Artificial intelligence is changing not only which technology products and services enterprises buy but also how vendors sell them. While customer-facing functions such as direct sales and support have started to adapt, partner channels have lagged: they often continue to use a traditional SaaS playbook to sell AI products and are among the last functions to undergo an AI-enabled operational transformation.
Channel partners matter: for leading software and technology vendors, partners drive roughly 60 percent of revenues on average. Yet two out of three technology vendors report capability gaps in their channels, and the top quartile of providers on partner management outperform the median by about twofold. This capability gap is especially consequential during the AI boom, as leaders rethink how AI reshapes external go-to-market and internal operations.
This article draws on interviews with industry leaders and market experience to identify decisive channel moves across five broad areas. Interviewees included AI solution decision-makers across international enterprises ranging from large companies (more than $10 billion revenue) to midsize players ($5–10 billion).
Rethinking partner models and deeper embedding
Traditional partner models were designed for distribution at scale: thousands of resellers, system integrators and ISVs organized by tier. AI requires a different approach. Enterprise customers increasingly need specialized partners with technical and domain expertise to shape architectures, integrate solutions, grow adoption and deliver measurable outcomes. Surveys indicate roughly half of enterprises are working with partners to design their AI and agentic architectures.
Four broad partner archetypes are becoming more important, aligned to how AI solutions are built, deployed and scaled. For channel leaders this implies a shift from broad partner coverage to more targeted orchestration: match partners to the roles and life‑cycle stages where they create the most value. This precision partnering becomes the foundation for enterprise adoption and measurable outcomes.
Continuous partner involvement across the customer life cycle
Traditional partner engagement often resembled a relay: vendors hand deals to partners at sale, partners hand delivery to customers at deployment. That construct cannot support AI sales, where integration complexity and consumption economics demand continuous partner involvement from POC through scaling.
The new motion is more like a barbell: partners are deeply embedded at both ends of the customer life cycle—early codesign and POC, and later adoption and expansion. Practically, vendors need to package AI around industry-specific data, workflows, use cases and compliance so partners can sell clear business outcomes and drive consumption demonstrated during POCs.
Channel leaders can redefine partner engagement rules: involve partners earlier in codesign and POCs, and hold them accountable through adoption and expansion rather than only rewarding deal closure.
New pay structures and incentives for AI economics
SaaS-era partner programs rewarded upfront bookings and contract value. In AI, value is realized through consumption and adoption over time, so applying old incentive structures to new economics creates costly misalignment. Channel leaders should build AI-specific program tracks that address what, how and whom they pay in AI channel sales.
Financial incentives can include shared development costs, dedicated technical teams to accelerate agent builds, and AI-specific SPIFFs and marketing funds tied to POCs and pilot scaling. Microsoft’s AI Cloud Partner Program, for example, grants partners Azure credits, product licenses and sandbox development environments; AWS offers generative AI POC funding for validated partners, typically covering up to 10 percent of costs, capped at $25,000.
Nonfinancial incentives matter as well: free-to-try agents with one-click deployment, priority placement of partner-built offerings on vendor marketplaces and 60-day early access to new AI products can all help align partner behavior with long-term consumption outcomes.
AI-driven channel productivity and operational reset
Many channel organizations still struggle with long deal cycles, high support overhead and underperforming partner revenue. AI and agentic workflows present an opportunity for a structural reset and significant efficiency gains. Across more than 30 curated use cases in sales, marketing, operations, programs and enablement, AI has shown potential productivity improvements of 30 to 45 percent.
The opportunity extends beyond cost reduction to faster pipeline velocity, higher win rates and more effective partner engagement. Salesforce’s 2026 State of Sales report found that 40 percent of partners have full access to company AI tools and a further 55 percent have partial access—reflecting vendor efforts to extend AI capabilities into partner ecosystems.
Valuable AI use cases for partner management include automated lead prioritization, dynamic offer support, partner-specific go-to-market plan generation and automated escalation handling—each capable of improving speed and reducing overhead.
Forward‑deployed engineering (FDE) and moving pilots to production
AI deployments are iterative, complex and tightly integrated with customer workflows—and most fail without the right on-the-ground support. Industry research shows organizations scrap more than half of AI POCs before production, and 42 percent of companies report abandoning most of their AI initiatives, up from 17 percent in 2024.
A critical organizational response is building forward‑deployed engineering (FDE) capacity at scale: engineers embedded directly inside customer operations for months. Running FDEs internally is expensive and hard to scale; one approach is to offer FDE capability as a service provided by select partners.
Partner-enabled FDE works because it closes gaps between product assumptions and operational reality, uncovering undocumented processes, legacy system complexities and misaligned incentives. Two capability sets make the partner-enabled model effective: deep industry expertise and global delivery capacity.
Some leading vendors have operationalized this model with measurable results. Salesforce launched its FDE Partner Network in April 2026 with more than 30 firms, including Accenture, Deloitte, IBM Consulting and PwC. By tying incentives to agents reaching production rather than deal closure, Salesforce leverages partners’ delivery and industry expertise to support scaled deployment and adoption; FDE partner network members have driven one‑third of successful Agentforce implementations to date.
Sequence of actions and the risk of standing still
Shifting to AI-first channel models requires a comprehensive overhaul: redesign the partner map, embed partners across the lifecycle, launch a dedicated AI partner program, invest in AI-driven channel productivity and restructure organizational capabilities to include FDE. Partners can evolve from resellers into cobuilders, depending on up-front investment and new financial models. Vendors that retain status quo risk misalignment with how AI is purchased, deployed and scaled, and may fail to realize desired outcomes.
The article is authored by Barathram Ananthakrishnan (partner, McKinsey Bay Area), with contributions from Pranita Sadavarte (consultant), Jeremy Schneider (senior partner, McKinsey New York) and Nitya Samuel (associate partner). The authors thank Angie Selzer‑Wong, Devina Sial, Dhruv Sriram and Rocío Marazuela for their contributions. The piece was edited by Daniel Eisenberg, executive editor in the New York office.
Conclusion
For software and technology vendors, moving from product sales to AI solutions is a fundamental transformation of partner ecosystems rather than an incremental change. Leaders who redesign partner maps, embed partners across the customer journey, create AI-specific incentive programs, scale FDE and invest in AI-driven productivity are best positioned to convert pilots into production and consumption-driven revenue growth.
