Agents Today #14 - AI Agents GTM Strategies: The Overlooked Role of Channel Partners in AI Agent Adoption
While consumer-focused AI applications have seen explosive growth through direct sales models, enterprise adoption follows different patterns... patterns that many AI startups are overlooking.
In today's rapidly evolving AI agent landscape, a critical disconnect is emerging between how AI startups are selling their products and how enterprises traditionally purchase technology. While consumer-focused AI applications have seen explosive growth through direct sales models, enterprise adoption follows different patterns... patterns that many AI startups are overlooking. This article examines the historical and ongoing importance of channel partners in enterprise technology procurement, with specific focus on how this affects AI agent adoption in enterprises.
Summary
Enterprise technology purchasing has historically relied on a complex ecosystem of channel partners—from global consulting firms to regional value-added resellers. These partners influence over 70% of enterprise IT spending, serving as trusted advisors who guide planning, implementation, and ongoing support. Despite this reality, many AI startups are attempting to sell directly to enterprises using consumer-oriented approaches, creating friction in the adoption process. This post explores the traditional enterprise procurement landscape, the various types of channel partners and their evolving business models, and how AI companies can leverage these established pathways to accelerate enterprise adoption. For AI startups targeting enterprises, understanding and embracing channel dynamics isn't just advantageous, it may be essential for sustainable growth and market penetration.
Mike's Insights
Throughout my career, I've had the unique perspective of working on all sides of the enterprise technology equation—as a buyer making enterprise purchasing decisions, as a seller at both startups and established tech companies, as a channel partner reselling hardware and software, and as a consultant advising on technology strategy. These experiences have taught me that enterprise technology adoption rarely happens in isolation.
What I'm observing in today's AI market: many AI startups are approaching enterprise sales with consumer-oriented playbooks, expecting Fortune 1000 companies to purchase and implement complex AI systems through direct channels like click through terms with a credit card on a webpage. This approach fundamentally misunderstands how large organizations evaluate, purchase, and deploy new technologies. The reality is that most enterprises rely heavily on trusted advisors, technology influencers and implementation partners—the same channel ecosystem that has facilitated technology adoption for decades.
The exception to this pattern exists primarily in the cloud provider space, where Google, Microsoft, and AWS have established marketplaces and channel structures that AI offerings can plug into. This is partly why we're seeing faster enterprise adoption of AI capabilities from these platforms compared to standalone AI startups.
For AI startups targeting enterprise customers, recognizing the continued importance of channel partners isn't about clinging to outdated models—it's about acknowledging the practical realities of how enterprises operate. Building effective channel strategies alongside direct sales efforts will be crucial for those aiming to penetrate enterprise markets at scale.
If there is enough interest on this topic, would be happy to dig deeper on each perspective.
Understanding Channel Partners
Understanding the different categories of technology channel partners and their historical functions can help identify their potential role in enterprise AI adoption.
The Shifting Revenue Models of Channel Partners
The revenue models for technology channel partners have evolved over time, influenced by changes in technology consumption patterns.
Traditional Revenue Models
Consulting firms typically generated revenue through consulting fees, charging clients on an hourly or daily basis, or based on specific project milestones. Their revenue was directly tied to the time and expertise they provided.
Value-Added Resellers primarily earned revenue through markups on hardware and software they resold to enterprises. They would purchase products at a discount and sell them at higher prices, with the margin representing their profit. Additionally, VARs often charged fees for value-added services such as installation, customization, and support.
System Integrators typically made money by charging for their services, including consulting, system design, implementation, and ongoing maintenance. Their revenue was often project-based, reflecting the complexity and duration of integration projects.
The Impact of Cloud Computing and SaaS on Channel Revenue
The emergence of cloud computing and widespread SaaS adoption significantly impacted traditional channel partner revenue models:
Managed Service Providers have largely shifted toward subscription-based pricing models. Instead of charging per incident or project, MSPs offer services through recurring monthly or annual fees, providing predictable revenue streams for themselves and consistent IT support costs for clients.
Value-Added Resellers have adapted their revenue models to include services related to cloud integration, cloud management, and ongoing support for cloud-based solutions. While they may still resell software licenses, their focus has expanded to encompass the value they provide in helping enterprises transition to and manage cloud environments.
The IT channel as a whole has witnessed a transition from one-time product sales to subscription-based, proactive IT support and services. This shift from CapEx to OpEx has fundamentally altered how channel partners earn revenue, moving from large, infrequent payments to smaller, regular payments through subscription services.
This shift toward recurring revenue models has required channel partners to focus on providing continuous value to customers, as their ongoing revenue is directly linked to client satisfaction and retention. For AI agents, which are often offered as subscription or cloud-based services, channel partners will likely adopt similar recurring revenue models, emphasizing continuous support, value delivery, and integration services for enterprise customers.
Channel partners continue to exert substantial influence on technology spending within enterprises.
Forrester Research estimates that 64% of overall spending ($4.7 trillion) in the technology industry flows through indirect sales channels.
Accenture suggests that, on average, 70% of revenue for high-tech companies is generated through channel partners.
Canalys reveals that partner-delivered IT technologies and services are expected to account for over 70% of the global total addressable IT market.
Go-to-Market Strategies of AI Startups Selling AI Agent Products to Enterprises
As AI technologies, especially AI agents, mature and become increasingly relevant for enterprise applications, AI startups are employing various go-to-market strategies to reach their target audience. Digging into these strategies reveals the current prevalence of direct sales models versus the adoption of channel partnerships.
AI startups are increasingly leveraging AI itself to enhance their sales and marketing efforts. AI agents are being used to automate tasks such as lead qualification, personalized outreach, and customer engagement, allowing startups to scale their sales operations efficiently. But their customers are not purchasing or making decisions with the same level of efficiency.
There appears to be an initial tendency among AI startups to favor direct sales models, particularly in the early stages of market entry. This preference can be attributed to several factors:
Control - One primary driver for direct sales is the desire for greater control over brand messaging and the overall customer relationship. AI, especially in its nascent stages, often requires nuanced explanations and careful positioning to highlight its unique value proposition and address potential customer concerns. Startups may believe that a direct sales force, deeply knowledgeable about their specific AI products, can better articulate this value and ensure a consistent brand experience.
Perceived Speed - Speed to market can also be a significant factor influencing the choice of sales strategy. AI startups, often operating in a rapidly evolving technological landscape, may perceive that building a focused direct sales effort allows them to reach customers and generate revenue more quickly, especially in the initial phases of their market entry. This direct approach can bypass the potentially time-consuming process of identifying, onboarding, and enabling channel partners.
Lack of Channel Experience - Concerns about the inherent complexity of channel relationships and the potential for conflicts of interest can also deter AI startups from heavily relying on indirect channels. Managing relationships with multiple independent partner organizations can be challenging, requiring significant resources and expertise. Startups with limited bandwidth may prefer the more direct control offered by an in-house sales team.
Complexity and Training - The highly technical nature of many AI products, including AI agents, can also lead startups to favor direct sales due to potential difficulties in effectively educating channel partners. Ensuring that partners possess the deep technical understanding required to accurately represent and support complex AI solutions can be a significant hurdle. Finally, AI startups may initially opt for direct sales to retain the full profit margin associated with their products and to maintain a direct line of communication for gathering crucial customer feedback, which is invaluable for ongoing product development and iteration.
Conclusion
The enterprise AI sales landscape is a complex challenge for vendors navigating the intersection of new technologies and established procurement practices. While direct sales approaches may work effectively for consumer and SMB markets, enterprise sales cycles typically involve multiple stakeholders, formal procurement processes, and established technology partnerships.
Channel partners continue to play a significant role in enterprise technology decisions, ~70% of B2B purchases occur through indirect channels. These channel partners provide essential services including technical expertise, integration capabilities, change management, and ongoing support that many enterprises rely upon for successful technology implementations.
For AI startups targeting enterprise customers, developing effective channel strategies represents a significant (and currently missed) opportunity to accelerate market penetration. By learning from historical technology adoption patterns and embracing collaborative partner models, AI vendors might be able to more effectively navigate enterprise sales cycles and drive growth in this challenging but lucrative market segment.
New sales models like product-led growth and marketplaces are influencing how enterprises make decisions. Rather than eliminating the channel, they are prompting it to evolve and in some cases reaffirming its importance as intermediaries and influencers who can translate AI into business value.
AI startups that recognize this reality and develop balanced go-to-market strategies; combining direct approaches for SMBs and leveraging channel partnerships for enterprise scale may be best positioned to succeed in the enterprise landscape.