case study

AI Accelerates Safety Alerts and Automates Estimation Workflows

A leading energy and communications infrastructure provider sought to drive internal efficiencies and enhance workplace safety using AI.

With crews regularly performing hazardous work, the company needed a partner to identify practical use cases, stand up impactful solutions, and deploy AI to ensure safety and efficiency.

AI Solution(s)

Strategy

Industry

Energy

Delivering cost-effective AI with both velocity and vigilance

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Vision‍

Establish a scalable, responsible foundation for AI adoption across a complex, distributed enterprise.

A leading energy and communications infrastructure provider sought to drive internal efficiencies and enhance workplace safety using AI. With crews regularly performing hazardous work, the company needed a partner to identify practical use cases, stand up impactful solutions, and deploy AI to ensure safety and efficiency.

Through a rigorous discovery phase, KUNGFU.AI and the client aligned on a high-value opportunity: automating the creation of daily safety alerts to reduce manual workload, increase accuracy, and improve on-the-ground safety outcomes.

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Challenge‍

From scattered ideas to strategic action.

Like many enterprises, this company faced a long list of AI possibilities but lacked a structured approach to prioritize, validate, and implement them. With limited in-house AI solution architecture expertise and a distributed operating model, they needed help to:

Vet and refine use case ideas
Rapidly prototype high-potential concepts
Avoid costly missteps in build vs. buy decisions
Stand up reusable governance frameworks and architectural patterns

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Breakthrough‍

From backlog to build.

KUNGFU.AI embedded deeply with stakeholders—interviewing domain experts, mapping opportunities, and developing a centralized AI backlog prioritized by feasibility and ROI.

From there, we:

Built a generative AI solution to automate the generation of safety alerts based on daily field activities. The system uses a Retrieval-Augmented Generation (RAG) pipeline to access the client’s comprehensive safety protocols and guidelines, ensuring alerts are accurate, relevant, and tailored to specific job tasks.
Integrated the solution into the client’s Microsoft Copilot deployment, enabling safety teams to generate alerts within familiar tools while selecting the document sets relevant to each team’s responsibilities.
Prototyped and validated additional use cases in safety and estimation, including AI- and RPA-driven automation for cost estimation workflows.
Developed reusable Azure architectural patterns to support scalable AI deployments and integrations across business units.
Provided vendor selection guidance to redirect efforts away from low-fit solutions and maximize internal development resources.
Delivered cross-functional training and governance artifacts—including Responsible AI principles and solution lifecycle documentation—to enable responsible scaling.

Outcome‍

A confident, cost-effective start to enterprise AI.

While many solutions are still being scaled, early progress has delivered measurable returns and positioned the organization to scale safely and effectively:

$300K–$2M in estimated cost avoidance from deprioritizing low-value initiatives
$100K–$500K in saved internal development time by redirecting a custom build to a vendor application
120–250 hours/year saved per operating unit expected from the automated safety alert solution
$25K–$125K/user in estimated annual savings per operating unit from automation in estimation workflows
Established AI governance and architecture playbooks to accelerate future builds and reduce implementation risk

With reusable frameworks, embedded safety automation, and a clear path to scale, this Fortune 500 company is now equipped to deliver AI with both velocity and vigilance.

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