Software & SaaS

Asimov AI requires client team leaders to complete intensive induction sessions on the nature and causes of algorithmic hallucinations in the legal domain before system use.

Palantir provides in-depth AI literacy resources for engineers using AI daily and customised resources for employees in customer-facing configuration of AI systems.

Palantir is rolling out an internal AI Literacy Hub for all staff, with resources aligned to roles covering AI foundations, risks, internal guidelines, policies and legal aspects.

Collibra trains technical teams on effective deployment of AI tools and engineers on how to stress test and monitor Collibra's generative AI products.

Collibra provides all employees with basic training on the value and risks of workplace AI, plus an AI hub explaining approved tools and risk mitigation.

Alteryx provides four live, role-based training courses per year to product and product engineering teams, plus product-development and governance training.

All employees with access to internal models must complete basic trainings and pass an assessment. Alteryx also provides on-demand AI training modules to all staff.

OneOrigin posting verbatim: excited about using AI tools, automation, and data in day-to-day recruiting.

OneOrigin's live Technical Recruiter posting frames the role as expecting comfort with AI, automation and data.

Figma State of the Designer 2026: 72% of designers use GenAI, 89% report being faster, 91% report better designs, +25% job satisfaction.

Aaron Levie's AI-first post (1 May 2025) laid out a company-wide expectation that every Box team run AI-first.

Krishna's May 2023 Bloomberg interview estimated ~30% of IBM's ~26,000 back-office roles could be replaced by AI over 5 years. IBM has publicly denied a blanket hiring pause.

Salesforce cut ~4,000 support roles in September 2025, explicitly citing Agentforce automation. Public evidence of an AI-driven operating-model shift.

Agentforce handles ~50% of Salesforce customer-support conversations. Applied AI is embedded in day-to-day service workflows.

Salesforce internally uses Agentforce for support and sales automation. Benioff has publicly stated AI now performs a substantial share of internal work.

Replaced a weekly long-form update with a live dashboard and short AI brief; freed capacity for forward-looking work. Redefined responsibilities and trained teams.

Connected calendar, CRM, email/Slack and note tools so one system produces briefings, flags conflicts and routes follow-ups; others reuse it.

Every week AI aggregates calendar, attendee context and prior threads into a fixed brief; they iterate prompts and spot hallucinations against source material and can cite time saved.

Uses AI to tighten emails or summarise meetings; priority routing and judgment work are unchanged and there is no repeatable workflow or measurement.

Enables teams across Zapier to independently ingest and use data through AI-powered systems with guardrails. Replaces manual work streams with durable AI systems.

Builds repeatable AI workflows that significantly reduce manual work in analysis, reporting or data development. Integrates AI into shared processes so work is more scalable.

Uses AI consistently across core workflows (analysis, modelling, engineering, experimentation), improving speed and quality while maintaining rigour and reliability.

Uses AI only for one-off tasks, not as part of regular workflows. Does not integrate AI into how work is planned, executed or delivered.

Shifts from backward-looking reporting to forward-looking AI models. Rebuilt tax ops around AI. Redesigned the close process with AI; cut close time materially.

Automates scenario modelling, reporting and insight generation. Built workflows for tax memo drafting and quarterly review. Automated reconciliations and journal entry exceptions.

Uses AI for forecasting, variance analysis and reporting, tax research and provision prep, close checklists and journal entry review. Builds repeatable workflows across FP&A, tax and accounting.

Uses AI for basic support tasks: summarising reports or explaining concepts. Tries AI for analysis but doesn't rely on it.

Rebuilt how the ecosystem function operates. How partners are sourced, tiered, enabled and measured is AI-native. Reallocated capacity to strategic partnership development.

Connected CRM, partner data and AI into one system. Partner tiering, outreach and performance tracking run without manual pulls. Built an enablement workflow the whole team uses.

Uses AI across partner workflows every week: research, outreach, co-sell prep and enablement materials. Built a repeatable workflow for partner QBR prep.

Uses AI occasionally for partner comms and to draft partner emails or materials. Tries AI for research but not embedded in workflow.

Rebuilt how corp dev works: sourcing, screening, diligence and memo generation are AI-first. Team focuses on deal judgment and relationship work.

Connected deal sourcing, research tools and AI into one pipeline; cut time-to-IC-memo. Built a target screening workflow that scores inbound opportunities automatically.

Uses AI across deal evaluation every week: market mapping, comps, CIM synthesis and IC memo drafting. Built a repeatable diligence workflow across multiple deals.

Uses AI for basic research only, like to summarise companies or markets. Tries AI for diligence but doesn't rely on it.

Rebuilt planning, reporting and decision systems around AI. Stopped legacy static reporting entirely; reallocated capacity to forward-looking analysis.

Connected data sources, reporting tools and AI into one system. Insights surface without manual pulls and the team runs it. Built automated reporting workflows.

Uses AI across reporting, analysis and synthesis every week. Built repeatable workflows for recurring analyses, now embedded in how the work gets done.

Uses AI occasionally for note cleanup, summaries and light analysis, but doesn't rely on it. Cannot point to evidence that reporting is faster or decisions are better.

Redesigned core sales motions around AI-native workflows. An AI agent analyses account history and generates a renewal brief so reps start at strategy, not data gathering.

Chains multiple AI tools into connected workflows: AI-driven account research feeds a personalised outreach sequence and a deal channel with auto-generated qualification analysis.

Runs AI-powered tools daily across core sales workflows: account research, usage analysis, call prep. Built a repeatable pre-call research workflow iterated over time.

Uses AI-generated call summaries after meetings and to draft outreach emails. Can't describe how AI has changed their win rate, deal velocity or pipeline quality.

Rebuilt legal ops so that intake, triage and routing happen largely automatically. Attorneys work from exceptions, not the full queue.

Converted the enterprise negotiation playbook into an AI-assisted workflow other teams self-serve. Built an AI legal response and ticketing system.

Uses AI across every contract: redlining, risk flagging, drafting, negotiation prep. Built a reusable library and reviews are faster and more consistent.

Uses AI for background research before drafting but the core review process is unchanged. Cannot point to evidence that reviews are faster or higher quality.

Stopped running a legacy programme entirely and rebuilt the function around AI-first delivery. Redefined team roles around the new operating model.

Has orchestrated end-to-end automation of a core People process (onboarding, hiring pipeline, reporting) with measurable results.

Uses AI daily across multiple parts of their role with repeatable prompt templates they refine each cycle. Sets direction for their team's AI experimentation.

Manually does work AI could meaningfully assist with and hasn't tested whether AI would improve it. Actively blocks their team from experimenting with AI.

Built a personalization engine that serves AI-generated campaign variants at scale, tied directly to pipeline. Restructured how the marketing team works.

Has run AI-driven experiments with measurable results and now defaults to this approach across campaigns. Built a content system that drafts, formats and schedules posts across channels.

Uses AI regularly across content, SEO analysis and performance review. Built a reusable prompt library for top content formats that the team now pulls from.

Uses AI for first drafts only. Output reads like unedited AI; hasn't developed a process for improving quality or adapting tone.

Has changed what work their team does, not just how fast they do it. Categories of work either no longer exist or run without human involvement.

AI use has a clear before/after story that spans months, not tasks. Produces work that others on the team use: a tool, a template, a process.

Has repeatable prompts for core parts of their job. Feeds relevant context into AI before complex work rather than asking isolated questions.

Asks AI one-off questions to look something up, then goes back to doing work the same way. AI is a slightly faster Google, nothing more.

The PM/Design role on their squad looks materially different than six months ago. Can show examples of redesigning how product ships: abandoning the previous status quo and reinventing the process.

Can point to entirely new skill sets developed through AI: writing SQL, doing data analysis, building dashboards. Building systems, not one-off features: pipelines of agents that take in customer feedback, write specs, prototype solutions.

Has a structured approach for generating specs and prototypes that they reuse and refine across projects. Uses AI to tap into user insights previously inaccessible due to technical limitations or data scale.

Uses AI for simple tasks like summarizing, writing or looking up information, but output reads like obvious AI slop. Cannot point to clear evidence that work is faster or higher quality.

Re-engineers how software gets built so AI becomes part of the operating model, not just an individual productivity boost; code production, review, testing and delivery are meaningfully restructured around it.

AI fundamentally changes how they engineer: they default to AI-first approaches where appropriate and have built workflows, tooling or practices that improve output beyond just themselves.

Uses AI regularly across implementation, debugging, testing and documentation, with concrete examples of better quality, speed or leverage. Shows real tool and model literacy.

Uses AI as a lightweight assist inside a mostly unchanged workflow; helps with snippets, debugging or summarization but does not materially change how they design, build, test or ship.