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Getting Started, Pro Tips, Strategy
October 13, 2025

The Ultimate Guide to Approaching Agentforce & Data Cloud

Heather Rinke

If you’re exploring Agentforce or Data Cloud, but feel unsure where to start—or how to ensure real outcomes—you’re not alone. In our recent webinar, “A No-Nonsense Guide to Launching Agentforce & Data Cloud,” we heard from marketing, RevOps pros, admins, and IT professionals across industries who are in the same boat.

The good news? You don’t need to launch a massive initiative to get started. But you do need a strategy. To help guide yours, I’ve shared the key concepts from the webinar where I spoke alongside Sercante’s VP of Growth & Alliances, Lauren Noonan, and Data Cloud Practice Director, Austin Frink, for our proven approach to creating a strategy for Agentforce & Data Cloud that sets you up for success.

A No-Nonsense Guide to Launching Agentforce and Data Cloud
Watch On Demand

Why having a strategy for Agentforce and Data Cloud is imperative

Implementing Agentforce or Data Cloud without a clearly defined strategy is like building a house without blueprints. According to RAND, over 80% of AI initiatives fail—not because the technology doesn’t work, but because teams skip over the foundational work of aligning their tools to real problems, realistic goals, and existing infrastructure.

What we’ve often experienced is that implementing these tools without a clear strategy often exposes existing challenges:

Data silos become more obvious.
Misaligned processes are harder to ignore.
Adoption falters because people don’t see the value.

When we’ve asked organizations about their vision or what they want to accomplish, we’ve heard many who say, “I want an agent” or “I want a unified profile.” While those are great aspirations, they are just starting points. A successful implementation requires more than a desire for automation or consolidated data. It requires a full vision of the why, the what, and the who.

Setting an impactful vision

Instead of focusing on the tool, focus on the challenge, the opportunity, and the people involved.

Ask yourself:

Why do we want an agent or a unified profile?
What business challenge are we solving?
What do we want the agent to actually do? / What will the data be used for?
How will the data or AI support better decisions?
Who will benefit, and how?

When considering these questions, be specific. If your starting point is, we want to have better segmentation or to deliver more personalized experiences, dig deeper. What would you like to segment by? Are there specific segments you’re focusing on for a business need? What part of the customer experience would you like to personalize more? Uncovering the more specific needs that lie within will help shape a more actionable vision.

Other questions you can consider for getting started to identify your use cases are:

Where are the friction points in your customer journey?
What repetitive tasks are your teams spending time on?
What segmentation or personalization capabilities are limited by your current data?

Use this framework to build your vision statement. Your vision should capture:

WHAT: The capabilities you’re adding
WHY: The impact those capabilities will have
WHO: The people in your organization who will benefit

This vision becomes your team’s north star. As you outline the use cases for Agentforce and Data Cloud, you will also need to define how they will impact the business.

Anchoring your Agentforce & Data Cloud use cases to business value

Lauren Noonan emphasized during the webinar, that this step is often missed, but it’s key to alignment. The use cases that drive meaningful business value are the ones that sustain momentum and stakeholder support.

To help frame your use cases in terms of the level of impact, consider impact levels such as the following:

High: Critical to strategic goals or revenue
Medium: Important contributor to organizational priorities
Low: Helpful improvements, but not game-changing

For example, one Agentforce use case that would be considered higher impact would be streamlining lead qualification and routing.

Use Case: AI-Powered Lead Qualification & Routing Agent
Scenario: Deploy an Agentforce assistant that engages with inbound demo requests, asks qualifying questions (budget, timeline, decision-maker status), and routes hot leads to the correct rep in real-time based on region, product, or account type.
Why It’s High Value:

Directly impacts pipeline acceleration and conversion rates
Reduces lead response time, which is closely tied to revenue performance
Supports strategic goals around improving sales velocity and rep productivity
Teams Impacted: Marketing Ops, Sales Development, Revenue Leadership, Prospects, Customers

Notice how the above framing points to impacts such as increased pipeline velocity and speed to lead which leads to higher conversion rates, all tied to revenue.

Medium to low-impact Agentforce use cases might include ones that result in internal efficiency gains, such as streamlining campaign brief creation or surfacing answers to FAQs faster using articles from your knowledge base. However, these lower-impact use cases are often a low-risk and lower level of effort to deploy, so they can be a great starting point as small efficiency gains do add up and free up your team’s time to focus on higher-impact initiatives. Continue to weigh this as you frame your roadmap of priorities.

The other detail to call out from the lead qualification and routing Agentforce example is that it outlined the people who would be impacted. Which is often overlooked, but absolutely necessary when creating your strategy.

Considering the level of impact on your people

Agentforce and Data Cloud aren’t just technical implementations—they’re changes to how people work. According to CIO Dive, 42% of businesses have scrapped most of their AI initiatives in the last year, where studies have shown that the teams that see success are ones that are customizing their use cases and prioritizing them according to their needs and impact. As Lauren shared in her previous article on how to create your AI roadmap, “your unique AI path is the only one that matters.”

You have a unique set of people at your organization with different skill levels, needs, and talents. Not considering a change management plan for how your Agentforce and Data Cloud rollout will be applied will often lead to low adoption rates, causing a low level of success and your initiative to fizzle out.

That’s why it’s so important to assess:

How will workflows shift?
What training or enablement will be needed?
How disruptive will this be to current roles?

Then classify the level of impact on your people:

High: Many roles and processes change significantly
Medium: Moderate impact with some changes to roles or responsibilities
Low: Minimal disruption to current workflows

Evaluating the level of business value alongside the level of impact on your people will help determine which use cases you might want to prioritize first based on the level of effort to deploy.

During the webinar, we showed the chart below as a visualization of the ideal intersection point, which is medium-to-high business impact and low-to-medium people impact, with a lower level of effort to deploy.

A chart that shows the business impact on the x-axis and the people impact on the y-axis with the level of of effort ranging from low to high above. On the chart, Customer Self-Help is plotted on the low to medium business impact level and medium people impact with a lower level of effort. Sales coaching/productivity is mapped at higher level of effort, business impact and people impact.

Take your considerations of the people impact a step further with this on-demand MarDreamin’ Session: Empowering Your People: Nailing Change Enablement for AI Rollouts by Director of Change Enablement at Sercante, Debra Engles.

The other aspect of your strategy that needs to be considered is the dependencies involved with deploying your use case for Agentforce and Data Cloud.

Understanding your dependencies

As Austin Frink and I emphasized in the webinar: implementation depends on more than just ideas. You need to understand the full picture of what it’ll take to make your use case a reality. That full picture includes the infrastructure that you have, including:

Technology: What do you already have? Where are the gaps?
Data: Do you have the right data sources? Are they integrated? Is the quality strong enough?
People & process: Who owns what? Where will the lift be felt most?

Dependencies aren’t necessarily roadblocks, but more factors that will help you to decide where you might want to start when it comes to implementing technology like Agentforce and Data Cloud.

The last piece to consider when defining your strategy is the metrics you’ll use to measure the impact of your Agentforce and Data Cloud initiative.

Defining your metrics for measurement

Your metrics will be used to help prove the business values that you defined earlier. For every use case, identify one or two key success metrics. Before you start your Agentforce and Data Cloud project, measure where you are right now to get a baseline, so that you can benchmark later on and compare your level of improvement.

Some ideas:

Customer satisfaction
Campaign performance
Churn rate

Ideas of metrics that could be associated with our Agentforce use case example of lead qualification and routing, would be speed to lead, conversion rate, and sales cycle time. If your use case will focus more on efficiency gains, consider the amount of time it takes your team to perform the task without the agent implemented, and then measure the hours of time that are saved as a result.

Pro tip: When bringing your strategy to stakeholders, share what your team will accomplish with the time gained back. This goes beyond just thinking through the metrics, and it will be key when articulating the full picture of the impact that implementing Agentforce and Data Cloud can have on your business.

Identifying a success metric is one part of it, you also want to make sure you have defined how that metric will be tracked and validated. For example, will it come from Salesforce, Data Cloud or an external system? Consider, are you capturing the fields that are needed for that metric today?

Also, don’t forget to capture a pre-project snapshot of your metric. Without a clean, documented baseline gathered before any implementation, you lose the ability to accurately show ROI.

Common misconception: you don’t need to start with a massive rollout

One of the most common misconceptions when teams are thinking about implementing Agentforce and Data Cloud is that it requires a massive rollout and overhaul. However, this is not the case.

You don’t have to ingest all your data to use Data Cloud. You don’t need to first implement Agentforce into every aspect of your business.

Start with:

1–2 use cases
Lower people impact
Medium-to-high business value

Clearly defining your use cases will inform you of the data that you’ll need, which will then point to the data sources required. The use case will then point to the agent that you’ll need and the teams or subsect of a department that will be impacted.

As the CMO of Mogli, Christina Scarmeas, shared during her conversation with us about how her team approached implementing Agentforce, “It’s okay to take a crawl, walk, run approach.”

Using AI and Agentforce to Make it Easier to Text on Salesforce
Watch On Demand

Which is why Sercante service offerings such as an Agentforce Quickstart or Data Cloud In-A-Box are so helpful for teams. They enable you to start small, accelerate time-to-value, and showcase the impact, to then scale the initiative to other areas of the business.

Let’s get started with your approach to Agentforce and Data Cloud

To recap, a successful approach to Agentforce and Data Cloud includes:

A clear, problem-driven vision
Use cases tied to real business value
Understanding the people impact
Mapping your technology and data dependencies
Identifying the metrics that will be used to measure impact

Having this all defined will help to serve as your team’s north star to guide a successful approach to Agentforce and Data Cloud.

Now I know how it can be overwhelming to think through all of this, especially with multiple department goals and initiatives, so if you’d like support with creating your Agentforce and Data Cloud strategy, reach out to the Sercante team. We’ll listen to understand your goals, challenges, and help bridge the gap between your vision and reality, for an impactful Agentforce and Data Cloud rollout.

Create Your Roadmap with the Experts
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Pro Tips, Strategy
September 12, 2025

The Proven Approach to Create Your AI Roadmap for Meaningful Impact

Lauren Noonan

If you’re feeling overwhelmed with AI at your organization, you’re not alone. In fact, during our recent webinar, AI Roadmap: The Strategy to Drive Growth with AI, 85% of attendees said they were either leading or supporting AI initiatives at their organization. And 70% of them admitted they had more questions than answers. Therefore, we’re sharing our proven approach for creating an AI roadmap that results in meaningful impact, to be a guide for growth leaders who are navigating AI for their organizations.

Why an AI roadmap matters

Just like any other solution, implementing AI without a set strategy and a plan for how it will be used, the goal you’re after, the dependencies to consider, and enablement for your team, is only going to hold you back from maximizing its value. If anything, diving in without a strategy will most likely highlight your gaps in processes, data, and alignment.

According to RAND, 80% of AI projects fail due to a misalignment of the problem being solved, not having adequate data, or a lack of infrastructure and readiness. These causes for failure can be avoided if teams take the time up front to think through their strategy. As the CMO of Mogli, Christina Scarmeas, shared from her episode, Using AI & Agentforce to Make it Easier to Text on Salesforce (Watch here), “The biggest thing that we learned was it’s okay to take a crawl, walk, run approach…if you put that infrastructure together and actually have a team that’s dedicated to the strategy behind [AI], the results come fast.”

Therefore, when navigating AI and rolling it out in your organization, don’t skip the important step of defining your strategy and building your roadmap. When considering how to start thinking through your strategy for AI, use the steps below from our proven approach to get started.

The CMO of Mogli, Christina Scarmeas, quote "If you have a team that's dedicated to the strategy of AI, the results come fast."

And if you’re ready to take action to start creating your customized AI roadmap with the guidance from our experts, check out our AI Workshop: Building Your Roadmap for Real Impact.

The Proven Approach to Create Your AI Roadmap

When thinking through your strategy for how to get started with AI, you want to start with your use cases and the goals you want to accomplish. Then, using your use cases and goals as the foundation allows you to tie to the business value and consider the data needed, the dependencies, and the impact it will have on your people for change management, so your first step is to frame your vision.

Start with the vision

To get the ideas started, think about your team’s daily workflows. Where are the friction points in their day? What repetitive tasks are slowing people down? Where is your time being taken, or where are you missing opportunities? That can start to highlight a few efficiency gains you could get from implementing AI for internal use cases.

Most importantly, you also want to think about your customer journey. Where are the friction points for the customer? What transition points could be made more seamless if AI were used? Taking this customer-centric approach will then highlight use cases where AI could be deployed for externally facing engagements.

As you identify your use cases, start defining the goals you want to accomplish. For example, if you implement AI to assist with lead qualification, what is the desired result you want to come out of that? What is the problem being solved, and what will success look like?

Then combine everything into one vision statement. The vision statement should capture:

WHO: The people involved who will benefit from the implementation
WHAT: The capabilities that will be added to your organization
WHY: The impact that is expected as a result

Director, Marketing Innovation & Technology, Zebra Technologies, Johanna Lehner, quote "The use case is the most important to define. Everything else should follow and be aligned."

This is the most critical step, and leaders who have started down this path of approaching AI for their organization agree. The Director of Marketing Innovation & Technology at Zebra Technologies, Johanna Lehner, shared on her episode, Future-Proofing Technology Through Impact-Driven Use Cases (Watch here), that“The use case is the most important thing you want to define…so that you’re thinking about what that process looks like in order to meet that goal or accomplish that use case in the most efficient and effective way possible. I would say that’s the most important thing, everything else should follow and be aligned with those goals that you’re trying to accomplish with the tool.”

Your vision becomes your north star that guides your team through the rest of the AI roadmap process.

Anchor it to the business value

This is where we shift from “wouldn’t it be cool if…” to “here’s why it matters.” For every use case you consider, think about what value it would add to your organization. Does it drive revenue? Improve customer satisfaction? Shorten the sales cycle? Some use cases will be mission-critical, while others might be helpful but not game-changing. For each use case, frame if it would be a high, medium, or low level of value.

High: Critical to the organization’s strategic goals
Medium: Important and contributes to the organization’s goals
Low: Has a minimal direct impact on the organization’s strategic goals, financial performance, or overall operations

Framing the level of business value will help guide the team’s decision-making later when choosing which use cases to prioritize first. But, in addition to the level of business value, you also have to consider the level of impact on your people.

Evaluating the impact on your people

Let’s be clear: AI is a people thing. Yes, it’s tech. But adopting it requires real humans to shift how they work. That means you need to map the impact. Will an initiative completely overhaul someone’s role? Or just take a few manual tasks off their plate? The more disruption, the more intentional you need to be about change enablement. Low-impact projects might require just a little training. High-impact ones? Those need a communication plan, champions, and a solid support structure. Therefore, for each use case, you’ll also want to define the level of impact on your people.

High: Many roles are impacted, many processes will change, and roles will be redesigned
Medium: Some roles are impacted, some processes will change, and roles might change slightly
Low: Very few roles are impacted, minimal process changes, and no role restructuring will be needed

As you consider the level of business value and how your people will be affected by the AI solution implemented, this thought process will highlight the level of effort that would be required for rollout. This is where finding the ideal sweet spot comes in for all three when considering which use cases to prioritize first on your roadmap.

Finding your sweet spot

Every use case you explore should be evaluated based on three things: the value it delivers, the impact on your people, and the level of effort it will take to implement. Think of this like a chart where you’re looking for where the ideal points intersect. The sweet spot with medium to high business value, relatively low people disruption, and feasible to get off the ground. We’ve seen many organizations start with internal processes to get some efficiency gains as a starting proof of concept. However, these small efficiency wins are not to be overlooked as they add up to greater impact and make the customer experience feel that much more seamless. experience feel that much more seamless.

A chart visualizing the examples of two AI use cases: Sales Coaching/Productivity and Customer Self-Help and where they fall on the chart measuring Business Impact on the x-axis, People Impact on the y-axis, and also the Level of Effort at the top.

For example, Advanta Health partnered with Sercante to implement Agentforce to help scale their member services team and address the needs of their growing customer base. The solution surfaced answers to FAQs faster, using their Knowledge articles, assisting the service team, and cutting down on their handle time, while making responses more consistent across the board. As a result, members reaching out with inquiries experience a more streamlined engagement that gets them the answers to their questions faster.

Advanta Health Scaling Member Services with Agentforce Read Case Study

Therefore, when considering which use cases to start with first on your roadmap, use the “sweet spot” to guide you, and don’t underestimate the impact that can come from small efficiency gains.

Once you start to get an idea of the use cases you might want to start with, based on where they land among the level of business value, people impact, and effort to deploy, the other important piece you need to consider, which will impact your level of effort, is your dependencies.

Map your dependencies

This is the part of the roadmap where things can get messy if you don’t slow down and look under the hood. Before rolling out anything new, you need to assess what it’s going to take to support it. Do your systems talk to each other? Is your data accessible and clean? Do you have a governance structure in place? Do your people have time and capacity to manage the shift? These aren’t roadblocks—they’re flags that help you build smarter. Ignoring them can cause your AI project to fail.

Here are some starting questions to ask yourself to help identify your dependencies:

Technology

What technologies do you already have?
Do you have gaps in technology needs based on the use cases?
What will it take to close these gaps?

Data

What data will AI need access to?
Do you have data sources that are not integrated today?
Do you have data quality issues?

People

What teams/roles are impacted by these AI use cases?
What amount of time will AI unlock for these teams? What can they focus on instead?

As you think through the dependencies for each use case, this can help give you a clearer idea of the level of effort it would take and then better inform which use cases truly fall in the sweet spot.

Then last, but certainly not least, if you didn’t already think through the metrics you’ll use to measure success when defining your goals in the first step, this is where you’ll want to do that.

Define your success metrics

If you want to prove AI is working, you need to measure it. Tie every use case to a clear outcome. That might be lead conversion, campaign ROI, sales cycle length, or something else entirely. Pick metrics that matter to your business and are realistically measurable. And don’t wait until you’ve rolled something out to start tracking. Get benchmarks in place now so you can show progress early and often.

A final word of wisdom

The final and last piece of wisdom when thinking about your approach to AI is your unique AI path is the only one that matters. Now is not the time to be a copycat with AI. The chatter I keep hearing is, “What are other companies doing? What are their use cases? How quickly can we implement those use cases at our organization?” And frankly, it feels dangerous.

Why? Because the last decade has left every single company with a unique technological fingerprint. We all have our own way of working, our own internal processes, and a pile of tech debt and data quality issues to go along with it.

Thinking you can just copy someone else’s AI strategy is like trying to replicate a recipe with totally different ingredients. The output is destined to be different. The truth is, the successful path isn’t about looking outward. It’s about looking inward.

Therefore, when thinking through your AI strategy, don’t be so concerned about what everyone else is doing, focus on what will drive value for your customers and your business.

Getting started on your AI Roadmap

Creating an AI roadmap is about solving real problems in a way that supports your people, your business goals, and your vision. It is your guide to rolling out a successful AI solution that empowers your people to focus on the most important initiatives and deliver a seamless experience for your customers.
If you’re ready to dive in to start building your AI roadmap and would like an expert guide to take you through step by step, of defining your vision and use cases and creating your prioritized roadmap, reach out to the Sercante team. We’ve helped many organizations get on the right path to maximizing AI for their organization and driving meaningful business impact with our AI Roadmap offering.

Discover what's possible with the AI Roadmap

Community, Data Management, Industry News, Pro Tips
September 10, 2025

Salesforce Related Data Breaches: What you need to know

Heather Rinke

Several data breaches affecting a wide range of companies that use Salesforce have been reported in recent weeks. These incidents have impacted organizations across various sectors, including technology, retail, and insurance. The exposed data has varied by victim but has commonly included customer contact information, internal business records, and even sensitive data like API tokens and credentials.

Sercante clients can be assured that our systems have not been impacted by these recent attacks, however we want to make sure that Salesforce customers are aware of these incidents and are equipped to safeguard their instances.

How the Breaches Occurred

The recent breaches are not due to a vulnerability within the Salesforce Core platform itself. Instead, threat actors have used sophisticated social engineering and supply chain attacks to gain unauthorized access.

One common method has been targeted voice phishing (vishing) campaigns. In these attacks, bad actors impersonated legitimate employees or IT support staff to trick victims into downloading a malicious replica of Data Loader and granting access to their Salesforce environments.

In a recent and widespread campaign, attackers leveraged compromised OAuth tokens for a third-party application, Salesloft Drift. By exploiting the integration between the app and Salesforce, the threat actors were able to export large volumes of data and credentials from numerous corporate Salesforce instances in what is called a “supply-chain attack”. . The attackers were able to steal “digital keys,” or authentication tokens, from the Drift app. They then used these stolen keys to access and steal data and credentials like passwords, API keys, and access tokens for other services that could be used to compromise other systems integrated with Salesforce.

This highlights a critical risk: while the core platform may be secure, its connections to third-party apps can introduce vulnerabilities.

Risk to Salesforce Customers

The primary risk to Salesforce customers lies in the potential for stolen data to be used for further attacks. Customer contact information and other details can be weaponized in targeted and highly convincing phishing and social engineering campaigns to gain access to other corporate systems. The exposure of sensitive information like API tokens and credentials poses a significant threat, as it can be used to compromise connected systems, such as other cloud platforms or internal networks.

UPDATE: If you are a Drift customer – Salesloft has announced plans to shut down its Drift chatbot following their recent security breaches. This no doubt presents a challenge to your website engagement strategy. The Sercante team is well-versed in the various conversational platforms that integrate seamlessly with Salesforce and can help you navigate this transition.

Recommended Actions for Protection

While Salesforce has taken steps to restrict the use of “uninstalled connected apps”, customers should take steps to protect themselves from similar threats:

Reauthenticate Drift Connections: Salesloft Drift customers will need to reauthenticate their Salesforce integration with Drift. It’s also advised that any and all authentication tokens stored in or connected to the Drift platform should be considered potentially compromised and update them immediately.
Rotate all credentials and keys: Immediately change any passwords, API keys, and other access tokens that were stored in your Salesforce instance
Investigate your Salesforce account: Look for any unusual activity in your Salesforce login history, audit trails, and API access logs from early to mid-August 2025. Look for suspicious logins or data access patterns, particularly from the user account associated with the Drift integration.
Audit Third-Party Apps: Audit your connected apps to make sure they are secure, and make sure that all third-party apps connected to your Salesforce account have only the minimum permissions they need to do their job and revoke access for any app that is no longer in use.
Secure APIs and Integrations: When configuring new integrations, restrict API access by defining trusted IP ranges and ensuring that connected apps have the most restrictive scope possible.
Apply the Principle of Least Privilege: Limit user permissions to only what is necessary for their job role. Restrict administrative access and minimize the use of permissions like “Modify All Data.”
Be on high alert for phishing: Warn your employees to be extra cautious about any unexpected or unusual emails, phone calls, or messages. The attackers may use the stolen contact information to try and trick people into giving up more sensitive data.
Rinse & Repeat: Security isn’t a set it and forget it function. It takes constant and consistent vigilance to protect your systems and data.

While the core Salesforce platform is secure, recent data breaches are a reminder that a company’s security is only as strong as its weakest link, which is often a third-party app or a human being. To stay safe, you have to be proactive. By using strong security practices, enforcing strict access rules, and training your team, you can drastically improve your defenses. Ultimately, keeping your data safe is a team effort—you, Salesforce, and all of your employees have a role to play.

If you’d like a guide to help you navigate how to optimize data protection in your organization with Salesforce, reach out to the Sercante team. Our experts can be your guide for impactful next steps.

Data Management, Marketing Automations, Pro Tips
July 29, 2025

Using Agentforce, Data Cloud, and Marketing Cloud Advanced to Improve Student Retention

Angelica Cabral

Student retention is one of the most pressing challenges in higher education—and it’s not just about keeping enrollment numbers up. It’s about making sure students feel supported, connected, and confident in their ability to succeed, and engaging them before it’s too late.

Almost a quarter, 22.3%, of first-time, full-time undergraduate freshmen drop out within the first 12 months, while 39% don’t complete their degree within eight years (Education Data Initiative).

Which is why it is imperative for institutions to be taking action to improve student retention. One of the ways they can is by maximizing the technology they have and tapping into the latest solutions available to gain a better view of their students’ journeys and take action to engage when it matters most.

In Sercante’s latest demo, we explored how Salesforce’s connected tech stack—featuring Data Cloud, Marketing Cloud Advanced, and Agentforce can empower institutions to identify and support at-risk students before they fall through the cracks.

In the demo, we followed the journey of Jason Smith, a sophomore whose profile revealed a 77% attrition risk. What followed is a case study in how smarter tech, working harder behind the scenes, can drive real results for both teams and students.

Unifying Student Data for Smarter Insights

Everything begins with data. Using Salesforce Data Cloud, we created a Unified Student Profile by integrating key data sources across systems: academic performance, course engagement, attendance patterns, and more.

This holistic view powered a propensity model that flagged Jason’s risk level at 77%. Instead of relying on gut instinct or outdated reports, the team now had real-time, actionable insight—and a clear signal that it was time to act.

From Insight to Action with Marketing Cloud Advanced

That’s where Marketing Cloud Advanced comes in. Once a student is identified as at-risk, every minute matters. This tool enabled us to build automated, personalized communication journeys, so students like Jason could receive the right message at the right time.

Jason’s message came from Sercante University: a friendly, timely nudge to connect with an advisor. And because it was based on real-time data from his unified profile, it felt relevant, not random.

A Seamless Path to Support with Agentforce

The magic moment? When Jason clicked the link and landed on a scheduling page powered by Agentforce.

Here’s where tech meets empathy. Agentforce’s AI assistant recognized Jason’s concerns about unavailable classes and provided instant, personalized guidance—helping him book an appointment with an advisor in just three clicks or less.

No back-and-forth emails. No waiting. No frustration. Just a frictionless experience that made Jason feel seen, supported, and empowered to move forward.

A Multi-Cloud Solution that Improves Student Retention

What made this work wasn’t just the data or automation—it was how these tools worked together to create a better experience for both students and staff.

For teams: The heavy lifting was handled behind the scenes. With Data Cloud pulling real-time insights, MC Advanced automating outreach, and Agentforce handling the scheduling, advisors could spend less time triaging and more time supporting.
For students: It felt easy, human, and personalized. Jason didn’t need to fight for support—it found him, right when he needed it. He got help fast, with minimal effort and instant gratification.

This Agentforce, Data Cloud, and Marketing Cloud Advanced, multi-cloud solution, is an example of when teams use the power of data and AI to engage their students when it matters most, before it’s too late. Intercepting more students like Jason can help institutions improve student retention while creating real connections that last to drive growth for the institution and in Jason’s educational journey.

Final Takeaway

When schools bring together data, automation, and AI, they unlock a more connected, proactive, and student-centered approach to retention.

Students feel seen, supported, and confident in what to do next
Teams get relief from manual processes and can focus on what matters most
Institutions see better outcomes—without burning out their staff

It’s not just about reducing attrition—it’s about building trust, creating moments that matter, and delivering an experience that helps every student thrive.

Want to see the full journey? Watch the demo here.

Looking to implement something similar at your institution? Reach out to the Sercante team.

Getting Started, Pro Tips, Technology
July 25, 2025

How to Overcome the 6 Biggest Obstacles to AI Adoption

Angelica Cabral

AI has the potential to fundamentally change the way we work—not just in theory, but in the day-to-day rhythms of marketing, sales ops, RevOps, and customer experience teams. At its best, AI can help us scale what works, automate what drains us, and create seamless customer journeys that feel personal, not robotic.

Every organization wants to implement AI, because they know the value, but teams are navigating real challenges: unclear use cases, disconnected data, limited internal expertise, and a natural hesitation that comes with change.

A recent Gartner survey found that 77% of executives believe AI will give them a competitive edge—but only 44%, meaning less than half, feel confident in their roadmap to get there.

To help teams navigate AI adoption, the experts at Sercante put together an AI Starter Kit filled with demos, real-life examples, expert recommendations, and insightful how-tos for overcoming the most common AI adoption obstacles, which is what inspired this article.

Here are the six most common AI adoption roadblocks the team has seen firsthand—and some practical, no-nonsense ways to work through them.

Obstacle #1 Data silos are stalling progress

You might be feeling this if…

Your team is using multiple tools that don’t talk to each other, reporting feels unreliable and takes forever, and you’re not quite sure where all your customer data even lives.

How to move forward:

AI can’t do its job if it doesn’t have access to clean, connected data. Start with a simple audit: where is your data, and who owns each piece? From there, focus on one high-impact use case and use integration tools (like a customer data platform or middleware) to bring data together. Keep it focused—you don’t have to solve the whole thing in one go.

Obstacle #2 Lack of trust in accurate results

You might be feeling this if…

There’s skepticism around AI recommendations, hesitation to take action on outputs, or concerns about compliance, bias, or lack of transparency.

How to move forward:

This isn’t just about proving that AI “works”—it’s about making people feel safe using it. Prioritize tools that show their work (think explainable outputs). Run small pilots to validate results and let the data do the convincing. Also appoint internal champions who can model responsible, thoughtful AI use.

If you’re just getting started, this article offers helpful grounding: 7 tips for how to get started with AI.

Obstacle #3 Skill gaps

You might be feeling this if…
AI feels too technical or intimidating for the team, and you’re relying on one or two people to drive all the innovation.

How to move forward:
You don’t need a team of data scientists to start using AI. Launch basic AI literacy training by role—what should a CX leader know about AI vs. someone in RevOps? Create safe spaces for learning and experimentation. And if there are areas where you need deeper expertise, don’t be afraid to lean on partners while your team ramps up.

Obstacle #4 No clear use cases

You might be feeling this if…

Your team has a shiny new AI tool, but no one knows what it’s for—or conversations are stuck at the “someday” level. Or conversations around AI remain at the hypothetical level, but no one is actually taking the plunge to use it daily and point to how it is helping them scale and be more efficient.

How to move forward:

Bring AI down to earth. Host simple workshops by function and explore high-impact, low-effort use cases: AI-generated email copy or campaign briefs, summarization, agentic lead qualification and routing, FAQ case deflection through knowledge article references. Document small wins and share them internally—that success story from the marketing team might inspire the sales org to try something next.

Obstacle #5 Resistance to change

You might be feeling this if…

There’s pushback from users or leaders who feel uneasy, or concerns that AI might replace jobs or change the nature of their work. Or you’re hearing team members say “But we’ve always done it this way.”

How to move forward:

There’s no way around it, change will always evoke emotions—discomfort, worry, anger, excitement—you get it. However, how we choose to respond is what is in our control, and we can either choose to keep our shields up with AI or we can see it as an opportunity to innovate, scale what we do best, and create even better experiences for customers.

For conversations with your team, consider reframing AI as a co-pilot that takes on the repetitive tasks, not a replacement for human expertise. Involve employees early and let them help shape how AI gets used. Highlight wins that make daily work easier and more efficient.

Obstacle #6 ROI concerns

You might be feeling this if…

Budget holders want to see results before approving spend, or it’s unclear how success will even be measured.

How to move forward:

In the beginning, when you’re identifying use cases, consider the metrics that will be used for each one to evaluate success and tie these to clear business outcomes.

Lauren Noonan, VP of Growth and Alliances at Sercante, quote: "What are you doing to do with the time you get back from using AI, and how will it impact the organization?"

In the beginning, it may be as small as time saved during campaign building, but when you multiply that time saved over the course of the year, and the amount of team members it affects, that will add up to big results. Then, as Sercante VP of Growth & Alliances, Lauren Noonan, shared on the Connections Recap session, answer the bigger question, “What are you going to do with the time you get back and how will it impact the organization?” When you answer that question, it will show leadership the existing gap between where you are now and the level of growth that could be reached if your team was using AI.

When you’re building your AI roadmap, include the expected short-term wins that will come with your initial low-level of effort use cases and the long-term goals the team is after to give your team a big picture that everyone can align on.

Overcoming to get started with AI

The road to AI adoption isn’t about flipping a switch. It’s about taking deliberate, doable steps—ones that meet your team where they are and build toward where you want to go.

Start by acknowledging what obstacles you and your organization align with the most and then work through the steps above to start to overcome. And if you’d like a third-party expert’s insight, the Sercante team can help. We’ve partnered with dozens of teams to move past the blockers and build AI strategies that actually work in the real world, and have guided them on the path toward driving growth with AI at their organization.

Getting Started, Industry News, Pro Tips, Real Talk, Technology
July 23, 2025

7 Tips for How to Get Started with AI

Christina Anderson

It’s not a secret, so many growth teams, marketing, sales, and customer success, want to be using AI to streamline processes and elevate customer experiences, but when it comes to adoption, they’re a little stuck on how to get started with AI.

After hearing a few of the experts at Sercante share their insights and having conversations with marketing leaders who have taken the plunge on applying AI to their initiatives, plus some first-hand experience with our own marketing, I was able to create this collection of tips for how to get started with AI.

For those thinking, TLDR, let’s cut to the chase, I recommend downloading Sercante’s AI Starter Kit.

Tip 1: Pinpoint the pain

After attending a Connections 2025 Recap Webinar, Sercante’s Salesforce Product Director, Heather Rinke, advised the audience to get started with AI by writing down the biggest pain points they have today.

What is a manual, repetitive, and mundane task, sucking up your bandwidth?

Then consider, of those pain points, which is the lowest barrier to entry? What might have dependencies that might need a little more technology configuration, data setup, or the involvement of multiple departments?

Focus on the low lift, but quick-win initiatives first. Often, this would be an internal process that you could see how it performs, measure impact, and then scale from there.

[Watch On-Demand 2025 Connections Recap]

Tip 2: Do your research

As Laura Curtis, Senior CRM & Marketing Automation Strategist at Sercante said on the Connections Recap, “You don’t know what you don’t know.”

In fact, 71.7% of non-adopters say “lack of understanding” is their biggest barrier to AI adoption (Influencer Marketing Hub AI in Marketing Benchmark Report). So if you’re feeling overwhelmed, you’re not alone. But it’s also fixable.

One way to start doing your research: Download the Sercante AI Starter Kit. It’s full of:

Real use case examples
Tips you can steal
Customer stories
Common pitfalls to avoid

Also, check out Rinke’s article, Five Tips for Getting Started with Agentforce.

Tip 3: Try out-of-the-box tools first

Don’t build the Death Star on day one.

There’s zero need to spin up a complex custom solution to get started. Many platforms—especially Salesforce Agentforce—already have out-of-the-box agents and AI functionality you can activate right now.

Use them.

Start small. Test how it works. See how it helps. Then decide if you want to scale or customize.

Tip 4: Get your data house in order (but don’t wait for perfect)

As Sercante VP of Growth & Alliances, Lauren Noonan, shared on the Connections Recap, “Very few people buy a home and it’s perfect.”

You can start with AI even if your data isn’t a 10/10. But the better your data hygiene, the better your AI output.

Start by asking:

What data is critical for our first use case?
Where does it live?
What’s messy that could block us?

Then clean it as you go. Consider what other data optimizations you can make along the way to support the future AI initiatives on your roadmap. Like updating the kitchen before you renovate the whole house, and then mapping out what renovations make sense to do next.

Tip 5: Measure what matters

But what metrics are worth tracking to measure the impact of AI?

It depends on your use case, but as an example, Noonan shared the idea of using AI to generate a campaign brief instead doing it manually.

How long does it take you to do it manually versus using AI?

Before getting started with using AI internally, get benchmarks for how long it typically takes your team do the processes you’ll be using AI for and then measure how long it takes after.

Now, the bigger question that Noonan posed is “What are you going to do with the time you get back and how will it impact the organization?”

If you’re trying to champion AI in your organization and get your leaders on board to support the initiative, answering questions like these can be a huge part of the business case—other than the increased efficiency, maximized output, and better customer experiences that’ll lead to growth.

Tip 6: Taking a crawl, walk, run approach to AI

As the CMO of Mogli, Christina Scarmeas, shared on my recent conversation with her on the Innovator Series about her team’s approach to using AI and Agentforce:

“One of the biggest things we learned was, it’s okay to slow down and take a crawl, walk, run, approach to AI. Take a step back, look at the infrastructure that we need to deploy, and what we’re trying to do with these agents. Be okay with having a phased approach with expectations of how it’s going to be implemented, and if you put that infrastructure together and have a team that is dedicated to the strategy behind it, the results come fast.”

[Watch the Innovator Series]

This aligns with Rinke’s advice of starting with an initiative that is low-barrier-to-entry, perhaps something internal where you can point to productivity gains.

A customer in the healthcare industry’s story of getting started with AI

For example, one of Sercante’s customers in the healthcare industry serving multiple practices implemented Service Agents with Salesforce Agentforce.

The Service Agents were set up to help the human agents query knowledge faster to support them in their calls to better serve patients and providers.

The solution has been rolled out to one practice area first, so the team can continue to learn and adapt the solution as needed before scaling to their other practice areas.

This is a great instance of a team starting small, with rolling out an AI-powered solution in one area of their business to then evaluate and see how it can be applied to the other areas of their business..

Tip 7: Build your AI roadmap

Once you’ve dipped your toe in, it’s time to zoom out.

As the Director of Marketing at Mogli, Evan Thomas shared on the Innovator Series:

“AI is the next evolution of technology and like all the tools that came before it, you have to have a plan. Just getting AI into your org isn’t going to fix it. You have to have a plan for that AI. What is it going to do for you? What is the process it is going to supplement or help your team focus on? What is the reason you’re doing it?”

Creating your AI Roadmap for quick wins and long-term success

Therefore, the next step is creating your plan or your AI roadmap. Identifying your pain points and use cases is the first step of this. The other piece of this is evaluating the customer lifecycle through the lens of the customer and identifying points of friction that could benefit from a solution to help your team scale and make the engagement seamless. However, as this can be overwhelming for teams to do, experts have started collaborating to put together resources for this.

Sercante’s session, AI Roadmap: The Strategy for Driving Growth with AI, is a great resource that includes insights from experts on how to approach creating a plan that:

identifies high-impact use cases
considers dependencies and how to address roadblocks
outlines key metrics to track for measuring success
clearly defines your path for quick wins and long-term success

Take advantage of training opportunities for creating your AI plan

Another option for getting started with creating your AI Roadmap is to get training. According to SurveyMonkey, 70% of employers don’t provide training on AI, even though 70% of marketers say it’s essential. Therefore, any time you can get training and advance your skills on how to approach AI or how to use it, take advantage!

One training offered is Sercante’s AI Workshop: Building your roadmap for real impact. This is the deep dive where the experts will guide you through creating your 30-60-90 day plan to help your organization get started with AI and scale for the future.

One last thing: Pick yourself in this era

CEO of Sercante, Andrea Tarrell, shared this sentiment during her opening keynote at MarDreamin’ Summit, encouraging the community to take action asking, “If not you, then who?”

This technology is at our fingertips, and it’s up to us to decide how we’re going to use it to streamline processes, meet customers where they are at scale, and create a truly seamless experience.

It’s time to get started with AI.