Solve It Sessions: Bring Your AI Challenges. Leave With Solutions

Expert advice from Tyler Calder (CMO, PartnerStack) and Justin Zimmerman (Founder, Partnerplaybooks). 

Snapshot

You are entering a moment where a small partner team can operate with the intelligence of a much larger one, but only if you stop treating AI as a novelty and start treating it as an operational layer. The opportunity is not simply to write faster emails or summarize calls. It is to spot partner risk early, understand how each affiliate prefers to work, trigger the right intervention, and make every outreach feel intentional at scale.

The risk is equally clear. As your program grows, manual lists, scattered notes, generic campaigns, and spreadsheet-based follow-up create blind spots. Great partners quietly go dormant. High-performing partners surge without anyone noticing why. Meanwhile, your team spends its time assembling data instead of helping partners win.

AI, partner relationship management platforms, CRM workflows, and connected data can change that. The best place to begin is not with a giant transformation. It is with a simple question: wouldn’t it be cool if your program could do this for you?

Keep reading to see how Tyler and Justin Zimmerman can help you identify partner risk, automate re-engagement, and create more personalized affiliate experiences at scale.

“The most important thing is just getting in and doing it.” – Tyler Calder

Table of Contents

• Snapshot
• Start with curiosity, not a perfect AI strategy
• Build an AI opportunity backlog
• Test workflows safely before company-wide access
• Why affiliate churn needs a smarter response
• Create a profile for how every partner wants to work
• Use performance changes as intervention signals
• Design a scalable partner re-engagement workflow
• Make activation campaigns less manual
• Recommended tools
• FAQs
• Conclusion

Start with curiosity, not a perfect AI strategy

You do not need to become a developer before you can build meaningful AI workflows for partnerships. That is one of the most useful shifts happening right now. Tools such as Claude, ChatGPT, Claude Code, and no-code automation platforms make it possible to move from an idea to a practical test quickly.

Tyler’s path is a useful model. Start by using an AI assistant conversationally. Move into collaborative workspaces as your use cases become more hands-on. Then, where it makes sense, use AI-assisted coding or technical workflows to build the things that previously felt inaccessible. The point is not that every partnerships professional should write code. The point is that you can now participate much more directly in creating the systems you need.

That matters because partnership teams sit close to some of the most valuable and fragmented business information in a company:

• Partner performance trends
• Referral and conversion data
• Sales calls and partner-introduction opportunities
• CRM activity and lifecycle stages
• Onboarding responses and enablement needs
• Email, Slack, WhatsApp, and call history
• Commission and incentive data

Historically, turning that information into action required analyst time, engineering support, operations resources, or lots of spreadsheet work. Now you can ask better questions, prototype workflows, and make a much clearer business case for what should be automated.

“Wouldn’t it be cool if we could listen to our call recordings and identify where a sales rep missed a potential partner introduction opportunity?”Tyler

That question captures the right mindset. Do not begin with, “How do I use AI?” Begin with, “What operational outcome would be genuinely useful?” A missed partner introduction on a sales call is not a writing task. It is a revenue opportunity hidden in unstructured data. That is exactly the kind of problem AI can help you investigate.

Build an AI opportunity backlog

A practical AI strategy begins with an idea backlog. Tyler calls these “wouldn’t it be cool” sessions. You set aside an hour with your team, take the pressure off, and generate possibilities. No one needs to prove that every idea will work. You are simply identifying friction, blind spots, repetitive tasks, and moments where more context would improve a decision.

This is particularly valuable for partner teams because the most useful automation ideas often come from the everyday work that feels too small to justify a project. A partner manager notices an affiliate becoming quiet. Someone spends an hour producing a campaign list. A high-performing partner has a sudden lift, but no one investigates until the following month. A sales call contains a partner-fit signal that never reaches the partnerships team.

Write down all of it. Tyler began experimenting with a list of hundreds of potential ideas, then worked through them one at a time. You do not need hundreds to begin. Ten good ideas are enough.

Questions for your “wouldn’t it be cool” session

• Wouldn’t it be cool if you knew which partners were likely to go dormant before they disappeared?
• Wouldn’t it be cool if a partner’s preferred communication channel was visible in your CRM?
• Wouldn’t it be cool if a performance drop automatically created a re-engagement recommendation?
• Wouldn’t it be cool if a performance spike alerted you to an emerging opportunity?
• Wouldn’t it be cool if onboarding responses automatically shaped each partner’s enablement journey?
• Wouldn’t it be cool if call recordings surfaced partnership opportunities for your sales team?
• Wouldn’t it be cool if campaigns were assembled from behavior, performance, and partner context instead of static lists?

Once you have ideas, ask an AI assistant to help you think through implementation. Describe the systems you have, the data you can access, and the outcome you want. Ask what data fields, triggers, decision points, and integrations may be required. You are not asking the model to make an unsupervised business decision. You are using it to turn a vague ambition into a more concrete workflow concept.

For more ways to turn partnership work into repeatable AI-supported workflows, explore this guide to building Claude Co-Work workflows for partnerships.

Test workflows safely before company-wide access

Sometimes your curiosity will move faster than your organization’s approved AI stack. That does not mean you have to stop learning. It means you need to test responsibly.

Tyler’s approach was to use free trials of software with MCP connections and test workflows with fake data. MCP, or Model Context Protocol, is a way for AI tools to connect with other systems and retrieve or act on information in a more structured way. If you are exploring this area, Anthropic provides an overview of the Model Context Protocol and why interoperable tool connections matter.

The important discipline is this: do not put sensitive company or partner data into tools that have not been approved for it. Use synthetic examples. Build sample partner records. Create a fictional performance dataset. Simulate a workflow from data intake through segmentation, message drafting, routing, and follow-up.

That kind of testing helps you understand:

1. What data you actually need. You may discover that partner performance alone is not enough. You also need communication preferences, lifecycle stage, channel type, or prior campaign history.
2. Where your systems connect poorly. A prototype often reveals whether your PRM, CRM, enrichment tool, and messaging platform can share the necessary information.
3. Which decisions should stay human. AI can flag patterns and recommend next steps. A partner manager may still need to decide whether a high-value affiliate gets a phone call, a custom incentive, or no outreach at all.
4. What a real implementation requires. Once you understand the workflow, your conversation with operations, security, product, or engineering becomes much more specific.

“Take advantage of some of these freemium tools and see how they work and connect.”Tyler

There is a major difference between casually trying AI and deliberately learning how systems work together. The second approach gives you a head start when your organization is ready to expand access.

Why affiliate churn needs a smarter response

Affiliate programs are especially well suited to this kind of automation because scale creates a brutal trade-off. You want to give partners thoughtful, individual support. But as your program reaches hundreds or thousands of affiliates, you cannot manually review every account, every month, and every change in performance.

That was the challenge raised by a SurveyMonkey affiliate-program leader working on a two-person team. With around a thousand affiliate partners, the goal was not simply to identify churn. It was to diagnose partner needs early, understand why performance changed, and send partners into relevant enablement or activation paths without turning the whole program into a manual list-management exercise.

That distinction matters. Partner churn is not always a cancellation event. In affiliate programs, it often looks like silence. A partner stops promoting. A formerly active source sends less traffic. A productive creator shifts attention elsewhere. A partner’s audience changes. A campaign underperforms. By the time a quarterly report makes the issue obvious, the relationship may already be cold.

Your program needs a way to identify the difference between normal variation and a meaningful signal. It also needs a response that is proportionate and personal.

“As the program grows, it can get messy and tricky to still understand what each partner is doing.”SurveyMonkey affiliate program leader

Create a profile for how every partner wants to work

Before you build a churn workflow, build context. Tyler’s core recommendation is straightforward: understand how each affiliate wants to interact with you.

Affiliates are not one uniform audience. Some want relationship depth. They want a call, strategic discussion, and the confidence that someone understands their business. Others want minimal interaction. They want clear terms, reliable tracking, timely payments, and the freedom to operate independently. If you use the same re-engagement sequence for both groups, you will make some partners feel ignored and others feel crowded.

Your partner profile should answer questions such as:

• What communication channel does this partner prefer?
• Do they respond best to phone, email, Slack, or WhatsApp?
• Do they want hands-on strategic support or self-serve resources?
• What promotional channel do they use?
• What did they say they needed during onboarding?
• How have they responded to previous outreach?
• What incentives, content, or enablement have historically worked?

You can gather some of this directly during onboarding. Ask how partners prefer to communicate and what kind of support is useful to them. You can also enrich the profile by examining historical interactions across your systems. An AI assistant can help synthesize notes, emails, CRM records, and engagement history into a consistent partner profile, assuming you are working within your approved data and privacy policies.

The objective is not to label people permanently. Preferences change. Instead, treat the profile as a working hypothesis that gets better over time. If a partner consistently responds to email but never accepts meeting requests, that should shape your next action. If another partner repeatedly requests calls, a generic automated email is probably not enough.

This is how you make automation feel less robotic. Automation handles detection and routing. Context determines the action.

Use performance changes as intervention signals

Once you understand partner context, you need a performance threshold that tells you when to intervene. Tyler shared a useful example from an affiliate program: a partner whose monthly volume fell roughly 35% below their usual historical performance was treated as a meaningful dormancy signal.

The exact percentage is not universal. Your program may need a different threshold depending on your attribution window, traffic volatility, seasonality, partner type, and baseline volume. A low-volume partner may show large percentage changes that are not meaningful. A major partner may require attention after a much smaller decline.

Still, the operating model is powerful:

1. Establish each partner’s historical baseline.
2. Measure recent performance against that baseline.
3. Identify a significant negative or positive deviation.
4. Trigger an action based on partner context.
5. Track the result and refine the decision tree.

Do not only look for downside. A partner who suddenly drives 60% more traffic than usual deserves attention too. That upside signal may reveal a new content format, audience trend, campaign, placement, or market opportunity. You may be able to help the partner lean in further while the momentum is real.

“If there is an outlier on the upside, we should probably dig into that and see if we can lean in further.”Tyler

This is where your PRM data becomes more than a reporting archive. It becomes an early-warning and opportunity-detection system. If you are still managing key partner data through spreadsheets, you are likely losing time and context between detection and action. The lessons from building a scalable PRM-centered partner tech stack are especially relevant here: connect your systems early, centralize workflow-critical information, and reduce referral or attribution leakage before it becomes a relationship problem.

Design a scalable partner re-engagement workflow

A strong re-engagement workflow is not one automated email. It is a decision tree that connects performance data with relationship context.

A practical partner dormancy workflow

1. Detect the change. Compare recent partner activity with the appropriate historical baseline.
2. Confirm the signal. Check for seasonality, tracking issues, campaign changes, or a known pause before treating the partner as at risk.
3. Pull partner context. Retrieve communication preference, program segment, channel, prior engagement, support needs, and relationship owner.
4. Choose the action path. Route a relationship-oriented partner to a manager task, a self-serve partner to targeted email, or a strategic account to a tailored plan.
5. Offer a relevant next step. This might be a call, new creative, an enablement resource, a campaign idea, or a time-bound incentive.
6. Measure the result. Track response, reactivation, lift, and whether the intervention should be repeated or adjusted.

For example, if a partner prefers phone conversations and has historically driven meaningful volume, your workflow might create a task for the affiliate manager: “Partner has declined 35% versus normal monthly volume. Call within two business days. Review recent performance and ask what has changed.”

If a different partner generally engages through email, the system could send a targeted sequence. Perhaps the first email asks whether their promotion strategy has changed. The next message offers fresh creative or product updates. A later step could introduce a temporary commission lift or an activation challenge.

Those are very different experiences, even though the initial signal was the same.

“It started with how that partner wants to be interacted with, then the delta in performance that suggests we need to intervene.”Tyler

Make activation campaigns less manual

Activation challenges can be effective, especially when you need a clear reason for a dormant partner to re-engage. The problem is operational drag. If you are manually creating lists, identifying eligibility, launching campaigns, checking performance, and following up across multiple tools, the process does not scale gracefully.

The short answer offered in the session is yes, much of this can be automated. The longer answer is that the implementation may be a little hacky, depending on your tool stack and the level of sophistication you need.

Some actions may live inside PartnerStack. Other steps may need to happen in HubSpot, Unify, or another connected system. That is not a failure. The goal is not to force every action into a single platform. The goal is to create a reliable workflow across the systems where your data and communication actually live.

What to automate first

• Performance-based partner segments
• Dormancy and upside-outlier alerts
• CRM tasks for relationship-led outreach
• Email enrollment for self-serve re-engagement
• Campaign eligibility lists
• Partner enablement recommendations
• Follow-up reminders and outcome tracking

Keep the first version simple. You do not need a fully autonomous campaign engine on day one. Start with a trigger that creates a clean segment and tells your team what to do next. Then add message generation, incentives, routing logic, and additional data sources as you gain confidence.

When you hit a technical limitation, make it a working session. Bring the desired outcome, the fields you have, the trigger you want, and the systems involved. A good customer success manager, product team, or technical partner can help you translate a business problem into an implementable workflow.

Recommended tools

Your exact stack will vary, but the discussion points to a practical category-based setup for AI-enabled partner operations.

• Partner relationship management: Use a PRM such as PartnerStack to manage affiliate relationships, partner activity, programs, and performance data.
• CRM and marketing automation: Use a system such as HubSpot to manage lifecycle campaigns, tasks, contact history, and outbound sequences.
• AI work environment: Use an approved assistant such as Claude or ChatGPT for idea generation, workflow design, synthesis, and structured analysis.
• Connected workflow tools: Use integration tools and MCP-enabled platforms to connect data sources and actions safely.
• Communication channels: Maintain partner preferences across email, phone, Slack, and WhatsApp rather than forcing every relationship into one channel.
• Data-sharing and ecosystem tools: Consider data collaboration tools where appropriate to improve account intelligence and identify partnership opportunities.

The technology is only valuable when the workflow is clear. Before adding a tool, define the signal you need, the decision you want to make, the action that should occur, and who owns the result. If you want to go deeper on AI-native partner data and PRM workflows, read how PartnerStack MCP can support smarter partner-program operations.

FAQs

How can AI help reduce affiliate partner churn?

AI can help you detect meaningful changes in partner performance, combine those changes with historical context, and trigger the appropriate follow-up. The strongest approach uses AI to assist with detection, segmentation, and recommendations while keeping relationship-sensitive decisions under human ownership.

What is a good churn threshold for affiliate partners?

There is no universal threshold. One affiliate program found that a decline of about 35% versus typical monthly volume was a meaningful dormancy signal. You should calibrate your own threshold using partner size, historic variability, seasonality, and program economics.

Should every dormant affiliate receive the same re-engagement campaign?

No. A partner who values calls and strategic support should receive a different response from a partner who prefers self-service email communication. Use communication preference, historical engagement, partner type, and performance context to route partners into appropriate paths.

Can these workflows be built entirely inside a PRM?

Some workflows can live in a PRM, while others may require CRM, marketing automation, or integration tools. The most practical setup is often a connected workflow where partner data, campaign execution, and follow-up tasks happen in the systems best suited to each job.

How should you start experimenting with AI if company access is limited?

Use approved tools and follow your organization’s data policies. You can still learn a great deal by creating synthetic partner data, using free trials, mapping workflow logic, and testing how tools connect without exposing real company or partner information.

Conclusion

You do not scale partner management by removing the human element. You scale it by using AI and automation to protect the human element for the moments that matter. Build your list of “wouldn’t it be cool” ideas. Learn what your data can tell you. Identify dormancy and upside signals early. Capture how partners prefer to work. Then route each partner toward a response that makes sense for them. When your systems can detect, contextualize, and organize the work, you get more time to do the part no workflow can replace: build stronger partnerships.