Year:
2026
Focus:
AI Design
For:
IBM
OVERVIEW
Redesigning enterprise AI to mitigate the mainframe knowledge extinction event
The mainframe powers the world's most critical infrastructure, supporting global commerce, banking, and healthcare. However, the senior system programmers (sysprogs) who maintain these high-stakes enterprise systems are retiring at an accelerating pace. This project focuses on designing human-centered AI integration strategies, aiming to compress a steep learning curve, capture vanishing institutional knowledge, and establish robust user trust frameworks within high-stakes engineering environments.
PROBLEM
Vast reference repositories that provide information without context
The retirement wave is triggering a massive knowledge extinction event. Senior engineers possess decades of tacit reasoning that is rarely written down, while junior replacements face a punishing multi-year onboarding process. While technical documentation is vast, it serves as a reference rather than a teacher; new learners struggle to interpret data in context and lack the confidence to act because they cannot safely predict the downstream consequences of their decisions on live production environments.
USER INTERVIEWS
Evaluating AI readiness and guardrails with global infrastructure operators
Primary research involved qualitative deep-dive interviews with 14 enterprise system programmers, engineers, and security specialists from internal teams, major financial and insurance institutions, and a management services provider. These sessions evaluated user AI readiness and mapped core expectations. The universal floor across all experience levels is a strict security boundary: AI must operate entirely under the user's existing authority level, generate clear uncertainty indicators, provide auditable logs, and require explicit human validation before executing any system adjustments.
COMPETITIVE ANALYSIS
Locating an unoccupied market opportunity in judgment transfer and learning
Evaluating the enterprise infrastructure market (including BMC, Broadcom, Instana, and OpenTelemetry) revealed that existing monitoring tools cluster heavily toward high automation and operational observability, focusing entirely on surfacing raw system states. Competitors like Hypercubic push toward compliance verification but lack educational depth. This left the high-value quadrant of judgment transfer and learning-focused tools completely open territory, positioning IBM to uniquely occupy the space by prioritizing why an option is recommended over others.
AFFINITY MAPPING
Synthesizing community sentiment with primary user behavioral insights
By synthesizing both secondary data (processing over 10,000 community posts across r/mainframe and r/sysprogs using machine learning methods like classification and sentiment analysis as well as Discord, and IBM Main) and primary insights from our 10+ stakeholder interviews, we grouped findings into the following core thematic clusters:
TARGET USER
Structuring system architecture to address distinct experience levels
The system architecture was structured to address three core user archetypes across the organizational spectrum: junior learners, mid-level operators, and senior experts.
I also considered the impact on the security side who represent governance and managed service perspectives as they are downstream users. They absorb the cost of every system programmer's decisions as incidents to triage, policies to re-explain, and audit trails to reconstruct.
OUTCOME
Delivering a validated strategic prototype for trust-based feature design
Through research that extended beyond the project's initial scope, I conducted and synthesized client interviews that directly shaped the direction of the team's agentic AI strategy. The resulting insights were shared with product leadership, senior technical stakeholders, and an IBM Fellow, helping align decision-making around validated user needs. This work culminated in a stakeholder-approved opportunity framework and prototype that connects prioritized AI capabilities to concrete business outcomes. The concept also established AI design principles emphasizing transparency, user control, and explainability, resulting in a strategic proposal for transforming tacit data into an experiential material layer that scales enterprise expertise responsibly.
Due to a NDA with IBM, all sensitive data, proprietary product features, and specific metrics have been anonymized or abstracted. I can only share a high-level overview of my work on this project publicly. However, if you are interested in learning more about what I worked on in more detail, please reach out to me privately!
