In a packed room at ELEVATE 2025, GTM executive leaders from top B2B companies like Adobe, Salesforce, Verizon Business, and more found themselves staring down a reality most teams are already feeling: customers expect measurable impact faster, QBRs arrive too late, data is scattered across tools, and teams are stretched to capacity. Add a Chief Revenue Officer who just announced that demand is up 20%, but hiring is frozen, and the mandate was clear:
“We need a new GTM playbook—one that reimagines Realize & Expand for an AI-native world.”
This was the starting point for Group 3 of our breakout workshops in the afternoon. What followed was one of the day’s most dynamic conversations, a mix of aspiration, constraint, and hard-won experience, resulting in a bold vision for what effective post-sale execution could look like by 2027.
Setting the Stage
The first question was immediate: "What if the platform could track realized versus promised value automatically?"
This sparked ideas about a unified AI system that not only reports outcomes but acts on them:
- Could AI choose the right channel based on customer needs?
- Could routine check-ins shift entirely to AI nudges and insights?
- Could expansion signals surface automatically without human interpretation?
The concept of an “AI-Driven Outcome Orchestrator” emerged, handling the operational grind while humans focused on strategy, expansion, and outcome design.
Humans as Outcome Architects
With AI taking over routine touchpoints, the group considered what becomes uniquely human. “Outcome Architect” described customer success managers and value engineers evolving into strategic, outcome-focused experts rather than schedulers or report generators.
Role redefinitions included:
- Value engineers moving away from repetitive data pulls
- Sales using AI signals to time expansion outreach
- Humans focusing on executive alignment, political navigation, and long-term strategy
Some questioned whether predefined roles even make sense in this future, suggesting AI could recommend which human to engage on an account.
Risks: Data, Trust, and Complexity
The group recognized that AI depends on quality data. Risks included:
- Fragmented data pipelines
- Customer skepticism of AI-led touchpoints
- Security and privacy vulnerabilities
- AI generating too much information for teams to handle
Without data readiness and trust, the benefits of AI cannot materialize.
Practical Plays for 3–6 Months
The discussion moved from vision to action:
- Telemetry and Data Readiness: Inventory and clean all data sources
- Role Envisioning and Alignment: Adjust responsibilities to interpret AI insights
- Security and Privacy Compliance: Ensure AI touchpoints meet the highest standards
- Tool and Tech Stack Evaluation: Verify platforms can support orchestration and value telemetry
- Cross-Functional Councils: Align product, customer success, value, sales, and operations for adoption and enablement.
These experiments were seen as achievable in the next quarter.
Exploratory Bets
Two long-term bets stood out:
- AI-Driven Outcome Orchestrator: Tracks value realized, drives touchpoints, personalizes post-sale experiences, surfaces expansion opportunities, and involves humans only when needed. This approach scales teams rather than replaces them.
- Adaptive Engagement with Fluid Role Definitions: AI triages, predicts, and routes, while humans deliver strategic partnership. AI creates capacity; humans create value.
Where the Group Landed
By the end, the group had:
- Named the AI-driven orchestration model
- Identified human role evolution
- Acknowledged data and security prerequisites
- Defined practical 90-day plays
- Outlined long-term exploratory bets
What the group described, an AI-driven system that tracks realized outcomes, orchestrates customer touchpoints, and surfaces expansion opportunities, aligns directly with the direction ViViEN™, Ecosystems’ Virtual Value Engineer, is heading.
The capabilities the group imagined mirror many of the exact gaps ViViEN™ is designed to close:
- Connecting promised vs. realized value through a shared Collaborative Value Assessment and Collaborative Success Plan system of record
- Automating data pulls so customer success managers and value engineers spend more time on strategy
- Triggering next-step recommendations tied to customer objectives and adoption patterns
- Creating consistency across roles by giving sales, customer success, and value teams a unified set of insights
In other words, the workshop described the future, and ViViEN™ represents the practical path toward it.
AI is not eliminating post-sale roles; it is elevating them. The future of “Realize & Expand” blends AI automation with human strategy, reshaping workflows, roles, and customer engagement. Group 3 designed a blueprint for the next era of customer success and expansion, proving that in a world of rising demand and static headcount, visionary thinking is essential.
To learn more about ViViEN™, contact our team today.
