Virtual Mix & Mingle: Ecosystem Leaders Lounge

Expert advice from Katie Landaal (Ecosystem Executive, ZoomInfo), Gwen Sioson (AI Strategic Partnerships Director, ActiveCampaign) and Justin Zimmerman (Founder, Partnerplaybooks).

Snapshot

You are entering a moment where AI fluency is becoming part of career fluency, especially in partnerships. The opportunity? Become the person who can connect scattered data, partner activity, customer signals, product needs, and business priorities into useful decisions.

That matters because ecosystem work has always been complicated. You are balancing affiliates, channel partners, resellers, agencies, systems integrators, technology partners, customer needs, sales motions, implementation work, and company goals. AI can help you manage that complexity, but only if you actively learn how to use it. Whether you work at a large AI-forward company or a tiny team, the real risk is staying passive while the work changes around you.

Keep reading to see how Katie Landaal and Gwen Sioson can help you build AI fluency, create useful agents, and grow your partnerships career.

“Your fascination will unlock opportunities to build new things in new places.” – Justin Zimmerman

Table of Contents

• Snapshot
• Partnerships work is bigger than one funnel
• Start with company priorities, not partner noise
• Build agents that give you back time
• Governance is what makes AI scalable
• Choose AI tools based on the work
• AI fluency is a career advantage, not a company perk
• Follow fascination and build in public
• Make AI learning visible across the organization
• FAQs
• Build the capability before you need it

Partnerships work is bigger than one funnel

When you lead an ecosystem, you are not managing one neat category of partners. You are managing a living network with different motions, incentives, buying stages, and operating rhythms.

Katie leads the partner ecosystem at ZoomInfo, covering everything outside direct sales. That includes top of funnel partnerships such as affiliates and channel partners, including resellers, distributors, value-added resellers, and managed service providers. It also includes middle and lower funnel relationships, such as global and regional systems integrators, solutions partners, and implementation partners.

Then there are technology partnerships. Integrations can support the entire customer journey, from a sales conversation to adoption and post-sale customer value. That ecosystem can include hyperscalers, ISVs, AI companies, LLM providers, and MCP partners.

The important point is simple: partnerships are not a side channel anymore. They touch acquisition, pipeline creation, conversion, implementation, retention, and expansion. As the partner ecosystem becomes more connected, the volume of decisions rises quickly.

• Which partner activities directly support company priorities?
• Where does an executive decision need to happen now?
• Which team or workflow is blocked?
• What is changing across partner performance?
• What needs attention before a small issue becomes a customer or revenue problem?

You cannot answer all of that well by keeping a longer to-do list. You need a sharper operating system.

“There can be a hundred to-do items with every single partner, big or small.” – Katie Landaal

Start with company priorities, not partner noise

AI does not solve prioritization for you if you have not decided what matters. Katie’s approach begins with alignment to company priorities. As company goals evolve, partner activity needs to roll up into those goals.

That sounds obvious, but it is easy to lose when you are deep in the operating details. A partner manager may have dozens of legitimate requests in a week. A new integration needs attention. A strategic reseller wants an escalation. A field team needs campaign support. An agency partner is waiting for enablement. A leader needs a board-level update.

Without a shared frame for priority, everything feels urgent. With one, you can ask a more useful question: Which action has the strongest connection to the company’s current objective?

Use two levels of prioritization

There is a meaningful difference between strategic prioritization and daily prioritization.

1. Strategic prioritization: Align partner programs to company-level outcomes, executive priorities, and the story you need to tell leadership.
2. Daily prioritization: Identify immediate decisions, risks, blocked work, upcoming meetings, and actions where your involvement will create leverage.

The first level helps you make sure partnerships are part of the business strategy. The second makes your calendar survivable.

This is where agents become useful. Katie uses AI agents to surface alerts, urgent attention items, decisions that need to be made, and work that needs to be unblocked. The agents also help prepare her for the meetings ahead. Instead of opening every system and manually piecing together context, you can begin with a structured readout of where your attention is most valuable.

That is not about removing judgment. It is about reserving your judgment for decisions that matter.

Build agents that give you back time

In partnerships, you have traditionally had to connect the dots manually. You checked partner updates, reviewed account context, asked sales for feedback, searched through support issues, prepared for meetings, and tried to turn it all into a useful point of view.

AI assistants can help connect those dots, but the best results come when you define a narrow, useful job for each agent. Do not start with “build me an AI agent for partnerships.” Start with a recurring point of friction.

For example, your first agent might help you:

• Prepare a daily brief of priority partner actions and decisions.
• Create meeting preparation using account, partner, and opportunity context.
• Flag stalled partner initiatives and identify likely blockers.
• Summarize repeated customer support themes that affect integrations.
• Organize partner program work by urgency, owner, and business goal.

Katie’s experience is a good reminder that agents become more valuable when they are connected to the environment where work already happens. An internal platform that can access the right infrastructure, integrations, and business context can make it much easier to create useful workflows.

Gwen has seen a similar model at ActiveCampaign, where an internal AI lab supports experiences across the organization. But Gwen also builds personal projects outside that environment. That combination matters. You can benefit from company resources while still developing your own practical instincts about how agents work.

“We have all these assistants that are helping us put all the dots together.” – Katie Landaal

Make agent building a team habit

You do not need every useful workflow to come from a central innovation team. Katie has seen every person on her team create agents, or collections of agents, for their own needs. The value compounds when people share what works, take pieces from one another’s approaches, customize them, and make them better.

This is the difference between having an AI tool and creating an AI-capable team. Individual experimentation creates local knowledge. Knowledge sharing turns that into organizational leverage.

If you are managing a partner team, make it normal for people to share:

• The problem they were trying to solve.
• The source systems and information used.
• The instructions that produced a reliable result.
• What failed or required revision.
• What another teammate could reuse.

For a broader framework on adopting AI without losing the human side of partnerships, read this practical AI playbook for partner teams.

Governance is what makes AI scalable

Creating agents is exciting. Maintaining them is the grown-up part.

At ZoomInfo, an agentic team manages and maintains AI agents across different workspaces. That structure matters because a large organization can quickly accumulate many nearly identical agents. One person creates a meeting-prep assistant. Another creates a similar version. A third makes a separate version for a different workspace. Before long, you have duplication, inconsistent outputs, unclear ownership, and wasted effort.

Governance does not need to mean making experimentation impossible. It means creating a way to preserve the good work and reduce unnecessary confusion.

A practical AI governance checklist

• Assign ownership: Know who is responsible for the agent and its workspace.
• Audit regularly: Find duplicate or outdated agents before they spread.
• Consolidate reusable workflows: Turn broadly useful agents into shared resources.
• Release improvements: Give teams access to better versions rather than forcing everyone to rebuild.
• Keep human review: Use AI to accelerate work, not to make unexamined decisions.

Monthly releases of generalized agents can be particularly powerful. When someone creates an agent that works across multiple functions, it can be refined and distributed across the organization. That gives people an easier starting point and prevents the organization from filling up with thousands of tools doing almost the same thing.

This operating discipline is especially relevant in partner organizations, where data tends to sit across many systems and teams. You can explore additional principles in 10 new principles for AI in partnerships.

“They constantly review them and try to bring them together as much as possible.” – Katie Landaal

Choose AI tools based on the work

There is no universal best AI platform for every partnership use case. The right choice depends on what you need to accomplish, how complex the task is, what information it requires, and whether you need research, automation, coding, or a customer-facing experience.

Gwen highlighted v0 by Vercel as a strong option for quickly building web app experiences through natural-language instructions. When you have a clear idea of what you want to create, a capable builder can translate that concept into something real without endless correction. Gwen used it to deliver a project in 48 hours.

Katie called out Perplexity for its ability to blend different capabilities and bring useful information together. For deeper research, especially questions involving customer support patterns, total addressable market analysis, recurring customer feedback, or product opportunities, Katie has found Anthropic’s models particularly thorough.

Gwen also distinguishes between models based on the job. For deep research, Anthropic can provide strong context. For scalable task automation, lower token consumption, and work built on a known foundation, ChatGPT and Codex may be a better fit.

Recommended tools

Your tech stack should begin with a use case, not a logo. These are the tool categories discussed and where they may fit.

• Vercel v0: Rapidly create web experiences and prototypes using natural language.
• Perplexity: Explore research questions and synthesize information across sources.
• Anthropic models: Conduct deeper analysis where context and detailed reasoning are valuable.
• ChatGPT and Codex: Support scalable task automation and foundational workflows.
• Internal agent platforms: Connect AI workflows to approved company systems, tools, and data.

The better question is not, “Which model wins?” It is, “What task am I trying to complete, what quality level do I need, and how will I validate the output?”

“How complex and really what you are trying to achieve points you to what you should be using.” – Katie Landaal

AI fluency is a career advantage, not a company perk

It is easy to assume that people at large, well-resourced companies have an unfair advantage. They may have internal AI labs, dedicated teams, connected data, and access to advanced tooling. Those things do matter. Real exposure to AI at scale can change how you think and how quickly you learn.

But the conclusion is not that you are stuck if you work at a small company, on a lean team, or as a solo operator.

Katie agrees that access and resources can be valuable, but the tools available today give you more room to create your own learning environment. Agents and workflows can help you access leverage that once required a much larger organization. Your responsibility is to keep learning, stay informed, ask questions, and actively use the tools.

The biggest shift is intentionality. Katie’s schedule can run from early morning through early evening, packed with calls. That is familiar to many partnership leaders. Yet once Katie intentionally blocked time to learn and build agents, the work created more capacity. The learning did not compete with productivity forever. It improved productivity.

Protect a recurring learning block

You do not need to dedicate every spare minute to AI. You do need a consistent block of time where experimentation is part of your job, not something you promise to do someday.

Use that time to:

1. Pick one frustrating recurring workflow.
2. Try an AI-supported version of it.
3. Compare the output with your existing process.
4. Adjust your prompt, inputs, or instructions.
5. Document what you learned and share it.

You can start with work. You can also start with life. Build something to organize meal preparation, manage a household schedule, plan groceries, or reduce a repetitive task. The point is to become comfortable with the cycle of giving instructions, testing outputs, correcting errors, and improving the workflow.

“When I started being really intentional about that and blocking time, I freed up more time for myself.” – Katie Landaal

Follow fascination and build in public

Gwen’s advice goes beyond learning a product or collecting certifications. Learn in the direction of what genuinely fascinates you. When you are building around something you care about, the work becomes firsthand knowledge. That knowledge makes you more credible and more likely to earn a place in important decisions.

Gwen is extending that learning through an AI builders experience in Las Vegas. The concept is refreshingly practical: sit with people, spend a few hours understanding their problems, and work through potential AI solutions together. This kind of hands-on problem solving expands your knowledge beyond your company, product, or vertical.

That is an important career move. You learn not only how a model works, but how real people define their problems, what good output looks like in different contexts, and where your assumptions fail.

If you want a structured view of how AI can improve partner workflows without replacing relationship-building, explore AI partner recruitment workflows.

Build your own practical learning loop

• Choose a project you care about.
• Talk to people who have a real problem to solve.
• Build a small solution, even if it is imperfect.
• Measure the quality of the result.
• Bring lessons back to your role and refine the project.

That is how you turn AI from an abstract trend into an operating capability. You do not need permission to become more curious. You do not need a perfect environment to begin. You need a problem, some time, and enough willingness to learn through trial and error.

“When you are learning about something you are fascinated about, it does not feel like work.” – Gwen Sioson

Make AI learning visible across the organization

Individual learning is necessary, but culture determines whether it spreads. Katie described several ways ZoomInfo creates room for learning. Teams have working sessions with AI experts where people can ask questions, build what they need, and get help. There is also designated focus time for learning and experimentation.

Leadership participation matters, too. When a CEO shares recent AI wins, explains personal experiments with agents, and creates space for others to share their work, it makes learning visible. It changes AI adoption from a private side project into an organizational expectation.

That does not necessarily mean every compensation plan or OKR needs an immediate overhaul. The more immediate move is often simpler: give people protected time and permission to learn. Reward useful knowledge sharing. Make practical experimentation normal.

If you lead a partnerships team, do not wait for a massive transformation program. Start with a recurring working session, a shared library of proven prompts and agents, and a regular moment where people show what they tried.

FAQs

How can you use AI in partnership management?

You can use AI to prepare for meetings, surface urgent partner issues, organize work by priority, identify blocked initiatives, summarize customer or support themes, and connect partner activity to company goals. The best initial use cases are narrow, repetitive, and easy to review.

Do you need an internal AI platform to build useful agents?

No. Internal platforms can provide better access to approved company data and workflows, but you can learn with commercially available tools. Start with a personal project or a contained work process that does not require sensitive data, then build your confidence through testing and iteration.

What is the best AI tool for partner teams?

There is no single best tool. Use web app builders when you need to prototype an experience, research tools for synthesis and analysis, deeper-context models for complex research, and automation-focused models for repeatable tasks. Match the tool to the task, data, quality requirement, and review process.

How should you govern AI agents at work?

Give agents clear owners, audit them for duplication, consolidate reusable workflows, maintain shared versions, and keep human accountability for decisions. Governance should make strong work reusable without stopping teams from experimenting.

How can you stay competitive if your company is not AI-forward?

Block time to learn, build small projects around problems you care about, test multiple tools, learn from other builders, and share your work. Your career advantage comes from practical fluency and curiosity, not only from the size of your company’s AI budget.

Build the capability before you need it

AI is changing the work of ecosystem leaders because it can help you handle complexity at a scale that was previously exhausting. But it will not replace the essentials of good partnerships: clear priorities, reliable data, sound judgment, customer understanding, and relationships built on trust.

Your advantage comes from combining those human strengths with practical AI fluency. Make time to learn. Build something small. Use it. Improve it. Share it. Follow the problems that fascinate you, because that is where you will gain the experience and confidence to shape what comes next.