What's the right first AI workflow for a Lincoln engineering team?

Code review summaries against your internal standards. High-volume, low-risk, surfaces the standards-discipline gap that's the foundation for everything else.

Should we use AI agents yet?

Probably not for production-facing autonomous work. For internal-only agents, reasonable today with strong human-in-the-loop.

How do we avoid the 'AI writes code we don't understand' failure mode?

Standards-based drafting. AI drafts against your standards library; engineers review against the same standards.

What about cybersecurity AI specifically?

Deloitte 2024 found cybersecurity has the highest GenAI ROI — 44% above expectations, a 27-point gap over below-expectations.

Does this work for state-contract engineering work?

Yes — but NITC Standard 8-609 governs AI used by state agencies. Engineering teams supporting state contracts get pulled into the OCIO security review and privacy impact assessment workflow.

For Lincoln engineering teams

AI for engineering teams in Lincoln

Code review, IaC standards, change requests, vulnerability triage, technical documentation. The platform-team operating model where AI drafts and humans approve against standards.

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What this team is doing in Lincoln

IT and engineering is the most-mature function for advanced GenAI deployment. Deloitte's Q4 2024 survey found 28% of organizations' most-advanced GenAI initiatives are in IT — the leading function. Cybersecurity within IT has the highest ROI: 44% of cybersecurity initiatives deliver ROI above expectations.

At a Lincoln mid-market engineering team — Hudl-class sports tech, Nelnet-class fin-services platform, Sandhills-class trade marketplaces, or the firms supporting state contracts — the workflow redesign that produces real impact is the platform-team operating model: AI drafts code, change requests, vulnerability remediations, and documentation against your internal standards. Engineers review and approve against the same standards.

This is also where the move from assistive AI to agentic AI shows up first. McKinsey 2025 found 62% of organizations are experimenting with AI agents, but only 23% are scaling them.

Workflows that fit this team

The AI-shaped workloads where this team gets the highest payback.

Code review summaries and pattern-checking — AI surfaces deviations from internal standards.
IaC standards-based drafting — AI drafts Terraform / CloudFormation / Pulumi from your patterns.
Change request population — AI drafts the change request from the diff and your change-control template.
Vulnerability triage — AI analyzes scan output against your context and drafts remediation.
Jira ticket grooming and refinement — AI drafts acceptance criteria and technical notes.
Technical documentation — runbooks, ADRs, postmortems.

Why this matters in Lincoln

Engineering is the function with the highest demonstrated AI ROI ceiling — and the most exposed to the assistive vs. architectural distinction. McKinsey 2025 found AI high performers are nearly 3x more likely to have fundamentally redesigned individual workflows.

Only 23% of organizations have scaled AI agents (vs. 62% experimenting). The gap is largely architectural readiness — APIs, audit trails, exception handling, governance for AI taking actions.

For mid-market engineering, the realistic path is sequencing: assistive workflows + governance foundations in year one, architectural workflows + agentic experiments in year two.

Common questions from this team in Lincoln

What's the right first AI workflow for a Lincoln engineering team?: Code review summaries against your internal standards. High-volume, low-risk, surfaces the standards-discipline gap that's the foundation for everything else.
Should we use AI agents yet?: Probably not for production-facing autonomous work. For internal-only agents, reasonable today with strong human-in-the-loop.
How do we avoid the 'AI writes code we don't understand' failure mode?: Standards-based drafting. AI drafts against your standards library; engineers review against the same standards.
What about cybersecurity AI specifically?: Deloitte 2024 found cybersecurity has the highest GenAI ROI — 44% above expectations, a 27-point gap over below-expectations.
Does this work for state-contract engineering work?: Yes — but NITC Standard 8-609 governs AI used by state agencies. Engineering teams supporting state contracts get pulled into the OCIO security review and privacy impact assessment workflow.

Sources

AI high performers are nearly 3x as likely as others to say their organizations have fundamentally redesigned individual workflows — The state of AI in 2025: Agents, innovation, and transformation, McKinsey & Company (QuantumBlack, AI by McKinsey), 2025
About 6% of organizations qualify as 'AI high performers' — those attributing 5%+ EBIT impact to AI — The state of AI in 2025: Agents, innovation, and transformation, McKinsey & Company (QuantumBlack, AI by McKinsey), 2025
62% of organizations are at least experimenting with AI agents — The state of AI in 2025: Agents, innovation, and transformation, McKinsey & Company (QuantumBlack, AI by McKinsey), 2025
Only 23% of organizations are scaling AI agents — The state of AI in 2025: Agents, innovation, and transformation, McKinsey & Company (QuantumBlack, AI by McKinsey), 2025
28% of organizations' most advanced GenAI initiatives are in IT — the leading function — Now decides next: Generating a new future — State of Generative AI in the Enterprise Quarter four, Deloitte AI Institute, 2025
44% of cybersecurity-focused GenAI initiatives are delivering ROI somewhat or significantly above expectations — Now decides next: Generating a new future — State of Generative AI in the Enterprise Quarter four, Deloitte AI Institute, 2025
Most advanced GenAI initiatives by function: IT 28%, operations 11%, marketing 10%, customer service 8%, cybersecurity 8% — Now decides next: Generating a new future — State of Generative AI in the Enterprise Quarter four, Deloitte AI Institute, 2025

Function

AI in software engineering

Playbook

AI architecture, not AI tools

Answer

what makes AI 'agentic'

Service

end-to-end AI engagement

Service

AI governance framework

Concern

the AI data leak risk

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