How to Prioritize Enterprise Projects Using AI Agents

Right now, almost every company I see is struggling with prioritizing their enterprise projects. They have too many project ideas flooding in, too many AI ideas. They're trying to reprioritize every quarter to determine which projects deserve funding. Where do they have resources available to execute projects? What fits their strategy? What did their competitors just do that may influence decisions? And they get stuck, paralyzed by the complexity, the scale, the unknown unknowns that keep surfacing and derailing the last month or two's worth of work. However, the most advanced enterprise teams are leveraging AI today to do the prioritization itself, running continuously so leadership sees an updated ranking the moment anything changes across their entire enterprise. So today I'll walk you through exactly how AI agents prioritize a portfolio of projects on autopilot in real time as things change. So your team can cut weeks off every cycle and stop funding projects that were never going to deliver in the first place, and instead start focusing on the projects that will create the most momentum and impact for your organization. Let's get into it. Before I show you how this works in practice, you need to understand what these new AI agents are doing across your portfolio. These agents work because they can see see literally everything across your portfolio at once. Things like all your projects, all your goals, your budget, your team capacity, and what your competitors are doing. No human can keep all that in their head and re-rank against it every time something changes. No one can update spreadsheets to keep these in sync anymore. Changes happen too fast, and the underlying projects' constraints, signals change before you can even think about their impact. It's a math equation where the variables change too fast, where new variables and concepts are injected or removed constantly. Humans in static systems and software just can't keep up. And even if they could, you couldn't hire the army of quants and data scientists you'd need to deploy. Fortunately, this is the perfect use case for modern agentic AI. There are 4 key signals the agents are watching on every single project in your portfolio. And the answer you get from these agents is only as good as the quality of these 4 inputs. So part of any deployment is connecting these signals. The first input is your overall strategy and goals. That means AI needs to understand your business, how you serve your customers, who your competitors are, your objectives, your goals, your OKRs, how they're broken down, your innovation objectives, and the priorities leadership has already set for the business. What is your strategic posture? Are you in a growth mode or are you trying to stabilize? You need to help AI understand how you make decisions, how you prioritize risk, what size of investments will you consider, how aggressive you are, are there any key timelines, executive pressures, or external commitments. Together, these define your strategy. Without this, any AI system has no way to tell whether any project moves the company forward or just keeps people busy. Expressing this for your organization is important. And if your AI agents understand it, they become extremely helpful. The second is business impact paired with how complex the project is to deliver. Not every project delivers the same impact. So what are the goals of a project? Its business case, projected revenue contribution or impact, cost to deliver, and how long it will take to get there. A high-impact project that takes a long time to deliver can easily lose to a series of smaller projects that can get done quickly and can make impact quickly. Now, in most companies, all the inputs for all of the project ideas are not there yet at any given time. So how do you make fair comparisons when one project has a deeply vetted business case and ROI projections, and 10 others don't have that yet. That's where a system like FourSquads helps a ton. For every idea, no matter how immature or small, AI agents automatically expand context, build initial and projected business cases and ROI, identify risks, alignment challenges, develop expected resource requirements and cost estimates, and the associated plan to confirm any assumptions if that particular project shows signals for being prioritized. This enables AI to start the prioritization engine running against your strategy signals in every potential project idea, 24 hours a day, 7 days a week, considering any changes that occur in real time. Always know what should be prioritized through clear AI recommendations on every project against every project. The third area is your real-world constraints, meaning your budget, your team capacity, and your existing commitments. Most conversations teams have about prioritizations ignore these factors until late in the process, which is why so many approved projects never get the resources they were promised. The best way is to connect these signals to AI in real time so they are always being mitigated against the prioritizations across your entire portfolio. Second to live connections is to bring questions to your AI system. Any modern application should be able to have a conversation with you. What does prioritization look like if we reduced our budget by 20%? Which projects should be stopped or sequenced to make X type of resource available for the top priority projects? If your prioritization system has awareness of and understands your strategy, all your projects, and your constraints, and in real time can think about the questions you ask, you can make decisions and impact. If you don't, You're blinded by the complexity, competing objectives, and lack of intelligence. The fourth area is your live performance signals. This is your portfolio performance data, market changes, competitor moves, all updating the AI system continuously. For example, if your competitor launches a new product, does this impact what's prioritized? If a KPI for one of your business segments is running 20% short after Black Friday, Does that change the priority across your portfolio? If you have to move teams from one location to another, does this change project priority? If you make a new acquisition, what changes? Once those 4 inputs are in place, the agents can tell you which projects to move forward with, which ones to drop, and which ones need more information before deciding. The first example happens the moment your budget changes. Maybe you got hit with a mid-year cut, got a new round of funding, or your CFO just signed off on a different quarterly allocation. The second any of that lands, you have way more approved initiatives than you can fund, and somebody has to figure out what survives. Most teams handle this by booking 2-week, 4-week prioritization sprint where finance, the PMO, and leadership go back and forth and decide which projects to keep funded. What AI agents do instead is give leadership the answer as soon as the new budget is set without your team needing to spend weeks getting alignment. Leadership instantly sees a ranked list of projects that fit the new budget. These projects, the projects that need to get cut because of the change and a clear reason for each decision. That's what I built Force Equals to do, a continuous transformation platform. They handle strategy, prioritization, planning, governance, alignment, collaboration, decisions, execution, and agent control for enterprise teams. With that said, let me actually show you what this looks like inside the platform.

Here in Forest Equals, the first thing you do against any of your organizations is you express your strategy. So this is where you identify your company, who you compete with, what your strategic posture is, the things that— key information about your industry and the size of your organization. This helps us understand who you are and what the scale of your business is. The next area is your business goals. This is where you can express specific business goals that you think are important, that you want our system to be matching against, implicitly matching against any of your particular projects in how it prioritizes across your portfolio. The next area is about how you make decisions, how you manage risk. Are there any key important timelines we need to understand related to your your business or commitments that you've made, and then the ability to load up any documents, uh, that express your KPIs, your business strategy, your innovation objectives, any strategy documents about your business. You can load them up here. Agents learn about you and your business, and then as you come into the portfolio, those KPIs dynamically apply themselves against your live portfolio of projects. So the The system very quickly and in real time analyzes different KPIs. You can add any KPIs that you want to manage. There's some here around managing the portfolio. In this example, you can see the way these widgets are created. You can create them by just chatting with our AI agent. Our AI agent can build them on the fly, automatically helps you understand what the insight is from any particular widget and what the recommended next step is. And then it guides you directly to any associated work to help you activate against that next step, which is really important. So for example, in this one over here, and there's an overlap detected between two projects. Here are those two projects for you to then look at. And then you can do what if, what would it take analysis to ask the AI agent, what happens if I combine them? What happens if I follow this recommendation and bring these projects together? As an example. The AI system also is looking at how to filter out and look at your portfolio in different ways across your, your broader portfolio. So what does that mean? That means you can create any filters leveraging AI. Again, you just chat with the system to create any filter that you want. I can just come in here and do a little chat with AI. It'll create a new filter so I can express any concepts that are important to me or my business, or that I want to evaluate this portfolio against. And when I'm prioritizing, AI is automatically managing against the entire portfolio, all these different signals. And it's looking at the portfolio to see where the projects have the closest support to your strategy, your constraints, real-time signals. This is dynamic. It changes as data changes automatically so that you always get a live understanding of what, what AI is recommending for your GOAT projects to get more momentum across your portfolio. It's also telling you which ones it believes you consider sequencing because there's, there's dependencies, which ones that need— you need to be refined, which projects are clear go signals versus projects that are no-go signals, ones that don't match your strategy and probably need to be deprioritized. All this happens in real time based upon the strategy and the underlying project data that's held within each project. And this is where AI is working deeply to make sure you have— each project has full context, a full understanding of, you know, who the stakeholders are that need to weigh in to expand that context, confirm against risk signals, an engine that's driving the exploration of context to ask and distribute questions to confirm information across your team, the ability for you to really quickly understand what the goals are for any project, align on those goals, align on the solutions that will deliver those goals, including the functional, technical, and operational requirements and approval flows for each requirement so that you get a highly refined, high-quality project that's— you have high confidence in moving to execution very fast. That's what this whole system is designed to do, help you prioritize that portfolio, and quickly move and get momentum for the right projects so you can get the impact in operations as fast as possible for your business on a continuous cycle of transformation.

So if you're leading a planning team and want every leadership meeting to start with a ranked portfolio already in front of you, you can try Force Equals by clicking the first link in the description.

Anyway, back to the video.

When leadership commits to a specific number, like projects that deliver $20 million in revenue impact this year, the second that number gets set, the AI agents work, work it across your entire portfolio and give you the smallest set of projects that hit, that can hit that target, ranked by their probability of getting you to the number. What the agents do behind the scenes is look at how much each project would bring in, how long it would take to see that money and how likely the project is to work. Then they hand you a working portfolio that hits the target with the reasoning for why each project is on the list and what would replace it if one of them got dropped. This works on any number leadership commits to, not just revenue. It works for cost reduction, time to market, customer retention, or any goal you can measure with it. And it changes how your teams plan entirely. The starting point becomes the outcomes leadership has to hit, and the agents bring you the most direct paths to get there. In enterprise planning, you rarely have just one thing changing at one—

at a time.

You usually have revenue target locked in, a budget you can't go over, a team capacity limit, a competitor that just made a move, 5 key resources that just gave notice. All happening together.

Uh, asking a human committee to weigh all of that and re-rank a portfolio of 100 projects against it within a week is not realistic if you want anything close to the truth.

What the agents do is take every one of those constraints and re-rank your entire portfolio against all of them together. The result is a working portfolio that fits inside each one of those limits at once, combined together, with each project labeled as something to do, to delay because you don't have the team for it or accelerate because of the competitive move. And every project still comes with a clear reason for why it's where it is. The, the most useful part is that any one of these constraints can change and the AI agents update the rankings without anyone having to ask or do any work. If a competitor launches something new tomorrow, the agents recalculate against that and update the rankings before your next leadership meeting. If your team capacity changes mid- quarter, or because a project is running slower, the same thing happens. Maybe 5 new great ideas were added overnight that really need to be considered. The agents also let you see what's driving the rankings by including or excluding specific inputs and watching how the rankings update. That gives you a way to pressure test alternatives before committing to anything, which is a big deal for any team that's locked themselves into a portfolio mid-quarter and watched the market change a week later and the resulting negative impact on what happens to the portfolio. We get in front of that with AI agents. Now that you've seen all of these examples, let's talk about what changes once your team is planning this way. The biggest difference is the prioritization stops being a 3-hour meeting that takes over your team's calendar every 30 days with the hundreds of hours of prep going into that meeting from across different team members in your organization. It simply becomes a prioritization ranking that's always up to date with the AI agents updating it the second anything changes.

The next thing is that every projection on the list comes with the AI's reasoning attached to it. Leadership can challenge the reason.

You can ask the system, the AI agents themselves, what would have to be true for a different project to rank higher, or pressure test the inputs themselves against any questions that you may want to ask the system. That changes what gets discussed because the question is no longer about who is right, who's the best prepared versus who's not as prepared, or who has more power versus who has less. The discussion is now rooted closer to the truth, closer to your strategy, backed by facts and data. Always, always in real time, always updated. Anything changes— a competitor signal, a budget change, a capacity drop, an idea that came from your team, a guardrail escalation by an AI agent that requires a change— all of those triggers an updated portfolio ranking that's ready for your team 24/7, which means you're no longer locked into prioritization decisions made 6 or 12 months ago that don't apply anymore. So this is what enterprise prioritization looks like when AI agents are running it continuously with the rankings updating the moment anything changes in the business. If you want your portfolio to be ranked this way with every project coming with AI's reasoning attached, you can try Force Equals by clicking the first link in the description. You'll no longer be locked into quarterly committee meetings watching approved projects go nowhere because the resources were never available, or making budget decisions in your leadership meetings without the data right in front of you, or understanding the impact of those decisions.

So again, you can try Force Equals by clicking the first link in the description.

Give it a test run or, or set up a pilot for your organization and prove the impact in your business in, in your live environment. And if you want to know what career paths are paying the most right now as AI changes the enterprise team, then check out this next video.