Pro Tips Archives

Pro Tips, Strategy
April 15, 2026

Moving Past Efficiency: How to Drive AI Seller Effectiveness in 2026

Behrang Asadi

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.

Unlock your data
The Era of Precision Growth in B2B
Trilliad Growth Imperatives 2026
Download eBook

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).

The State of AI in Enterprise
Closing the Gap Between Investment and Impact
Why 95% of AI pilots fail to deliver results
The 4-pillar playbook to fix it
Download Report

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.

Discover what's possible with the AI Roadmap
Learn More

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.

Sandler seller training reinforcement loop

(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.

Unlock sales growth with an optimized CRM seller experience
Contact Us

Marketing Automations, Pro Tips
April 10, 2026

Saving Time with Flow Templates in Agentforce Marketing

Mike Morris

Agentforce Marketing (aka: Marketing Cloud Next) introduced marketing teams to Salesforce Flow for the first time. While flows provide increased capabilities over tools like Engagement Studio in Account Engagement and Journey Builder in Marketing Cloud Engagement, building them can be time-consuming and even intimidating to new users.

In this post, we’ll explore how to create and use templates to increase efficiency while taking full advantage of the power of Flow.

Non-Admin Flow Types

“Non-admin” flows power Agentforce Marketing and allow marketers to automate key processes without needing administrator-level permissions. Common use cases include sending emails, delivering SMS messages, and creating Salesforce records.

There are two primary Flow types within Agentforce Marketing:

Form-Triggered: These have a 1-to-1 relationship with a marketing form. They are used to create or update Salesforce records, manage consent, and handle immediate post-submission tasks.
Segment-Triggered: These are primarily used to send individual or a series of emails and SMS messages. They also power Path Experiment (available in the Advanced Edition) and provide access to additional flow elements allowing marketers to build customized journeys.

Flow is extremely powerful, but getting started can be tough. The Flow canvas is a “blank slate” that requires thought and configuration.

Let’s take a look at how marketers can simplify and scale their efforts using flow templates with a real-world scenario.

Example Scenario

The marketing team has a high volume of assets that they would like to gate on the website using Agentforce Marketing forms. They need to ensure that existing contacts or leads are updated when a form is submitted before creating any new lead records. Additionally, they must generate consent records whenever a user opts in to marketing communications and add them to the correct Salesforce campaign for tracking purposes.

User Setup and Permissions

To replicate the experience of a marketing user, all flows and images in this post were created with a Salesforce user with the following profile and permissions. These are reflective of a standard marketing user.

Profile
- Standard User
Permission Sets
- Marketing Cloud Admin
- Tableau Next Included App Business User (This provides access to the Marketing Performance Dashboards)
CMS Contributor Role
- Content Manager

Form and Flow Templates

The first step is determining the fields that need to be included on the form. The best approach is to take a minimalistic approach and only ask for information that will be used or is needed for lead routing.

Hidden fields on forms are very useful for capturing form-specific data for use in your flow. For example, you can use them to automatically pass the Campaign ID and Campaign Member Status.

Creating the Form Template

Select the “Marketing” app from the App Launcher.
Click the “Content” tab to enter the Salesforce CMS.
Click on the “Content Workspace for Marketing Cloud” workspace.
Click the “Add” button and select “Content”.
Choose the “Form” CMS content type.
Add Data Source as “Lead”.

Drag the input fields into the form, and configure the labels, unique names, and determine if the field should be required or hidden.
Set the desired action at form submission (show thank you message or redirect).
Add a title, API Name, and description and then save.

Here’s an example of my form. Note that all fields have been set to required (with the exception of the opt-in checkbox). It’s important to know that Flow will overwrite data in Salesforce if a submission is received with blank data. If optional fields are needed, formulas can be created in the flow to protect data.

Creating A Flow

From the form you just created, click “New Flow” in the flow section.
Select “Open Flow in Flow Builder”.

Customize the flow as needed and save.

Customizing the Flow

The flow needed to meet our requirements is a little complicated, but that’s the whole point of this post. We don’t want the marketing team building this logic repeatedly.

This flow was built by a user with the “Marketing Cloud Admin” permission set. The “Marketing Cloud Manager” role lacks access to certain required elements. If you’d prefer, your Salesforce Administrator can also set up this template for you.

Flow Summary

Here’s a summary of the actions being performed in the flow.

Creates an opt-in consent record if the consent box on the form is checked.
Finds existing Salesforce contacts based on the email address and last name in the form submission.
- Updates existing contact records, if found.
Finds existing Salesforce leads based on the email address and last name in the form submission.
- Updates existing lead records, if found.
- Created new leads, if matching records are not found.
Retrieves campaign members based on the CRM ID of the person who submitted the form and the campaign ID included in the hidden field on the form.
- Updates the campaign member status of existing members to the value included in the Campaign Member Status hidden field from the form.
- Created new campaign members using the hidden fields from the form.

Saving the Flow as a Template

We can now complete the creation of the flow template.

Save As Template

From the Flow tab, open the latest version of the flow.
Delete the associated form from the “Start” element.
- Click “Edit” next to “Event: Form Submission”.
- Click the “X” to delete the form.

Click “Save As New Flow”.
- Add the flow label and a detailed description of what the flow does and when it should be used. The API Name will automatically populate based on the flow label.

Example of how to add a flow label and form description

Note: You may run into an issue saving due to the presence of the Consent Request element. If this happens, delete the field reference in the Contact Point value and save.

How to solve the issue of being unable to save

Click “Save As New Version”.
- It’s important to create a new version of the flow before saving as a template. The initial version of the flow will be v0.
- Saving a new version will increment the version number and ensure your updates are available in the flow template.
Click the dropdown to the right of the “Save As New Version” button and select “Save as Template”.
Confirm that the template has been created by completing the following steps:
- Click the Flows tab.
- Click the “New” button.
- Enter the template name in the search box..

How to confirm that a template has been created

Using Your Templates

Now that all the heavy lifting is done, you or your team can use your form and flow templates to quickly support additional assets. Here’s how.

Repeat the following steps for all new assets

Create the Salesforce campaign that will be used for the form and the campaign members.
- When creating the campaign from the Marketing App, do not select a campaign template.
- Selecting a template will result in an additional flow being created.

Do not select a template at the following screen.

Do not select a campaign template when creating the campaign from the marketing app

Navigate to the form template in the CMS.
Select the “Clone” from the dropdown menu to the right of the form name.

Name the new form based on the asset name.
Update the Campaign ID and Campaign Member Status (hidden field values) and Form Submission action.

How to update the campaign ID and campaign member status

From the Flow tab, click the “New” button.
Search for and select the gated content template.
Click “Edit” next to “Event: Form Submission” and select the cloned form.
Update the Contact Point value in the Consent Request element to reference the email address from the associated form.
Click “Save” and name the new flow.
- The Consent Request element can be a bit finicky. If you see an error after updating the Contact Point, just delete the element and add it back.
Exit the flow details page and associate the flow to the correct campaign using the “Associated Record” lookup.

How to associate the flow to the correct campaign using the "Associated Record" lookup

Return to the CMS and publish the form (this will also activate the flow).
Add the embed code to the web page promoting the asset.

Save Time and Increase Efficiency with Templates

Building the initial form and flow template featured in this post took approximately one hour. Because the logic is quite involved, I spent a portion of that time testing to ensure everything functioned exactly as intended.

Once the templates were ready, I put them to the test with a stopwatch. While the steps in the “Using Your Template” section might look detailed, I was able to create a brand-new form and flow in just 4 minutes and 1 second. That is a massive 93% time savings!

Beyond just saving time, templates ensure accuracy and process consistency. Asking multiple people to manually replicate the complex requirements stated in this post would almost certainly result in errors.

If you frequently build forms and flows with similar structures, do yourself a favor and templatize. Your future self (and coworkers) will thank you.

Sercante is recognized as a Marketing Cloud Growth and Advanced Implementation Expert and has the expertise to support your Agentforce Marketing needs. If you’re interested in support with Agentforce Marketing, reach out to us and let us know how we can help.

Pro Tips, Strategy
April 8, 2026

AI Answer Engine Optimization: A GEO Strategy for B2B Brand Discovery

Behrang Asadi

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.

Getting Started, Pro Tips, Strategy
April 2, 2026

Dispelling the Myth of Data Perfection: A Prioritized Data Roadmap for AI

Austin Frink

In the current market landscape, Chief Revenue Officers and growth leaders are facing a fundamental breakdown of the traditional go-to-market (GTM) engine. As organizations scramble to gain traction by implementing AI pilots, they often hit a wall: the daunting reality of their own data. Many leaders feel overwhelmed, thinking they have to have perfect data before seeing any real progress. However, this is a myth that stalls momentum. It doesn’t take perfect data to achieve real results with AI. It takes the right data for the right outcomes. The solution to moving beyond this roadblock is a data roadmap for AI that prioritizes based on business impact.

By shifting the focus from total data readiness to targeted, phased integration, organizations can finally start building an integrated data layer, the connective tissue needed for a modern growth engine. This was the resounding theme from Trilliad’s 2026 Growth Imperatives that industry leaders shared as being a top priority for GTM leaders to focus on this year to unlock sustainable success. It is the data that serves as the foundation to power impactful AI, analytics, and workflows across the customer lifecycle to deliver the connected customer experiences modern buyers expect. Which is why it is so critical for teams to move past a notion of ultimate data readiness and start prioritizing the data that needs to be connected now to start gaining real momentum.

Download the 2026 Trilliad Growth Imperatives eBook

The AI Mandate Reality Check for a Data Foundation

Data continues to be the foundation that powers experience, but with the AI era, it has a newfound importance. Sercante’s State of AI in Enterprise report reveals that organizations are dealing with more tech than ever before, yet they have yet to see meaningful results from their AI initiatives.

The average large enterprise now manages a technology stack of over 600 applications, leading to unparalleled volumes of fragmented data (WalkMe Inc.).
Currently, 56% of executives have yet to see a true impact on the bottom line from their AI deployments (Oxygen Staff).
87% of AI failures are rooted in poor data quality (RAND).

It is true that organizations need a solid data foundation to gain impactful outputs from AI. However, the belief that every one of those 600+ apps must be connected simultaneously is a recipe for gridlock. To gain momentum, leaders must identify the specific data needed for the intended outcomes and build from there.

“Uncertainty is chaos. It drains energy. It drains time. It feels out of control. Building a plan gives a sense of control. It alleviates the chaotic, stressful, anxiety-feeling that leaders feel and makes their path clear.”

Lauren Noonan, VP of Growth & Alliances, Sercante

This sense of control with data begins when the pursuit of perfection is replaced with a disciplined, strategic roadmap.

Download The State of AI in Enterprise report

A Prioritized Data Roadmap for AI

Creating a data foundation is not an “all or nothing” technical project. It is a strategic initiative that should be grounded in measurable, targeted business outcomes. It’s not about gaining more data. It’s about unlocking the right data. It can be overwhelming for leaders to think about connecting all their systems. That’s where the roadmap comes in. It gives leaders a clear plan to move forward with to start gaining real traction with their data and AI.

“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

Establishing the vision for your data

Answering the question of “what is the end goal?” is the first step in setting your vision. Without a vision to anchor your data and AI initiatives, it can feel like departments are running in several different directions, which can cause more silos and make it feel like busy activity without a lot of real results.

“Without a vision to ground your strategy, technology can feel like motion without progress.”

Jenna Packard, Strategy Director, Sercante

By defining the end goal first and the metrics that will be used to measure success, you create a filter for prioritization. This ensures the data initiatives are aimed at enhancements that drive measurable growth.

A sequenced path for your data and AI

As Jenna Packard shared in her article, The AI Roadmap for Enterprise, “a roadmap is highly sequenced, helping organizations understand which capabilities to build first and how each piece creates a foundation for the next.” A roadmap, or vision map, captures your end goal and measurable outcomes, then organizes the technology and data required to make it happen.

When creating the roadmap and thinking about what should come first, consider the following:

Specific Data Sources: Which data sources must be unlocked to fuel the AI to get the results we’re after?
Integration Effort: What is the technical and operational burden to enable this specific capability?
Support Systems: What is the infrastructure needed to ensure data connections run smoothly?
Data Integrity: Is there anything that needs to happen first to ensure that data will be clean, accurate, and actionable?

Answering these questions will allow the team to start placing the actions that need to happen on the roadmap to unlock the right data for the intended outcome. The end result gives teams a clear path forward to gain momentum, replacing reactive data fixes with impactful strategic discipline.

Getting Started with Your Prioritized Data Roadmap for AI

Data perfection is not a realistic end goal, and waiting for it only stalls progress. It takes a disciplined approach to step back, identify the end vision with measurable business outcomes, and then let that guide your data roadmap for AI. By prioritizing the right data for the right outcomes, you unlock the ability to gain real, measurable results with AI, fuel impactful analytics, and deliver connected customer experiences that modern buyers expect.

If you’d like support with creating your vision map for data and AI, reach out to the Sercante team. We partner with go-to-market teams daily to establish strategic roadmaps and design data foundations that result in scalable, impactful AI solutions.

Learn more about the vision map for data and AI

Pro Tips, Strategy
March 25, 2026

The AI Roadmap for Enterprise: A Framework for Identifying Value-Driven AI Use Cases

Jenna Packard

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.

Download the 2026 Trilliad 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.

Privacy & Compliance, Pro Tips
March 20, 2026

The Silver Lining of the Great Reset: Modernizing Marketing Governance

Kyle Simenson

On January 21, 2026 Salesforce deployed an urgent security patch to address high-severity vulnerabilities. While this patch was necessary against potential data exploits, the side effect resulted in every tracked link in every email sent from Salesforce Marketing Cloud Engagement (SFMC) prior to that date to be instantly deemed invalid.

So what does that actually mean? It means for organizations and brands with everything from multi-channel journeys, long-running welcome automations, or newsletters with a multitude of links, it was a strategic wake-up call.

The Silver Lining: Disruptive Innovation

It’s easy for many to look at this as a catalyst to hit the ejection button, but in reality, this is a classic example of “Disruptive Innovation” – an event that causes immediate pain but ultimately forces deep change. And history is full of these! Such as…

The Morris Worm (1988) when a Cornell graduate student released what was intended to be a small experiment to “gauge the size of the internet” which ended up crashing 10% of the world’s connected computers. BUT this was the literal birth of modern cybersecurity and led to the CERT (Computer Emergency Response Team).

The Knight Capital Glitch (2012) had software deployment gone wrong when the Knight Capital’s trading algorithms went rogue, buying and selling millions of shares in seconds. The results were a loss of over $400 million in 45 minutes. Knight Capital nearly went bankrupt, but it forced the financial sector (and eventually big tech) to adopt “Kill Switches,” automated deployment pipelines, and strict “Canary Testing” (where updates are rolled out to 1% of users first), which is now the gold standard for DevOps and Deployment Governance.

A simple, unpatched Apache Struts vulnerability led to The Equifax Breach in 2017 and the theft of personal data for 147 million people. The positive was that it put security front and center with leadership. Before Equifax, many C-suite executives viewed security as an “IT problem,” but after, it accelerated the adoption of laws like GDPR and CCPA, giving consumers more rights over their data.

With great resources from long-time Marketing Cloud Engagement users like Adam Thul from Polaris on how to fix things (see post here), history has a way of repeating itself, so this incident is the perfect catalyst to audit your instance through the lenses of governance, security, and long-term strategy.

Marketing Governance Framework 101

Governance isn’t about red tape. It’s about creating a “Golden Path” for your marketers. An effective model should be built on the pillars of ownership and stewardship. Executive Sponsors need to align marketing goals while managing the corporate risk and driving the overall vision. Product Owner(s) need to prioritize the backlog and manage the “Source of Truth” for data. Finally data stewards need to handle the day-to-day hygiene and ensure the integrity of subscriber data and integrations. Wrap all of this within business units that create data boundaries and sharing when necessary. This is essential and table stakes for global brands to ensure that a marketer in New York cannot accidentally email a customer list from Tokyo, while also maintaining regional compliance structures like GDPR and CCPA.

Embracing Modern Security

Salesforce has significantly tightened the screws on platform security, not only in the link security patch in January, but also API protocols. Taking a step back and ensuring identity and access management is in place so the overall “house” has the necessary locks and who has the keys needs are addressed. Multi-Factor Authentication (MFA) has to be a non-negotiable requirement. Ensuring all users (including API users) are routed through MFA or Single Sign-On (SSO) using SAML 2.0. At the user level, make sure custom roles are in place to restrict access to sensitive features like Automation Studio or Setup. Defaulting to the “everyone is an Administrator” is not the path.

Agentic Era Compliance

With the shift toward Agentforce Marketing and AI-driven agents, compliance is no longer a “set and forget” task. Consent Management has to be top of mind as regulators are utilizing tools to verify opt-outs, so preference centers must be integrated directly with the organization’s internal “Source of Truth” (ideally via Data 360) to reflect opt-outs in real-time.

Within the lens of AI transparency, maintaining an audit trail of decisions and edits needs to be put in place, especially if Einstein or Agentic workflows are generating content. This is increasingly required under new 2026 state privacy laws like Kentucky and Indiana. Finally, purging old Data Extensions and subscriber records that haven’t engaged in 18–24 months.

Here is a monitoring schedule that can be a baseline to build off of:

Task	Frequency	Purpose
User Audit	Quarterly	Deactivate dormant users and verify permission sets.
Setup Audit Trail	Monthly	Review who changed critical configurations or deleted Data Extensions.
Health Check	Weekly	Monitor automation failure rates and API limit usage.
User Audit	Quarterly	Deactivate dormant users and verify permission sets.

The Great Reset: Modernizing Marketing Governance

As we move forward in 2026, the most successful Marketing Cloud Engagement instances will be the ones that prioritize establishing a data foundation grounded in a marketing governance framework rooted in trust. Treating security as a feature, rather than a hurdle, to protect the most important aspects: a brand’s reputation and customers’ data.

If you’d like support with establishing your data foundation, governance, and security, reach out to the Sercante team. Our experts partner with marketing teams daily, designing and architecting data layers and frameworks that build trust and deeper customer relationships.