Milena Traikovich is a prominent figure in the marketing technology landscape, specializing in demand generation and the strategic optimization of lead lifecycles. With a career rooted in deep analytics and performance-driven initiatives, she has become a go-to expert for businesses navigating the complexities of modern SaaS ecosystems. In this discussion, we explore the seismic shifts occurring within the partner programs of industry giants like Salesforce and HubSpot, examining how these changes signal a move toward hyper-specialization in the age of artificial intelligence.
The conversation covers the transition from broad partner networks to elite, competency-based structures and the financial implications of mandatory platform engagement. We also delve into the technical challenges posed by autonomous AI agents, the competitive battle between CRM-centric stacks and hyperscalers, and what these trends mean for the future of consultancy.
Salesforce is moving from a four-tier system to a simplified structure consisting of Select and Summit levels. How will this reduction in tiers and credentials redefine “elite” status, and what specific technical benchmarks must a firm meet to prove they can deliver safe, verifiable AI outcomes?
The shift from the traditional four-tier model—Base, Ridge, Crest, and Summit—to just Select and Summit is a deliberate attempt to restore prestige to the top of the ladder. Previously, the ecosystem had become so crowded that thousands of partners held high-tier status, which diluted the perceived value of being a “Summit” partner. By slashing nearly 170 legacy badges down to 28 core competencies, Salesforce is forcing firms to prove they aren’t just generalists but possess “outcome architecture” capabilities. To achieve this elite status now, a firm must demonstrate mastery in deploying Agentforce and ensuring that AI agents are secure, compliant, and produce verifiable results. It’s no longer about how many people you have certified in basic functions; it’s about having the proven technical depth to manage complex, high-stakes AI transformations without compromising data integrity.
HubSpot now requires a monthly membership fee that is waived only for partners who are active users of the platform. Why is hands-on experience with tools like Breeze AI becoming a prerequisite for partnership, and how will this “thinning” of the ecosystem impact the overall quality of service?
HubSpot is essentially sunsetting its low-cost, passive partnership model by introducing a $400 monthly fee for those who aren’t deeply invested. The logic is simple: if you aren’t living in the product every day, especially with new tools like Breeze AI, you cannot possibly guide a client through a sophisticated implementation. This “thinning” of the herd will likely remove agencies that only occasionally dabble in HubSpot or treat it as an afterthought in their tech stack. While this might lead to a smaller total number of partners, the overall quality of service should rise significantly because the remaining players will be those who are “truly operating inside the ecosystem.” We are moving away from a volume-based game toward a model where the partner’s own business serves as a case study for the platform’s power.
The rise of autonomous AI agents introduces risks such as hallucinations and incorrect automated workflows. What internal shifts in data architecture expertise are required for partners to mitigate these risks, and how can agencies transition their billing models away from manual configuration toward high-level strategic oversight?
The risk of AI hallucinations is real, and the stakes are much higher when autonomous agents are interacting with customers or executing workflows without human intervention. Partners must now pivot their internal hiring and training toward deep data architecture and platform engineering rather than just simple “point-and-click” setup. If the underlying data is messy, the AI agent will inevitably fail, which reflects poorly on both the agency and the SaaS platform. Because automation is rapidly shrinking the hours needed for manual configuration, agencies can no longer rely on the old “billable hours for implementation” model. They must instead transition to high-level strategic oversight and “outcome-based” billing, where the value provided is the safety and efficiency of the AI system rather than the time spent clicking buttons.
Major SaaS platforms are currently competing with hyperscalers and specialized AI labs to remain the primary operating layer for businesses. When consultants influence software selection, how can partners effectively argue for the continued relevance of a CRM-centric stack over a direct-to-AI or hyperscaler-led approach?
This is the front line of the “AI platform war,” where SaaS giants are fighting to keep their seat at the table against hyperscalers like AWS, Google, and Microsoft. Partners play a critical role here because they are the ones in the boardroom influencing the final decision; as we’ve seen, there are often seven different partners involved in a single large enterprise deal. To argue for a CRM-centric stack, partners must emphasize that platforms like Salesforce and HubSpot provide a ready-made, secure operating layer for business workflows that “direct-to-AI” tools lack. If a consultant recommends going straight to OpenAI or a hyperscaler, they are often asking the client to build the governance and workflow infrastructure from scratch. The winning argument is that a CRM-led approach offers a safer, faster route to AI maturity because the business logic and customer data already live within that ecosystem.
What is your forecast for the future of partner ecosystems?
I believe we are entering an era defined by “the great thinning,” where the total number of partners will shrink while the technical capability of those who remain will skyrocket. The days of the generalist marketing agency that claims to be an expert in five different CRMs are numbered; the requirements for certification and platform engagement are becoming too rigorous to maintain that level of breadth. In the next few years, we will see a landscape dominated by highly specialized firms that have chosen a side—becoming either a “Salesforce shop” or a “HubSpot shop”—to meet the deep technical benchmarks required for AI deployment. Ultimately, the success of these SaaS platforms won’t just depend on their internal code, but on whether their partner ecosystems are strong enough to capture the lion’s share of the AI economy before the hyperscalers move in.
