Board AI Governance
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.
Delivering cost-effective AI with both velocity and vigilance
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.
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
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.
