How Measurement Transforms L&D From a Cost Center to a Strategic Partner with Christina Castelli of Accenture
In this episode of Learning Leader Spotlight, Christina Castelli, Head of Learning Operations and Measurement at Accenture, explores how learning organizations can better connect their work to business outcomes while navigating the rapid rise of AI. In conversation with host Chenier Mershon, she shares practical strategies for measurement, challenges common assumptions about ROI, and reframes AI as a tool for augmentation rather than replacement. The discussion offers a grounded, actionable perspective for learning leaders seeking to improve both the credibility and impact of their function.
Christina Castelli leads learning operations and measurement at Accenture, where she has spent the majority of her career across consulting, learning design, and internal capability development. Her work has focused on building scalable systems that connect learning initiatives to measurable business outcomes. With experience spanning social learning, instructional design, and learning analytics, Christina brings a comprehensive view of the learning function, balancing creativity with operational discipline and business alignment.
Measurement Must Start with Business Outcomes
A central theme of the conversation is the importance of defining business outcomes before designing measurement strategies. Christina emphasizes that traditional approaches often begin with performance metrics such as satisfaction or completion rates, without first asking what success looks like for the business.
Her framework reverses this approach. By identifying outcomes upfront, teams can determine which metrics are meaningful and avoid collecting data that does not answer a real question. For instance, satisfaction data is only valuable if it connects to outcomes such as engagement or retention.
This shift also creates clearer accountability. When both learning teams and stakeholders agree on outcomes in advance, measurement becomes a shared responsibility, not a post-hoc justification exercise.
The SKAI Model: Flexible, Outcome-Driven Measurement
Christina describes Accenture’s internal measurement framework, known as the “SKY model” as spoken, consisting of four levels: Satisfaction, Knowledge, Application, and Impact. While structurally similar to the Kirkpatrick model, its distinguishing feature is how each level links directly back to business outcomes.
Rather than treating measurement as a hierarchical pyramid that must culminate in ROI, this model allows flexibility. Not every program needs to measure every level. For example:
- A short learning experience may not justify impact-level measurement
- Behavioral change may matter more than knowledge retention in certain contexts
- Application data may be more relevant than test scores
By connecting each measurement level to a clearly defined outcome, learning teams can tailor their approach and avoid unnecessary complexity.
Standardization Enables Scale and Efficiency
Another key insight is the value of operationalizing measurement through organizational standards. Christina explains that without a consistent approach, teams spend excessive time reinventing measurement strategies for each program.
At Accenture, this challenge was addressed by:
- Establishing a standard measurement framework
- Creating a “beginner’s guide” for accessibility
- Defining default methods for data collection based on program size
- Partnering with analytics teams to build dashboards for scalable insights
This structure reduces ambiguity and increases efficiency. It also helps stakeholders interpret results consistently, strengthening trust in the learning function.
The broader implication is that measurement maturity is not only about sophistication but also about repeatability and clarity.
Moving from Justification to Insight
Christina highlights a common pitfall in learning organizations: the tendency to use metrics to prove success rather than generate insight. This often results in selective reporting, inflated narratives, and an overreliance on “smile sheets.”
She contrasts this with a mindset focused on partnership and transparency. When outcomes are agreed upon upfront, learning teams can openly report whether those outcomes were achieved. If they are not, the conversation shifts from blame to problem-solving.
This approach changes stakeholder relationships. Instead of defending programs, learning leaders become collaborators who provide actionable insights into what is working and what is not.
The distinction between justification and insight is subtle but significant, influencing both credibility and strategic impact.
AI as an Augmentation Tool, Not a Replacement
Christina offers a clear and pragmatic perspective around AI. She acknowledges widespread apprehension but reframes AI as a tool whose impact depends on how it is used.
Her guiding principle is simple: if individuals ask AI to replace their work, it will. If they use it to augment their capabilities, it enhances their effectiveness.
In the context of learning development, she identifies a critical distinction:
- Tasks such as summarizing content can be automated easily
- Higher-value activities such as structuring learning, designing experiences, and creating meaningful scenarios require human judgment
By using AI to generate options while retaining responsibility for decision-making, learning professionals can expand their creative capacity rather than diminish their role.
Critical Thinking as a Differentiating Skill
Closely tied to AI adoption is the growing importance of critical thinking and discernment. Christina notes that polished, AI-generated content can appear credible while lacking substance.
The ability to evaluate quality, identify gaps, and refine outputs becomes essential. This requires genuine understanding of the subject matter, not just familiarity with tools.
She emphasizes that human insight, emotional awareness, and contextual understanding remain irreplaceable. These capabilities enable learning professionals to create experiences that resonate and drive behavior change.
Personalization as a Long-Awaited Opportunity
One of the most forward-looking themes is the potential of AI to enable personalized learning at scale. Christina points out that one-to-one coaching and tailored feedback have always been recognized as the most effective learning methods, but historically they were not scalable.
AI now makes this level of personalization more feasible. This creates an opportunity for learning leaders to rethink their role, moving beyond traditional course design to:
- Designing adaptive learning paths
- Building conversational learning tools
- Enabling individualized feedback experiences
This shift represents a significant evolution in how learning functions deliver value to the business.
Practical Takeaways for L&D and Business Leaders
Define Outcomes Before Designing Measurement
Leaders should ensure that every learning initiative begins with a clear articulation of business outcomes. Measurement strategies should be derived from these outcomes, not applied as an afterthought.
This approach improves alignment, clarifies expectations, and ensures that data collection efforts are purposeful and relevant.
Adopt a Standardized Measurement Framework
Organizations benefit from establishing a consistent measurement model and providing practical guidance for implementation. This reduces duplication of effort and enables comparability across programs.
Standardization also simplifies stakeholder communication, as results are presented in familiar formats.
Prioritize Insight Over Performance Narratives
Learning leaders should shift their focus from proving value to generating actionable insights. This involves:
- Reporting results transparently
- Identifying gaps and opportunities
- Engaging stakeholders in problem-solving discussions
By doing so, learning functions can strengthen their role as strategic partners rather than service providers.
Use AI to Enhance, Not Replace, Expertise
Professionals should approach AI as a collaborator. Effective use involves generating ideas, exploring alternatives, and accelerating tasks while retaining ownership of decisions and outcomes. This approach preserves the human elements of learning design while improving efficiency and creativity.
Invest in Critical Thinking and Content Expertise
As AI-generated content becomes more prevalent, the ability to evaluate and refine information becomes increasingly valuable. Leaders should prioritize developing these skills within their teams.
Deep understanding of content areas enables better decision-making and ensures that learning experiences remain meaningful and impactful.
Experiment with AI in Structured Ways
Christina suggests practical methods to reduce barriers to adoption, such as setting aside dedicated time for exploration. Treating AI experimentation as a routine activity allows professionals to build familiarity and confidence without immediate pressure for results.
Using AI as a thought partner, rather than a tool for specific outputs, can also uncover new applications and efficiencies.
Reimagine the Role of the Learning Professional
The emergence of AI presents an opportunity to redefine roles within the learning function. Instead of focusing solely on content creation, learning professionals can expand into areas such as:
- Learning experience design
- Personalized learning pathways
- AI-driven coaching and feedback systems
This evolution aligns more closely with long-standing goals of improving individual and organizational performance.
Learning Trends Shaping the Future
Optional Resources or Tools Mentioned
- Kirkpatrick Model: A traditional framework for evaluating training effectiveness
Closing Reflection
This conversation underscores a pivotal moment for the learning profession. The combination of outcome-driven measurement and AI-enabled capabilities offers a path toward greater strategic relevance. By focusing on business alignment, embracing transparency, and leveraging AI thoughtfully, learning leaders can move beyond proving value to delivering it in more targeted and meaningful ways.
Listen to the episode here.
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