AI Leaders Build Harnesses to Turn Employees into Power Users
Top companies capture 75% of AI gains by using it for growth and reinvention, not efficiency—building internal systems like RAMP's Glass that connect tools, share skills via a 350+ marketplace, and provide persistent memory, making every employee effective on day one.
AI Leaders Drive Growth, Not Just Efficiency
Leading companies secure 75% of AI's economic gains by treating it as a growth technology, being 2-3x more likely to pursue new opportunities and 2.6x more likely to reinvent business models. McKinsey's analysis of 20 AI leaders across industries shows AI transformations deliver 20% EBITDA uplift, break-even in 1-2 years, and $3 incremental EBITDA per $1 invested—far beyond productivity tweaks. They target economic leverage points like supply chain integration (e.g., Toyota's AI breakthroughs in automotive) and build enduring systems around AI, not just deploy tools. Data is the key constraint, requiring ongoing enrichment as an operational discipline. Senior business leaders must gain AI expertise, combining domain knowledge with tech skills, while keeping 70%+ of AI talent in-house for people-led transformations. Agentic engineering—ingesting unstructured data, adding agents, automating guardrails, and codifying playbooks—is the next frontier, with speed as the core advantage amid shrinking skill half-lives.
Institutional AI Requires Coordination Beyond Individuals
Individual AI boosts personal productivity 10x, but no company has seen 10x value without institutional AI processes that align efforts. George Sulka's A16Z piece outlines seven pillars, starting with coordination: without defined roles, OKRs, and communication for humans and agents, AI creates chaos—like employees with mismatched prompting styles generating incompatible outputs. Institutional AI filters noise from exploding content, enforces professional objectivity over personal alignment, and scales revenue, not just saves time. PWC echoes this: top 20% firms don't just use AI better; they use it differently for structural change.
RAMP's Glass: Raise the Floor with Shared Harnesses
RAMP built Glass, an internal AI workspace, because models are ready but setups are painful—only 9% of employees used AI daily until day-one access with SSO integrations, persistent memory from tools like Slack/Notion/Linear, and scheduled automations (daily/weekly/cron, even offline). Three principles: (1) Preserve full power-user upside (multi-window workflows, deep integrations) rather than dumbing down; AI tutors handle complexity. (2) Share breakthroughs via Dojo marketplace (350+ reusable skills, e.g., Zendesk workflow pulling tickets/account health for resolutions). (3) Product enables learning—Sensei AI recommends top 5 skills by role/tools/history; memory synthesizes sessions daily. Why build in-house? It's a moat for productivity, enables same-day fixes via Slack triage, and informs external products. Results: New hires know teams/projects/tools instantly; non-engineers run automations once needing 6-month engineers. Learn by doing—skills/memory show 'what good looks like,' accelerating mastery faster than workshops. This organizational harness engineering compounds advantages, evolving agentic work into everyone's capability.