Scaling Business Impact & EQ with AI Strategy @ AWS
Transforming a service-based EQ training business model into a scalable AI-driven product, solving large-scale client engagement, impact measurement, and revenue challenges.
BACKGROUND
At Amazon Web Services (AWS), an internal department was focused on developing emotional intelligence (EQ) within Amazon and its large enterprise client network, with a business mandate to onboard more client operations to AWS. Consulting with renowned I/O psychologist Daniel Goleman, their theory was that stronger organizational EQ leads to better communication, which fuels innovation. With more innovative clients, the department anticipated that more client operations and services would be onboarded onto the AWS network.
To do this, department leaders traveled globally, delivering in-person EQ training to AWS clients, but continually faced three major challenges:
✘ Limited Engagement
They could only reach and train a handful of clients at a time.
✘ Poor Measurement
They relied almost entirely on client surveys to measure impact & success.
After a few years running this playbook, AWS leaders questioned the sustainability and effectiveness of this model, and internally, the department began to question their long-term viability.
ACTION
Collaborating with an AI development firm and leveraging contemporary psychological research and practices, we conceived and designed a proprietary AI platform (Beta), operating on AWS, aimed at redefining EQ development at scale.
Through interactive journaling and guided self-reflection, the AI offered leaders and employees a secure, intensely personalized space to develop the emotional understanding of their internal systems — subconscious biases, blindspots, triggers, roles, and behavioral patterns.
As users developed greater emotional awareness, the AI helped to identify and map how each of their subconscious patterns intertwined to shape their individual life, particularly with respect to colleague relationships and workplace dynamics. Insights were framed using a simple and intuitive language users could naturally adopt and immediately apply in their daily interactions. Building off this momentum, the AI coached users in how to communicate, relate, and work more intentionally and emotionally grounded with others.
✘ No Onboarding
They had no direct link of translating EQ training to increased AWS adoption.
RESULTS
Through the AI platform, we transformed AWS’ EQ development model from an expensive, one-time, high-touch training to a scalable, data-driven product that promoted ongoing behavioral change across organizations.
While emotional makeup of each user remained uniquely their own, the platform was adept at standardizing a plain and shared language. This provided teams with commonalities in navigating emotions, supporting peers, and building trust and emotional resilience in their relationships, cultivating better constructive communication as a group to drive innovative thinking.
For AWS, the product offered an opportunity to:
✔ Increase Engagement
Equipping AWS with a platform to continue to educate and engage clients past one-time trainings.
✔ Measure Impact
Replacing unreliable survey data, with hard quantifiable engagement data.
✔ Supercharge Onboarding
Providing a tangible, scalable means to onboard more client operations to AWS via a new AWS product.
LESSONS
EQ Training is Costly, Inconsistent, and Hard to Scale → Companies already invest millions in coaching and well-being services, often spending $2K–$7K per employee per year to enhance leadership skills, innovation, and team performance. While valuable, coaching varies widely — training methods are inconsistent, insights aren’t standardized or easily shared, and measuring impact with hard, quantifiable data is impossible. These challenges make is hard to build, iterate, and scale a unified culture across large workforces. By shifting from high-touch, service-based EQ learning to a scalable, AI-driven product, organizations can maintain the depth of personal development while ensuring consistency, accessibility, and measurable business impact, at significantly reduced costs.
AI as a New Alternative → Beyond coaching, organizations have historically relied on one-time workshops and silo’d certificate programs and courses to develop EQ. These are adept at inspiring temporarily, but rarely drive lasting behavioral change, particularly across a large workforce. AI introduces continuous, interactive learning, enabling organizations to develop EQ at scale through recurring, deeply personal touchpoints that reinforce learning over time, adaptive, dynamic feedback that tailors insights to each user’s unique emotional landscape, and anonymized real-time data insights that move beyond self-reported survey results.
Personalized Learning with Standardized Results → EQ is deeply personal; every individual operates within their own unique emotional patterns, biases, and experiences. At the same time, there is huge benefit in standardizing aspects of EQ, particularly with respect to language and methods, to improve communication, alignment, and culture across an organization. AI offers a means to bridge this gap, personalizing EQ development at an individual level while standardizing a common framework and language for teams to communicate emotions effectively. The result — colleagues understand each other more clearly, emotions are internalized less often, and mutual trust can be developed more quickly, thereby fostering a collective culture where constructive discussions can be had without damaging relationships.
— this project is still actively being refined and expanded within AWS.