Non Techie - Ai Brief

Non Techie - Ai Brief
People, Work, and AI in Practice 2025
A practical brief for HR and business leaders using AI as an extra set of hands, not a science project.
Issue I · December, 2025
Adoption
AI moved from experimental to operational in HR this year. What began as cautious pilot programs evolved into daily workflow tools. Organizations stopped asking "should we use AI?" and started asking "how do we use it responsibly?" The shift wasn't dramatic—it was incremental, practical, and focused on solving specific pain points rather than wholesale transformation.
Where It Showed Up
AI found its footing in talent acquisition, learning design, and HR service delivery. Recruiters used it to draft job descriptions and screen resumes. Learning teams turned policies into practice scenarios. HR service desks deployed it to draft responses and surface knowledge base articles. These weren't moonshot projects—they were practical applications that saved hours every week.
What Didn't Happen
The revolution didn't arrive. AI didn't replace HR professionals or automate entire departments. Many organizations struggled to move beyond proof-of-concept. Data governance concerns stalled initiatives. Some tools promised too much and delivered generic outputs. The gap between vendor promises and ground-level reality remained wide, particularly for small teams without dedicated IT support.
The Opportunity Now
Focus on one workflow that creates measurable value in the next 90 days. Pick a repeatable process where AI can draft, summarize, or sort—and humans can review and decide. Document what works, drop what doesn't, and build from there. The opportunity isn't in doing everything with AI; it's in doing one thing well enough that it becomes part of how your team actually works.
AI in HR: Year in Review
What actually changed for HR this year—and what to do next
The pressure to "do AI" hit HR teams hard this year. Executives asked for AI strategies. Vendors promised transformation. LinkedIn filled with success stories. Meanwhile, most HR teams were already stretched thin—managing open requisitions, updating outdated policies, answering the same benefits questions for the hundredth time, and supporting managers who needed help yesterday.
This tension—between the hype and the reality of everyday HR work—defined the year. Leaders thought 2024 would bring dramatic automation, intelligent chatbots handling complex employee inquiries, and AI-powered talent intelligence transforming workforce planning. Some of that happened in well-funded enterprises with dedicated AI teams. For most organizations, the reality looked different.
What Leaders Expected
Comprehensive AI-powered HR platforms
Automated end-to-end recruitment
Intelligent employee self-service
Predictive workforce analytics
Seamless integration across systems
What Actually Happened
Point solutions for specific tasks
AI-assisted drafting and screening
Template generation and FAQs
Basic pattern recognition in data
Manual workarounds and exports
Where AI actually delivered value, it behaved less like a revolutionary technology and more like a capable junior employee. It drafted job descriptions that HR could edit. It summarized interview feedback into coherent notes. It turned policy documents into plain-language FAQs. It sorted resumes into initial categories for human review. These weren't transformational—they were practical, time-saving, and genuinely useful.
Experiments stalled in predictable places. Data privacy concerns halted pilots before they launched. Integration challenges meant AI tools lived in isolation from core HR systems. Outputs required so much editing that teams questioned whether they were saving time. Training managers to use new tools proved harder than expected. Many "AI initiatives" never left the planning phase, casualties of competing priorities and resource constraints.
The most successful implementations shared common characteristics: they solved a specific, repeatable problem; they kept humans in the decision loop; they started small and scaled based on results; and they measured impact in hours saved or quality improved, not in "AI adoption rates." Organizations that treated AI as a tool rather than a strategy found practical applications faster than those pursuing comprehensive transformation.
Why This Brief & Who It's For
For HR Leaders & Practitioners
This issue speaks directly to HRBPs, People Operations leads, Talent Acquisition managers, Learning & Development professionals, and HR service delivery teams navigating the intersection of AI capabilities and real-world constraints.
For Small Business Owners
If you're running a growing company without a dedicated HR department, wearing multiple hats, and trying to professionalize people processes while keeping the business moving forward—this is for you.
Situations This Issue Addresses
Talent Challenges
Hiring has stalled because you can't keep up with requisition volume. Job descriptions take too long to write. Candidate screening is overwhelming. Interview feedback is scattered and inconsistent.
Operational Overload
Onboarding is messy and inconsistent. Managers are overwhelmed and undertrained. HR admin work crowds out strategic priorities. The same questions get asked repeatedly.
Resource Constraints
Your team is too small for the workload. You can't afford enterprise HR tech. You need practical solutions, not comprehensive platforms. Quick wins matter more than long-term roadmaps.
How to Use This Issue in the Next 7-30 Days
Week 1: Read through the "Where AI Actually Showed Up" section. Identify one workflow that matches a current pain point on your team. Don't pick the biggest problem—pick one that's repeatable and measurable.
Weeks 2-3: Choose one free tool from Section 6. Run a small experiment with it on your identified workflow. Document what happens: time saved, quality of output, what required human review.
Week 4: Decide what you'll keep, modify, or drop. If it worked, document the process so someone else on your team can replicate it. If it didn't, note why and move on. The goal isn't AI adoption—it's solving a real problem.
Where AI Actually Showed Up in HR
Talent Acquisition: From Hype to Quiet Utility
Corporate HR Lens
Large organizations deployed AI across the recruitment lifecycle with varying success. Job description generation became standard practice—recruiters provided key requirements and AI drafted descriptions that matched company voice and compliance standards. Resume screening tools sorted hundreds of applications into initial categories, flagging relevant experience and skills for human review.
Interview support tools transcribed conversations and generated summary notes, freeing interviewers to focus on candidate interaction rather than note-taking. Some teams used AI to draft follow-up emails and generate interview questions based on job requirements. The technology worked best when it accelerated existing processes rather than replacing human judgment on candidate fit and potential.
Small Business Lens
Small business owners and lean HR teams used AI differently—more scrappy, more immediate. They asked ChatGPT to turn rough bullet points into professional job postings. They generated interview questions for roles they'd never hired before. They used AI to draft outreach messages to passive candidates and create structured evaluation rubrics.
The barrier to entry was lower—no enterprise software required, just a browser and clear prompts. Results were more variable because there was less infrastructure to catch errors, but the time savings were immediate. One-person HR departments found AI particularly valuable for creating consistency in hiring processes that had previously been ad-hoc.
Learning & Upskilling: Less Content, More Capability
Policy to Practice
L&D teams used AI to transform dense policy documents into practical learning scenarios. Instead of telling managers what the harassment policy says, AI helped create realistic situations managers might encounter and guided them through appropriate responses.
Manager Support
Training content shifted from theoretical to applied. AI generated coaching conversation scripts, performance review templates, and difficult conversation frameworks tailored to specific team challenges. Managers got just-in-time support rather than generic training.
Documentation Creation
Small businesses turned institutional knowledge into transferable process documentation. Experienced employees described how they did their jobs, AI organized it into structured SOPs and checklists, and new hires had clear guidance instead of tribal knowledge.
HR Service & Support: Draft First, Human Always
Corporate HR Lens
HR service desks experimented with AI-assisted response drafting. When employees submitted tickets, AI suggested responses based on policy documents and knowledge base articles. HR professionals reviewed, edited, and personalized before sending. The technology helped maintain consistency while reducing response time from hours to minutes.
Some organizations used AI to cluster similar tickets and identify common pain points. Instead of treating each "how do I change my 401k contribution?" question as unique, AI surfaced patterns that suggested FAQ updates or process improvements. The best implementations kept humans firmly in control while letting AI handle pattern recognition and initial drafting.
Small Business Lens
Without dedicated HR service desks, small businesses used AI to build evergreen answer repositories. They compiled frequently asked questions about benefits, PTO, expense reimbursement, and company policies. AI helped structure answers clearly and maintain consistent tone across all documentation.
The result was a simple, searchable HR playbook—not a sophisticated knowledge management system, but a Word document or Notion page where employees could find answers without interrupting the owner or HR lead. Updates were manual but infrequent. The time savings came from answering each question once well rather than repeatedly via email or Slack.
Best Free Tools This Issue
1
ChatGPT (Free Tier)
Best for: Drafting job descriptions, generating interview questions, turning policy documents into plain-language FAQs, and creating email templates.
Why it earned a spot: Zero cost to start, no technical setup required, and genuinely useful for text-heavy HR tasks. The free tier handles most small-to-medium HR content needs without requiring paid subscriptions.
2
Notion AI (Free Trial, Starter Plan)
Best for: Building searchable HR knowledge bases, creating SOPs and process documentation, and organizing team information in one collaborative workspace.
Why it earned a spot: Combines documentation with AI assistance for summaries, action items, and content generation. Small teams can manage their entire HR knowledge repository here with minimal learning curve.
3
Google Forms (Free)
Best for: Employee surveys, feedback collection, onboarding checklists, and simple data gathering without enterprise survey platforms.
Why it earned a spot: Everyone already has access through Google Workspace. Responses auto-populate to Sheets for analysis. Not AI-powered itself, but pairs perfectly with ChatGPT for analyzing open-ended feedback at scale.
4
Loom (Free Tier)
Best for: Creating training videos, recording process walkthroughs, and delivering personalized manager coaching without scheduling live sessions.
Why it earned a spot: AI-generated transcripts make videos searchable and accessible. Turn one recorded explanation into reusable training content. Free tier allows up to 25 videos—sufficient for core HR processes.
5
Calendly (Free Tier)
Best for: Scheduling interviews, manager check-ins, and employee meetings without endless email chains.
Why it earned a spot: Eliminates scheduling friction that slows down hiring and performance conversations. While not AI-specific, it solves a time-drain problem that AI can't—coordinating calendars across time zones and availability.
From This Issue to a 90-Day Plan
Use this as a working template with your team. The goal isn't to "implement AI"—it's to solve one specific problem measurably better than you do today.
1
Phase 1: Days 1-30
Diagnose One Workflow Honestly
2
Phase 2: Days 31-60
Design and Run One Real Experiment
3
Phase 3: Days 61-90
Decide, Standardize, or Stop
Phase 1 (Days 1–30): Diagnose One Workflow Honestly
Corporate HR Teams
Review your last 10 requisitions. How long did job description creation take? How many screening calls happened before you found qualified candidates? Where did interview feedback get lost or delayed? Look at ticket volume in your HR service desk—which questions repeat most often?
Talk to your team about where they feel most behind. Don't pick the workflow with the most strategic importance—pick the one where current delays are most painful and where you can clearly measure improvement. Candidate screening, interview guide creation, and FAQ response drafting are good starting points.
Output: One clear sentence describing the workflow you'll focus on. Example: "We will reduce time spent drafting job descriptions from 3 hours to 45 minutes while maintaining quality and compliance standards."
Small Business Owners
Where did you actually use AI in the last month? If you haven't used it yet, what HR task makes you groan when it appears on your to-do list? Writing job posts? Creating offer letters? Answering the same benefits questions? Documenting how your top performer does their job so you can hire another?
Pick one pain point that happens repeatedly—not a one-time project. You want something you'll do again next month and the month after. Focus on processes where "good enough, consistent" beats "perfect but sporadic." Employee handbooks, interview questions, and onboarding checklists are practical starting points.
Output: One sentence describing what you'll improve. Example: "I will create a standardized onboarding checklist so new hires get consistent experience regardless of which team member trains them."
Phase 2 (Days 31–60): Design and Run One Real Experiment
01
Map the Current Process
Document every step in your chosen workflow. Who does what? Where does work wait? What decisions require human judgment versus just executing a known process?
02
Assign AI vs. Human Steps
Mark which steps AI could draft, summarize, sort, or suggest. Keep all decision-making with humans. AI proposes, people decide. No exceptions.
03
Set Clear Guardrails
What must AI never do? What outputs require review before use? What data can't be shared with AI tools? Document these constraints before you start.
04
Create Your Prompts
Write specific instructions for the AI. Test them. Refine them. Generic prompts produce generic results. Specific prompts with context and examples produce useful drafts.
05
Run It for Real
Use your AI-assisted process for the next 5-10 instances of this workflow. Not a demo—real work with real consequences. Track what happens.
Corporate HR Example
Map current screening process → Assign AI to initial resume categorization, keep human review for all advancement decisions → Set data privacy rules for candidate information → Create prompts for resume analysis against job requirements → Screen next 50 applicants using new process.
Measure: Time from application to first phone screen, quality of screened candidates (measured by hiring manager satisfaction), any compliance concerns.
Small Business Example
Document current onboarding chaos → Assign AI to draft checklists and email templates, keep human delivery of all content → Ensure AI doesn't access confidential company information → Create prompts for "first day checklist for [role type]" → Onboard next 3 hires using new process.
Measure: New hire confidence after week one (simple survey), time spent by owner/manager on onboarding tasks, completeness of process (nothing forgotten).
Phase 3 (Days 61–90): Decide, Standardize, or Stop
Corporate HR: Keep, Fix, or Stop
Review your measurements. Did the process save time without sacrificing quality? Did it introduce new problems that offset the benefits? Were there unexpected challenges in adoption or compliance?
If it worked: Document the workflow. Train the team. Make AI assistance the default for this process. Create templates for the prompts that worked best. Establish review protocols so quality stays consistent.
If it partially worked: Identify what to keep and what to modify. Maybe AI screening works but your prompts need refinement. Maybe the time savings are real but you need better integration with your ATS. Fix the specific problems and run another 30-day cycle.
If it didn't work: Stop. Document why. Share learnings with your team. This isn't failure—it's data. You learned one thing AI can't do effectively in your context. That's valuable. Pick a different workflow or wait for better tools.
Output: One repeatable workflow where AI is now "part of the team," documented clearly enough that a new team member could follow it without asking questions.
Small Business: Default or Abandon
Was using AI faster than your old method? Was the output good enough that you'd use it again without hesitation? Did it create consistency you didn't have before?
If yes: This becomes your default process. Write it down—not a formal SOP, just clear enough that you'll remember how you did it six months from now. Save your best prompts. Next time this situation arises, you have a reliable starting point instead of reinventing the wheel.
If no: What specifically didn't work? Was the tool wrong? Were your prompts too vague? Did you pick a workflow that actually required more human nuance than you realized? Sometimes the answer is "AI isn't right for this"—and that's fine. Move on to a different pain point or stick with your current method.
Output: One documented process that's now part of how you run your business, simple enough that you'll actually use it when things get busy.
How CultureConnectAi Partners With You
CultureConnectAi doesn't sell AI transformation or comprehensive technology roadmaps. We help HR leaders and business owners turn vague pressure to "do something with AI" into one or two specific use cases tied to measurable outcomes. No jargon, no year-long implementations, no dependence on enterprise IT resources.
Our approach starts with identifying workflows where AI can genuinely help—not every workflow, just ones where drafting, sorting, or summarizing creates immediate value. We design simple processes where AI handles initial work and people make all decisions. No automation for automation's sake. No tools that require specialized training. Nothing that creates more work than it saves.
We run 60–90 day experiments with clear decision points at the end. You'll know exactly what worked, what didn't, and what you're keeping. If an AI application doesn't deliver measurable value, we stop and redirect to something else. The goal isn't AI adoption metrics—it's solving real problems for your team and your employees.
For Corporate HR
Identify high-impact, repeatable workflows
Design AI-assisted processes with clear guardrails
Run structured pilots with real teams
Build internal capability, not vendor dependence
For Small Businesses
Focus on immediate pain points, not future capabilities
Use free or low-cost tools you already access
Create simple, documented processes
Build consistency without adding headcount
Engagements Include
Workflow diagnostics and prioritization
Pilot design and implementation support
Prompt engineering and template creation
Team training and documentation
We work with you as practitioners, not theorists. CultureConnectAi combines deep HR expertise with practical AI implementation experience—we've run these experiments ourselves, made the mistakes, learned what actually works in resource-constrained environments. We help you avoid the expensive detours and focus on applications that deliver value in weeks, not quarters.
Interested in exploring how AI could solve a specific challenge on your team? Reach out to discuss a focused engagement. We'll start with one workflow, run a real experiment, and help you decide what's worth scaling. No long-term commitments, no comprehensive roadmaps—just practical help solving one problem well.
Personal Notes
On Animals and Responsibility
Andrea has always cared deeply about animal welfare. Through her beaded bracelet business, The Charmed Bee, she donates 10% of all sales to the Humane Society and licensed animal rescues. This isn't marketing—it's conviction. Where money and attention go matters. It's a statement of values, a deliberate choice about what deserves support and protection.
The same principle applies to AI at work. How we choose to use these tools—or not use them—affects real people. Every decision to automate or augment work has downstream consequences for employees, candidates, managers, and teams. Technology isn't neutral. Deployment choices reflect priorities.
At CultureConnectAi, we believe AI should serve people, not replace them. It should save time for work that requires judgment, empathy, and relationship-building—the distinctly human parts of HR that matter most. Where we direct these capabilities says everything about what we value. Use AI to amplify human capability, not substitute for human connection.
On The Charmed Bee
The Charmed Bee is Andrea's beaded bracelet business, where small decisions show up quickly. A clasp that's slightly too tight makes a beautiful bracelet unwearable. A color combination that looks good in isolation might clash when worn daily. Design choices matter because people actually use these pieces—they're not decorative objects, they're part of everyday life.
Andrea approaches AI the same way: start small, test in real conditions, keep what works, drop what doesn't. No grand visions or comprehensive strategies. Just practical craft—trying something, seeing how it performs under actual use, and refining based on results. The Charmed Bee succeeds because Andrea pays attention to what customers actually wear and adjusts accordingly.
AI in HR deserves the same grounded approach. Test one process. See if people actually use it. Measure whether it makes work better or just different. Keep the parts that deliver value. Abandon the parts that don't. This isn't defeatist—it's disciplined craft. Good work comes from iteration and honest assessment, not from believing your first design will be perfect.
Verification Notes
This issue of the CultureConnectAi Brief draws on multiple sources to ensure accuracy and practical relevance. Content reflects both direct client work and broader industry research. We prioritize verifiable information over speculation and clearly distinguish between observed trends and predictive statements.
Data Sources and Methodology
Direct client engagements across corporate HR departments and small business contexts, representing organizations from 20 to 5,000+ employees
Industry research from Gartner, Deloitte, and SHRM on AI adoption patterns in HR functions during 2023-2024
Vendor briefings and product testing with leading HR technology providers
Surveys and interviews with HR practitioners conducted through professional networks and industry events
Analysis of job descriptions, HR technology reviews, and implementation case studies published in trade publications
Verification Standards
Where specific statistics appear in this issue, they reference published research from recognized industry analysts or aggregated data from CultureConnectAi's client work. Percentage figures and adoption rates cite specific sources. Claims about tool capabilities reflect direct testing and documented user experiences, not vendor marketing materials.
Unverified statistics and speculative projections are explicitly excluded from this brief. When discussing future trends or potential applications, language clearly indicates these are possibilities rather than established facts. "Could enable" and "may support" distinguish forecast from current reality.
Continuous Improvement
The AI landscape changes rapidly. What's true today may not hold next quarter. We update perspectives based on new evidence and welcome corrections when our understanding misses the mark. This brief serves as a snapshot of current practice, not a permanent statement of how AI will always function in HR contexts.
Questions about specific claims, data sources, or methodologies? We're happy to provide additional context or clarification. Reach out directly and we'll share supporting documentation or explain our reasoning in detail.