Executive Presentation  ·  2026
AI Initiative
APPSolve
Measured results from our own builds. A path to scale them. The decisions that make it happen.
400h Project
400h → 50h 8x faster
Effort reduction on our own pilot
Dev Workflow
4h → 15min 16x faster
Maintenance screen build time
PM Turnaround
3 days → 1 prompt
Working mockup + spec + ClickUp task, automatically
02  ·  Where We Are Today
The Problem We're Solving
Three friction points that slow delivery, fragment knowledge, and expose the team to unnecessary risk.
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Development Velocity
Delivery is slower than it needs to be.
Large projects take hundreds of hours. Maintenance screens take half a day. Every sprint, those hours are available to be recovered. They aren't yet, because the workflow isn't standardised.
400 hours per large project  ·  4 hours per maintenance screen
Knowledge Fragility
Critical knowledge lives in one person.
Client-specific customisations, undocumented processes, tacit system context. When that person is unavailable, the team loses days recovering what should already be written down. The bus factor is real.
No source control  ·  Undocumented customisations  ·  OneDrive-only
Output Inconsistency
AI is already being used. Without a standard.
Individuals are using AI tools right now, each with their own approach. The results vary. Some are good. Many need significant rework. Without a shared workflow, the potential stays with the individual.
Untracked usage  ·  Inconsistent output  ·  No verification layer
03  ·  What Changes
Why AI? And Why Now?
Three results from our own development work, produced with a standardised AI workflow. Each measured. Each reproducible.
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8x
Faster Delivery
400-hour project reduced to 50 hours. Same developer, same quality standard, standardised AI workflow applied end-to-end.
400h Project
16x
Build Speed
Maintenance and customisation screen builds from 4 hours to 15 minutes. Repeatable by any developer following the workflow.
Dev Workflow
1
Prompt
PM mockup turnaround: 3 days to one prompt. Produces a working, on-brand screen plus the use case and technical spec. ClickUp task created automatically.
PM + ClickUp
04  ·  How It Works
The Scaffold: What Makes It Work
The numbers on the previous slide aren't produced by "using AI." They're produced by a structured, tested, verified generation system.
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Architecture
Design Token Layer
tokens.css  ·  109 CSS vars (truth source)
8,294-line compiled CSS  ·  multi-tenant brand
Component Layer
7 Razor shell fragments  ·  18 atomic widget primitives
25 BMP menu icons  ·  brand assets
Composition Layer
14 YAML recipes (A–M)  ·  design-system.yaml (Claude reads first)
prompt.md hard rules  ·  14 slot-substituted init templates
Quality Layer
verify.js live-vs-replica probe  ·  38 verified test screens
Returns tokens + shellRects + primitiveStyles
Claude reads the design system first, follows the composition rules, generates on-brand output, then verifies against a live replica before the screen is considered done. Generic AI gives you a rough idea. The scaffold gives you a production-ready screen.
The Pipeline: 1 Prompt to ClickUp Task
01 ///
PM writes one prompt
Describes the screen or feature needed
↓
02 ///
Scaffold generates on-brand screen
Reads recipes, applies tokens, verifies against live replica
↓
03 ///
Use Case Builder produces spec
Structured use case + technical spec for developers
↓
04 ///
ClickUp task created automatically
Work item tracked from brief to delivery. No manual handoff.
05  ·  The Developer Scaffold
How It Works for Developers
One /scaffold init deploys a project-aware AI infrastructure. Code-specific enforcement. Knowledge that persists.
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Scaffold Architecture
scaffold/
├─ CLAUDE.md.tmpl • project identity + key commands
├─ Conventions • Things That Will Bite You
├─ hooks/ • 14 Python scripts, one concern each
guard_style.py • doc_check.py • memory_extract.py
└─ discovery/stack_markers.json • 8 stacks auto-detected
7 Commands  •  Any Project
/scaffold init · Bootstrap CLAUDE.md, docs/, hooks/, settings.json
/scaffold doctor · 26-validator health check. Flags drift + broken files
/scaffold upgrade · Pull latest templates without touching project files
/scaffold repair · Auto-fix broken scaffold state (doctor + fix failures)
/scaffold discover · Re-scan project: update overview + code fingerprint
/scaffold modules · List all modules with their status for this project
/scaffold tour · Walk through what each enabled module does here
Hooks are copied per-project. Self-contained and portable.
The Pipeline: 1 Session to Persistent Knowledge
01 ///
Developer opens any project
One-time /scaffold init deploys CLAUDE.md, hooks, and settings. Never repeated.
↓
02 ///
Session starts. CLAUDE.md auto-loads
Stack, conventions, gotchas, and key commands injected before the first prompt. No re-explaining the codebase.
↓
03 ///
Claude generates convention-aware code
kebab-case files, PascalCase classes, correct API shape, right branch naming. All from context, not instruction.
↓
04 ///
Session ends. Knowledge persists
Retrospective written. Corrections extracted as memory. Gotchas captured. Next session starts with everything the last session learned.
06  ·  Internal Pilots
Proven Results From Our Own Team
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Development Results  ·  Scaffold Applied
87.5%
Effort Reduction
400-hour project — 50 hours delivered. Same developer. Same quality standard. The scaffold applied at each stage: spec, code scaffolding, tests, documentation.
94%
Build Time Saved
Maintenance screens: 4 hours to 15 minutes. Repeatable by any developer following the standardised workflow.
Our own team. Our own work. Not vendor projections.
PM Case Study  ·  Full Pipeline
From brief to tracked work item in one prompt.
A PM wrote one prompt. The scaffold generated a working, on-brand mockup. The use case builder produced the spec. ClickUp created the task automatically. 3 days of work compressed to minutes. The developer starts from a verified, tracked work item with no manual handoff.
BEFORE / AFTER
3 days → 1 prompt → ClickUp task
Mockup + use case + spec + work item. No manual step.
Proof 01
400h Project Efficiency
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The Scenario
A development team quotes a large project: 400 hours estimated. With the AI scaffold applied end-to-end, initial UAT delivery came in at 50 hours.
Same developer. Same quality standard. The scaffold handled spec generation, code scaffolding, test generation, and documentation at each stage. The 87.5% reduction is the scaffold working, not luck.
What this means for APPSolve
Every large project in the pipeline carries the same opportunity. At current project volumes, this multiplier translates directly to delivery capacity, client satisfaction, and margin.
Before
400h
MANUAL WORKFLOW
After (scaffold applied)
50h
AI SCAFFOLD APPLIED
The Result
87.5%
EFFORT REDUCED
Same developer. Same quality standard. 8x faster delivery. UAT-ready output, not a prototype.
Proof 02
PM Workflow & ClickUp Pipeline
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The Scenario
A PM writes one prompt. Three minutes later: a working, on-brand, interactive mockup exists. The use case is built. The technical spec is written. A ClickUp task is created and tracked.
Without the scaffold, this PM was spending 3+ days per deliverable with inconsistent results. With it, the entire brief-to-tracked-work-item pipeline runs in one session. The developer starts from a verified, documented task. No manual handoff. No lost context.
The scaffold is the difference
The mockup generator reads the design system, follows composition recipes, applies the correct tokens and components, then verifies against a live replica. The output isn't "an idea." It's a production-ready screen that passes verification. That's what makes the ClickUp task trustworthy enough to send to development immediately.
01 ///
PM writes one prompt
Describes the screen or feature. No wireframe, no spec document, no design brief needed upfront.
02 ///
Scaffold generates verified mockup
Reads design-system.yaml, follows recipe rules, applies correct tokens and components, verifies against live replica. Output is production-ready.
03 ///
Use Case Builder produces spec
Structured use case + full technical spec for developers. Same format every time. No variation, no gaps.
04 ///
ClickUp task created automatically
Work item created with spec attached and context preserved. Development starts from a tracked, verified work item. No manual handoff required.
Proof 03
Developer Centralisation
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The Scenario
A senior developer holds critical knowledge: client-specific customisations, undocumented processes, tacit system context. If they're unavailable, the team loses weeks recovering what should already be written down.
The AI-augmented workflow eliminates this fragility. Code is documented as it's written. Customisations are committed with context. A junior developer following the scaffold can pick up where the senior left off with minimal handover. The knowledge lives in the process, not the person.
GitHub as the foundation
Source control for Acumatica customisations, reports, and client repos, combined with AI-generated context in every commit, creates a centralised, transferable knowledge base. This is the infrastructure that makes the team resilient and makes GitHub adoption the obvious next step.
01 ///
Before: Knowledge in one person
Customisations undocumented. Context in one developer's head. Any disruption means recovery time, rework, or client impact.
02 ///
After: Knowledge in the process
Every commit includes AI-generated context. Documentation is a byproduct of development, not a separate task. Any developer can continue the work.
03 ///
The Outcome: Team resilience
Bus factor eliminated. Onboarding time reduced. Client delivery continuity guaranteed even when the person who built it is not available.
Proof 04
Resource Planning & Project Management
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The Scenario
Project scoping, resource allocation, and timeline estimation are time-intensive and often inaccurate, leading to scope creep, underquoting, and resource bottlenecks.
With AI-assisted project management workflows, teams generate detailed work breakdowns, estimate effort from historical patterns, flag resource conflicts before they occur, and keep project documentation current without manual overhead. Planning that used to take days compresses to hours.
What changes at APPSolve
Accurate scoping means accurate quotes. Accurate quotes mean healthier margins. Resource planning that surfaces conflicts before sprint start means fewer mid-project disruptions and more consistent delivery across clients.
01 ///
Scoping and estimation
AI generates detailed work breakdowns from a brief. Effort estimates drawn from historical project data. Quotes become data-driven, not gut-feel.
02 ///
Resource allocation
Cross-project capacity checks run automatically. Conflicts surfaced before they become blockers. Right skills on right work, without manual juggling.
03 ///
Documentation as a byproduct
Meeting notes, status updates, and handover docs generated from context. Project managers spend time managing, not documenting.
11  ·  The Direction
Cloud-First Now. Local AI Next.
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Phase 1  ·  Active Now
Cloud AI: fastest path to results
Cloud AI delivers capability right now, without infrastructure investment before governance and skills are in place. Start with cloud, where the gains compound fastest and risk is lowest. The scaffold runs on cloud first.
Why the timing matters
The AI space is maturing, not just growing. Tooling, orchestration patterns, and collective understanding are converging. Each maturity cycle builds on the last. Teams with structured workflows capture each improvement as it lands. Teams without foundations spend each cycle catching up. The gap widens with every wave.
Phase 2  ·  Once Foundations Are Set
Local AI deployment
Once governance, standardisation, and team capability are in place, local deployment becomes viable. The scaffold built now makes local deployment responsible later.
Data stays on your infrastructure. No external exposure.
Natural progression from the cloud foundations being built now
In Pipeline  ·  Proof of Direction
Gemma 4 E4b
1
Active Client
First Local AI Deployment
Document classifier POC, approved and in active development. Local inference, no external data exposure.
12  ·  The Initiative
Our AI Initiative: What We're Building
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01 ///
Education
Structured AI literacy for every developer: tools, prompting, and responsible use. Built on the failure modes already documented so teams skip the learning curve.
02 ///
Standardisation
Defined AI workflows and proven methods. Codified processes that replicate the 15-minute builds and 50-hour projects across every team and every client engagement.
03 ///
Policy & Governance
Clear policies on what tools to use, how data is handled, and what outputs require validation. Removes ambiguity and makes AI use auditable.
04 ///
Measurement
Tracking time saved, quality improvements, and ROI. Makes the business case visible and reportable at every leadership review.
05 ///
Team & Resourcing
One person owns standardisation, iteration, and feedback. The PM workflow went from concept to V4 because one person championed it. That ownership model, applied at team level, is how this scales.
06 ///
Executive Alignment
Three asks: public leadership commitment, approval for the short-term productivity buffer during upskilling, and policy sign-off. These are what move this from individual effort to company capability.
13  ·  Honest Disclosure
Understanding the Adoption Curve
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There is a learning dip. We are planning for it.
When developers begin adopting AI, initial productivity drops 20-30% as they map the learning curve. This is expected and temporary. The real budget cost is billability, not cash. The scaffold shortens that dip significantly, because the failure modes are already documented. Teams that follow it recover faster and stabilise higher.
+5× +3× +2× +1× 0 -1× -3× -5× +3x early win (2–5× range) −3x dip (matches early gain) +2x stable (2–3× range) Weeks 1–4 Month 2–3 Month 4–5 Month 6+ BASELINE
Why the field is accelerating
LLM inference costs fell 40x per year (Epoch AI). The same AI capability costs 280x less than two years ago (Stanford AI Index 2025). Every month of delay means competitors access better tools at lower cost. The adoption curve gets steeper, not flatter, the longer you wait.
01 ///
The Dip Is Temporary
Productivity drops 20-30% as developers adjust. Those who persist (especially with scaffolded guidance) recover and exceed their original output.
02 ///
The Scaffold Shortens It
The failure modes are already mapped. The scaffold gives developers a structured path through the learning curve, not trial and error.
03 ///
Stagger the Rollout
One team at a time. Keeps delivery stable, lets us refine the rollout, and surfaces problems early without broad impact.
14  ·  The Ask
What We're Asking For
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01
Budget Approval
The real budget is billable time, not direct cash. People's billability dips temporarily as they upskill. Approval means accepting a short-term billability buffer while teams clear the adoption curve. The payback begins immediately after and compounds with every project.
02
Executive Sponsorship
Visible leadership endorsement to signal this is a company priority, not a team experiment. Without it, individual effort cannot produce company-wide change.
03
Dedicated Headcount
One person with clear ownership to drive standards, training, and continuous improvement. This moves the initiative from person-dependent to institution-dependent.
04
Policy & Governance Sign-off
Executive approval of an AI usage policy, formalising what is encouraged, permitted, and required. This protects the business and makes AI use consistent and auditable.
15  ·  Q&A Preparation
Addressing Your Concerns
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"Isn't this moving too fast?"
Our results come from structured pilots with proven processes. The scaffold already exists. We're not asking to abandon discipline. We're asking to codify it. Standardisation is the opposite of recklessness. It is how we make what works repeatable.
"If this is working, why isn't it spreading already?"
Results don't spread on their own. They spread when someone structures the evidence clearly enough that a decision becomes easy. You're experiencing that right now. The initiative formalises that process so it isn't dependent on one person doing it. That's how individual results become company results.
The Data Is Clear  ·  The Results Are Real  ·  The Risk of Delay Is Measurable
We move from pilots
to practice together.
Four decisions. One direction. Secure our competitive position.
→Approve the budget to scale our AI initiative
→Provide executive sponsorship to signal company-wide commitment
→Authorise dedicated headcount to own and drive AI standards
→Sign off on the AI governance policy to protect the business
APPSOLVE  ·  AI INITIATIVE  ·  2026