Accelerated Innovation

Identifying & Understanding Your EDD Capability Gaps

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Identifying & Understanding Your EDD Capability Gaps

Description

This capability focuses on how effectively an organization identifies and understands the specific capability gaps within its Evaluation Driven Development (EDD) processes. It includes assessing where solution evaluations fall short, pinpointing root causes of underperformance, and building a clear picture of improvement needs.

Why it's Important

A well-defined understanding of EDD capability gaps is essential for driving meaningful, data-backed GenAI improvements. Without accurate gap identification, teams risk optimizing the wrong areas, applying misaligned fixes, or missing key failure points entirely. Consistently identifying where evaluations break down-whether in model, data, UX, or governance layers-enables more targeted enhancements, faster iteration cycles, and greater GenAI solution reliability. As organizations mature, a scalable and precise approach to understanding EDD gaps becomes a foundational enabler for safe, intelligent GenAI advancement.

Why it's Challenging @ Scale

  • Lack of evaluation coverage across layers: Many teams only assess GenAI performance at the output level, overlooking root causes in model, data, or UX layers.
  • Fragmented signal sources: Teams often struggle to connect signals across systems (e.g., model telemetry, user feedback, human review), leading to incomplete gap identification.
  • Ambiguous ownership of evaluation processes: It’s often unclear who owns evaluation or is responsible for investigating capability gaps across GenAI teams.
  • Manual, unscalable methods: Teams rely on ad hoc reviews or spreadsheets, which become unmanageable as GenAI usage expands.
  • Gaps in slice-aware diagnostics: Most evaluations fail to account for variations across slices (e.g., user segments, query types), masking high-impact issues.

Complexity

High: This capability requires cross-functional coordination, tooling integration, and diagnostic depth to evaluate GenAI performance beyond surface-level outcomes.

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Taking Action

Though most organizations begin their GenAI journey with significant knowledge gaps, there are targeted actions that can be taken to accelerate the process. Select your group’s current maturity, based on your assessment results, and act today.

The most important part of any journey is starting… To move from “Exploring” to “Experimenting”, focus on the following key actions:
  • Explore Key Concepts & Best Practices: Complete the Iteratively Tuning Your GenAI Solutions workshop (2 hrs.) to understand foundational key concepts and explore applied best practices
  • Assessing Your Solution’s Performance.
  • Identifying and Prioritizing Improvement Opportunities.
  • Actioning Improvement Opportunities.
  • Understanding the Interdependent Nature of GenAI Solutions.
  • Making Data-Driven ‘Go / No-Go’ Decisions.
  • Define Your Action Plan: Outline concrete, prioritized steps your organization will take to implement GenAI Strategy.
  • Align on your Current State and define your Target State.
  • Create an actionable enablement plan.
  • Define target timeline and measures of success.
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame.
  • Evaluate Existing Gaps Using SME Input: Launch a mini-review session with SMEs to identify where EDD evaluations are falling short.
  • Introduce a Gap Tagging Framework: Pilot a standardized framework for tagging issues by layer (e.g., model, UX, data) during evaluations.
  • Create a Feedback-Linked Gap Dashboard: Build a simple dashboard to visualize gaps using existing feedback and evaluation signals.
To move from Experimentation to “Lifting-Off”, prioritize the following actions:
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including:
  • Optimizing Your Data
  • Optimizing Your Model(s)
  • Optimizing Your Natural Language Understanding & Intent Classification
  • Optimizing Your GenAI Search
  • Optimizing Your GenAI Retrieval
  • Optimizing Your GenAI Responses
  • Optimizing Your Safeguards
  • Optimizing Your GenAI Solution Costs
  • Optimizing Your GenAI Support
  • Optimizing Your EDD Approach
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale
  • Assess Your Proposed Solution or Process: Evaluate how capability gaps are currently identified and tracked across GenAI layers.
  • Define in-scope Processes and Guardrails: Document which gap types and evaluation signals should trigger prioritization or follow-up.
  • Close any Data or Measurement Gaps: Establish consistent tagging and tracking across evaluation types to support slice-aware diagnostics.
  • Define Your Adoption & Scaling Plan: Create a structured roadmap for how GenAI solutions will be rolled out across teams, workflows, or business units
  • Define Your Phased Implementation Plan: Stage deployment of EDD diagnostics starting with high-risk or high-impact GenAI flows.
  • Build Awareness and Finalize Enablers: Share gap identification guidance, tagging standards, and slice-aware evaluation tools across teams.
  • Operationalize Your Comms Plan: Regularly communicate common gaps, fix rates, and learning outcomes to drive team engagement.
To move from Lifting-Off to “Accelerating”, prioritize the following actions:
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases
  • Standardize Gap Identification Criteria: Align teams on consistent definitions and tags for common GenAI capability gaps
  • Build Layer-Specific Diagnostic Templates: Create templates for structured evaluation across model, UX, and data layers
  • Embed Gap Reviews into PMLC Milestones: Require capability gap reviews during key phases of the product lifecycle
  • Accelerate Your Adoption: Intensifying efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Expand Evaluation Coverage Across Journeys: Ensure capability gap identification spans both internal and external GenAI use cases
  • Equip Teams with Self-Serve Diagnostic Tools: Provide tools and checklists to help teams identify EDD gaps independently
  • Launch Gap Identification Office Hours: Offer recurring support sessions to help teams refine their evaluation practices
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Spotlight Gap-Driven Improvements: Showcase how identified gaps led to measurable solution enhancements
  • Share Examples of Effective Diagnostics: Highlight strong examples of slice-aware or multi-layer evaluation
  • Recognize Evaluation Champions: Acknowledge individuals or teams who improved gap detection or diagnostics
The “Accelerating” stage represents “Target State” for many capabilities. “Breaking Away”, on the other hand, suggests that the specific Capability represents a clear competitive advantage for your business
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine
  • Embed Diagnostics into Authoring Pipelines: Integrate capability gap checks directly into GenAI workflow and deployment tools
  • Provide Real-Time Evaluation Signals: Enable evaluators to flag capability gaps while reviewing outputs in context
  • Harmonize Evaluation Across Teams: Align diagnostics and gap-tracking methods across business units and use cases
  • Leverage Automation: Using GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Automate Gap Detection and Tagging: Use AI to identify and categorize capability gaps based on evaluation data
  • Suggest Root Cause Pathways Automatically: Leverage metadata to recommend likely causes or follow-ups for observed issues
  • Train Models to Pre-Flag Known Gaps: Fine-tune models to detect common failure patterns before human review
  • Evolve & Further Accelerate: Continuously refining GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Refresh Gap Detection Frameworks Based on Feedback: Update evaluation approaches based on performance data and reviewer input
  • Extend Gap Tagging to New Modalities: Apply diagnostic techniques across multimodal GenAI solutions
  • Benchmark Gap Resolution Velocity: Track and improve the speed at which capability gaps are identified and resolved

Key "Watchouts"

As you take action you’ll want to avoid:

  • Focusing only on surface-level symptoms: Limiting evaluations to output errors without diagnosing root causes reduces improvement impact
  • Inconsistent tagging across teams: Without shared standards, gaps are categorized differently-making patterns and trends difficult to track
  • Ignoring slice-specific failure modes: Capability gaps often appear only in certain user segments or contexts, and broad metrics can obscure them
  • Relying solely on human evaluation: Manual review is slow and variable-automated diagnostics improve consistency and scalability
  • Delaying diagnostic enablement: Postponing the rollout of gap tracking slows improvement cycles and prevents learning at scale

Targeted Benefits

While Identifying & Understanding Your EDD Capability Gaps can be challenging, its benefits are clear and compelling, including:

  • Faster iteration cycles: Early detection of root causes enables more efficient solution tuning
  • More reliable performance: Identifying hidden issues prevents recurrence and strengthens GenAI solution quality
  • Scalable diagnostics: Consistent gap tracking supports broader rollout without sacrificing evaluation depth
  • Smarter resource allocation: Pinpointing high-impact gaps helps teams focus where it matters most
  • Competitive differentiation: Superior diagnostic practices enable faster improvement and better outcomes than peers

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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