Scaling AI Discovery Through Innovation Sprints

01 /// The Challenge

From AI Curiosity to Business-Ready Use Cases

As interest in AI accelerated, teams across the client experience space surfaced many ideas, but the hardest part was turning that enthusiasm into business-ready opportunities. Work was often framed as “can we use AI here?” rather than grounded in user needs, measurable outcomes, and clear constraints. Without a shared structure for prioritization and alignment, it was difficult for leaders and teams to decide what to pursue, what to defer, and how to connect initiatives to business outcomes, especially when data readiness, integration complexity, and change management varied across teams.

02 /// The Strategy

A Repeatable Sprint Framework for AI Discovery and Alignment

I used innovation sprints as a repeatable way to move teams from AI ideas to decisions. Across 20+ sprints, my approach paired human-centered problem framing with an enterprise lens, helping teams define where AI could add value and what would matter for adoption.

To scale that approach, I designed two AI-focused workshops:

• Cross-functional AI discovery workshop (Sales, Marketing, Client Experience): brought together 30+ participants to build empathy for priority users, identify high-priority opportunities, and filter out ideas that would not get meaningful lift from AI.
• Senior leader alignment workshop: shifted the conversation from use cases to capabilities, enabling leaders to align on cross-pillar business capabilities AI could enable and to clarify how priorities connected back to business priorities.

• Prioritization framework

  Each pillar evaluated opportunities across three dimensions:

  • Customer Needs.
  • Fit with AI Capabilities (including agentic and analytical patterns where relevant).
  • Implementation Considerations, with data readiness consistently the primary constraint, alongside integration complexity and change management.

• Outputs
  • Empathy Maps per pillar capturing priority users, needs, and context.
  • Pillar-specific Value Maps organized around Customer Needs, AI Capabilities, and Implementation Considerations (with pillar-specific sub-considerations).
  • Prioritized lists of opportunities per pillar, grounded in the empathy and value mapping outputs.
  • Cross-pillar readout synthesizing patterns, major learnings, and shared constraints.
  • Executive capability lineage and value chain view, mapping business priorities to pillar priorities and cross-pillar capabilities AI could enable, highlighting where linkage was unclear and required refinement.

• Examples of prioritized opportunities
  • Marketing: strengthen compliant content workflows using agentic support, constrained by dispersed and context-dependent disclosure guidance and data readiness.
  • Sales: improve territory pulse and decision support using agentic and analytical capabilities, constrained by data readiness and the need for reliable feedback loops to support trust.
  • Client Experience: automate triage for cases “not in good order” by identifying gaps and exceptions, constrained by sensitive data handling and feedback loops to improve quality and accuracy over time.

• Examples of cross-pillar capabilities assessed
  • Prospecting and lead triage.
  • Content creation and review workflows.
  • Client meeting preparation and follow-up.
  • Client feedback analysis and triage.
  • Compliance risk detection in content and data.
  • Contact center workflow support.

03 /// The Outcome

Faster Alignment, Faster Decisions

The sprint approach helped teams and leaders move faster from open questions to clearer decisions. Instead of debating AI in the abstract, the workshops created shared context around user needs, priority opportunities, and feasibility constraints, reducing siloed prioritization and improving decision quality.

Across pillars, teams used the empathy and value mapping outputs to validate and refine existing initiatives, compare workshop priorities to current roadmaps, and reconsider sequencing where constraints (especially data) made certain ideas less viable in the near term.

At the leadership level, the capability lineage and value chain mapping made alignment and gaps visible, including where certain strategies or capability linkages to business priorities were fuzzy and needed refinement before deeper investment. Follow-up execution and refinement was owned by delivery leaders (for example, senior architecture and data science leadership), supported by the alignment artifacts produced through the workshops.