As we step into 2026, the landscape of Go-To-Market (GTM) strategies is evolving rapidly.
In this insightful discussion, Sayanta Ghosh and Nikhil Ojha explore the journey of NREV and the innovative paths they are taking to address the challenges in GTM implementations.
With a focus on democratizing technology and leveraging AI, this blog post delves into the key themes and strategies that will define the future of GTM.
1. The Shift Towards Democratization in GTM
Sayanta Ghosh opens the conversation by highlighting the need for a shift from agency-based implementations to a more democratic approach.
He emphasizes that operators in GTM should have the capability to build and strategize without being bogged down by the complexities of technology.
Nikhil Ojha agrees, pointing out that the objective for NREV in 2026 is to simplify the user experience, making it easier for GTM operators to execute their strategies using AI.
2. Understanding User Challenges and Opportunities
Nikhil shares insights from the past six months, noting the diverse range of use cases NREV has been able to address.
He explains that while AI can take users part of the way, it often requires human intervention to complete tasks.
The challenge lies in minimizing the learning curve for users, which NREV aims to tackle by making its platform more user-friendly.
3. Balancing AI and Human Input
A significant point raised during the discussion is the balance between AI-driven agents and traditional workflows.
Nikhil illustrates this with a real-world example: if an AI agent were to handle 5,000 data rows independently, the inconsistency in outcomes could lead to chaotic results.
Instead, NREV is developing agentic workflows that maintain reliability while integrating AI to enhance functionality.
This ensures that while AI aids in building workflows, the overall structure is maintained systematically to avoid errors.
4. Learning from Industry Giants
Sayanta reflects on Salesforce's experience, where they acknowledged that agents would not replace human roles.
This learning has informed NREV's approach to build systems that prioritize accuracy and reliability, especially when dealing with sensitive data in CRMs.
The ultimate goal is to create a GTM process that is AI-native yet devoid of embarrassing mistakes, leveraging the strengths of both agents and workflows.
5. The Role of Knowledge Graphs in Enhancing AI
The conversation shifts towards the importance of knowledge graphs in automating workflows.
Nikhil explains that knowledge graphs allow for efficient data retrieval and context management, which are crucial for AI agents to operate effectively.
By structuring information in a graph format, NREV can ensure that AI has access to the relevant data needed to make informed decisions, thus enhancing its capabilities.
6. The Future of GTM with AI
As NREV looks ahead, Nikhil shares plans for integrating a consultancy-style AI agent that will assist users in strategizing and implementing best practices.
This AI agent will act as a guide, helping human operators navigate through complex decisions and optimize their GTM strategies based on previous learnings and outcomes.
Conclusion:
The discussion between Sayanta Ghosh and Nikhil Ojha sheds light on the transformative journey of NREV as it adapts to the evolving needs of GTM operators.
By focusing on democratization, user-friendly design, and the strategic use of AI, NREV is poised to redefine the landscape of GTM in 2026.
Key takeaways include the importance of balancing AI and human input, the role of knowledge graphs in enhancing AI capabilities, and the vision of creating a seamless workflow system that empowers users to achieve their goals with confidence.