Claude AI Training & Enablement — for teams rolling out Claude

Give your teams an approved way to use AI before shadow AI becomes routine.

I'm Daniel Michael Newland, an AI training and enablement consultant at TechStack Consulting LLC. I design Claude training and help teams apply Claude to their actual work — the documents, decisions, and reviews they handle every day.

I help companies put approved AI tools to work before shadow AI becomes routine. I design training by department, build custom workflows with clear review steps, and give leaders reporting they can use to guide the rollout.

My flagship work is a complete Claude training program I designed and delivered for an enterprise rollout at a regulated components manufacturer — three levels delivered live, week by week over a month, each course building on the one before it, alongside self-paced materials, scenario-based throughout, and built for a regulated environment where some data must never touch an AI tool.

Role: AI Training & Enablement Consultant Practice: TechStack Consulting LLC For: Business leaders · L&D & enablement · IT leaders · Team managers Format: Live week-by-week training + self-paced video & supporting docs

Discuss your Claude rollout See the enterprise case study Try the Service Desk demo View résumé

Daniel Michael Newland — AI Training & Enablement Consultant, TechStack Consulting LLC

3Training levels — Beginner, Intermediate, Advanced

60 minPer session — delivered live week by week over a month, with self-paced materials

DocsSupporting documentation for each video, simulation, and training course

100%Synthetic examples — no real company or controlled data in any exercise

Reported results — training data & attendance

400+Users trained across the program

97%Success rate reported from the training data

3.0 → 5.0Participant confidence rose from 3.0 to 5.0 on a 5-point scale

Results as reported from the training data and attendance.

Flagship engagement — case study

Claude training for 400+ users at a regulated components manufacturer

This is the work most relevant to a company planning its own rollout, so here is how it came together — the need, the constraints, what I built, and the follow-up I prepared.

1 · The training need

The manufacturer was rolling Claude out to staff with widely different starting points — some had never written a prompt, others were ready for technical tools. The company needed one program that could take a first-time user to safe, confident everyday use, and give more technical staff a path beyond that — structured as a progression, with each course building on the one before it, rather than three disconnected sessions.

2 · The constraints that shaped it

The company manufactures specialized components for customers in regulated markets, including government-related work. Export-control and controlled-data requirements shaped the training. For this engagement, CUI, ITAR, and EAR material was a hard boundary: that data never goes into Claude. The training also had to work in two modes at once: live sessions people attended week by week, and self-paced material hosted on the company's own SharePoint / Stream environment that learners could revisit between sessions. And it had to teach client-approved data handling and output verification as habits — not as a disclaimer at the end.

3 · What I designed and delivered

48 hours of live instructor-led training across the month. Beginner, Intermediate, and Advanced each ran for two weeks, with overlapping schedules. Each level was offered twice a day, four days a week, in 60-minute sessions. That adds up to 16 hours per level.
Self-paced materials alongside the live delivery — narrated video and supporting docs learners could work through on their own and revisit between the live sessions, so the live weeks and the self-paced study reinforced each other.
Narrated video produced for SharePoint / Stream, so staff could watch, pause, and revisit material inside the company's own environment.
Supporting documentation for each video, simulation, and training course.
Scenario-based knowledge checks in every session: realistic workplace situations where the learner decides what to do, then sees what good looks like.
Simulations pairing a learner action with an expected Claude response, so people build an accurate picture of how Claude behaves — including when its output needs human review.
Provisional reporting on participation, attendance, and exercises — reporting based on participation, attendance, and the exercises was made available and provided provisionally as the live delivery ran, so the client had visibility during the month rather than only at the end.
Synthetic examples throughout. Every prompt, document, and exercise was invented for training, so learners could practice real decisions without using real or controlled data.

Instructional designNarrated videoSharePoint / StreamSupporting docsScenario-based checks

4 · The follow-up I prepared

Training that ends at delivery leaves adoption to chance. During the live delivery, reporting based on participation, attendance, and exercises was provided provisionally — an interim view while the program was still running. The reported results from that training data and attendance were: 400+ users trained, a 97% success rate, and participant confidence rising from 3.0 to 5.0 on a 5-point scale. I also prepared a 90-day readout with Phase 2 recommendations: a structured review of how the program landed, what learners still needed, and what the next phase of enablement should cover. That gives the client a decision point based on the first phase — what to extend, what to adjust, and where to go deeper — instead of a one-off training event with no next step.

Reported results — from training data & attendance

400+Users trained

97%Success rate

3.0 → 5.0Participant confidence — from 3.0 to 5.0 on a 5-point scale

These figures are reported results from the training data and attendance for this program.

Why these deliverables matter to you

The design choices above are deliberate. Running the three courses live, week by week over a month, means each session can assume — and build on — what was practiced the week before, instead of restarting from zero. Scenario checks let employees practice decisions they will actually face at work, while the stakes are still zero. Supporting docs and self-paced video give them a reference they can revisit mid-task, weeks after the session. Simulations show where Claude's output needs human review before anyone acts on it. And provisional reporting on participation, attendance, and exercises gives the client an interim view of engagement during delivery.

The program

Curriculum — three levels, one progression

Each level is a 60-minute session. During the enterprise rollout, the three were delivered live, week by week over a month — a month's worth of live training where each course built on the one before it — alongside self-paced materials learners could use between sessions. Together they take a learner from “I've never prompted” to Claude Code and reusable Skills. The level-by-level breakdown below shows how the topics are organized for a buyer planning a rollout.

Beginner — Foundations

Audience: staff meeting Claude for the first time, whatever their role.

Objective: finish the session able to ask Claude for everyday help, improve the result, and check the output before using it — while keeping unsafe material out.

Prompting with clear instructions and context — what to include, what to leave out
Iteration — reading the first answer and asking again, better
Verifying output — where Claude can be confidently wrong, and the checks that catch it
Safe inputs — what must never be pasted into Claude
Everyday practice on synthetic examples, closed by a scenario-based knowledge check

Intermediate — Workplace workflows

Audience: staff already prompting with confidence, ready to connect Claude to the tools they work in.

Objective: handle more involved, multi-step workplace tasks and understand how connectors and reusable Skills fit an organization's approved setup.

Stronger prompting and verification for multi-step, real-work tasks
Microsoft 365 connector concepts — working with Claude inside existing documents and workflows, as configured and approved by the organization
Introductory examples of reusable Claude Skills — what a Skill is, and when one is worth using
Checking connected-tool output before acting on it
Scenario-based knowledge check

Advanced — Claude Code and Skills

Audience: power users and technical staff. This is where the technical material sits — most employees will not need this level, and buyers can scope it to selected users.

Objective: get a working Claude Code setup, create a basic Skill, and run a practice workflow that ends with reviewing the output before it is used.

Claude Code set-up — getting a working environment running
Basic Skill set-up and authoring — creating and configuring a reusable Skill
A practice workflow with Claude on a technical task, including review of the output
Keeping controlled data out of AI-assisted development
Scenario-based knowledge check

FORMAT 01

Narrated video

Professionally narrated sessions produced for SharePoint / Stream hosting. Your team watches inside your own environment, and learners can rewatch a segment whenever a task calls for it.

FORMAT 02

Supporting documentation

Supporting documentation for each video, simulation, and training course.

FORMAT 03

Live delivery + self-paced support

The courses ran live, week by week over a month, each building on the last. Simulation frames, scenario checks, narrated video, and supporting docs gave learners self-paced material to work through and revisit between the live sessions.

Supporting materials

Training materials library

I created supporting documentation for each video, simulation, and training course. Learners can use it during training and refer back to it on the job.

3 sessions covered — Beginner, Intermediate, Advanced 5 training formats 100% synthetic examples throughout

Formats I created

PDF guides

Step-by-step references, scenario sheets, and handouts learners can keep open while they work.

MP4 training videos

Recorded walkthroughs learners can pause and replay as they practice.

MP3 audio narration

Audio narration learners can listen to separately from the video.

PowerPoint presentations

Slides that structure live instruction, demonstrations, and discussion across the three training levels.

Interactive simulations

Workplace scenarios where learners make a decision and get feedback. Try the synthetic Service Desk example.

Organized by session

Beginner — Foundations

Video support docs — companions that follow each Beginner video segment
Prompting quick references — clear instructions and context, on a keep-at-your-desk sheet
Safe-input handouts — what must never be pasted into Claude, in plain language
Scenario sheets — first-week workplace situations with a decision to make
Simulation worksheets — learner action paired with the expected Claude response

Intermediate — Workplace workflows

Video support docs — companions to the connector and Skills videos
Microsoft 365 connector guides — step-through sheets for working in existing documents
Skill example sheets — what a Claude Skill is, with worked introductory examples
Prompt-pattern references — reusable patterns for multi-step, real-work tasks
Scenario sheets — connected-tool situations, including checking output before acting on it

Advanced — Claude Code and Skills

Video support docs — companions to the Claude Code and Skill set-up videos
Claude Code set-up checklists — getting a working environment running, step by step
Skill set-up worksheets — creating and configuring your own Claude Skills
Scenario sheets — technical situations for code and repository work
Safety reference sheets — keeping controlled data out of AI-assisted development

Organized by type

Supporting docs for the videos

Written to fit the videos segment by segment. A learner can pause the video and work from the doc, then keep it as the reference version of that part of the session.

Supporting documentation for each video, simulation, and training course
Step-through guides and set-up checklists
Quick-reference sheets for mid-task lookups
Worked examples — all synthetic — matching the examples on screen

Scenario sheets

The paper side of the scenario-based method. Each sheet presents a realistic workplace situation, asks the learner to decide, and shows what good looks like — including the safety call.

Scenario and knowledge-check sheets for every session
Simulation sheets pairing a user action with the expected Claude response
Decision exercises on hallucinations, verification, and data boundaries
Every situation invented for training — no real or controlled data

About this catalog: this section describes the library's scope and organization. The documents themselves are part of the training delivery and are not hosted on this portfolio site — learners receive them with the training program.

Enterprise AI enablement

Train each department on the work it actually does

I work with department leads, IT, and Security to choose workflows worth piloting, define approved tools and inputs, and teach people where to verify outputs or escalate. Here are examples we can practice with synthetic data.

HR

Draft onboarding guides from approved policies. Have HR verify policy language before it reaches employees.

Sales

Build account briefs from public sources and draft follow-ups. Verify claims and commitments before sending.

IT

Turn approved change notes into rollout checklists and user communications. Confirm rollback steps with the change owner.

Cybersecurity

Practice incident handoffs using synthetic alerts. Separate confirmed facts from hypotheses before escalating.

Finance

Practice drafting budget variance explanations and close summaries using synthetic figures. Finance reconciles totals to approved source reports and reviews the explanation before sharing it.

Operations

Draft SOP updates and shift handoffs. Have the process owner check exceptions and approve changes.

Infrastructure

Draft runbooks and maintenance plans from sanitized examples. Test commands and dependencies in a sandbox.

Applications

Turn requirements into acceptance criteria and test cases. Review coverage and edge cases with engineering.

Service Desk

Triage sample tickets and draft replies from approved knowledge articles. Practice when to escalate to a specialist.

Custom workflows

I turn a use case into a repeatable process with approved inputs, reusable templates, a named reviewer, and an escalation path. A Service Desk pilot could walk agents through ticket classification, knowledge lookup, drafting, and review before sending.

Custom reporting

I build a readout around the data you have, such as attendance, scenario performance, confidence, and pilot usage where approved telemetry exists. Leaders can see where teams need coaching and which workflows are ready to expand.

Illustrative plan · Projected targets

Sample enterprise Claude rollout plan

This example models an organization with 2,000 employees and an initial cohort of 400 people, or 20% of the workforce. The figures below are planning targets for the sample organization.

November 2026

Target date for completing training with the initial 400-person cohort.

10–12 competencies

Track skills such as prompting, output verification, workflow automation, and using Claude Projects and Artifacts. Use scenario assessments to check what participants can apply.

80–120 tested prototypes

Target this range across AI agents, automated workflows, and workspace tools. Test with approved inputs and record what each prototype is ready to handle.

20–30 approved deployments

Target this subset of the tested prototypes for production after business owner sign-off, security review, and validation of human review steps.

Interactive portfolio sample · Synthetic data

Finance simulation · Review a budget variance

You’re preparing the monthly close narrative. Approved October figures show software spend of $132,000 against a $120,000 budget. The ledger includes an $8,000 renewal posted early. The remaining $4,000 is still under investigation.

Example reporting workflow · Synthetic data

CFO dashboard · From close data to a decision

October example. Dollar amounts are in USD. Variance is actual minus budget, and the percentage uses budget as the denominator.

Actual software spend

$132,000
Approved close extract

Budget

$120,000
Approved October plan

Unfavorable variance

$12,000 (+10%)
$8,000 explained · $4,000 open

Controller review

Open the supporting records, compare them with the simulated AI draft, and decide whether the explanation is ready to publish.

$8,000 · Early renewal

Synthetic ledger entry SW-104 records an $8,000 renewal in October. The approved budget scheduled it for November. This supports a timing explanation for $8,000 of the October variance.

$4,000 · Unreconciled spend

Synthetic close item SW-119 ties to the ledger. The software cost owner has yet to confirm what drove the spend. Keep the item open pending reconciliation.

Software spend was $12,000 over budget (+10%) because a renewal posted early. The full variance is a timing difference, so the forecast can stay as is.

How the dashboard gets published

Reconcile. Finance ties the close extract to the ledger and checks period, account mapping, and budget version.
Calculate. The reporting layer computes variance from approved values. The drafting tool receives only the reconciled figures and documented explanations.
Draft. AI proposes a short narrative and labels the unresolved $4,000. Finance checks each statement against the source.
Approve and publish. The controller signs off on the figures and narrative before publication. The open item stays visible with an owner and follow-up.

Example decision: Ask the software cost owner to reconcile the remaining $4,000 before changing the forecast. Keep the renewal timing difference visible in the close narrative.

Interactive portfolio sample · Synthetic data

Service Desk simulation · Evaluate agent judgment

Take a ticket from triage to human review. This scripted exercise runs in your browser. All tickets, knowledge articles, and report figures are fictional. No requests go to an AI model or ticketing system. See the sample department report.

What this exercise measures

Can an agent route a ticket correctly, use approved guidance, catch an unsupported claim in an AI draft, and distinguish reply approval from permission to reset an account?

How a manager uses the results

Record the first decision at each checkpoint. Coach on missed steps and require a fresh scenario before agents use this workflow on live tickets.

Ticket SD-1042 · Locked out after replacing a phone

“I replaced my phone and can’t get an authenticator prompt. Could you reset my MFA? I’m locked out of the employee portal.”

Step 1 of 4 · Triage

Where should this ticket go?

The employee reports an MFA problem affecting one account.

Sample report · Synthetic data

Service Desk pilot readout

Four-week sample for a fictional 24-agent Service Desk. Counts show which decisions agents made correctly on their first try in a practice exercise.

Participation

24 of 24 agents completed the exercise.

Review checkpoint

18 of 24 (75%) approved only the reply and held the reset pending verification.

First-attempt decisions by workflow stage
Stage	Correct	Rate
Triage	22 / 24	92%
Knowledge lookup	21 / 24	88%
Draft response	20 / 24	83%
Human review	18 / 24	75%

Decision for the Service Desk lead

Keep the pilot in practice mode. Six agents missed the review decision on their first try. Run a short exercise on the difference between approving a reply and authorizing an account change, then retest with a fresh scenario before expanding.

Sample readiness criterion for expansion: all 24 agents hold the reset pending identity verification on the retest.

How I teach

Approach

How I help people learn

I’m trained to facilitate learning for people with different needs, experience levels, and preferences. I explain the task, demonstrate it, give learners time to try it, and provide feedback they can use on the next attempt. Then I revisit the same decision in a different scenario so they can practice applying it on their own.

Learners can follow a live PowerPoint walkthrough, revisit an MP4 demonstration, listen to MP3 narration, or refer to a PDF guide while they work. In a simulation, they make a decision themselves and see the consequence. A Service Desk learner, for example, practices approving a reply while holding an MFA reset until identity is verified.

Workplace decisions, not trivia

Knowledge checks are workplace situations, not quizzes. For example, an illustrative scenario: Claude drafts a summary of a synthetic report and states a figure that isn't in the source. The learner decides — send it, fix it, or check it first — then sees the better move and why.

Realistic responses, not just prompts

Simulation frames pair each learner action with an expected Claude response, including a plausible-sounding answer that is subtly wrong. Learners calibrate what Claude will actually do before the stakes are real.

Data handling practiced, not posted

Data rules are practiced inside the exercises, using synthetic documents. When a scenario includes material that resembles restricted data, the practiced move is to stop and use the approved route — so the habit forms during training, not after an incident.

Follow-up after training

Reporting based on participation, attendance, and exercises was provided provisionally during the delivery, and a 90-day readout and Phase 2 proposal close the loop: what landed, what didn't, and what enablement comes next. Reported results from the training data and attendance: 400+ users trained, 97% success rate, confidence 3.0 to 5.0 on a 5-point scale. The implication for a buyer is simple — you get a basis for deciding the next investment instead of guessing.

The non-negotiable

In every piece of training I produce for this kind of environment: controlled and sensitive data (CUI / ITAR / EAR) never enters an AI tool, examples are synthetic, and training reinforces client-approved data handling and output verification. Those boundaries are set with the client for each engagement.

How I can help your team

Three ways a company typically scopes work with me. Formats, timelines, and scope are set together — none of these is a fixed package.

ENGAGEMENT 01

Team training

I build a learning path around your audience, its work, and your organization's rules for Claude — like the three-level program I delivered live, week by week over a month, alongside self-paced materials, for the enterprise rollout.

ENGAGEMENT 02

Rollout & workflow consulting

We identify suitable first use cases, map where human review belongs in each workflow, and decide what to pilot first — so adoption starts with work that is safe to try and easy to judge.

ENGAGEMENT 03

Advanced enablement

For selected users whose roles fit the tools, I help people get started with Claude Code, reusable Skills, or approved Microsoft 365 workflows — with the same verification habits applied.

A first conversation covers four things: the target audience, your current Claude access, the priority workflows you're considering, and any data restrictions the work has to respect. From there we can tell whether you need training, consulting, or both.

Discuss your Claude rollout

Related work

Beyond the classroom

The regulated manufacturing rollout is my flagship engagement. The work below is supporting evidence — each piece connects to a capability a training buyer is actually hiring for.

AI explainer & training video production

For Retention IQ, I produced a series of AI-narrated explainer films — HVAC, MedSpa, Dental, and General editions (roughly 2½–3 minutes each), plus a set of short-form clips — scripting, voice selection, and production end to end. For a buyer, this is the same production capability behind training video: material people will actually watch, made to order.

ScriptingAI narrationVideo production

AI & automation consulting — APT

A consulting engagement with APT, the Association of Public Treasurers of the United States and Canada (nonprofit), through TechStack Consulting LLC — experience working inside a member organization's real processes, which is where rollout and workflow advice has to land.

ConsultingNonprofit sectorTechStack Consulting LLC

CRM automation builds — GoHighLevel

I built and ran a GoHighLevel prospecting system for Retention IQ: researched contacts imported and tagged with per-contact research notes, segmented campaigns, and delivery tracked through to opens and replies. The relevance to enablement work: building a workflow people can actually use, then checking whether it works.

GoHighLevelAutomationCampaigns

Daily practitioner, not just trainer

I run my own consulting practice on the tools I teach — Claude and Claude Code, ChatGPT, and Supabase back ends — so the training reflects current, hands-on practice rather than slideware about tools I've only read about.

Claude CodeChatGPTSupabase

Competencies

Skills at a glance

Claude training & enablementProgram design & delivery

Instructional designScenario-based checks, simulations

Output verificationHallucination awareness & review habits

Claude CodeSet-up & coaching

AI toolsFluent in ChatGPT, Codex, Claude Cowork, and Claude Code

Claude SkillsSet-up & basic authoring

Microsoft 365 workflowsConnector concepts, approved setups

AI data safetyCUI / ITAR / EAR engagement boundaries

Narrated video productionSharePoint / Stream delivery

Technical writingSupporting documentation for each video, simulation, and training course

CRM automationGoHighLevel builds & campaigns

For applications

Résumé — Daniel Michael Newland

For recruiters and hiring managers: the résumé below is the one to attach. Download it as a text file, or copy it straight into an application form.

Contact me about a role

Daniel Michael Newland

AI Training & Enablement Consultant · TechStack Consulting LLC · danielmichaelnewland@outlook.com · linkedin.com/in/dannynewland · Remote, United States

AI training consultant who designs and delivers practical Claude enablement for real teams. Designed and delivered a complete Claude training program for an enterprise rollout at a regulated components manufacturer — three courses delivered live, week by week over a month, each building on the last, alongside self-paced materials, curriculum, narrated video, supporting documents, scenario-based assessment, and provisional reporting on participation, attendance, and exercises — and produces AI-narrated educational video and workflow automation builds through his own consulting practice. Reported results from the training data and attendance: trained 400+ users, 97% success rate, participant confidence rose from 3.0 to 5.0 on a 5-point scale.

Experience

AI Training Lead — Claude Training, Enterprise Rollout, a regulated components manufacturerClient engagement · 2026

Designed and delivered 48 hours of live instructor-led training across Beginner, Intermediate, and Advanced. Each level ran for two weeks on an overlapping schedule, with two 60-minute sessions per day, four days per week. Self-paced materials supported live instruction.
Curriculum covered prompting with clear instructions and context, iteration, output verification, Microsoft 365 connector concepts, introductory Claude Skills examples, Claude Code set-up, and basic Skill authoring.
Replaced quizzes with scenario-based knowledge checks and simulations pairing a learner action with an expected Claude response.
Produced narrated video for SharePoint / Stream and supporting documentation for each video, simulation, and training course.
Reporting based on participation, attendance, and exercises was made available and provided provisionally during the live delivery.
Reported results (from training data and attendance): trained 400+ users; 97% success rate; participant confidence rose from 3.0 to 5.0 on a 5-point scale.
Built the program for a regulated environment: CUI / ITAR / EAR data never enters Claude, every example is synthetic, and training reinforced client-approved data handling and output verification.
Prepared a 90-day readout with Phase 2 enablement recommendations.

AI & Automation Consultant — TechStack Consulting LLCOngoing practice

Consulting engagement with APT (Association of Public Treasurers of the United States and Canada), a nonprofit client.
Produced AI-narrated explainer and training films for Retention IQ — HVAC, MedSpa, Dental, and General editions plus short-form clips — scripting, voice selection, and production end to end.
Built a GoHighLevel CRM automation system for Retention IQ outreach: researched, tagged contacts with per-contact notes, segmented campaigns, and tracked delivery and engagement.
Runs the practice day-to-day on Claude / Claude Code, ChatGPT, and Supabase — training content reflects current hands-on use.

Core skills

AI tools: Fluent in ChatGPT, Codex, Claude Cowork, and Claude Code.

Claude training & enablement · Instructional design (scenario-based assessment, simulation) · Output verification & hallucination awareness · Claude Code set-up & coaching · Claude Skills · Microsoft 365 workflows · AI data safety (CUI / ITAR / EAR) · Narrated educational video production · Technical writing & supporting docs · GoHighLevel automation

Training formats delivered

Live week-by-week training (three courses over a month, building sequentially) · Self-paced narrated video (SharePoint / Stream) · Supporting documents & handouts · Scenario-based knowledge checks · Simulation exercises · Provisional reporting on participation, attendance & exercises · Adoption readout & Phase 2 planning

Start a conversation

Hiring for AI enablement or planning a rollout?

If you are hiring, tell me what you need this person to own. If you are planning a rollout, tell me which teams need training, what tools are approved, and where the work gets stuck today.

Discuss your Claude rollout View résumé