At our recent Global Channel Leaders Forum event in Los Gatos, AI was an unavoidable topic. But unlike many discussions, this one focused less on future potential and more on practical application inside partner programs today.
Moderated by Kenneth Fox, CTO and Founder of Channelscaler, the panel featured:
- Kaushik Ram, Senior Director, Global Partner Program at Broadcom
- Brian Kroneman, AVP, WW Channel Programs & Strategy at SentinelOne
- Thomas Schwab, Channel Chief at Netgear
- Scott Goree, Senior Vice President of Partners and Commercial Sales at Optiv
What emerged was a clear message: AI is only valuable when it solves real operational problems.
The Reality Check on AI Adoption
Early in the discussion, we ran a quick audience poll:
- Many organizations are being asked about AI
- Far fewer have implemented meaningful use cases
- Even fewer are seeing real value
This reflects a broader industry pattern – AI is a priority, but execution is still catching up. And that’s where channel leaders have an opportunity.
Where AI Is Actually Making a Difference
Rather than broad transformation, the most impactful AI use cases are emerging in high-volume, repetitive workflows. Examples discussed included:
1. Deal Registration & Decisioning
AI can pre-process deal registrations, enrich data, and present recommendations, reducing manual effort and speeding up response times.
This matters because speed directly impacts revenue. Slow responses lead to lost deals.
2. Partner Insights & Health Monitoring
Understanding partner performance, engagement, and growth potential has traditionally been manual and fragmented.
AI allows teams to:
- Analyze large volumes of interaction data
- Identify trends and risks
- Surface actionable insights ahead of partner reviews
This shifts channel teams from reactive to proactive.
3. Content & Enablement Personalization
One of the most practical use cases is helping partners access the right information at the right time.
Instead of expecting partners to search through large content libraries, AI can:
- Recommend relevant assets
- Summarize key information
- Support real-time learning during sales cycles
As Kroneman noted, “partners don’t consume content proactively, they use it when they need it.”
AI as a Growth Engine
From the partner side, Goree highlighted a different but equally important use case: pipeline generation.
By combining:
- Customer data;
- Historical win/loss data and
- Market signals
AI can predict what customers are likely to buy next. This allows partners to approach customers with highly relevant, timely propositions, often before formal demand is visible.
The Risk: AI Without Purpose
A consistent theme throughout the discussion was the risk of implementing AI without clear outcomes.
“AI for the sake of AI” doesn’t deliver value.
Instead, successful teams are asking:
- What manual processes can we reduce?
- Where are we losing time or revenue?
- What decisions can we improve with data?
Only then does AI become meaningful.
The Shift: From Experimentation to Expectation
While adoption is still early, there’s a growing sense that AI will soon move from competitive advantage to baseline expectation.
What feels innovative today, AI-driven insights, automation, personalization, will quickly become standard. And organizations that delay, risk falling behind.
Final Thought
AI won’t replace partner programs, but it will reshape how they operate.
The winners won’t be those who talk about AI the most. They’ll be the ones who apply it where it matters:
- Faster decisions
- Better insights
- Stronger partner engagement
Because ultimately, AI is not a strategy. It’s an enabler of execution.
Want to understand the foundation behind these AI use cases? Read our previous post, Designing for the Partner Reality (Not the Vendor Ideal), where we explore why partner-centric design is critical to making any innovation, including AI, actually work.
