About project

The Challenge

While repetitive projects offer efficiency opportunities, they also highlight a major operational challenge: accessing historical knowledge quickly and accurately. Employees previously had to manually search through multiple repositories, leading to:

• Time-consuming workflows
• Risk of inconsistencies in reports and project outputs
• Difficulty leveraging past project insights for decision-making
• Limited scalability in task automation

Without a robust AI-driven solution, McFarland Johnson risked slower project turnaround, higher operational costs, and potential errors in client deliverables. The company recognized that leveraging artificial intelligence could be transformative – but only if implemented securely and aligned with corporate privacy requirements.

Technology & Approach

To achieve the dual goals of robust data security and operational efficiency, we implemented a solution that addressed both knowledge infrastructure and process optimization:

Our Solution

Dedicatted implemented a comprehensive AI-powered knowledge management solution using Amazon Q, designed to transform McFarland Johnson’s access to project information and workflow efficiency. Key components of the solution included.

• Internal Virtual Assistant: Amazon Q was deployed as a secure, AI-driven assistant that provides instant, contextual answers based on historical project data.
• Centralized Knowledge Base: A dedicated repository was created to store all project-related documentation in a structured, searchable format. Authorized users can continuously add new documents, ensuring the system evolves over time.
• Automated Document Generation: Routine outputs, such as project reports, resumes, and client requests, are generated automatically, following predefined templates or replicating the structure of previous projects.
• Granular Access Controls: Two levels of permissions were implemented – subscription type (Pro vs Lite) and departmental access (Marketing, Divisions, or both) – ensuring information is available only to the right employees.
• Transparency & Trust: Every response includes active links to source documents, allowing employees to validate AI-generated insights and maintain accountability

Adoption has been robust throughout the laboratory. Managers highly commended the role-based controls, which ensured compliance while maintaining workflow efficiency. Additionally, the direct updating of the knowledge base has facilitated the system’s adaptation to evolving research priorities and regulatory standards.

Outcome

The deployment of Amazon Q delivered significant, measurable improvements across McFarland Johnson’s operations:

• Reliable AI Performance: Amazon Q consistently provides accurate, context-aware responses, enabling employees to make informed decisions quickly and confidently.
• Streamlined Workflows: Routine tasks, such as generating reports, resumes, and client requests, are now automated, freeing employees to focus on strategic, high-value work
• Access to the system is carefully managed through tailored controls, including subscription types (Pro vs. Lite) and departmental permissions, ensuring the right information reaches the right teams while maintaining data security.
• Adoption was further supported by two targeted workshops: one for non-technical end-users to maximize daily utility and another for IT staff responsible for managing and maintaining Amazon Q internally.

What began as a struggle to manage vast amounts of project data has become a story of operational excellence and AI-powered transformation. Amazon Q has turned manual, repetitive tasks into automated, reliable workflows, freeing teams to focus on strategic thinking and creative problem-solving. McFarland Johnson now stands at the forefront of intelligent knowledge management, ready to harness AI as a true competitive advantage for years to come.