At the second annual Global Channel Leaders Forum (GCLF) in London, Channelscaler convened senior channel leaders to discuss the future of partner ecosystems and how organizations can scale partner-driven revenue in an increasingly complex landscape.
Moderated by Kenneth Fox, CTO & Founder of Channelscaler, the panel brought together:
- David Hiscock, SVP Global Channels at Ribbon Communications
- Kelly Woodfin, EMEA Commercial Director at Ricoh Europe
- David Fisher, Head of Distribution EMEA at Zoom
Among the many topics discussed were partner experience, solution selling, and ecosystem complexity. But one theme generated significant discussion: the role AI will play in transforming how partner programs operate and scale.
But the conversation quickly moved beyond hype. The real focus was on how AI can be applied in practical ways to improve partner productivity, insight, and engagement.
AI is now part of every channel conversation
Every roadmap includes it. Every strategy references it. But most organisations are still in the same place:
Experimenting, without seeing consistent impact.
The reason is simple.
AI doesn’t create value on its own. It amplifies what’s already there. And if your workflows are unclear, AI won’t make them better.
One of the clearest takeaways from the panel was:
AI only works when it’s applied to something specific.
Not “improving engagement.” Not “driving efficiency.”
Actual workflows:
- onboarding
- content discovery
- customer adoption
If you can’t point to the workflow, AI has nowhere to land.
Start with friction, not features
The better way to approach AI isn’t to ask, “what can we automate?” It’s to ask:
- where are partners getting stuck?
- where are teams wasting time?
- where are decisions being delayed?
Those friction points are where AI delivers immediate value.
Because you’re not introducing something new, you’re removing something unnecessary.
The biggest constraint isn’t technology, it’s data
This came through strongly across the panel.
AI depends on:
- accurate data
- consistent inputs
- clear ownership
Without that, outputs become unreliable. And once trust drops, adoption follows quickly.
As one panelist put it:
“AI is great, but you’ve got to feed it with data.”
That’s the part many strategies underestimate.
Where AI is already working (and why)
The most effective use cases aren’t complex. They’re practical.
- guiding partners to the right next action
- surfacing relevant content automatically
- identifying churn risk from usage data
- highlighting upsell opportunities early
- automating repetitive tasks (RFPs, support workflows)
These work because they:
- reduce effort
- improve speed
- support better decisions
They don’t try to reinvent the model. They improve how it runs.
“Make it real” – the simplest rule that works
There’s a lot of abstraction around AI. But the most useful advice from the panel was grounded:
Focus on real use cases.
Make it relevant to:
- specific roles
- specific industries
- specific decisions
Because when AI feels generic, it gets ignored. When it feels relevant, it gets used.
AI is a multiplier, not a strategy
This is the key shift. AI doesn’t replace:
- programme design
- partner strategy
- operational clarity
It strengthens them.
If your foundation is strong, AI accelerates everything:
- faster onboarding
- better engagement
- clearer insights
- more proactive execution
If it’s not, AI exposes the gaps faster.
The takeaway
AI isn’t about being ahead of the market. It’s about removing friction at scale.
The organisations seeing real impact aren’t doing everything. They’re doing the right things in the right places.
Start small.
Stay focused.
Tie everything to a real outcome.
That’s where AI stops being a conversation and starts becoming a competitive advantage.
