Benchmarking the Business Case: Sizing Up Knowledge Management Investments.

About the Client

Company W is a global management consulting firm.

Industry Context

Professional services firms’ investment in knowledge and AI infrastructure has scaled dramatically across the industry since 2016. Company W’s own research finds that CEOs plan to double their AI spending in 2026, and leading consulting firms now treat knowledge and AI infrastructure as core, revenue-generating capabilities rather than back-office support functions. That kind of large, fast-scaling investment decision benefits most from rigorous peer benchmarking — understanding how much comparable firms actually spend, on what, and with what staffing model, before committing further capital. The disciplined, evidence-based approach to KM investment decisions that Iknow’s 2016 benchmarking study supported reflects the same data-driven investment posture that has continued to guide Company W’s scaling of knowledge and AI infrastructure, resulting in a business now generating billions of dollars in annualized revenue.

Current Situation

Having already built substantial internal KM capability, Company W’s program leadership needed external comparative data that internal metrics alone could not provide — specifically, how Company W’s KM investment measured up against other leading professional services organizations — to inform future budgeting and investment decisions and to support conversations with senior stakeholders.

• No external benchmark existed. Company W had no reliable way to know whether its KM staffing, offshore support, and technology investments were aligned with those of peer organizations.
• Highly sensitive data. Internal cost and staffing figures were competitively significant, meaning peer firms would only participate under strict confidentiality.
• Genuinely different KM programs. KM costs and program scope varied significantly across firms by services offered, organizational size, and geography, making raw, unadjusted comparisons misleading.
• Difficult-to-capture personnel costs. The majority of KM spending was personnel-based, spread across formal and informal roles, including part-time or unbudgeted roles embedded within business groups rather than a centralized KM budget.
• Recruiting a credible peer set. The study needed roughly 15 large, global professional services firms willing to share competitively sensitive information for a comparative benchmarking exercise.

• Benchmark knowledge management costs across Company W and its close peers in management consulting and other leading professional services organizations.
• Capture and normalize cost data by spend type, service type, organizational size, and geography.
• Develop practical, comparable cost metrics usable for Company W’s future investment decisions.
• Identify key insights and leading practices from peer firms worth further assessment by Company W.
• Deliver a sanitized, transferable cost model Company W could apply on an ongoing basis.

How Iknow Structured the Work

Iknow conducted the study in three phases — preparation, data collection, and analysis and reporting — with confidentiality-protecting data handling built into every stage, given the competitively sensitive nature of the information involved.

Key Activities & Decisions

• Preparation. Iknow built and tested the survey and data collection approach and secured participation from roughly 15 large global professional services organizations, each of which was required to have a defined KM capability and program leadership in place.
• Data Collection. Each participating firm’s head of KM or another designated senior contact completed an online survey, followed by a telephone interview with Iknow to clarify and deepen the data.
• Data Normalization & Analysis. Iknow normalized the raw data to account for differences in KM program scope and services, organizational size, geography, and firm strategy, and built practical cost metrics usable for cross-firm comparison. Because the majority of KM costs were personnel-related, Iknow focused on capturing clean, normalized headcount and FTE data, along with staff locations, and ensured the process captured all relevant roles, including part-time or unbudgeted roles embedded in business groups. Technology enablement costs were captured by major KM platform category, distinguishing new investment from ongoing spend, and cost data was mapped against services delivered and key program activities to compare spending by major activity.
• Reporting & Wrap-up. Iknow completed the full data analysis, delivered a comprehensive project report, and transitioned a sanitized version of the benchmarking model to Company W for its own continued use.
• Confidentiality Protocol. Raw participant data was never shared among the firms; each participating organization received a summary report in exchange for its participation.

Stakeholders & Collaboration

Iknow worked directly with Company W’s KM program leadership to define the study’s scope and worked independently with the heads of KM, or senior designated contacts, at roughly 15 participating peer firms — serving as the neutral third party trusted to collect, normalize, and analyze competitively sensitive data on everyone’s behalf.

Securing Participation on a Sensitive Topic

Recruiting roughly 15 competing and peer firms to share detailed cost and staffing data required a high level of trust in the process. Iknow addressed this with a strict confidentiality protocol — never sharing raw participant data among firms — and by offering each participant a summary report as a direct incentive to take part, with Iknow itself serving as the independent party that holds and analyzes the underlying data.

Making Fundamentally Different Programs Comparable

KM programs across firms differed enough in scope, services, size, and geography that a raw cost comparison would have been misleading. Iknow addressed this with a systematic normalization methodology, adjusting for these differences before producing any cross-firm cost metric.

Capturing True Personnel Costs

Because KM staffing was often distributed informally across business groups rather than centralized and fully budgeted, a simple headcount count risked significantly understating true personnel investment. Iknow addressed this by building an explicit process step to capture part-time and unbudgeted KM-related roles, ensuring the resulting personnel and FTE data was both clean and complete.

Operational Outcomes

• Delivered practical, comparable KM cost metrics across roughly 15 leading professional services organizations, spanning personnel, technology, and other program costs.
• Delivered key insights and leading practices from peer firms for Company W’s further assessment.
• Delivered a full project report and a sanitized, transferable benchmarking model handed off to Company W for ongoing use.

Strategic and Organizational Outcomes

Company W used the benchmarking results directly to help optimize its own investments in knowledge management — informing specific organizational, budgeting, and investment decisions, giving Company W’s leadership external, credible data to bring into stakeholder conversations, and equipping the KM program to answer specific spending questions with real comparative evidence rather than internal assumptions alone.

Timeline to Impact

Iknow completed the preparation, data collection, and full analysis and reporting phases within the three-month engagement, delivering both a completed benchmarking study and a reusable model Company W could continue to apply going forward.

Core Skills

• Knowledge management benchmarking
• Market & competitive research
• Cost & investment analysis
• Confidential multi-party data studies

Methods & Frameworks

• Survey design and multi-firm data collection
• Data normalization methodology
• Cost-metric development
• Confidentiality-protected benchmarking protocol