Using AI for Research and Insights

Introduction

For a long time in Customer Success, the solution to the massive increase in workload that comes along with scaling customer bases was to hire more people. More CSMs means more work done right?

But this model is reaching its limit. Customers want something different now. They expect businesses to understand them on a deeper level, anticipate their needs and risks, and provide instant value. And the cost of adding enough people capable of handling these new expectations while managing onboarding, renewals, and expansion is beyond what most businesses are willing to allocate to CS. 

So what can you do when your CS team doesn’t have the manpower, and each CSM is stuck trying to be everywhere at once? You take advantage of next-generation AI technology to make a new operational model. 

Our earlier modules were focused on you: how to upskill as an individual, strengthen your AI literacy, and integrate AI into your day-to-day workflow. This module is different, because we go beyond personal capability and look at what it takes to bring AI-powered change to an entire Customer Success function.

If you’re a CS leader, this module can give you a practical blueprint for guiding your org through that transformation. And even if you’re not in a leadership role yet, this content will help you understand how CS organizations evolve, how AI reshapes operating models, and how you can demonstrate strategic thinking that positions you for the next level in your career.

In this module, you’ll learn how to use AI to restructure your CS org:

• How to assess your organization’s readiness for AI before introducing automation or agents.
• How to design an AI-specialized Customer Success team, with clarity on mindset, learning, and collaboration.
• How to build the technical architecture required for AI, like data layers, integrations, governance, and infrastructure.
• How to identify and implement high-impact AI workflows, from proactive risk detection to automated onboarding.
• How to create a practical implementation roadmap, so you can adopt AI in a way that is safe, phased, measurable, and aligned with your company’s CS strategy.

The goal is to give a clear blueprint for transforming your CS organization into one built for the AI era. With these insights, you can be more predictive, more efficient, and more aligned to revenue impact than ever before.

Chapter 1: The Step-by-Step Guide to Leading Effective AI Transformation Across Your Team

An organization-wide AI shift has to be a people and system transformation happening at the same time. This module is designed to help you navigate both. On one hand, your team needs the mindset and skills to work effectively alongside AI. On the other hand, your organization needs clean data, clear workflows, and stable processes to support agentic execution.

This kind of holistic AI adoption requires a methodical approach that assesses and strengthens the foundations of the org. This is what creates the stability and clarity that allows for AI to operate effectively across your Customer Success function.

This roadmap outlines a simple path CS leaders can follow to make their teams, data, and workflows AI-ready. You can adapt the structure based on your team size, product complexity, and available resources. The chapters that follow this will elaborate on how to practically implement these steps. 

Step 1: Audit How Your CS Organization Operates Today

Before you automate anything, make sure the underlying system actually works.

Your operating model forms the ground that AI stands on. A clear audit helps you identify what is stable, repeatable, and ready.

Questions to ask:

• Are customer journeys (onboarding, renewals, escalations) defined and followed?
• Are roles clear across CS, Sales, Support, and RevOps?
• Is the customer journey visible in one place?

You can refer to Chapter 1 of this module for more details on this step. 

Leadership Action:
Run a simple gap analysis. Identify the top 3 operational inconsistencies slowing your team down today.

Step 2: Clean and Align the Data Layer

AI will only ever be as good as the data it is trained and triggered on.

Customer Success teams often work across many tools, like CRM, CSP, billing, support, and product analytics. When these systems don’t agree, AI can’t produce reliable insights.

Here are some areas to evaluate:

• Health score inputs
• Product usage tracking
• Sentiment signals
• Cross-tool consistency

AI requires:

• Clear, automated inputs
• Consistent customer fields
• Structured signals
• Minimal duplication

The data doesn’t need to be perfect, but it should ideally be trustworthy, stable, consistently updated.

Additionally, to make AI effective, CS leaders need to shift away from legacy segmentation models like “SMB / Mid-Market / Enterprise”. 

It’s much better to adopt more dynamic, data-driven segments based on value potential, product behaviors, support needs, and strategic importance. This ensures AI can trigger the right actions at the right time, using clear decision logic. 

Leadership Action:
Pick one area (health score, usage data, sentiment) and clean it before expanding to others.

Step 3: Align the Team and Build Change Readiness

AI adoption requires culture change, not just new tools.

Even the best-designed AI workflows will fail if your team isn’t fully aligned with the AI shift. The team needs to understand exactly why AI is being introduced, how it supports their work, and in what ways the human role evolves when working with AI. 

As a CS leader, it’s vital that you foster a workplace where the whole team sees AI as a natural extension of their own capabilities. So how do you go about creating an environment where the team is enthusiastic about adopting AI?

• Encourage your team to check out this course to build a shared understanding about AI in CS
• Create reward systems for experimentation with AI, like a mini-hackathon, or a monthly “best workflow improvement” award.
• For technical concepts that go beyond the average CSM’s role, bring in outside technical help or an AI Ops Lead to guide implementation. It would be frustrating for CS teams to try and build advanced architecture themselves, since that’s just not where their skills are suited for. 

We can’t stress enough on how important communication, training, and clear ownership are. Without this, AI just becomes a novelty in the workspace. 

Chapter 3 has more in-depth tips to offer on creating an AI-enhanced team. 

Leadership Action:
Run an internal “AI expectations” session. Show simple examples of how AI will help their work.

Step 4: Modularize Key Workflows

AI cannot operate inside inconsistent processes.

To prepare for automation or agents, you must break workflows into modular “building blocks” that have:

• Clear ownership
• Clear triggers
• Clear inputs and outputs
• Defined fallback logic

AI needs structured processes it can follow reliably. Start with one journey (onboarding or renewals) and break it into 10–15 steps. 

Leadership Action:
Create a modular workflow map for one lifecycle stage.

Step 5: Prepare for Agents 

Agents require a stronger foundation than basic workflows.

AI agents represent the next maturity level of automations: they act without waiting for a human to interpret dashboards.
But this only works when the organization has clarity around:

• AI Architecture (which we will explore in Chapter 4)
  Step ownership
• Guardrails
• Failure modes
• Fallback owners
• Observability and feedback

Have a look at Chapter 4 for more information on this sort of preparation. 

Leadership Action:
Review how agent-ready your org is with the AI Ops Lead or RevOps. Identify 2–3 workflows that could eventually move to agentic execution.

Step 6: Pilot, Measure, and Scale

Transformation happens through gradual iteration.

Once your foundations are in place, run a small, controlled pilot:

• Choose one workflow (e.g., onboarding nudges, renewal prep, risk alerts).
• Define success metrics.
• Assign ownership (typically the AI Ops Lead).
• Test for 4 - 6 weeks.
• Capture learnings and refine.

Leadership Action:
Select your first AI workflow pilot and set a clear review date.

With the right sequence, the right roles, and the right culture, every CS organization can build a scalable foundation where AI amplifies human talent.

For even more detailed guidance on implementing this roadmap, feel free to check out our ebook: How to Build an AI-Ready CS Function: Fixing the Foundations, which contains a series of worksheets for making your CS organization AI-ready.

Chasing every new tool or trying to automate everything at once is probably more trouble than it’s worth. Look to have a smarter, more intentional way of working, and gradually make your workspace more AI-native. Bit by bit, you’ll be able to deliver Customer Success the way it was always meant to be: strategic, valued, and deeply connected to customer outcomes.

Chapter 2: Auditing the Organization

Before you bring AI agents, predictive models, or automated workflows into your Customer Success function, you need one crucial thing: a clean, reliable foundation. It’s true that AI has the potential to accelerate whatever it touches. But if your processes are inconsistent, your data is unreliable, or your segmentation logic is outdated, AI will only amplify the mess. 

This chapter is all about making sure your system is strong enough to support automation, prediction, and eventually, agent-led execution.

Audit Your Operating Model

An operating model is basically how your CS team runs today: your segmentation, processes, workflows, ownership, and documentation. Consider it the “software” of the department. We need to fix the buggy system before we layer on AI. Here’s what we’re looking for:

• Are engagement models standardized?
  If each CSM runs onboarding, QBRs, or renewals differently, you can’t automate or templatize anything. AI workflows need clear rules of engagement.
  
  
• Is resource allocation logical?
  Does your current coverage model align with effort vs. value? AI enables dynamic coverage, but only if the baseline is defined.
• Documentation hygiene
  Are onboarding flows, QBR templates, renewal steps, and escalation protocols written down and accessible?
  
  
• Clear ownership
  Does every step in the journey have a defined owner? AI needs to know when to act, and when to hand off to a human.
  
  
• Failure modes
  What happens when a customer doesn’t respond, complete a task, or hit a milestone? AI needs predefined fallback logic.

After going through this checklist, note down the inconsistencies. Identify the top workflows that are undocumented, or too dependent on individual CSM styles. Look for patterns where your team is forced to rely on judgement because there are no set standards for action. Those areas cannot be automated yet.

Audit the Data Layer: Hygiene, Integration, Telemetry

If your product data is partial, your health score inputs are subjective, or your CRM contradicts your billing tool, AI tools cannot reason or act reliably. Here are some key categories in the data layer you should review:

• Are segments accurate?
  Many teams still rely on static firmographics (ARR, employee count, region). These say nothing about behaviour, growth potential, or risk. AI workflows work best with dynamic, signal-based segments that adapt as customers do.
• Health score inputs
  ‍Most teams still rely on subjective indicators or outdated survey metrics. AI-ready health scores must be real-time, and behavioural.

• Product usage telemetry
  ‍Logins aren’t enough. You need feature-level usage, events tied to Time-to-Value, and automatic tracking.

• Sentiment & qualitative signals
  ‍This includes calls, emails, tickets, and Slack threads. These are rich sources of risk and intent, so it’s a big waste to leave them unused. Your systems must capture and structure them so AI can interpret them.

• Cross-tool alignment
  ‍If ARR differs between HubSpot and your CSP, or if renewal dates aren't synced, AI will hallucinate. Make sure the data is consistent across your tools. 

You can measure your current data quality by assessing the above categories. Identify what’s clean, what’s missing, what’s contradicting, and what’s inaccessible.

These gaps directly determine what AI agents will be able to do in later chapters, so try fixing them first. 

Audit Workflow Modularity: Breaking Processes Into “Automation Blocks”

Your workflows can’t be massive, linear, 20-step journeys. AI needs modular chunks to function well. Basically, create small blocks with clear inputs, outputs, owners, and failure states. 

When you break a workflow apart, you’ll notice that each step naturally falls into one of three categories:

• Steps that repeat across 80% of customers
  These steps are predictable, structured, and follow the same logic every time. Consider them prime automation candidates.
  ‍Example: Sending a welcome email.

• Steps that require human nuance
  These must remain human-led or shared. While you don’t automate these steps, you can design workflows that let the AI assist while the CSM leads the interaction.
  Example: Presenting a QBR.

• Steps that can be triggered by data
  These are steps that depend on a clear input like usage or sentiment, and can run automatically when conditions are met. These set the foundation for future AI automation.

Now that you know the types of steps, how do you actually do the modularity audit? Here is a simple process to follow:

Step 1: Pick one workflow (e.g., onboarding, renewal).

Choose one journey you repeat often and want to eventually automate or improve.

Step 2: Break it into atomic steps.

Each step should be one action, not a cluster of actions.
Example:

• “Schedule kickoff call”
  
• “Send training resources”
  
• “Check milestone completion”

Step 3: Label each step using the three categories.

This tells you:

• What can be automated immediately
  
• What should stay human-led
  
• What can eventually be handed to an AI agent

Step 4: Assign ownership (Human-led, AI-led, or Shared).

This ensures every step has:

• A clear owner
  
• A clear trigger
  
• A clear output

This modularization process allows you to slowly replace human-executed blocks with AI-executed blocks over time, ensuring continuity of service.

While this chapter wasn’t about implementing anything, it’s an unskippable step in our journey to build the AI-powered CS org.

If you complete this audit properly, everything in the following chapters will be significantly easier, safer, and more impactful.

Chapter 3: Navigating the Technical Architecture For AI Workflows

It’s time to actually get into the technical foundations of adding automations and agents with AI to your CS org. Many promising AI projects fail because leaders aren’t sure what foundational pieces need to be in place. It’s possible for AI to dramatically improve how your CS org operates, but only if the underlying architecture is set up correctly.

This chapter takes you through the architecture behind agentic workflows. You don’t need to have in-depth technical knowledge of AI for this. What you need is to understand the principles behind AI architecture, know who you need on your team, and know how to collaborate with them to implement it safely and effectively. 

Understanding the AI Agent Architecture

An AI agent can observe, reason, plan, and act. This makes it far more suitable for dynamic customer scenarios like churn prediction, expansion opportunity detection, or onboarding orchestration than standard automations. 

The Core Components of an AI Agent

1. The Brain (LLM)

This is the primary reasoning engine (e.g., GPT-4o, Claude 3.5 Sonnet). It interprets intent, analyzes data, and decides the next step.

2. Memory (Short-Term + Long-Term)

AI agents require two kinds of memory:

• Short-term memory:
  Maintains the active context of the current workflow, e.g., “I’ve already checked support tickets; next I should look at usage.”
  
  
• Long-term memory (Vector Database):
  Stores knowledge base articles, playbooks, previous interactions, and rules the agent must follow. This is where RAG comes in (more on this later).

Without memory, the agent behaves inconsistently or forgets essential rules 

3. Tools (Action Layer)

Tools allow agents to interact with your real systems through APIs:

• Query Salesforce or HubSpot
• Check usage in Mixpanel or Amplitude
• Pull Zendesk tickets
• Send a Slack message
• Draft an email
• Update the CRM

This is what separates AI agents from simple copilots: they can execute.

4. Planning Module

This is where the agent transforms a high-level goal into sub-tasks. Instead of executing a single instruction, the agent uses the planning module to figure out what steps are required to accomplish an outcome. It’s similar to how a CSM mentally works through a problem before taking action

Example:

If the goal is to reduce churn risk for Account X, the AI might come up with a plan like this:

1. Check product usage
2. Analyze support tickets
3. Review sentiment
4. Synthesize findings
5. Recommend action
6. Notify the CSM

AI roles your team needs

The most important hires or collaborators are the AI Operations Lead, and the RevOps / Data / Engineering Partners.

The AI Operations Lead (Your Technical Translator)

This is the single most important role for a CS org adopting AI.

They:

• Understand enough AI to architect workflows safely
• Understand enough CS to ensure the AI aligns with real needs
• Sit between RevOps, Product, Engineering, and CS
• Maintain prompts, automations, health score logic, and agent behavior
• Ensure AI doesn’t break workflows or create technical debt

This role takes the technical burden off the CS team and ensures the implementation has ownership.

RevOps / Data / Engineering Partners

You’ll need periodic involvement from:

• RevOps: to unify CRM, billing, and lifecycle data
• Data teams: to improve data hygiene and usage telemetry
• Engineering: for deeper integrations or product instrumentation

CS teams trying to independently build the agent architecture will almost certainly run into roadblocks. It’s far more practical to co-own the process with these teams.

How CS Leaders Should Work With Technical Partners

1. Start by defining the business problem

Telling your technical partner you need “an onboarding agent” isn’t very helpful by itself.
They need clarity on the pain point:

• “CSMs spend 10 hours/week sending repetitive onboarding emails.”
• “We want AI to surface risks earlier than humans do.”
• “We need a structured way to turn customer signals into actions.”

Define the outcome you desire, and they’ll work on the technical solution to achieve it. 

2. Assign one owner for AI workflows

This is normally the AI Ops Lead. They become the single-threaded owner of:

• Prompts
• RAG data sources
• Agent behavior
• Troubleshooting
• Quality control

3. Maintain “human-in-the-loop” oversight

Even the best agentic systems require human checkpoints:

• Reviewing draft emails
• Approving risk recommendations
• Validating expansion signals
• Escalating sensitive issues

Agents might assist, but they are not meant to operate unchecked.

Low-Code Implementation: The Ops Stack

While the previous section is about the best case scenario where you have access to technical partners, not every CS team has a team of engineers who has the bandwidth to constantly implement systems for them.

Luckily, a growing low-code ecosystem means you don’t absolutely need an entire team of engineers. Low-code tools can make it easy for CS Ops to build agentic workflows visually.

Key Tools in the Low-Code Stack

1. n8n / Azure Logic Apps

Drag-and-drop workflow builders with native AI nodes. It’s useful for orchestrating multi-step agent tasks.

2. Velaris Bridge (specialized for CS)

Built specifically for Customer Success workflows:

• Multi-system integration
• Event-driven triggers (e.g., “Health score turned Red”)
• Predictive play activation

3. Make 

Great for API stitching, enrichment, and passing structured data into the agent.

4. Zapier

A widely used platform for connecting apps and automating workflows without code.
It’s useful for building simple automations, such as sending an email when a customer reaches a key milestone.

If you severely lack technical resources, Zapier is your best bet. It's possible to make effective automations with zero code through Zapier. 

Check out Module 3 if you haven’t already to watch guided walkthroughs of building CS agentic workflows!

Guardrails 

As soon as agents touch customers or draft content, you need to be careful. AI has to be introduced responsibly, with clear ownership, documentation, and fallback logic. Guardrails exist to ensure your agents behave consistently and safely, protecting your customers and teams from unintended consequences. 

While guardrails are not mandatory for early experimentation, the more your agent automates customer-facing tasks, the more important these safeguards become. In their absence, human-in-the-loop oversight might be non-negotiable.

Guardrails broadly fall into three categories:

1. Software Guardrails

Use tools like:

• NeMo Guardrails
• Patronus AI
• Llama Guard

Using these tools, you can define rules or categories of unacceptable content (e.g., “never mention discounts,” “no medical advice,” “no promises about unreleased features”).

The tools act like filters, preventing AI from:

• hallucinating discounts
• inventing features
• generating toxic or risky language
• making promises CS can't deliver

2. Ethical guardrails

When AI agents begin interacting with customers or influencing decisions, transparency and ethical safeguards become essential. If a customer is interacting with an AI (e.g., in a portal or assistant), you must disclose it. 

Customers should know when AI has contributed to a response, and every AI-enabled workflow should have a clearly defined human owner who can step in when needed. 

It’s equally important to protect customer data by limiting what the agent can access, masking sensitive information, and ensuring compliance with privacy standards. This not only prevents unwanted consequences, but strengthens the trust your customers have with you through transparency. 

3. Escalation Guardrails

Hard-code rules where the agent must hand off to a human. Operational guardrails are often implemented through your workflow orchestration layer (Velaris Bridge, n8n, HubSpot, Logic Apps) and do not require advanced engineering.

Examples:

• Sentiment < –0.5
• Keywords like cancel, breach, lawsuit, refund
• Complaints about security or compliance
• VIP account interactions
• Contract escalations

Having the right guardrails in place significantly reduces the risk to customer relationships

The building of technical architecture is a vital stage in our roadmap to building an AI-powered CS org. Understanding the core components of an agent, grounding it in your own data, and implementing the right guardrails all work together to make AI reliable.

With these elements in place, every agent you implement can operate effectively, safely, and in service of better customer and business outcomes.

Chapter 4: Improving AI Adoption Across Your CS Team

The journey to becoming an AI-powered Customer Success organization starts with a mindset change. Specifically, you have to make sure that your existing team embraces AI as a tool that is part of their everyday workflow. 

The goal of this chapter is to help you make your existing team more AI-native. Your CSMs have to know how to, and be eager to, use AI in delivering value. 

The AI-Native Mindset

Most successful workplaces already foster a culture of continuous learning and improvement. We just need to extend that outlook to AI, and make sure our team is maximizing the benefits it brings.

Here’s what an AI-native mindset looks like:

• Embrace automation: Instead of fearing AI will replace jobs, the mindset shift involves showing CSMs that automation is a way to free up their time from routine tasks (e.g., email drafting, health score updates, data entry). Now they have more opportunities to focus on higher-value, strategic activities.
  
  
• Data-driven decision making: AI offers powerful data insights, but these are only valuable when team members know how to interpret and act on them. Encourage your team to trust AI insights while refining them through their own expertise.
  
  
• Curiosity and experimentation with AI: Encourage your team to adopt a mindset of curiosity toward AI. Rather than seeing AI as a fixed tool, help them understand it as a dynamic resource that can evolve and adapt. Promote experimentation by letting CSMs test out different AI-driven processes. 

Making CSMs More AI-Native: Learning and Development

One of the most important aspects of improving AI adoption in your team is ensuring that your CSMs have the right skills to engage with AI systems effectively. This involves developing a strong AI learning culture where team members are confident in their ability to integrate AI into their day-to-day workflows.

Here’s how to foster AI literacy and development within your team:

1. Regular Training and Workshops

Start by offering training that focuses on how AI tools work and why they’re beneficial to your team. The goal isn’t just to teach the basics of using the tools but to help CSMs understand how these tools can enhance their current tasks.

Topics for training might include:

• How AI analyzes customer data and provides insights.
• How to interpret AI-generated suggestions for risk management, upsells, and customer satisfaction.
• The basics of AI-driven automation, such as sending personalized emails or flagging at-risk accounts.

2. Encourage Continuous Learning

AI is evolving rapidly, and the tools you’re using today might be different from what you’ll use tomorrow. Encourage a growth mindset within your team and make learning about AI part of their role. This can be done through:

• Monthly AI learning sessions or webinars.
• Access to online courses about AI in Customer Success or related fields.
• Peer-led knowledge-sharing sessions, where team members share best practices, tips, and use cases for AI in their daily work.

3. Build a Feedback Loop

Create space for CSMs to experiment with AI tools, and actively solicit feedback. This will not only give CSMs the opportunity to voice concerns or suggestions, but it will also create a collaborative learning environment where AI tools can be refined over time to better meet your team's needs.

Regularly ask:

• What AI-driven tasks or workflows have been most beneficial to your role?
• Where are you seeing AI make an impact in terms of efficiency or accuracy?
• What are the gaps in the AI tools you're currently using?

AI-Native People Skills

One of the greatest strengths of Customer Success teams is their empathy, relationship-building skills, and deep understanding of customer needs. AI, for all its power, cannot replicate these human qualities.

As you integrate AI into your workflows, it’s essential to upskill your team in AI-native people skills. These are the skills that will enable your team to work alongside AI effectively, while still maintaining the high-touch, human elements that customers value most.

Skills to Develop:

• AI Interpretation and Action: Teach CSMs how to interpret AI’s insights and recommendations and how to act on them. This includes trusting the AI’s data and blending it with their own expertise.
  
  
• Strategic Use of AI: Help CSMs develop a mindset where they use AI for strategic decision-making. For instance, knowing when to trust AI predictions and when human intuition is necessary.
  
  
• Customer-Centric Communication: While AI can automate much of the communication process, CSMs need to focus on humanizing those communications to keep customers engaged and satisfied.

Cross-Functional Influence

When trying to make your Customer Success team more AI-native, it’s important to recognize that AI adoption is an organization-wide effort. To truly maximize the power of AI, CSMs need to work hand-in-hand with other departments, like Product ,Engineering, Marketing and Sales.

Here’s how to strengthen cross-functional influence and build AI workflows in collaboration with other teams:

1. Align AI Goals Across Teams

AI has to drive shared outcomes. For AI workflows to succeed in CS, they must be aligned with broader organizational objectives.

What to Do:

• Collaborate with RevOps: Ensure that AI-driven health scores and predictive models align with revenue targets, customer segmentation, and forecasting metrics.
  
  
• Work with Product Teams: Leverage AI to surface feature usage trends and early signals of feature adoption. This can guide Product Managers on the roadmap and ensure alignment between customer needs and product development.
  
  
• Coordinate with Sales Teams: Share insights about customer health, usage patterns, and churn risk to create a smoother handoff between Sales and CS teams. A more informed handoff enables better relationship building with customers and reduces friction in the sales-to-CS transition.

2. Collaborate on Data Flow and Integration

One of the biggest challenges in building effective AI workflows is ensuring that data flows seamlessly between systems. Without consistent, real-time data from other departments, your AI workflows risk becoming incomplete or inaccurate.

What to Do:

• Integrate Data Sources Across Teams: Work with Engineering and Data teams to set up integrations between CS platforms (like your CRM, health score system, and customer data platform) and other tools used by different departments (e.g., Salesforce, Zendesk, HubSpot, etc.).
  
  
• Define Data Ownership: Clearly define who owns each data source. It could be the Sales team for opportunity data or the Support team for ticket history. Understanding ownership will prevent data silos and ensure that each department feeds the right data into your AI models.

3. Involve Other Teams in Workflow Design

Designing AI workflows is not just the job of the CS or AI Ops team; the design needs cross-functional involvement. To build workflows that actually solve problems and deliver value, you need input from those who understand the needs of customers, products, and business goals.

What to Do:

• Collaborate with Product for AI-Driven Feedback Loops: Work with Product teams to ensure that AI workflows integrate feedback loops, such as customer sentiment analysis, feature requests, or product pain points.
  
  
• Involve Marketing for AI-Driven Campaigns: Marketing can benefit from AI-driven segmentation and customer insights to craft targeted campaigns. For example, AI-based usage patterns can inform targeted campaigns for high-value customers, or predictive churn signals can trigger re-engagement campaigns.
  
  
• Align with Support to Enhance AI’s Problem-Solving Capacity: Support teams know where the most common issues lie. Work with them to integrate support ticket data into your AI workflows, allowing the system to trigger automated resolutions or escalate more complex issues to the appropriate CSM.

4. Build Shared AI Workflow Ownership

Effective AI workflows rely on shared ownership across teams. No single team owns AI in isolation; it's about creating a culture of collaboration and shared responsibility for AI tools and workflows.

What to Do:

• Create Cross-Functional AI Task Forces: Build a task force composed of representatives from CS, Product, Engineering, Marketing, and RevOps. These teams can meet regularly to review AI tool performance, identify issues, and prioritize new AI initiatives.
  
  
• Implement Agile Feedback Loops: Build agile feedback loops where all teams provide regular input on AI performance, including usability, accuracy, and customer impact. Regular feedback ensures that AI workflows continue to evolve and meet the needs of all teams.

5. Drive Change Management Across Teams

For AI to be successfully integrated into workflows across the organization, there must be a change management strategy that includes training, communication, and support for all teams involved.

What to Do:

• Educate Teams on AI Benefits and Use Cases: Run educational sessions for other teams, especially Sales and Product, to help them understand how AI-driven insights and workflows benefit them directly. For example, Sales can use AI-generated insights to forecast revenue or identify high-priority accounts.
  
  
• Empower Teams to Experiment: Encourage cross-functional teams to experiment with AI workflows in a low-risk environment. Give them access to AI tools, and allow them to pilot use cases that drive their success. In turn, this creates momentum for broader AI adoption across the organization.

By continuously developing the right mindset, skills, and collaborative processes, your team has an easier path to embracing AI-enhanced workflows.