AI Agent Operational Lift for 1898 & Co. in Kansas City, Missouri

Why AI matters at this scale

1898 & co. operates in a sweet spot for AI adoption: a mid-market professional services firm with deep domain expertise, a focused client base, and enough scale to justify custom tooling without the inertia of a massive enterprise. With 201-500 employees, the firm can deploy AI to amplify its consultants' capabilities rather than replace them—a critical distinction in high-trust advisory work. The industrial cybersecurity niche is data-rich but analyst-poor; every client engagement generates valuable structured findings, network diagrams, and threat assessments that currently sit in documents and brains. AI can unlock that latent knowledge.

The economics are compelling. Consulting firms typically see 60-70% of revenue go to labor costs. If AI can reduce report drafting time by 40%, proposal generation by 50%, and threat research by 30%, the firm could improve utilization rates and project margins by 5-10 percentage points. For a company with estimated revenue around $75 million, that translates to millions in bottom-line impact.

Three concrete AI opportunities

1. Automated deliverable generation

The most immediate win is using large language models (LLMs) to draft client deliverables. 1898 & co.'s consultants spend significant time translating technical findings into executive-ready reports. By fine-tuning a model on past deliverables—with strict data isolation per client—the firm can generate first drafts that consultants then review and refine. ROI comes from faster project closeout, higher consultant utilization, and the ability to take on more engagements without proportional headcount growth.

2. OT-specific threat intelligence pipeline

Industrial control systems face unique threats that generic cybersecurity feeds miss. Building an NLP pipeline that ingests vendor advisories, CVE databases, and dark web chatter, then maps them to client-specific OT architectures, creates a proprietary intelligence product. This can be sold as an ongoing subscription service, moving the firm from pure project revenue toward recurring revenue streams. The initial build requires data science talent but leverages existing client relationships for distribution.

3. Internal knowledge assistant

A retrieval-augmented generation (RAG) system trained on the firm's methodologies, past project artifacts, and industry standards would serve as an always-available expert for junior consultants. This accelerates onboarding, ensures consistent quality, and captures institutional knowledge before senior staff retire. The technology is mature and can be deployed on private cloud infrastructure to meet client confidentiality requirements.

Deployment risks specific to this size band

Mid-market firms face unique AI risks. First, client data sensitivity in critical infrastructure is extreme—any breach of OT network data could have national security implications. This demands on-premises or private cloud deployment, increasing infrastructure costs. Second, the firm likely lacks in-house AI talent; hiring or contracting data scientists competes with billable consultant roles. A phased approach starting with low-risk internal tools before client-facing applications is prudent. Third, change management is real: senior consultants may resist tools that seem to commoditize their expertise. Leadership must frame AI as an augmentation layer that lets them focus on the strategic work they value most.