Why AI matters at this scale

Slalom is a global consulting firm with over 13,000 employees, focused on strategy, technology, and business transformation. It partners with leading cloud providers like AWS, Google Cloud, and Microsoft to deliver custom solutions across industries. At this scale—operating in dozens of markets with thousands of concurrent client projects—AI is not a luxury but a necessity for maintaining competitive advantage, operational efficiency, and innovation velocity. For a people-centric business like consulting, AI augments human expertise, allowing consultants to focus on high-value strategy and relationship-building by automating routine tasks. Furthermore, Slalom's clients increasingly demand AI-integrated solutions, making internal mastery a prerequisite for credible advisory services.

Concrete AI Opportunities with ROI Framing

1. Automating the Sales & Proposal Lifecycle

Developing an internal generative AI platform to draft proposals, statements of work, and project plans can drastically reduce non-billable hours spent on business development. By analyzing historical RFPs and successful proposals, an AI engine can generate first drafts tailored to specific client industries and requirements. The ROI is clear: reducing the sales cycle by even 15-20% directly increases revenue capacity and allows senior talent to engage in more strategic pursuits. Initial development costs would be offset within quarters by improved win rates and consultant utilization.

2. Enhancing Delivery with Knowledge Management AI

Slalom's vast repository of past project artifacts, methodologies, and code is an underutilized asset. An AI-powered knowledge graph can connect consultants to relevant prior work, best practices, and subject matter experts in real-time. This reduces redundant research, accelerates onboarding, and ensures solution quality. The impact is measured in reduced project ramp-up time and increased deliverable consistency, leading to higher client satisfaction and potentially higher margin retention.

3. AI-Augmented Custom Software Development

As a firm that builds custom applications for clients, integrating AI coding assistants (like GitHub Copilot) into its development standards can boost developer productivity by 20-30%. This translates to faster time-to-market for client solutions and the ability to handle more projects with the same resource base. Additionally, AI can be used for automated testing and code review, reducing bugs and technical debt, which lowers long-term support costs and improves profitability.

Deployment Risks Specific to a 10,000+ Employee Organization

Rolling out AI at Slalom's size presents unique challenges. First, change management is critical; consultants may perceive AI as a threat to their expertise or resist new workflows. A clear communication strategy positioning AI as a co-pilot is essential. Second, data governance becomes complex across numerous client engagements and internal systems; establishing robust data access, security, and ethical use policies is paramount to maintain trust. Third, integration fatigue is a risk; AI tools must seamlessly fit into existing workflows (e.g., Microsoft Teams, Salesforce) to avoid productivity loss. Finally, skill gaps need addressing; widespread training programs are required to elevate the entire organization's AI literacy, not just a specialized team. Successful deployment requires executive sponsorship, phased pilots, and continuous feedback loops to adapt tools to real user needs.