Embedding AI-Enabled Engineering in a Healthcare Technology Organization

IN A NUTSHELL

Strategic Partner for AI Adoption

Practices Embedded Across Engineering Teams

High-Trust, Regulated Environment

Client Overview
The Business Challenge
Ciklum's Approach: A Consultative Partnership for Capability Building
What Made This Approach Different
The Results

01

Client Overview

The client is a healthcare technology company providing cloud-based platforms for pharmacy benefit management and advanced data analytics. Its modular solutions enable streamlined claims processing and deliver real-time insights for better decision-making. The company serves pharmacy benefit administrators, health plans, and health systems, while also powering a consumer savings platform that helps users access affordable prescription medications.

Scientist reviewing data on a tablet in a laboratory

500+

EMPLOYEES

United States

LOCATION

Healthcare

INDUSTRY

02

The Business Challenge

Healthcare technology operates in a high-trust, highly-regulated environment where engineering decisions carry significant weight. The client’s leadership recognized that AI-enabled engineering practices could accelerate their product development, but adoption posed real challenges. Their engineering teams were experienced and effective with established workflows. Introducing new AI-assisted tools and methodologies required more than technical implementation. It required building confidence, demonstrating value in context, and ensuring that new practices would take root sustainably rather than being abandoned after initial experimentation.

03

Ciklum's Approach: A Consultative Partnership for Capability Building

Ciklum became a strategic partner focused on capability transfer rather than simply delivering a product. We embedded with the client’s Product and Technology teams to introduce AI-enabled engineering practices through hands-on collaboration. Our approach prioritized working alongside their engineers, demonstrating value in real project contexts, and ensuring that new practices would be owned and sustained by internal teams long after the engagement evolved.

Before introducing new tools and methodologies, we partnered with Product and Technology leadership to establish shared context. We ran a structured Product Discovery process to identify features, functionalities, and user journeys. This wasn't just about defining what to build. It created a concrete environment where AI-enabled practices could be demonstrated and evaluated against real requirements. Engineering teams could see how AI tools performed on their actual challenges, not abstract examples.

We introduced AI-assisted tools for rapid prototyping, code generation, and validation. Rather than mandating adoption, we worked side-by-side with engineers to demonstrate practical applications. This included using AI tools to accelerate prototype development, validate solutions against requirements, and identify edge cases earlier in the development cycle. The focus was on showing, not telling, allowing engineers to evaluate the practices firsthand.

Healthcare technology requires careful consideration of compliance, data handling, and auditability. We worked with the client to establish appropriate guardrails for AI tool usage, ensuring that AI-assisted development practices aligned with their regulatory obligations. This included defining which tasks were appropriate for AI assistance, establishing review processes, and documenting practices for compliance purposes. The goal was adoption that the organization could defend and sustain.

The measure of success wasn't whether practices were used during the engagement. It was whether they would persist afterward. We focused on knowledge transfer through pairing, documentation of workflows, and gradual handoff of ownership to internal teams. We also worked with engineering leadership to identify champions within their organization who could advocate for and support continued adoption across other teams and projects.

04

What Made This Approach Different

Many AI adoption efforts fail because they focus on tools rather than people. We approached this as a change management challenge as much as a technical one. By embedding with the client’s teams, demonstrating value in their specific context, and building internal ownership, we helped ensure that AI-enabled practices would spread organically through the organization rather than requiring ongoing external support.