Where are the implementation cost-reductions ?

I’ve been implementing D365 since it first became available. Over the years, the improvements have been both incremental in the short term and fundamental in the long run. Cloud, AI, and modern architecture have reshaped what’s possible.

But what puzzles me is this: the costs of implementing D365 and transforming business operations haven’t changed in any dramatic way. In short—it’s still as expensive as before. D365 projects remain a significant investment. I haven’t seen groundbreaking cost reductions nor revolutionary improvements in project timelines.

Is it the complexity of the businesses we serve that keeps costs high?
Or is it the way we implement?

We now have more tools than ever before: preconfigured templates, industry accelerators, AI-assisted data migration, automated testing, low-code/no-code extensibility. But has any of this translated into faster, leaner projects? Or do these same tools just create space for more scope, more configuration, and more “what if we also…” discussions?

Maybe the real challenge isn’t the technology at all, but people. Business transformation has always been more about change management than software deployment. Even with better platforms, organizations still struggle to align processes, culture, and governance. Could these softer elements be the real bottleneck—and no technology ever deliver the cost reductions we expect?

Or is it us—the implementers?
Do we hold on to project models that worked in the past instead of fully embracing new approaches? Are we overcomplicating, or simply responding to inherent complexity?

And perhaps there’s another angle: the way projects are guided from the top. Do managers at implementation partners truly understand the realities of modern D365 projects? Or are decisions sometimes made with outdated assumptions about effort, scope, and methodology? It’s a delicate question—but if the leadership guiding these projects hasn’t evolved as quickly as the technology, could that also explain why costs remain stubbornly high?

And what about the customers?
Do they sometimes expect D365 to be a silver bullet, expanding scope beyond what’s realistic? Does the push for customization and perfection undermine the potential for a lean, standard-first approach?

If costs haven’t dropped, maybe the question shouldn’t stop at cost. Perhaps the value and revenues for companies implementing D365 have increased—making the same (or even higher) implementation spend worthwhile. Have organizations gained agility, sharper insights, or stronger customer engagement that offset the cost? If so, maybe the calculation has shifted from cost reduction to value creation. If not, then the cost question becomes even more urgent.

Looking back, I see remarkable progress in the platform itself. Yet when I look at implementation costs, I can’t shake the question: have we really moved forward in how we implement?

So the question remains: Have you actually seen D365 implementation costs go down—or is the real story in the value delivered?


Some facts to reflect on

  • Implementation still costs 2–5× license fees — $50K in licenses often means $150K–$250K first-year total (source).
  • Timelines haven’t collapsed — large D365 projects still average around 14 months (source).
  • Value is real — IDC found organizations gained an average of $20.6M in annual benefits after D365 implementations (source).

Chatty have helped with this post, but all content is mine.

D365 : Is AI fast enough ?

The short answer is no!  It is and will be too slow for a long time!  But slow does not necessarily mean useless. We must set realistic expectations and create use cases where it is OK to be slow.  I work a lot with performance enhancements and tuning Dynamics 365.  I understand the underlying platform and architecture, how data is stored and fetched from Azure SQL and computed on.  I see the latency and most importantly see the effect of tuning algorithms. 

For “close-to-real time” scenario’s, AI/Copilot does not even come close to what we see in algorithmic performance.  Let’s say you have an eCommerce site, or a POS.  The user selects the products, and we need to present a price within a few milliseconds.  Algorithms can do that.  AI cannot.  But AI solutions are excellent to build and adjust the business data and rules used for a pricing engine, when done in the background by AI agents.

This means that at the AI can set up and feed Dynamics 365 with the right data/setup to fulfill defined goals and scenarios. In the future I expect we can have AI Agents connected through MCP that monitor current sales data, cost changes, competitor pricing,  and availability.  We can have AI Agents that adjust and review current pricing and come up with recommendations on what to change.  Price adjustments are then a logical business decision based on actual data to optimize revenues.

Today I see that the algorithmic performance is very fast, but the human reaction time/processes to adjust pricing and related data is slow.  In the future pricing will be based on defined goals given to your Dynamics 365 agent.  This agent will then perform analysis, run simulations, ensure approvals, monitor effect and adjust with optimalizations.  But also communicate the price change effects to those responsible.  The agents will enter the data into the forms and tables, making it ready to be used fast algorithmic applications.

We then get the best of two worlds;  Fast algorithmic real time performance, mixed with the slow asynchronous AI that analyze and to all the heavy lifting to find better pricing and ensure better profitability.

So don’t fire your traditional developers yet.  They are still needed to create lightning-fast real-time algorithms.   

(This article was written without the use of any slow AI’s)