Artificial intelligence (AI) is redefining how B2B go-to-market (GTM) organizations connect with customers and drive growth. Automation has reached a level of intelligence once considered impossible, yet in practice much of that power still sits unused.
In a rush to integrate AI, teams have focused on efficiency and have overlooked collaboration, creating siloed insights that push teams ahead but leave the organization behind. Over time, that disconnected movement will create instability instead of scalable success.
As AI becomes central to GTM strategy, the challenge is no longer adoption but integration. Data remains locked inside walled gardens that limit visibility and slow progress. Efficiency has increased, while intelligence remains divided, so growth is hindered.
Closing the Gap
That’s the gap the Model Context Protocol (MCP) is designed to close. As an open standard, MCP allows AI models and GTM applications to exchange context seamlessly. It is a shared language that bridges disconnected systems, making data and tools portable across environments. In practice, it gives GTM platforms a unified framework for communication— a universal connector that allows information to move without custom integration.
When tools share context, intelligence compounds. What once required a dozen manual steps now happens effortlessly in the background, freeing teams to focus on strategy and execution. It’s the point where MCP shifts from concept to capability—the connective layer that transforms automation into intelligence.
Moving from Silos to Shared Understanding
GTM professionals move through a maze of tools every day. Ad campaigns are built in one platform, executed in another, and measured in a third. Each transition slows momentum and pulls focus from meaningful work. According to Demandbase’s State of B2B Marketing Report, only 45% of B2B marketers feel confident they can connect data across teams. It adds up: with so many disconnected handoffs, the steps that should signal progress often become an impediment.
When context flows freely, productivity looks different. People spend less time learning tools and more time making decisions that drive results. Traditional boundaries between marketing, sales, and operations fade as teams operate from a unified view.
MCP turns disconnected data into usable intelligence across the stack. It lets context flow between platforms so intelligence moves across applications instead of staying trapped inside an application. Teams don’t need new software or retraining; AI integrates directly into the tools they already trust.
Driving Alignment
While MCP addresses complex technical needs, its effect is human. It keeps technology moving at the pace of people. It creates space for judgment, strategy, and trust—the parts of work that can’t be automated.
Instead of starting the day buried in spreadsheets, a marketer can begin with a single question: Who should we reach out to next? With MCP, AI draws from a company’s customer intelligence to deliver an answer that’s immediate and reliable. Insights appear in the same workspace, ready for action.
Said another way, MCP drives alignment between teams on a system level. When teams operate from shared intelligence, accountability feels natural and collaboration happens with ease. GTM begins to function as one cohesive system that’s efficient, contextual, and built for measurable results.
Future-ready GTM, No Rebuild Required
That flexibility gives organizations a foundation that evolves with them, keeping data connected and context intact as technology shifts. It allows teams to deploy AI and modernize their tools and systems without major rebuild efforts. When context moves through an open protocol instead of a closed system, companies maintain control over their information and have confidence in its accuracy.
MCP expands opportunities for the next generation of GTM professionals as well. For new talent entering the field, success should depend on strategic thinking and creativity, not on relearning software with every platform change. With MCP, their knowledge and skills are portable and not tied to the ability to learn and execute strategies on specific GTM platforms.
That is sustainable intelligence— practical, continuous, and designed for the people who depend on it.
Defining Factor
The transformation of AI in GTM isn’t about another model or interface. It’s about the infrastructure that connects them. The MCP establishes that foundation, linking systems so data and people can operate in sync. It gives organizations the ability to scale intelligence without losing control or adding complexity. When context moves freely, insight follows naturally.
This new layer of interconnection will become a defining factor in business success. Companies that will engage with it— building their GTM strategy around a shared context— will create adaptive and intelligent systems that scale. Organizations that fail to engage and continue to build or buy tools without considering this connective layer will struggle to see a return on their AI investments and eventually ROI will plateau.
MCP marks the shift from isolated AI adoption to an integrated, connected ecosystem. The future of GTM belongs to organizations that treat connection as strategy.
Harshal Dedhia is the Vice President of AI for Demandbase. In his role, Harshal is responsible for pioneering the future of AI while ensuring that innovation translates into usability, impact, and success for customers. Harshal has over 20 years of experience in data science, machine learning, and data engineering and has worked to push the boundaries on agentic AI systems. At Demandbase, Harshal leverages his experience building and deploying large-scale AI systems to re-imagine how businesses operate through engaging, personalized AI experiences that drive meaningful outcomes. Harshal serves as a member of the San Francisco CDAO Community governing body and has supported data science efforts from previous roles with Houzz, Apartment List, and Ten-X. Harshal holds an MS in Industrial Engineering and Operations Research from the University of California, Berkeley, and has completed all 3 levels of the CFA exam.
