The conversation around AI in enterprise software has shifted. A year ago, most Microsoft Dynamics users were asking whether to adopt AI. Today, the question is "How fast they can make it useful?" Organizations that have already deployed Microsoft Copilot across their Microsoft Dynamics 365 environments are operating at a measurably different speed. Those that have not are competing against themselves.
In partnership with ERP Software blog, Solver along side seven other Microsoft Dynamics community experts, comprised of VARs and ISVs who work in these environments every day, created a whitepaper that distills the key insights from a panel report. Their perspective is practical, not theoretical, and it cuts through the noise around AI adoption to focus on what actually produces results for finance and operations teams.
Where AI Is Delivering Real Value Inside Microsoft Dynamics
The biggest gains from AI in Microsoft Dynamics are not coming from grand automation projects. They are coming from the elimination of friction in daily work.
When Microsoft Copilot is embedded directly into Microsoft Dynamics Business Central, Microsoft Dynamics 365 Sales, and Customer Service workflows, teams can surface insights without switching tools, answer operational questions without waiting on a report, and catch anomalies before they become problems. The compounding effect is significant: fewer escalations, faster closes, better decisions.
“AI in Microsoft Dynamics adoption drives measurable productivity gains and smarter decision-making through embedded insights, predictive analytics, and natural-language interactions directly within CRM and ERP workflows.”
— Tad Remington, Chief Commercial Officer, Solver
Across the organizations covered in the panel report, five categories showed the clearest and fastest returns:
- Faster insights without added complexity: Teams identify trends, risks, and anomalies and act on them rather than waiting to report on them after the fact.
- Reduced manual workload: Automation of routine processes, from time entry to invoice approvals, frees people for higher-value work.
- Better exception management: AI flags policy violations, spending anomalies, and overtime risks, keeping compliance intact without requiring manual oversight at every step.
- Improved consistency: When AI applies rules and patterns uniformly, the variability that comes from manual processes disappears.
- Higher adoption of Microsoft Dynamics itself: Natural language queries lower the barrier for non-technical users, which means more of the organization actually uses the system.
What Gets in the Way: Common Misconceptions and Adoption Pitfalls
The organizations that struggle with AI adoption tend to share the same misunderstanding: they treat Copilot as a fix for problems that exist upstream of the tool. If workflows are inconsistent, data is messy, or roles and permissions are poorly defined, AI amplifies those issues rather than resolving them.
“AI delivers real value when it augments finance teams and strengthens existing workflows, not when it tries to fix poor data or disconnected systems magically.”
— Marcel Syriani, COO, DATABASICS
Another common trap is assuming that enabling Solver Copilot automatically drives adoption. Without guidance, training, and clear examples tied to the user’s actual job, most people will ignore the feature or use it sparingly. The tools are only as useful as the context around them.
The straightforward prescription from the panel experts: clean your data, tighten your processes, define access and permissions clearly, and then introduce AI as a decision-support layer. Not a magic button. Teams that follow this sequence see adoption happen naturally because the value is visible from the first interaction.
A Practical Approach to Introducing AI Without Overwhelming Your Team
Getting AI into Microsoft Dynamics does not have to be a large-scale transformation project. The experts in the panel report consistently recommended a phased, use-case-led approach:
“The companies who get the most value out of AI are the ones who set clear expectations, start with focused use cases, and roll AI out in a way that fits how their teams actually work.”
— Marcel Chabot, Development Practice Manager, Technology Management Concepts (TMC)
How to Measure What AI Is Actually Delivering
ROI on AI investment in Microsoft Dynamics is measurable, but the right metrics are operational, not abstract. The panel report identified five categories that finance and operations leaders should track:
“Organizations are realizing tangible gains from AI across their operations, including lower logistics, labor, and inventory carrying costs alongside healthier inventory turns and improved cash flow.”
— Nathan Bensch, Enterprise Technology Officer, enVista
Keeping Humans in the Loop: AI as a Decision Support Tool
Every expert in the panel report made the same point in different ways: AI works best when it supports human judgment, not when it replaces it. By surfacing insights, highlighting patterns, and flagging risks, Copilot frees users to focus on the decisions that require context, relationships, and accountability.
“AI can’t fix broken processes on its own, but it can empower humans to make smarter, faster decisions. By highlighting risks and uncovering insights, AI gives people the tools they need to improve operations and drive meaningful organizational change.”
— Kirk Southcott, Partner, Ternpoint Solutions
Three principles support this balance across the organizations seeing the best results: using AI to surface insights while keeping humans responsible for final decisions; explaining AI limitations so users understand how recommendations are generated; and creating the conditions for adoption by making Copilot genuinely useful in daily workflows. Trust in AI follows trust in the outcomes.
Where the Platform Fits Into the AI Picture for Microsoft Dynamics Users
For finance teams running Microsoft Dynamics, the platform extends AI-powered intelligence beyond the ERP into the full planning and reporting lifecycle. Solver connects to Microsoft Dynamics 365 through patented QuickStart integrations and surfaces AI-driven insights through Solver Copilot, which includes a Help Agent for product and application questions and an Analysis Agent for anomaly detection, trend identification, and predictive recommendations. The AI assistant is available in the United States.
The result is a finance team that does not just wait for the ERP to tell them what happened. They get ahead of variances, close faster, and present more accurate forecasts to leadership. For organizations already invested in the Microsoft ecosystem, Solver’s xFP&A platform is designed to accelerate the time-to-value of that investment.
Is Your Organization Ready for AI in Microsoft Dynamics?
Before expanding AI deployment, ask five questions:
- Is your data accurate, consistent, and easy for AI to access?
- Are your key processes clearly defined and standardized?
- Are your teams open to learning and using AI in daily workflows?
- Do you have clear goals for what AI should achieve?
- Is there a plan in place to guide adoption, training, and governance?
If most of these answers are yes, you are in a strong position to move forward. If several are not, the investment in getting there will pay back faster than you expect once AI is in place.
Get the Full Report
The insights above come from a panel report compiled by ERP Software Blog, Solver and seven other Microsoft Dynamics community experts across implementation, consulting, and ISV perspectives. It covers AI adoption strategies, ROI measurement, and how organizations are balancing automation with human decision-making in real operational environments.
