Nailing Discovery in Your Planning Process Using AI Agents

I'm about to show you exactly how to nail the discovery phase in your planning process using AI agents. After working with multiple enterprise planning teams across hundreds of initiatives, what I typically see is the discovery phases ending not because they're done, but because the project has been in them too long and power and politics push them forward. Information gets spread across documents and emails with no single view of where the project stands. Stakeholders get pulled in only after decisions were already made, and requirements that look complete turn out to be assumptions nobody ever confirmed. The result is weak project charters and the lack of alignment to fix them before they impact execution downstream. Basically, low confidence, low trust in the early stages of the project planning cycle is pretty much the norm in the enterprises that I've been talking with for the last 5 years. So in this video, I'm going to cover why discovery phases often can't reach completion on their own, how AI agents can handle every stage from context capture to stakeholder alignment to approvals and what a clean handoff to execution actually can look like. Let's get into it. So the core problem is that information in early planning has no permanent home. There's no system for it historically. Requirements get documented in one file, decisions get made over email or Slack, and context sits in someone's head from a meeting nobody fully captured. And when you ask where the project stands, you're building the answer from scratch every time because there's no single view that's actually current, comprehensive, or complete. And there's no signals that alignment has actually been achieved. From there, the process turns into coordination overhead instead of actual discovery work. Whether it's who owns a decision, who needs to weigh in, or who has authority to approve, these questions get asked, partially answered, and asked again 2 weeks later when a new stakeholder gets added. So you end up spending all your time keeping everyone aligned instead of actually making progress. And underneath all that, critical pieces are missing that no one can identify in the moment. The data backs this up too. Over 50% of software projects end up with incorrect or incomplete requirements, not because requirements weren't written, but because they were never fully confirmed. Gaps were missed. Over 85% of projects that fail or require rework fail because they lack the right set of stakeholders and project subject matter experts weighing in. It's no wonder that the requirements were, were not correct when it came to execution. Dependencies aren't fully mapped, so risks don't surface until execution is already underway. So discovery ends not because everything is accounted for, but more likely because the project can't stay in discovery indefinitely. And often someone in power is pushing to move it to execution even though there are key gaps. By the way, if your discovery process looks anything like this, I built a platform called Force Equals to fix it. If you're curious about how it works, I'll leave the link at the top of the— in the description. Now let's get into what running discovery with agents looks like. Here's what changes when AI agents handle each stage of the process. The first thing, agents do is capture context continuously instead of relying on documents to stay current. Every piece of project context gets pulled together and structured automatically. So goals, decisions, constraints, open questions, and the reasoning behind each choice is captured, and nothing important ends up buried in someone's inbox or an outdated slide. From there, agents identify what's missing before it hardens into an assumption. Agents catch things like context gaps, undefined requirements, and unmapped dependencies before they turn into execution problems 3 weeks into the build or even 6 months down the road. Agents map stakeholders dynamically instead of building that list once and never updating it. So AI agents identify who needs to be involved, what roles do we need to engage for this unique project by looking deeply at the context. Who are the subject matter experts? That need to weigh in, who owns which decisions, and who needs to approve what. And, and that view updates as the project scope changes. So there's no late additions and no stakeholders who show up weeks later after a key decision was already made. And because all of that context is being captured continuously, outputs like business cases, risk assessments, ROI models build and are updated automatically. Stakeholders are always looking at the same current version instead of working off documents that are 2 weeks out of date. Then agents drive collaboration forward instead of waiting for meetings to happen. They ask follow-up questions, push for clarity, and bring stakeholders into alignment across goals, requirements, and solutions. Approvals also get structured with clear owners and defined sign-off. So, so nothing stalls waiting on somebody. When agents have brought discovery to completion, the handoff to execution is a completely different experience. Execution teams no longer have to spend the first few weeks rediscovering context that should have been captured already and re-updating project charters. They walk in with every decision documented, every requirement confirmed, and every assumption already surfaced. And because nothing is missing, they can move immediately instead of stopping to reclarify things that were supposed to be already settled. That context carries directly into the tools execution teams are already using. So there's automated handoff into these systems. When planning is high quality, risk mitigated, and aligned, seamless handoffs to development workflows, in tools like Cursor, Claude, project management platforms like Jira or monday.com, and procurement systems can happen every time without manual effort and intervention and rework. And full context gets carried forward automatically. There's no manual handoff gaps and no summarizing a discovery doc for a team that wasn't in the room. And the result is execution that starts clean instead of spending the first few weeks catching up or redoing what discovery planning should have already figured out. Teams have what they need from day one. So the momentum from planning carries through instead of resetting again at handoff. And when something does need to be revisited, every decision is traceable back to its source. That's the kind of leverage that compounds across every project that your team runs. It's the type of accountability that enterprises want and need to drive efficiency and accountability for decisions. Creates the confidence you need to have a more confident planning process that you know is going to result in execution success for all your projects. Now, if you want your planning process to work exactly like this, you can try out Force Equals for free by clicking the first link below this video. You'll no longer have to deal with requirements that look complete but turn out to be assumptions. You won't need to try to identify and align stakeholders pulled in after decisions are already made. Your execution teams won't be spending the first 2 months managing escalations and rediscovering what was supposed to be settled in planning. So again, you can try it completely for free by clicking the first link in the description. And if you want to learn how to create a project intake process more efficiently, then check out this next video.