Partner Data, AI, and Trust: The Future of Co-Selling

Partnership programs today face a critical need for efficiency and precision, driven by the current economic climate and the challenge of achieving "more with less." Partner Ecosystem Optimization begins with the strategic task of identifying the Ideal Partner Profile (IPP) to avoid wasting time on the wrong alliances. Dina Moskowitz’s perspective on the common pain points is clear: companies struggle with an existing ecosystem but realize they are losing focus and wasting time on the wrong partners. The core mission of her platform is to develop innovative data mining techniques to profile companies, making it easier for partner organizations to target them precisely. This precision means understanding a partner’s average transaction size, their Ideal Customer Profile, and their ability to influence revenue, moving past a "wait and see and hope and pray" mentality.

Theresa Caragol adds the essential strategic framework for this build-out, introducing the science behind Channel Partner Strategy. She emphasizes that getting the strategy right is the foundation of success, encompassing a joint vision, trusted relationships, business acceleration (where data intelligence serves as a key propeller), and community building centered on lifetime value, rather than just transactions. Achieve Unite’s approach, complemented by partner data intelligence, dramatically accelerates this process, ensuring that new builds and segment expansion efforts reach the right partners more quickly. This joint work enables companies to transition rapidly from a general strategy to identifying specific partners and geographies for recruitment, ensuring that the foundational business proposition is right before making a significant investment.

The two use cases—building a new channel and optimizing a large, existing ecosystem—are both addressed by first getting the strategy right, then operationalizing it with data. Dina’s comprehensive database, which she refers to as a solution to map “Jay McBain’s blob of partners,” continuously mines B2B technology companies globally. This data is structured to allow granular searches across resellers, ISVs, MSPs, SIs, and more. The goal is to provide clients with a comprehensive view of their total addressable market of productive partners, enabling them to abandon the "spray and pray" approach and instead target alliances based on shared technical stacks (e.g., Cisco or Juniper partners) and customer objectives. This structured approach to identifying the right partners at the right time is the first step in accelerating successful partnerships.

The success of any partnership program, once partners are recruited, hinges on effective enablement and Co-Selling motions. Theresa Caragol notes that generic training is no longer enough; enablement must drive behavior changes in people. This is where Partner Data Intelligence becomes a real-time, personalized tool. Partner Optimizer provides managers with territory-specific data and insights that can be used to form stronger, more relevant value propositions for the end customer and stronger business propositions for the partner. This intelligence, combined with AI, rapidly creates targeted messaging and strategic initiatives, such as identifying the best vertical markets to target, thereby accelerating the entire process from recruitment to activation. The difference is moving from talking at partners to talking with partners, building a foundation of trust based on a clear understanding of their business.

In co-marketing, the traditional one-size-fits-all approach is obsolete. The key determinant of success for co-op or MDF investments is a partner's marketing competency, which necessitates a more precise approach to evaluation. Teresa and Dina both stress the need for a maturity model, which defines different activities for partners based on their stage of engagement—from a Stage One partner needing basic growth activities to a Stage Five partner who receives custom, high-touch attention for campaigns. This precision, or "precision-based" approach, moves past the random acts of marketing that dominated the past.

The shift in marketing spend is also a key theme. While 90% of buying research happens online, there is a resurgence of events, driven by the need for strategic, multi-party engagement. Theresa suggests that the future of go-to-market involves an ecosystem of partners (vendors, partners, hyperscaler) focusing on a strategic initiative, a set of accounts, and then a few highly strategic events, rather than a “peanut butter” spread of effort. This movement away from generic marketing and into highly targeted, account-based marketing, with digital channels like LinkedIn, is how to win. It acknowledges that direct selling is changing, as buyers use trusted advisors (who may not be transacting partners) early in the decision-making cycle, making influence and the Co-Selling motion more critical than ever.

The conversation concludes with an in-depth focus on Co-Selling, emphasizing the roles of hyperscalers, AI in Partnering, and the irreplaceable human element. Dina explains that today's Co-Selling often centers around hyperscaler marketplaces (Azure, AWS, Google Cloud). However, listing in a marketplace is not a "floodgate opener" for sales; it’s an infrastructure for transacting. Smaller companies still need to find partners within that ecosystem who match their IPP and can help them sell, as the hyperscalers themselves prioritize the top several hundred. Partner Optimizer’s intelligence, therefore, remains essential for identifying the best-fit co-sell partners for a given product or opportunity.

Theresa Caragol details how her new Co-Selling programs leverage AI and data to train sellers and partner marketers. The mechanics involve collecting data from multiple companies, feeding it into a private AI, which then generates joint value propositions, targeted messages for specific customers (based on their market activity), and tailored business propositions for partner executives. This integration of data and AI in Partnering dramatically accelerates the seller's process—allowing them to focus on networking and building the funnel, not on generic prep work.

Both leaders agree on the critical role of AI in their platforms: Dina utilizes AI/ML for highly efficient data mining, finding partners globally, enhancing search algorithms, and transforming raw insights into conversational recommendations. However, a vital conclusion is that AI is a tool to augment the best human efforts, not a replacement. Dina cautions that while AI is great, without human intention and hypothesis—knowing why you are building a campaign—the AI can lead to "hallucinations" or simply a wrong path in partnership strategy. The future of Co-Selling lies in combining human intelligence with digital intelligence to deliver a quantifiable outcome, where trust remains the core driver of success.