Explain how Gartner is using AI to drive both revenue growth and operational efficiency across its business.

Gartner is leveraging Artificial Intelligence (AI) as a dual driver for its business: serving as the primary topic of client demand to fuel revenue growth, and as a core tool to transform internal operational efficiency.

1. AI-Driven Revenue Growth

Gartner’s revenue growth is being catalyzed by unprecedented demand for AI-related expertise and improved client engagement through new tools.

• Massive Demand and Pipeline: AI was the single largest demand topic in 2025 across all enterprise roles. Roughly 40% of Gartner's content and client interactions in 2025 were AI-related. This has driven a double-digit increase in the new business pipeline.

• Retention via AskGartner: To improve user experience, Gartner launched AskGartner, a GenAI tool that summarizes proprietary insights for licensed users.

• Rollout: After two years of development, it was rolled out to 100% of licensed users by October 2025.

• Impact: Early data shows that clients using AskGartner spend more time on the platform and have notably higher retention rates than those who do not.

• No "AI Module" Pricing: Gartner does not sell AI research as a separate module; it is included in the base license, reinforcing the value of the subscription as client priorities shift.

2. Operational Efficiency and the "75% Reduction"

Gartner has implemented over 50 internal AI applications to improve associate productivity.

• The 75% Publishing Time Reduction: Management reported a 75% reduction in average publishing time compared to 2024.

• Meaning: This refers to the speed at which a piece of research moves from an analyst's insight to a published document available to clients.

• Impacted Processes: Analysts use advanced proprietary AI tools to assist in content production. A neural network-based system also systematically determines the "trending" topics of greatest interest to clients in real-time, ensuring analysts write about the most impactful subjects.

• Translating Gains: These efficiency gains have translated into a 31% year-over-year increase in content published per analyst. Faster creation allows Gartner to respond to rapid market changes (like AI breakthroughs or tariff updates) much more quickly.

• Internal Workflows:

• Sales Productivity: AI tools are used to hone sales skills and help representatives better articulate Gartner's value proposition.

• Analyst Scheduling: AI and natural language processing (NLP) automate the complex task of matching 500,000 annual client requests with the right analyst in the correct time zone and language.

• Information Gathering: AI now synthesizes publicly available information, a task previously performed by teams of humans.

3. Broader AI Strategy and the Gartner Moat

Gartner’s long-term strategy focuses on using AI to augment, rather than replace, its human experts while expanding its competitive moat.

• The Moat vs. LLMs: Management argues that public Large Language Models (LLMs) do not threaten their core business because LLMs lack access to Gartner’s several hundred terabytes of proprietary data, including:

• 500,000 one-on-one client conversations annually.

• 27,000+ technology vendor briefings.

• 3 million+ technology reviews.

• Industry-leading proprietary benchmark databases (IT Key Metrics).

• Mission-Critical Journeys: Management views the business as helping clients with multi-year journeys (e.g., cybersecurity strategy, AI ROI) rather than just answering discrete questions.

• Human Analysts vs. AI: Over the long term, Gartner sees analysts as the foundation of its Insights. AI tools like AskGartner allow clients to "self-serve" the basics, which leads to deeper, higher-value follow-up discussions with human analysts. Management reports that the number of clients considering AI as a substitute for Gartner is "extremely small".