A GILD Research Report
This report was independently researched and produced by GILD ahead of the GILD AI and the Future of Internal Productivity event.
The research surfaces key patterns, decisions, and tradeoffs related to AI and the Future of Internal Productivity and is provided to attendees after the event to support continued reflection and application beyond the live discussion.
It is shared with attendees as a post-event resource to extend the value of the session and support continued application inside their organizations.
The GILD AI and the Future of Internal Productivity event was underwritten by Tecla.
GILD independently researched, authored, and produced this report and retained full editorial control, analysis, and conclusions.
This research report is published after each GILD event as an additional value-add for attendees and registrants. It is designed to help CEOs, founders, and COOs move faster inside their organizations on the topic discussed.
The core conversation stays private by design. This document stands on its own as a research-backed, operator-grade asset that leaders can confidently share internally.
All insights are synthesized from external research (late 2025–early 2026) and translated into practical guidance.
AI has entered a new phase inside modern technology companies. Adoption is no longer optional, experimentation is no longer impressive, and leadership teams are increasingly judged on whether AI investments translate into real productivity gains.
By late 2025, AI is embedded across most organizations, yet only a small percentage have achieved meaningful operational or financial impact. The gap between adoption and value is driven not by technology limitations, but by workflow design, operating models, and leadership decisions.
This report examines:
AI usage inside technology companies has reached near-saturation. Most organizations now use AI in at least one internal function, and investment levels have grown at historic speed.
What has not kept pace is enterprise-wide productivity impact.
Despite this surge, value capture remains limited.
The gap between AI adoption and value capture defines the current enterprise landscape.
Companies using AI in at least one function
Companies achieving EBIT impact from AI
The AI productivity conversation has shifted from tools to systems.
AI initiatives are now expected to:
Leaders are increasingly evaluated on whether AI changes how work gets done, not whether tools exist.
Many organizations experience delayed gains due to unchanged workflows and operating models.
Organizations move through pilot excitement, reality dip, workflow redesign, and finally scaled gains.
Y-axis: Productivity Impact
X-axis: Phase
AI agents are increasingly used for multi-step internal tasks, particularly in engineering, IT, and operations.
Automation has entered a new phase where AI is no longer limited to "assistive" outputs like drafts and summaries; it is now used to execute multi-step work and make operational decisions. The old "automate first, patch later" approach is breaking because modern automation is probabilistic, context-sensitive, and often opaque. Leaders are reporting measurable operational consequences, often tied to inaccuracy, while boards acknowledge that governance maturity is trailing deployment velocity.
February 24, 2026
The research examines how leaders are evaluating, measuring, and scaling AI investments to drive measurable business outcomes, and is provided to attendees after the event to support continued application beyond the live discussion.
February 11, 2026
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