Scaling discovery workshops with AI-driven knowledge capture and requirements traceability

Discovery is where every successful ERP or CRM project truly begins. It is the phase where project leaders uncover needs, map priorities, and align stakeholders. But as project volume increases or client complexity grows, the traditional discovery workshop model begins to break down.

Most discovery and requirements gathering workshops depend heavily on experienced facilitators, in-person collaboration, and manual synthesis of notes. This works well for a single project with a small team. It becomes a bottleneck when your organization is running multiple implementations at once, across different verticals and stakeholder groups.

Microsoft Partners who want to scale need a better way to deliver consistent, high-quality discovery without adding more consultants or cutting corners.

At Tato, we help System Integrators solve this challenge with AI-powered discovery tools. By using automated requirements capture and continuous learning tools based on past projects, we enable teams to scale workshops without losing depth, accuracy, or stakeholder trust.

The scaling problem with traditional discovery

Every discovery session aims to surface key information: What are the client’s goals? Who are the stakeholders? Where are the inefficiencies and risks? And what will success look like for each group involved?

In theory, a well-run workshop answers all of these. In practice, most discovery efforts rely too much on human memory, personality, and improvisation.

Here is where traditional discovery methods often fall short at scale:

• Each facilitator brings a different style and level of experience
• Workshops become less interactive as more stakeholders join
• Critical inputs are missed because there is not enough time for follow-ups
• Notes are manually transcribed and interpreted differently across teams
• Synthesis takes days or weeks, slowing down the momentum of the project

As projects scale, this creates inconsistency, and while some teams run excellent discovery, others miss key details. The client experience varies based on who leads the session and how well they document it.

These gaps do not just create inefficiency. They increase delivery risk. If stakeholder input is missed or misinterpreted during discovery, the entire project starts with the wrong assumptions.

What clients expect from the discovery process

Clients today expect more from discovery than a few generic workshops and a templated document. They want to feel heard. They want to see that you understand their business. And they want a discovery process that respects their time, not one that stretches on for weeks.

The challenge is that stakeholder availability is often limited. Key users are busy. Decision-makers are spread across departments and geographies. Pulling everyone into live workshops can be costly and disruptive.

This is especially true in verticals such as healthcare, professional services, and manufacturing. Industries where business-critical processes cannot be paused just to participate in discovery. To meet these expectations, partners must build discovery systems that are flexible, consistent, and scalable. That is where AI helps.

How AI improves stakeholder engagement

Tato’s AI-powered discovery tools solve the scaling problem by enabling users to gather requirements and centralize all of the discovery data on one platform.

Instead of relying on a single workshop, our system collects input from multiple stakeholders asynchronously. It uses AI to identify risks, assumptions, and missed requirements to ensure that your team gets the project completed the right way.

Here is how this approach improves the discovery process:

1. Personalised intake

Project managers do not need the same questions as operations leads. Tato's tools work for all stakeholder role in the business and the system being implemented, whether it is Dynamics 365 Business Central, Finance, or Sales.

2. Unified engagement

Stakeholders can complete their input on their own schedule. This reduces the burden of scheduling and increases participation rates, especially from users who are hard to reach during workshops.

3. Structured outputs

The system organizes responses into clear summaries tied to business processes, goals, and technical implications. This structure creates a source of truth for consultants, solution architects, and project managers.

By streamlining the discovery process and using AI, you turn discovery into a living, collaborative process instead of a one-time event.

What this means for delivery teams

• When discovery is consistent, your delivery teams benefit immediately.
• Solution architects work with complete, accurate context
• Consultants avoid redoing discovery during design
• Project managers have clearer insight into risks and dependencies
• Documentation stays standardised across clients and verticals

Most importantly, your teams can take on more projects without sacrificing quality. AI handles the repetitive parts. Your experts focus on the strategy, context, and collaboration that add real value.

Case scenario: scaling discovery across 10 ERP clients

Consider a Microsoft partner onboarding 10 new ERP clients in one quarter. Without a scalable system, they would need to run 10 live workshops, manage 100+ stakeholder interviews, and document thousands of data points by hand.

With Tato:

• Knowledge from each project can help educate the process for the next
• Stakeholder input feeds directly into structured summaries
• Project leads can review discovery data within hours, not weeks
• Consultants focus their energy on solution design, not information gathering
• The project are completed on time and on budget while enabling your team to do more

Instead of asking, “Who has capacity to run discovery next week?” your team asks, “How do we refine and validate what we’ve already collected, and ensure customer satisfaction?”

How this improves the client experience

Discovery is often the first major engagement a client has with a partner after signing a project. A poor experience here sets a negative tone.

When clients experience AI-enhanced discovery, they notice the difference:

• Their time is respected
• Their input is taken seriously
• They receive structured feedback and summaries quickly
• They are asked insightful, relevant questions

This builds trust. It also demonstrates the kind of digital maturity clients want to see from their implementation partner.

Clients expect partners to be efficient, organized, and technically capable. A streamlined discovery process proves that your firm understands not just the software, but also the business processes and communication dynamics required for a smooth implementation.

Turning discovery into a repeatable system

At Tato, we help partners turn discovery into a repeatable, scalable system, not a workshop dependency. This structure does not eliminate human expertise. It supports it. Your team stays focused on strategic conversations while the platform handles intake, documentation, and alignment tracking.

You reduce the variability in your project starts and create consistent, high-quality experiences across your client base.

A smarter way to start projects

Scaling ERP delivery is not just about hiring more people. It is about building systems that work across clients, regions, and industries. Discovery is the best place to start.

With AI-driven prompts and automated stakeholder intake, you can:

• Increase discovery capacity without burning out your team
• Improve documentation quality and consistency
• Reduce handoff time between presales and delivery
• Strengthen client engagement from the start

If your team is still running discoveries the old way - scheduling back-to-back meetings, scrambling for notes, and reinventing questions every time - it is time to modernize. Let’s scale smarter. Tato helps Microsoft partners scale discovery without sacrificing quality, accuracy, or client trust.