy. – Needs proof before adoption.	– Build trust through small, low-risk AI pilots. – Share clear success stories and data demonstrating measurable impact. – Provide step-by-step guidance and support. – Encourage dialogue and address fears openly.
Quiet Adopter	Low	High	– Uses AI tools mainly out of necessity. – Quiet about AI adoption. – Focused on practical efficiency gains. – May not explore beyond immediate tasks.	– Offer role-specific training to optimize use. – Recognize and reward efficiency gains. – Provide clear guidelines and best practices. – Encourage sharing of successes to build confidence.
Evaluator	Moderate	Moderate	– Experiments selectively with AI. – Pilots new tools before wider adoption. – Comfortable but not fully integrated AI. – Focused on risk/benefit analysis.	– Provide frameworks for experimentation with measurable goals. – Offer mentorship or coaching on AI integration. – Ensure clear criteria for success. – Encourage peer learning and collaborative evaluations.
Enthusiast	High	Moderate	– Curious and explores AI possibilities. – Advocates for AI adoption. – Uses AI consistently but not fully optimized. – Prioritizes exploration over impact at times.	– Channel curiosity into strategic initiatives. – Offer advanced training and sandbox environments. – Help prioritize high-impact use cases. – Provide opportunities to mentor others and share knowledge.
Trailblazer	Highest	High	– Early adopter and AI advocate. – Drives innovation and integration. – Mentors others and promotes AI transformation. – Regularly experiments and measures results.	– Empower them as change champions. – Provide access to cross-functional projects. – Recognize leadership in AI adoption. – Align their efforts with strategic business objectives to maximize measurable impact.

(Source: Sercante, 2026)

Identifying these mindsets within the organization enables leaders to tailor their change enablement plan to address the AI mindsets of the people. For example, a “Skeptic” needs different reassurance than an “Enthusiast” who may already be experimenting with tools outside of the core systems.

To further verify and understand the existing AI mindsets, observe how people are executing the processes today that will involve the AI solution, and listen to what the team is already saying about AI. 

Verifying AI Mindsets: Observing the Real Flow of Work

From a technical standpoint, AI solutions need to align with the core processes happening across the customer lifecycle, meaning they need to be designed so that they support how people actually work. From a change enablement perspective that considers the human factor of AI adoption, there needs to be an understanding of where skills and attitudes are today to provide impactful communication and training materials. To do both, consider conducting Day in the Life exercises. 

Day in the Life exercises involve sitting with team members to observe how they use AI and other systems to execute core processes. This practice helps to discover the “real” flow of work versus the documented one. By observing these daily habits, skill gaps can be identified along with true AI mindsets, and solution designers can ensure the final AI solution removes friction rather than adding it.

During these exercises, it is important to listen to the people executing the process to understand what they are saying about AI, allowing further verification of the AI mindset and also proactive planning for change enablement materials that will address differing levels of baseline adoption.

Confirming AI Adoption: Listen to the Voice of the User

In change enablement, silence does not imply consent. If people are not providing initial reactions or feedback, find out why. A “Quiet Adopter” might be using the tool out of necessity while still harboring deep skepticism that could eventually lead to disengagement.

Listen to what people are saying about the technology. When AI is brought up in conversations and team meetings, how do people seem to react? If they are not saying anything at first, what does their body language communicate? Paying attention to the words and expressions people are using about AI will support understanding of the current AI mindsets in the organization and further inform how to tailor the change enablement plan to successfully address the human factor of AI adoption.

Considering the Human Factor in AI Adoption: Developing Change Enablement 

To address uncertainty, existing AI mindsets, and bridge any skill gaps that would limit the success of an AI rollout, there needs to be an effective change enablement plan implemented. As a starting point, consider this four-phase Change Enablement Checklist:

1. Identify & Respond: Perform stakeholder impact assessments to identify unique role-based needs and concerns. Uncover the uncertainties and AI mindsets.
2. Define & Design: Collaborate with tech teams to streamline processes before designing the automation, ensuring human expertise and intervention points are clearly defined.
3. Listen & Inform: Provide regular, transparent updates that explain the “why” behind the change and the long-term roadmap. Continual clear communication builds trust to ease uncertainty and help shift AI mindsets.
4. Prepare & Sustain: Offer role-based training and post-launch support tools. Remember, users only retain about 34% of training within 24 hours (Harvard Business Review). Sustaining AI adoption is where the real value is realized.

Take the Next Step

To take a deeper dive into this human-centric approach, watch the on-demand MarDreamin’ session: Empowering Your People: Nailing Change Enablement for AI Rollouts.

If you need support with implementing a change enablement strategy or conducting team readiness analysis, creating learning materials, or developing your AI roadmap, reach out to the Sercante team today.

Prioritizing the Human Side of AI for Sustained Success

To mature from siloed AI experiments to operationalized processes that power a modern growth engine, the human factor must be integrated into every stage of planning. Taking the time to understand the team’s AI mindset, current usage, and skillset, and tailoring the change enablement plan to meet them where they are with post-launch support, allows the people involved to shift from uncertainty to sustained, confident AI adoption. Gaining measurable business impact with AI isn’t just about the technical or data factors. It’s also about mobilizing the people involved to trust it, master it, and use it to execute the strategy to reach the organization’s growth goals.