# 24G — The Knowledge Company for the AI Era > 24G captures institutional knowledge and transforms it into AI-ready capability for enterprises in automotive, manufacturing, and distributed workforces. ## Company Overview 24G is a knowledge infrastructure company based in Troy, Michigan. Founded in 2007, 24G helps enterprises capture what their best people know — the expertise trapped in heads, folders, and disconnected systems — and turn it into a governed foundation that both the workforce and AI can use. 24G evolved from a digital agency toward workforce enablement, addressing organizational knowledge barriers through the DRIVE Platform. Their clients span automotive, manufacturing, and distributed workforces — industries where institutional knowledge loss is measured in billions of dollars annually. Today, 24G is the knowledge infrastructure partner for enterprises serious about AI adoption that actually delivers ROI. ### Core Products **Knowledge Transformation Service (KTS)** KTS is a professional service engagement where 24G's knowledge engineers capture, structure, and govern institutional knowledge. Engagements run 8–16 weeks, starting with a 5-day Knowledge Architecture Sprint ($7,800). KTS produces an AI-ready content corpus, knowledge governance framework, and custom AI Expert and Coach agents. Full transformation engagements run up to $25K+. Deliverables include: SME knowledge capture, tribal knowledge documentation, AI-ready corpus development, custom AI Expert and Coach agents, training and communications assets, and a knowledge governance framework. **DRIVE Platform** DRIVE is 24G's AI Knowledge Operating System. It activates structured knowledge where work happens — unifying learning, communications, AI coaching, and performance support in one platform. Starting at $2,500/month with 3 months of direct support included. Features include AI-first Resource Library, role-based AI Experts and Coaches, learning paths and micro-training, targeted communications engine, engagement, gamification and rewards, unified analytics and measurement, and platform tiers that scale with the organization. DRIVE is not a traditional LMS — it combines structured learning, AI-powered coaching, targeted communications, and real-time performance support, all powered by the organization's own structured knowledge corpus. **AI Readiness Assessment** A free, 30-minute conversational evaluation of an organization's knowledge infrastructure. Produces an AI Readiness Scorecard, knowledge structure analysis, technology and data health review, operational maturity assessment, strategic alignment evaluation, quick-win recommendations, and recommended next steps. Available at https://futureready.24g.com/quickcheck ### Key Performance Metrics - 50% faster time-to-productivity - 40–60% less performance variance - Measurable ROI in 6 months - 1,000+ successful projects completed - 750% average ROI across engagements - 100,000+ employees enabled - Millions of pages of content produced ### Company Values - **Knowledge First** — Structured, governed knowledge is the foundation of every successful AI deployment. - **Measurable Outcomes** — Every engagement is tied to business results: faster onboarding, less performance variance, higher ROI on AI spend. - **Human + AI** — 24G captures what makes the best people great and makes that accessible to everyone. People are not replaced; their expertise is preserved and scaled. ### How It Works — Three Steps 1. **Assess** — Take the free AI Readiness Interview. Receive a scorecard and recommended entry path. 2. **Build** — KTS knowledge engineers capture, structure, and govern institutional knowledge. 3. **Activate** — DRIVE delivers knowledge where work happens — with AI Experts, learning paths, and analytics. --- ## Pages ### Homepage **URL:** https://24g.com/ The homepage introduces 24G as the Knowledge Transformation Company with the headline "Employees Leave; Their Knowledge Doesn't Have To." It positions the company's core value proposition: capturing expertise trapped in heads, folders, and disconnected systems and turning it into a governed foundation that both the workforce and AI can use. The page highlights four critical knowledge gaps that block AI adoption (AI investment failure, institutional knowledge loss, fragmented knowledge, shadow AI risks), introduces the two core offerings (KTS service and DRIVE platform), and features recent blog posts. Primary CTAs direct visitors to the free AI Readiness Assessment at https://futureready.24g.com/quickcheck. ### Solutions **URL:** https://24g.com/solutions The Solutions page details two complementary approaches to knowledge infrastructure: KTS and DRIVE. KTS (labeled "Service / 01") covers how 24G captures expertise trapped in team members' heads, scattered documents, and informal workarounds, transforming it into a structured foundation for both the team and AI. The page lists five outcomes KTS delivers, displays pricing ($7,800 starter, up to $25K+ full transformation), and breaks down the four deliverables: Knowledge Architecture Sprint, AI-Ready Content Corpus, Knowledge Governance Framework, and Custom AI Agents. DRIVE (labeled "Platform / 02") is described as the Knowledge Operating System that delivers knowledge in the workflow, with seven platform features listed and performance stats (50% faster time-to-productivity, 40–60% less performance variance). The page also includes a pricing teaser, FAQ accordion, and a CTA for the free AI Readiness Assessment. **FAQ covered on this page:** - What is Knowledge Transformation? KTS captures expertise trapped in heads, documents, and informal workarounds and transforms it into structured, AI-ready knowledge infrastructure. - How long does a KTS engagement take? Most engagements run 8–16 weeks. The Knowledge Architecture Sprint ($7,800) takes 5 days. - What's the difference between KTS and DRIVE? KTS is the service that captures and structures knowledge. DRIVE is the platform that activates it. - Do we need KTS before DRIVE? Not necessarily. Organizations with well-structured existing knowledge can start with DRIVE directly. ### Pricing **URL:** https://24g.com/pricing The Pricing page presents three tiers side by side: the free AI Readiness Assessment, KTS (from $7,800), and the DRIVE Platform (from $2,500/month). Each tier lists its features in detail. The page includes a "How it works" section describing the three-step Assess → Build → Activate process, a FAQ accordion covering six questions (AI Readiness Assessment, KTS timelines, DRIVE platform contents, starting with the assessment, industry focus, and how DRIVE differs from a traditional LMS), and CTAs for the free assessment. **Pricing summary:** - AI Readiness Assessment: $0 (free, 30-minute conversational evaluation) - Knowledge Transformation Services (KTS): from $7,800 starter - DRIVE Platform: from $2,500/month (3 months of direct support included) ### About **URL:** https://24g.com/about The About page opens with the mission statement: "Our Mission Is to Turn Knowledge into Business Performance." It presents four company metrics (1,000+ successful projects, 750% average ROI, 100,000+ employees enabled, millions of pages of content produced). The "Who We Are" section describes 24G's evolution from a digital agency to a knowledge infrastructure company. The "Our Story" section explains how building DRIVE to solve the knowledge problem for their own clients led to the full Knowledge Operating System. Three company values are articulated (Knowledge First, Measurable Outcomes, Human + AI). The page features the full leadership team with photos and LinkedIn links. ### Contact **URL:** https://24g.com/contact The Contact page presents a form with fields for name, email, and message for beginning a knowledge transformation engagement. The page headline is "Start Your Knowledge Transformation Journey Today." Physical address is listed: 400 W Maple Rd, Troy, MI 48084. ### Blog **URL:** https://24g.com/blog The Blog page lists all published articles covering knowledge management, AI readiness, and workforce transformation topics. Articles are displayed in a grid with thumbnail images, publication dates, titles, and descriptions. The page CTA directs readers to the free AI Readiness Assessment. --- ## Blog Posts ### AI-Ready Content Corpus: The Complete Guide to Structuring Knowledge for AI Agents **URL:** https://24g.com/blog/ai-ready-content-corpus **Date:** April 9, 2026 **Author:** Scott Wiemels This article explains why most enterprise AI investments underperform: the underlying content corpus is not structured for machine consumption. It details the three requirements an AI-ready corpus must satisfy (structural consistency, semantic clarity, contextual richness) and walks through the 10 essential components of AI knowledge infrastructure: taxonomic structure, metadata schema, content chunking strategy, semantic markup, source attribution, version control, quality metrics, access control, integration interfaces, and a governance framework. The article covers the four-phase corpus development process (Content Inventory and Audit, Structuring and Standardization, Semantic Enhancement, Quality Assurance) spanning 13–16 weeks for large corpora. Key finding: organizations that skip corpus architecture see 40–60% retrieval accuracy drops and 3–5x more hallucinations; those who build it properly achieve 80%+ user satisfaction and 3–5x ROI. ### Knowledge Architecture Sprint: What It Is, How It Works, and What You'll Get **URL:** https://24g.com/blog/knowledge-architecture-get-ai-ready **Date:** March 31, 2026 **Author:** Scott Wiemels This article explains 24G's 5-day Knowledge Architecture Sprint — a $7,800, time-boxed engagement adapted from the Google Ventures Design Sprint methodology. It compresses 4–12 weeks of traditional discovery and planning (typically $50,000–$150,000) into one high-impact week and produces 50–80 pages of deployable deliverables. The article covers the three business triggers that drive the decision to run a Sprint (AI readiness gaps, key employee departure, scaling challenges), the day-by-day deliverable structure, and the complete output: Current State Assessment Report, Knowledge Taxonomy Framework, Corpus Development Roadmap, AI Readiness Report, 90-Day Implementation Plan, and an Investment Proposal for CFO review. A documented ROI case shows a company invested $47,000 in Phase 2 implementation following their Sprint and prevented an estimated $840,000 in knowledge loss — a 1,787% return. Three post-sprint options are described: DIY implementation, KTS engagement, or DRIVE platform deployment. ### Tribal Knowledge Loss Prevention: The $2.3M Problem Every Manufacturer Faces **URL:** https://24g.com/blog/tribal-knowledge-loss-prevention **Date:** March 3, 2026 **Author:** Scott Wiemels This article documents a case study of an aerospace parts manufacturer that lost $2.3 million in four months after a senior machinist retired — compared to an estimated $10,300 cost to capture his knowledge beforehand (a 22,000%+ prevention ROI). The loss breaks down as: $340,000 in lost productivity during the knowledge gap (new machinist ran at 62% of predecessor's pace for 14 weeks), $180,000 in training through trial and error (127 rejected parts, 89 rework parts, 180+ engineering hours), $420,000 in quality issues (three customer installation failures due to unwritten supplier knowledge), and $1.2M+ in lost customer relationships. The article defines the seven types of tribal knowledge that disappear when experts leave (process optimization, equipment troubleshooting, quality control intuition, supplier intelligence, customer relationship history, safety workarounds, efficiency hacks) and presents a 90-minute interview framework for capturing expert knowledge. A regional fabricator case is cited: using structured capture, new welders reached 78% of master-level quality in 8 weeks versus the historical norm of 12 months. ### Subject Matter Expert Knowledge Capture: 7 Proven Interview Techniques **URL:** https://24g.com/blog/subject-matter-expert-knowledge-capture **Date:** February 17, 2026 **Author:** Scott Wiemels This article addresses the Cognitive Curse of Knowledge — why subject matter experts genuinely cannot articulate their most valuable tacit expertise when asked generic questions. Once a skill is truly mastered, the brain relocates it to procedural memory where it operates automatically. The article presents a preparation protocol (48-hour pre-interview questionnaire, background research, psychological safety framing) and seven targeted elicitation techniques: the Reverse Chronology Method (start from most recent complex problem, work backward), Scenario Simulation (present-tense narration activates procedural memory), Mistake Autopsy (analyzing others' errors reveals the expert's own mental models), Teaching Observation (recording an expert training a new hire captures unplanned knowledge nuggets), Decision Point Mapping (mapping micro-decisions converts "it depends" into operational flowcharts), the "What Would You Do If..." series (hypothetical edge cases stress-test mental rules), and Artifact Analysis (walking through recent real work products surfaces implicit quality standards). The article covers converting captured content into deployment-ready artifacts: enhanced process documentation, decision support flowcharts, and mistake prevention checklists. Outcomes reported: 60–70% reduction in direct SME interruptions, 30–40% faster new hire ramp time. ### Docebo Alternatives 2026: 11 AI-Powered Learning Platforms Compared **URL:** https://24g.com/blog/beyond-the-powerhouse-11-ai-powered-learning-platforms **Date:** March 17, 2026 **Author:** Scott Wiemels This article compares 11 AI-powered learning platforms against Docebo for organizations that find Docebo's cost ($25,000+ annual entry, 6–12 month implementation), complexity, or content-readiness assumptions misaligned with their needs. The article evaluates each platform across four critical dimensions: knowledge infrastructure capabilities, AI maturity, content creation support, and implementation timeline. Platforms reviewed: 24G DRIVE (knowledge transformation first, Corpus-to-Capability approach), 360Learning (collaborative, $8/user/month, 2–4 week deployment), Absorb LMS (strong UX for mid-market), Degreed (LXP for skills and career pathing), Cornerstone OnDemand (integrated talent suite for large regulated enterprises), TalentLMS (lowest entry cost at $59/month for 40 users), Litmos (fastest deployment at 1–2 weeks), Thought Industries (customer education), and Skilljar (customer education at scale). Key recommendation: DRIVE is best for organizations that recognize their content gap as the actual problem to solve rather than assuming content is already structured and ready. ### Knowledge Management Transformation: The Complete 2026 Implementation Guide **URL:** https://24g.com/blog/knowledge-management-transformation-guide **Date:** January 27, 2026 **Author:** Scott Wiemels This article frames knowledge management transformation as a strategic crisis in the AI era: organizations lose an average of $5.3 million annually to siloed, uncaptured, and lost institutional knowledge, and AI tools can only amplify knowledge that has already been structured. The article presents the Corpus-to-Capability methodology — a five-stage lifecycle covering Capture, Curate, Structure, Transform, and Deploy — and a 5-phase implementation framework: Phase 1 Discovery and Audit (Weeks 1–2), Phase 2 Architecture Design (Weeks 3–4), Phase 3 Capture and Curation (Weeks 5–10), Phase 4 Structuring and Transformation (Weeks 11–14), and Phase 5 Deployment and Adoption (Weeks 15–16+). The article warns against four common failure patterns: technology-first thinking (buying a platform before defining the architecture), treating knowledge like content (migrating old PDFs instead of extracting decision frameworks), perfection paralysis (delaying launch until all knowledge is documented), and ignoring the human layer (building a system users bypass for hallway conversations). A 30-day action plan is included to begin generating value immediately without waiting for a complete transformation. --- ## Case Studies ### 90% of AI Investments Fail to Deliver Measurable Transformation or ROI **URL:** https://24g.com/cases/90-ai-investments-fail This case study addresses the root cause behind the widely cited statistic that 90% of AI investments fail to deliver measurable transformation or ROI. The core thesis: AI tools are only as good as the knowledge they can access. When expertise is trapped in heads, scattered across folders, and locked in disconnected systems, no AI investment can compensate. 24G's approach fixes the knowledge foundation first — before technology selection — so that AI amplifies structured capability rather than amplifying chaos. This case links to the three related knowledge problems: institutional knowledge loss, fragmented knowledge, and shadow AI risks. ### Institutional Knowledge Is Walking Out the Door with Every Departing Employee **URL:** https://24g.com/cases/institutional-knowledge-loss This case study addresses the systemic loss of institutional knowledge that occurs every time a key employee leaves. The expertise that best people carry is not just documented in SOPs — it includes judgment calls, workarounds, and the context behind decisions. When it leaves, it does not come back. 24G's mission here is preserving that institutional knowledge before departure, capturing the tacit expertise that lives in expert heads and converting it into governed, retrievable knowledge infrastructure. Related problems include AI investment failure, fragmented knowledge, and shadow AI risks. ### Fragmented Knowledge Costs Companies Up to 60% of Organizational Productivity **URL:** https://24g.com/cases/fragmented-knowledge This case study documents the productivity cost of knowledge fragmentation: SOPs in SharePoint, tribal knowledge in Slack, training in the LMS, policies buried in email. When nothing connects, people spend more time searching than doing, and AI cannot help with what it cannot find. 24G addresses this by unifying scattered knowledge into one governed foundation — a single source of truth that makes knowledge accessible to both the workforce and AI systems. Related problems include AI investment failure, institutional knowledge loss, and shadow AI risks. ### Decaying Data and Shadow AI Create Massive Compliance and Security Risks **URL:** https://24g.com/cases/data-shadow-ai This case study addresses the compliance and security risks created when employees resort to consumer AI tools because enterprise knowledge is inaccessible. Proprietary information leaks and regulated data flows through ungoverned channels when enterprise knowledge systems are too difficult to use. 24G's solution ensures that the demand for AI tools runs through the organization's own infrastructure — with governance, access controls, and current, accurate data — rather than around it through shadow AI use. Related problems include AI investment failure, institutional knowledge loss, and fragmented knowledge. --- ## Team - **Scott Wiemels** — CEO & Head of AI Enablement. LinkedIn: https://www.linkedin.com/in/scott-wiemels-bbb9567/ - **Chris Pearce** — Director of Knowledge Engineering. LinkedIn: https://www.linkedin.com/in/christina-pearce-1aa18b239/ - **Dallas Smoke** — Chief Technology Officer & Head of Delivery. LinkedIn: https://www.linkedin.com/in/dallas-smoke-955a8b16/ - **Kevin Schneider** — CISO & Head of AI Governance. LinkedIn: https://www.linkedin.com/in/kevin-schneider-/ --- ## Contact Headquarters: 400 W Maple Rd, Troy, MI 48084 Website: https://24g.com LinkedIn: https://www.linkedin.com/company/24gcompany/ Free AI Readiness Assessment: https://futureready.24g.com/quickcheck