Executive Summary
DynaExperts faced a recurring challenge: repetitive, time‑consuming tasks slowing down their Business Central App based development projects. By strategically applying AI – starting with automated code documentation and expanding into solution architecture and code scaffolding – they cut documentation time by 35%, reduced error rates by 20%, and boosted developer morale. This case study explores how DynaExperts turned AI into a productivity powerhouse without compromising quality – and why developers aren’t being replaced, but elevated.
Client Background
Company: DynaExperts Consulting LLP
Industry: Microsoft Dynamics 365 Business Central consulting & development
Team Size: 2–10 developers and architects
Prior AI Experience: Minimal – primarily experimental
In a competitive ERP consulting market, DynaExperts needed to deliver high‑quality solutions faster and more consistently. They decided to harness AI in Business Central development to accelerate routine work, while keeping human expertise at the forefront.
Challenges
- Documentation Overhead
Writing thorough AL code comments, release notes, and AppSource‑compliant docs consumed up to 20% of each sprint. - Boilerplate Scaffolding
Creating tables, pages, reports, and APIs involved repetitive patterns that drained developer creativity. - Solution Design Friction
Drafting architecture outlines and data models from scratch led to blank‑page syndrome and extended planning cycles. - Quality & Control
Early AI experiments risked producing incorrect code or missing edge‑case logic without rigorous review.
AI‑Driven Solution
1. Automating Code Documentation
Approach: Developers fed completed AL code into an AI assistant with prompts like “Generate concise comments and parameter descriptions for this codeunit.”
Outcome: AI produced first‑draft documentation in minutes. Humans reviewed and refined – raising documentation consistency and clarity.
Impact: Documentation effort ↓35%; developer time freed for high‑value tasks.
“Automating docs was our low‑hanging fruit – AI comments went from ‘nice to have’ to mission critical overnight.”
2. AI as a Co‑Architect
Approach: During solution design sessions, architects prompted AI: “Outline a data model and page extensions for integrating a customer portal with BC.”
Outcome: AI delivered a draft architecture complete with table relationships, event‑queue suggestions, and indexing recommendations.
Human Role: Reviewed, refined, and selectively adopted AI suggestions – preserving deep domain knowledge.
Impact: Planning cycles accelerated by 25%; fewer design iterations needed.
“AI didn’t replace our architects – it gave them a turbo‑charged brainstorming partner.”
3. Streamlining Code Scaffolding
Approach: Integrated GitHub Copilot into VS Code for AL. Prompts like “Scaffold a table extension with fields A, B, C, plus a page and report layout.”
Outcome: Copilot generated boilerplate code stubs instantly. Developers focused on business logic and edge cases.
Impact: Routine setup tasks sped up by 60%; developers report a “pair‑programmer” experience.
4. Prompt Engineering & Governance
Internal “Prompt Cookbook”: Documented best‑practice prompts for documentation, architecture, and scaffolding.
Human‑in‑the‑Loop Reviews: Mandatory code and doc reviews to catch AI‑introduced quirks.
Data Security: Anonymized client details in prompts; exploring private AI instances for sensitive code.
Iterative Refinement: Logged AI errors to refine prompts – ensuring continuous improvement.
“We treated AI like a junior developer: talented, but always supervised.”
Results & Metrics
| Metric | Before AI | After AI | Improvement |
|---|---|---|---|
| Documentation Time | 20% of sprint | 13% of sprint | ↓ 35% |
| Average Error Rate in Scaffolding | 5 errors/week | 4 errors/week | ↓ 20% |
| Architecture Planning Time | 8 hours/model | 6 hours/model | ↓ 25% |
| Developer Satisfaction (survey) | 3.8/5 | 4.5/5 | ↑ 18% |
- Developer Morale: Higher engagement – teams enjoy solving complex problems rather than repetitive tasks.
- Quality: Fewer late‑stage bugs; clearer documentation accelerated onboarding for new hires.
- Speed: Faster project delivery timelines without sacrificing depth or customization.
In our earlier blog, “Will Copilot Replace Business Central Developers?”, we argued that developers who evolve won’t be replaced by AI. This case study brings that thesis to life: by embracing AI as an assistant rather than fearing it as a replacement, DynaExperts elevated their entire development practice.
Key Takeaways
- Start Small: Automate documentation first – easy wins build team confidence.
- Keep Humans in Control: Always review AI output; maintain domain expertise as the guiding force.
- Scale Gradually: Expand AI use to architecture and scaffolding once initial successes are proven.
- Invest in Prompt Crafting: A shared “prompt cookbook” maximizes AI relevance and quality.
- Monitor & Refine: Track metrics and feedback to continuously improve your AI workflow.
Ready to leverage AI in Business Central development without losing control?
DynaExperts is here to guide you – whether it’s building your internal AI‑powered workflows, sharing our prompt‑tuning playbook, or co‑developing your next BC solution.
📩 Contact us at snehanshu@dynaexperts.com or visit dynaexperts.com to explore how we can accelerate your ERP projects together.
By combining human expertise with AI efficiency, DynaExperts transformed both their processes and their people. If you’d like to see these strategies in action, let’s start a conversation today.