MAICON Cleveland 2025: From Experiments to Digital Teammates

I went to MAICON Cleveland last month to hear from leaders sitting at the intersection of AI and marketing and to see what’s real—not just what’s trending. The answer came through loud and clear in the halls, keynotes, and conversations: AI is no longer a novelty project. It’s becoming an operating layer made up of digital teammates that carry real work.

Here’s the simplest way I can say it after three days on the ground: if organizations aren’t AI‑empowered by the end of 2025, they’ll spend 2026 trying to catch up. That’s not a threat; it’s a practical observation. I saw teams move faster, measure better, and de‑risk decisions because they’ve stopped treating AI as a tool and started treating it as part of the team—with owners, boundaries, and success criteria.

What “digital teammate” really means

“Digital teammate” isn’t a metaphor for a human. It’s a governed, named service that performs a role—with a mission, inputs/outputs, access guardrails, an SLA, and an owner. When you design teammates this way, they plug into workflows the same way a new hire would: scoped, accountable, and measurable. That framing changes everything. It replaces the “try this cool model” energy with a practical question: What role does this teammate play, and how will we know if it’s doing the job well?

Session moments that stuck

1. Building AI Marketing Teammates (Liza Adams, GrowthPath Partners)

Liza’s session, How to Build AI Marketing Teammates: Custom GPTs in Action That Transform Teams, clicked with me because it focused on designing teammates, not just using them. The strongest idea: treat each teammate like a service with a charter—give it a name, a scope, and a way to instrument outcomes. She walked through how these teammates can coordinate, just like specialized roles do on a high‑performing team.

• Mindset shift: It’s more than a tool. Think team structure—who does what, how handoffs work, and how performance is measured.
• Practical design: Start small. Give each teammate a single job (e.g., Brief Builder, QA Auditor, Attribution Analyst), define inputs and outputs, and hold it to a simple SLA (quality and latency).
• Governance: Set access rules, version control, and an audit trail from day one so you can scale with confidence.

What I’m taking back: We’ll publish a Teammate Directory that lists each teammate, its mission, inputs/outputs, owner, and SLA—and wire those entries into our existing release and QA checklists so nothing runs “off to the side.”

2. Becoming an AI‑Driven Leader (Geoff Woods, AI Leadership)

Geoff’s theme was leadership courage and clarity. Not bravado but behavior. He made a compelling case that the C‑suite can accelerate adoption by visibly using digital teammates to make better decisions and by normalizing the idea that speed and governance can co‑exist.

• From fear to frameworks: It’s reasonable to worry about risk; it’s not reasonable to avoid structure. Define the risks, set guardrails, and move.
• Show your work: Weekly, leaders should publish one AI‑assisted decision brief—what was asked, how analysis was generated and reviewed, what was decided, and what to watch next.
• Culture follows habit: When executives demonstrate this rhythm, teams mirror it—and the organization’s “AI stance” becomes consistent, not ad‑hoc.

What I’m taking back: We’ll formalize an Executive AI Review rhythm: one strategic decision, one risk to mitigate, and one experiment to ship each week—each supported by a concise, AI‑assisted brief and a follow‑up check on outcomes.

Patterns we saw everywhere

• Measurability beats mystique. The most credible teams didn’t talk about “magic.” They showed before/after numbers: cycle‑time, accuracy, rework rates. Digital teammates were instrumented like services, not gadgets.
• Guardrails are baked in, not bolted on.  Access, data lineage, and escalation paths were part of the design, not a later compliance patch. Shadow‑mode testing was common: let a teammate run silently, compare its output to human benchmarks, then promote it when it’s consistently hitting the SLA.
• Compound gains live in the handoffs.  We saw a lot of energy in WebOps and RevOps: content production, QA, release automation, lead routing and enrichment, attribution. These are perfect proving grounds because the metrics are clear and the pain is visible.
• Catalogs > one‑offs. Teams that are progressing fastest keep a simple internal catalog of digital teammates, with dependencies and performance notes. That visibility makes it easier to reuse, improve, and govern at scale.

What we’re doing next at Digital Polygon

Our job at Digital Polygon is to help organizations turn this insight into operations they can trust—WebOps that ships faster and breaks less, RevOps that sees more and wastes less, and HubSpot that works as a single revenue engine. Here’s what we’re acting on first:

1. Publish the Teammate Directory. Each entry includes a mission statement, inputs/outputs, data/permission scope, SLA, owner, and rollback plan. This makes teammates discoverable and accountable from day one.
2. Pilot 2–3 focused marketing/RevOps teammates. Examples: a Brief Builder for campaign strategy drafts, a QA Auditor for content consistency and compliance, and an Attribution Analyst to reconcile contact and deal data. Each pilot uses baseline metrics and a clear “promote/demote” rule tied to SLA performance.
3. Embed governance in our WebOps release process. No teammate goes live without shadow‑mode testing, an audit trail, and an escalation path. We treat these like any other component in the stack—reviewed, versioned, and monitored.
4. Make leadership’s use visible. We’ll publish short decision briefs that show how digital teammates inform choices—assumptions, alternatives, mitigations, and outcomes. The goal isn’t theater; it’s consistency.