Full Guide to Agile Enterprise Project Management Using AI

Over the next 3 years, the average enterprise will need to deploy somewhere between 500 and 2,000 AI agents across every line of business in every workflow in their organizations. That's more projects than most enterprises have run in the past decade combined. And the way these companies plan and run projects today is nowhere close to being ready for it. So in this video, I'm gonna walk you through a full guide to Agentic Enterprise Project Management. Using AI so your team can plan more projects in less time and actually get them delivered and meet this new wave of increased scale of AI deployment across enterprises as they transform into native agentic enterprises themselves. Let's get into it. So every enterprise initiative starts as an idea, but most companies lose the best ones before they ever get evaluated because either The intake process is so annoying that people give up on submitting them, or the power and political dynamics create risks that people are not willing to navigate. And too often the ideas that do get through usually get prioritized based on who has the most influence in the room, not on what would actually move the business forward. So with the required volume of projects coming over the next few years, getting this first step right is what determines whether your team is working on the right things or just the loudest things. This is why the first step is making it easy for ideas to come into your portfolio management system, no matter how rough or immature they are. What you want is anyone in your company being able to drop in a rough idea and then have AI agents do the heavy lifting from there. So let me actually show you what this looks like inside Forcequals, which is my AI-powered enterprise planning platform to help companies transform continuously to become the next versions of themselves as a genic enterprises. Within 30 seconds, the agents start building out context behind the idea. Things like the rough business problem it solves, the rough scope, the business case, the projected ROI, the impact on revenue, the cost, what constraints and risks might stack up against this project. Who the stakeholders might need to be to, that have to get involved to mitigate risk and, and close context questions that are still open. What used to take a project manager 2 weeks of prep work happens before you finish a cup of coffee. From there, the AI agents rank that idea against everything else sitting in your project pipeline. The system looks at your company's goals, your strategy, your budget, what's working in the market, what your competitors are doing, what your live project signals are. Then it tells you whether any individual project should move forward, if you should pass on it, or if you should refine the idea to improve it. So that may or may not fall into what you want to prioritize for your business. If your budget changes, the AI agents adjust and recalibrate the expected impact timelines and costs with your strategy and readjust the prioritization recommendations and cut line instantly in real time as that data is updated. Which means your prioritization meetings stop being a debate about who wants what and start being a clear discussion about which projects are actually worth funding right now. And if there's a question on a particular project, the AI agents can answer complex questions. For example, why it falls where it does in prioritization. What could be done that would impact how it's prioritized? The conversation becomes based on data and truth and what's best for the business outcomes. By the way, if you want to follow along with what I'm showing you in the platform, you can try FourSquares by clicking the first link in the description. It's designed specifically for enterprise planning and continuous transformation, focusing highly on how to prioritize the right set of work. And the same intake and prioritization workflow I just talked about and walked you through is on the very first screen. Anyway, let's keep going. Once an idea gets a go signal from our AI agents saying this project should be prioritized because it's a great match to your priorities, your strategy, this particular project should be prioritized. The idea starts to gain momentum within how we drive collaboration across your organization to fully build out a plan. A go signal is an indication the project needs to gain momentum. It needs expanded context planning, stakeholder alignment, clear goals, detailed business case, risk mitigation, guardrail design, detailed requirements for delivery, and presentations and supporting assets for different stakeholders so they can understand and buy in. This part normally takes the longest because it's where lining up stakeholders, gathering requirements, and getting approvals all happen. And it's also where the part where AI agents save you the most damn time because they handle all of what I just talked about. The work that used to require hundreds of hours and dozens of meetings gets done automatically by AI agents as it guides your team through decisions and approvals. Detailed planning happens faster when AI is guiding your team rather than waiting for your team to guide AI. The first things that agents do is build out the stakeholder map for the projects based on unique context of the project and what type of project it is. The AI agents identify which roles need to be involved across your organization. Things like security, compliance, legal, operations team who'll run the day-to-day for any particular business unit or workflow, end users, finance, and subject matter experts on the technical side. The AI agents are going to organize all that collaboration and activate that collaboration. So instead of finding out 4 months in that you forgot to bring in compliance, you have the right people lined up before any decisions get locked in. From there, the agents run the planning across every angle a complex project actually needs to cover. The system works across dozens of areas like problem statements, business case, financial modeling, risk, build versus buy decisions, guardrail design, for AI systems governance and approval workflows. For each one, it generates the right questions and assigns them to the right stakeholder based on their role, which means everyone gets their questions answered quickly instead of taking 6 weeks to— of back and forth meetings to get through resolution of open items. AI agents identify where there are discrepancies from different stakeholders and engage automatically to drive consensus. Risks are flagged dynamically and addressed dynamically. And alongside the planning, the agents suggest the goals and solutions for the project based upon the context and how they fit back to the organization's strategy and work those for confirmation, sign-off, and approval across the right stakeholders at the right moments. Each goal comes with detailed description, how to measure success against the goal, priority, an owner, and its current approval status. The same thing happens with solutions with a system systems recommend what to build and ties each solution back to the goal it supports. You can accept what the AI agents suggest, edit it, add your own ideas, add your own solutions. The whole thing runs through the approval workflow inside the platform. AI agents drive the approvals across your team, capture all decisions so that you have traceability and accountability as a standard. Once the plan is built and approved, the next step is getting it into the tools your execution team actually uses. So the handoff is where most enterprise projects go wrong because the execution team only sees a fraction of what got decided during planning. Execution teams end up spending their first couple weeks rediscovering what should have been settled already. And that's when the project starts going over budget and falling behind schedule. So what you want is to send the plan directly into the execution tools without losing anything. For development work, the platform starts building solution prototypes automatically as the project evolves. So detailed visualizations are available while planning, and they're exported with the final project prototypes and requirements into tools like Claude, Cursor, Lovable, so dev teams can pick up exactly where planning left off. This really speeds the time because you have nearly fully developed prototype that's vetted by your team, across your team, by all the right stakeholders with all the right requirements that's been tested in the planning phase, handed off in full form directly to dev. For project management, it sends the plan into Asana with its assignments, Jira, Monday, with all the goals, dependencies, ownership already mapped. And for projects that need procurement, AI agents match your requirements against shortlists of vendors who can actually deliver on the project with details on where and how they fit. So your procurement team isn't starting their search and evaluations from scratch. This allows execution to start with everything in place from day one. So there's no kickoff meeting where half the team is hearing about the project for the first time. No requirements rewrite because dev team didn't trust what was handed over. And no 3-month delay while procurement runs its own evaluation, the planning already complete. Now, the last step in running an agile enterprise project management with an AI system is using the results from live projects to help you decide what your team should be prioritizing next. In most enterprises, once a project ships, the team moves on to the next thing, and the data and impact that project generates never gets used to improve the next decision or to help you understand what needs to be prioritized next as you transform your organization. So the way to fix this is to connect the live results from the business back to the prioritization step. And if you're using Force Equals, the platform tracks how live projects are currently performing. Things like adoption, revenue impact, customer behavior, all those feeds come back into the prioritization view and collide against your strategy to help you understand what needs to be prioritized out of the next set of opportunities and ideas. So the next time your team ranks projects, the AI system is looking at what's actually working in your business against your portfolio. For example, if a project was supposed to deliver at 30%, but it's running only at 7%, does that impact what we should prioritize next? Or if there's an escalation that indicates a key system needs an update, or maintenance, can that quickly get into the queue and should it be prioritized, planned, and executed for the required change with, with urgency? Operations now need this level of responsiveness built into their infrastructure because there's just too many projects, too many ideas competing for space. Every round of planning makes the next one stronger. You start cranking a transformation flywheel that drives impact and momentum. Better intake leads to better prioritization. Better prioritization leads to more momentum for the right projects, which leads into better plans. Better plans lead to cleaner handoffs to execution, and cleaner handoffs produce live results faster that make the next round of decisions sharper. That's what Agile Enterprise Project Management with AI actually looks like in practice. To pull it off, you need a planning operating system design exactly for this moment. And if you run projects this way consistently, your team will end up planning more projects in less time with a higher hit rate on the ones that actually deliver business results fast. With that said, if you want to run an enterprise project through the exact system I just walked you through, you can try FourSqualls by clicking the first link in the description. It's designed specifically for enterprise planning and it automates hundreds of hours of work in every project, which adds up to thousands of hours saved across an organization every year. Not to mention the increased business results from consistently having the most impactful projects successfully delivering for your business. So again, you can start for free by clicking the top link below this video or set up time for a deep dive with our team or to deploy a pilot to prove impact in your business. And if you want to go deeper on specifically how to prioritize enterprise projects using AI, then check out this next video.