AI Tool Overload? A Guide to Managing Your Options
Tool Overload: Daily launches of new AI tools overwhelm project managers, complicating smart purchasing decisions.
Use Cases First: Prioritize understanding specific AI needs before adopting tools to avoid inefficient solutions.
Test Comparatively: Evaluate AI tools by comparing results across multiple platforms, not in isolation.
Augment, Don't Replace: Consider enhancing existing tools with AI agents instead of hastily switching platforms.
Strategic Adoption: Focus on aligning AI tool adoption with long-term organizational strategies and process improvements.
There are now somewhere between 500 and 1,000 new AI tools launched every single day. For project managers and PMO leaders, that number isn't exciting — it's exhausting. Every week brings a new platform promising to automate your status reports, predict your risks, and optimize your resource allocation. And every week, organizations are making five- and six-figure purchasing decisions based on demo videos and sales decks.
Emmanuels Magaya, Founder of Project Managers Africa, has tested over 100 AI tools and built a consultancy around helping organizations make smarter decisions about the tools they adopt.
Recently, he joined DPM podcast host, Galen Low, to share a perspective that cuts against the grain of the current hype cycle: slow down, get specific, and stop letting the tool lead the strategy.
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Here's the exact framework he laid out on the show to help you do the same.
Start With Use Cases, Not Tools
The most common mistake organizations make isn't choosing the wrong tool — it's choosing a tool before they know what they need it to do. Magaya is direct about this. "The first step is to understand what exactly do you want AI to do for you," he says. "Don't try to use AI for everything. Look at some of the simple things that you do in your day-to-day that are routine, that are mind work, that take you time."
Don’t try to use AI for everything. Look at some of the simple things that you do in your day-to-day that are routine.
That sounds simple, but most teams skip it. They see a competitor using a new platform, or someone in leadership reads an article, and suddenly there's pressure to adopt something quickly. The problem is that urgency without clarity leads to the exact situation Magaya spends most of his consulting work fixing.
His other warning is equally important: don't assume your processes are ready for AI just because the tool is. "Don't rush to the tool," he says. "First ask, does my process support AI? What we found is there are broken processes out there where putting AI will just help it become more broken." If your resource planning is inconsistent or your project data is messy, an AI layer won't clean it up — it'll just make the mess run faster.
How to Evaluate and Compare AI Tools
Once you've defined your AI use cases, the next step is testing — and Magaya has a specific methodology for it. The biggest mistake people make when evaluating tools is testing them in isolation, judging one against an abstract standard rather than against a direct competitor.
His fix is straightforward: "Never use one tool to get AI results. Try typing the same prompt in Gemini, Claude, whatever tool that you have access to, then compare the two." Running identical prompts across multiple tools and AI models surfaces differences in quality, depth, and relevance for your specific use case.
This article has been adapted from a DPM podcast episode titled, You Don't Need a New AI PM Tool – You Need to Fix the One You Have. See more DPM podcasts here.
But the quality of your output is only as good as the quality of your input. "If you are not good at prompting, you may not get a good result even with the right tool," Magaya says. "So you have to develop the prompting skill." This is a variable most evaluation frameworks ignore entirely — teams test a tool, get a mediocre result, and write it off when the real problem is the prompt.
He also cautions against judging a tool on first impression. "Play around with your tools, try out different features. You will find that even the tool that you originally thought was not that good might actually serve one purpose really well."
Most platforms ship with embedded features that users never discover because they go straight to the primary interface and never explore further.
What to Do When Your AI Tool Doesn't Fit
Even with a thorough evaluation process, organizations sometimes find themselves six months into a deployment realizing the tool isn't delivering what was promised. When that happens, Magaya recommends a structured diagnostic before making any decisions about next steps.
"First understand what you expected from the tool," he says. "Then you do what we call a gap analysis. You do a gap analysis of expectation — what I'm expecting the tool to do versus what the tool is actually delivering." That gap analysis determines everything: whether the issue is fixable, whether it's worth staying, and what it would actually cost to leave.
And that cost can be significant. "The impact of making a wrong choice can be massive. It can even cost millions, showing up in customer satisfaction, customer delivery, product quality, etc.," Magaya says. The financial hit of a bad contract is obvious, but the downstream effects on delivery quality and team morale are often what really stings.
The impact of making the wrong choice can be massive – showing up in customer satisfaction, customer delivery, product quality, etc.
One factor that's easy to underestimate and that Magaya flags as the most important consideration after identifying friction points, is project data. "How much data had we fed this tool? This probably is the most important point after the friction point," he says.
"You might not even be able to recover that." Unlike a spreadsheet you can export and import elsewhere, what an AI tool has learned from your team's behavior, your project history, and your organizational patterns isn't transferable. The further down the road you are with a tool, the higher the cost of reversing course.
How to Augment Your Existing Tool With AI Agents
Here's where most conversations about AI tools get stuck: the choice gets framed as stay or leave. Magaya offers a third option that more organizations should be considering — augmentation. Rather than replacing a tool that has gaps, you build agents and automated workflows that fill those gaps while keeping your existing platform intact.
Think of Agents as Modular, Not Monolithic
The modular nature of agentic AI makes this more practical than it sounds. "With agentic AI, the beauty of it is you can remove agents you don't need or that do not work well, and put new ones in," Magaya explains. "So if you establish an agentic PMO, you've got an agent that does your risk prediction. You've got your AI agent that does capacity planning. You've got one that just does the scheduling or calendar management." Each agent handles a separate function, which means you can add or remove capabilities without overhauling your entire stack and you can ensure each is optimized for that specific task.
With agentic AI, the beauty of it is you can remove agents you don't need or that do not work well, and put new ones in
Start Small Inside Your Existing Tool
The starting point doesn't have to be complicated. "If you've got a tool already that you have, first of all, does it support AI?" Magaya says. "If you've been using the mainstream tools like Monday.com, Asana, Jira, then they are generally doing good work staying up to date with AI enablement. You can start there. So, you're starting small. Maybe it's just an AI agent that just checks notes after meetings — let's pull out the transcript, let's create action items, and then we'll send an email." That's a low-risk, high-value entry point that doesn't require a full architectural overhaul.
Plan Before You Build
For teams that need to go further — connecting tools like n8n, Make, or Zapier to build more sophisticated workflows — Magaya stresses that the technology is the easy part. The hard part is knowing what you want to build before you start building it. "You can't start building a house without a plan, and that's where many people make mistakes with AI," he says.
"When people hear about n8n, Make, Zapier, they run straight to start making workflows. But, what is your workflow all about? Create a mind map in tools like NotebookLLM first." The planning step isn't optional — it's what determines whether the workflow you build actually solves the problem and understands the flow of information or just adds more complexity.
You can’t start building a house without a plan, and that’s where many people make mistakes with AI.
Once the architecture is clear, the implementation becomes significantly more manageable. "You can keep your tool, but just make sure somebody that understands automation processes is the one that creates the workflows. That way they're integrated properly."
Treat AI Agents Like Members of Your Team
Augmentation isn't just a technical decision — it requires a shift in how you think about your team's composition. Magaya argues that PMO leaders need to start counting agents in their headcount. "There's also a change management element here," he says.
"The team needs to adjust to having AI as part of their team. Going forward as a PMO, when you're now doing your head count, let's say your team is 50 — now you need to count the AI agents as part of the team. So if you've got, let's say, 10 AI agents, you are actually a team of 60, because we have what we call an AI RACI chart where on the RACI chart — who's responsible, who's accountable, who's consulted, and who's informed — there's a section where AI is performing certain functions."
Going forward as a PMO, when you’re now doing your head count, now you need to count the AI agents as part of the team.
That framing matters because it forces clarity. When an agent has a defined role on the RACI, the team knows exactly what it's responsible for, what oversight looks like, and where the human handoff happens. It also makes it easier to identify which agents are underperforming on which parts of the workflow.
From Reactive to Predictive
Treating agents as team members unlocks a capability Magaya considers one of AI's most underutilized advantages in project management. "When you have an AI-enabled PMO with AI agents and assistants working cohesively, the agents can actually inform you of risks based on data collected in places like email and Microsoft Teams," he says.
That's a fundamentally different operating model from what most PMOs are used to. "Before, traditional PMOs were managing risks reactively — oh, something's about to happen, okay, let's fix it," Magaya says. "But with AI, AI can tell you 12 months down the line what might happen just based on data." The teams doing this well aren't just using AI to save time on admin. They're using it to see further ahead than was previously possible.
AI can tell you 12 months down the line what might happen just based on data.
Let Strategy Lead, Not Tools
The organizations that will get the most out of AI aren't the ones adopting the most tools — they're the ones being most deliberate about how and why they adopt them. That deliberateness starts before a single tool is purchased, and it starts with a clear picture of what your processes look like and where your organization is headed.
As Magaya puts it: "Imagine yourself using AI effectively 12 months down the line. How would your organization look? How would your customers feel? That's how you need to look at it." Start there, work backwards, and let the tools follow the strategy — not the other way around.
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