AI Adoption Operationalization
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As automation and citizen development gained momentum, Investment Operations and Technology leadership knew there was opportunity to improve efficiency, but they lacked a holistic understanding of employees’ real needs, concerns, and readiness for change. The risk was building an automation strategy around assumptions instead of reality.
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For automation to succeed, the organization needed more than tools. It needed employee trust, leadership alignment, and a clear view of where citizen development could create meaningful value without increasing confusion or resistance. Leadership also needed confidence that the strategy reflected how teams actually worked.
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Identifying where the business was ready for stronger AI & automation support. Understanding the real jobs-to-be-done of employees and challenging leadership assumptions. Aligning business and technology stakeholders around a shared strategy for citizen development and automation. Translating interview insights into practical AI enablement tools that could support associates in their day-to-day work.
How I Approached It
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I conducted 1:1 interviews with leaders to uncover their fears, concerns, and assumptions about automation and citizen development. I focused the work on teams with the highest ambiguity and technical uncertainty, using pain-point frequency, intensity, and density to guide where to go deeper.
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I designed a prioritized interview backlog and interviewed multiple teams in Investment Operations. This research invalidated a critical assumption: employees were not afraid AI or automation would take their jobs. Instead, they wanted low-value work automated so they could focus on higher-value work.
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To help leaders act on the findings, I created empathy-driven artifacts, including personas and a critical user journey tied to new fund launches. I also piloted Timeular devices to gather qualitative, real-time activity data and reduce memory bias in how work was understood.
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I facilitated a two-part design thinking workshop with business and technology leaders. The first session used a pre-read and Socratic discussion to align on the problem space. The second focused on problem framing, solution generation, and assumptions mapping so leadership could move from opinion to action.
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To make the work more actionable for associates, I helped enable the team with customized AI chatbots trained on interview insights, workflows, and the realities of how different teams worked. Rather than treating AI as a generic tool, I shaped these bots to reflect the specific context of the associates’ day-to-day responsibilities so they could act as thought partners in the flow of work.
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Because the AI tools were grounded in real workflow knowledge, associates could use them to reduce time spent on lower-value or repetitive tasks and redirect more energy toward higher-value work, judgment, and problem-solving. This made AI adoption feel practical and relevant, rather than abstract or imposed.
Outcomes
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Through direct employee research, I surfaced a critical insight: employees were not primarily afraid automation would take their jobs. They wanted repetitive, low-value work automated so they could focus on higher-value work. This shifted the conversation from fear-based assumption to evidence-based strategy and gave leadership a stronger foundation for decision-making.
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I helped business and technology leaders build shared understanding of the problem space, the employee realities behind it, and the most credible path forward. That alignment made it easier to move from fragmented opinions to a roadmap leaders could support across functions.
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The work contributed to a measurable reduction in operational defects tied to new fund launches. This demonstrated that human-centered research and better alignment were not just useful for culture or communication - they translated into meaningful business and workflow improvement.
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With stronger clarity, leadership support, and employee buy-in, citizen development expanded across every major Operations department in less than nine months. This showed that the organization was not just experimenting with automation - it was building a scalable internal capability.
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By translating interview insights into customized AI chatbots trained on team workflows and work patterns, I helped make AI more usable and relevant in day-to-day operations. These tools acted as thought partners for associates, helping them offload lower-value work and focus more on higher-value contribution.