One of the most critical phases of a Microsoft Dynamics implementation is requirement gathering. While this phase is necessary, relying on spreadsheets, Word documents, and email threads leads to delays, inconsistencies, and increased costs that affect both partners and their clients. As language models gain traction, it's time to ask: How much is the reliance on manual requirement gathering really costing your consulting firm? In this post, we will go through the hidden costs of manual requirement gathering and why AI can solve this problem that affects Dynamics partners in project delivery.
Manual requirement gathering is obsolete.
Consultants spend weeks gathering and organising client requirements. Typically, scoping workshops are structured on pre-built agendas, which forces the consultants to follow a rigid script. The problem with this approach is that it leads the team to discuss requirements as itemised to-do lists.
During the analysis workshops, consultants follow the agenda topics and take notes. Sometimes, when working on complex projects, a senior consultant is joined by a junior who takes notes while the senior or lead consultant discusses the requirements with the client's team.
At the end of every analysis workshop, the consultants discuss the requirements; notes are reworked and copied to spreadsheets that include other requirements, the project scope, and other details depending on the partner methodology. The time allocated to write-ups and documentation typically doubles the time spent with the client discussing the requirements, if not more.
Further clarification is sometimes required after reviewing the delivery team's arrangement of the requirements in a solution design document or a spreadsheet. This clarification process can take emails or even more meetings and more write-up time.
Risks associated with manual requirement gathering
Microsoft recommends using a process-focus approach to requirement gathering based on the Business Process Catalogue. Nevertheless, whether partners use fit-gap analysis or business process reengineering, consultants always end up with lists of requirements in their notes, spreadsheets, client documents, and emails.
Organising all this documentation can take days of work and exposes the consulting team to the risk of missing out on critical requirements. The risks generated by such a process are many; the most common are:
Scope creep
Misunderstanding requirements
Increased costs
When conducted using notes, spreadsheets, and emails, the requirement-gathering phase increases the risk of missing critical design details that will impact the project during user acceptance testing (UAT).
Presales and requirement gathering. Different scope, similar risks.
Presales teams discussing Dynamics deals with new prospects face similar problems even if the scope of their work is different.
The presales analysis does not typically need to be very detailed. Still, the number of documents exchanged with the prospects during this phase can even exceed the documents produced by the delivery team in the analysis phase.
Getting the requirements right during the presales phase can sometimes make the difference between winning or losing a deal.
Requirements lists do not describe processes.
Even when the costs and the risks associated with capturing requirements manually in fragmented documentation are affordable, the other challenge with manual requirement gathering is that a list of requirements does not describe how organisations operate. Presales, functional consultants and solution architects must translate the requirements into a solution that explains how the client's team can operate using their new Microsoft Dynamics application.
The consulting team often reviews missing documents, long email chains, and Teams call recordings, which increases the costs and time required to deliver an estimate, a proposal, or the solution design before the building phase can begin.
Use AI to streamline requirement gathering.
In the pursuit of greater efficiency, streamlined processes and automation, language models offer the perfect solution to reducing costs and, in some cases, eliminating the risks associated with the current tools and methods used during the requirement-gathering phase.
Why an AI tool can help streamline requirement gathering?
Language models can understand natural language and identify key points, create summaries, and organise insights. The ability to converse with an AI application about the content of calls, emails, and messages makes AI the perfect solution for requirement gathering, reducing the scoping time of a typical Dynamics implementation from weeks to hours.
In addition to the ability to transcribe meetings, which can streamline note-taking, AI can help keep everything together thanks to the following:
Compare the requirements with Dynamics features and capability when used in combination with a knowledge base
Help the delivery team identify gaps using an AI chatbot, almost like a custom ChatGPT or a dedicated Copilot
Automate the creation of project documentation, including Word documents and spreadsheets, or even automate the creation of work items in DevOps via APIs or low-code automation apps like Power Automate.
Use unstructured data from multiple sources such as Emails, clients' documents saved in SharePoints and chat history.
What do Dynamics Partners Need to Get Started
Putting together an AI consulting agent is more straightforward than it might sound. First, start with Microsoft Teams and use the meeting transcript as the first source of data for the AI agent. Automation can then be used to save the transcripts in cloud storage, such as Azure. Use the same storage to save other project documentation, such as client documents.
After that, give your AI agent access to a Dynamics knowledge base; this can be the official Microsoft training material on learn.microsoft.com. Lastly, ensure that you prompt your agent with project details such as company name, industry and size, and names and roles of key stakeholders.
Conclusion: The Cost of Staying Manual
Continuing to rely on manual fit-gap analysis is costing Microsoft Partners time, revenue, and consultant productivity. AI-powered solutions offer a way to reduce inefficiencies, improve accuracy, and close deals faster.
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