AMS vs AI: the real cost of a D365 ticket in 2026

Your AMS contract states a monthly retainer — say €15,000/month for a team of 2 part-time developers. Dividing by the number of tickets resolved per month (say 12), you get €1,250/ticket. But this calculation is misleading because it does not account for the total cost.

First, the management burden on your side: weekly prioritization meeting (1h × project manager + CIO + 2 business owners = 4h/week × €50/h average = €200/week = €800/month). Follow-up meetings, reminders, testing, post-delivery corrections... Add 20% to the direct cost, or €3,000/month extra.

Second, the cost of delay. A ticket that takes 10 days instead of 1 hour means 9 days during which a team works with a degraded process or manual workaround. If this team delivers €500/day of added value and the delay degrades 20% of their productivity, that is 0.2 × €500 × 9 = €900 of indirect cost per ticket. In the end, the true cost of a standard AMS development ticket is between €2,000 and €3,500 — not €1,250.

SKALP AI charges €200 per resolved ticket. Full stop. No hidden fees, no billing if the ticket is not resolved. But to be honest, let us add ancillary costs: the CIO or project manager's time to write the ticket (15 minutes × €100/h = €25), reviewing and approving the PR in Azure DevOps (10 minutes × €100/h = €17), and business testing (30 minutes × €50/h = €25).

True total SKALP AI cost: €200 + €25 + €17 + €25 = €267/ticket. Let us say €300 to be generous and include all imaginable coordination costs. That is the honest benchmark.

Direct comparison: €300 (SKALP AI) vs €2,500 (AMS, median estimate) = ratio of 1:8.3. For complex tickets (integrations, advanced reports), the ratio can reach 1:15 to 1:20. For simple tickets (form modifications, field additions), the ratio is 1:5 to 1:7. In all cases, the AI model is structurally cheaper — not because of lower quality, but because of fundamentally different efficiency.

The first objection is always quality: 'code generated by an AI in 1 hour cannot be worth code developed by an experienced consultant in 5 days.' This intuition is understandable but empirically false for common patterns. The AI generates code that strictly follows Microsoft conventions, includes unit tests, and documents every method. The experienced consultant sometimes does the opposite — especially when under deadline pressure.

The second objection: 'the €200/ticket model cannot be profitable — there must be a catch.' The answer is simple: the model is profitable because AI reduces development time by 95%. A development that cost 5 hours of consultant work now costs 20 minutes. The margin is in scale, not in the individual ticket. And for the client, the provider's margin is irrelevant — what matters is their own ROI.

The third objection: 'how do you know AI won't introduce bugs in production?' Answer: the same way you know it about a consultant — via unit tests, integration tests, business acceptance testing, and final validation in Azure DevOps. The validation process does not change; what changes is the code producer.

Here is the simplified calculator we offer every interested CIO. Step 1: estimate your current average cost per resolved D365 ticket (include direct AMS/T&M costs and indirect management costs). Step 2: estimate the number of tickets you resolve per month (include all those you defer for lack of budget).

Step 3: calculate the difference: (current cost − €300) × number of tickets/month = monthly savings. Example: (€2,500 − €300) × 20 = €44,000/month in savings. Over a year: €528,000. Even on a more conservative basis of 10 tickets/month with a current cost of €1,500: (€1,500 − €300) × 10 = €12,000/month, or €144,000/year.

Step 4: factor in the value of unprocessed tickets. If your backlog contains 50 tickets awaiting budget, their uncaptured business value often represents 5 to 10× their development cost. By delivering them at €300/ticket instead of deferring them at €2,500, you unlock considerable value while reducing your costs. This is the double benefit of the SKALP AI model.