Agentic AI vs Generative AI … Discover what agentic AI is, how it works, real use cases and practical steps to get ready now
Artificial Intelligence has come a long way in the last few years, thanks to pioneers like Open AI and Microsoft, but a new phase is unfolding faster than most of us could ever have realised.
Agentic AI, sometimes called autonomous AI or AI agents, isn’t just about answering questions or generating text on demand like previous uses (looking at you ChatGPT).
Instead, it’s about intelligent systems that can act on your behalf, make decisions and even pursue goals with minimal human intervention.
From donor engagement to client reporting and membership renewals, agentic AI is already reshaping how organisations work, and with Microsoft embedding these capabilities into familiar tools like Copilot, Power Automate and the wider Power Platform, the opportunity has never been more accessible.
As this new wave of autonomous systems accelerates, leaders and teams face a critical question: Are you ready to harness agentic AI responsibly and stay ahead of your competitors?
Agentic AI might sound like the next big buzzword, but the concept is surprisingly practical… and closer than you might think.
Before diving into how it works in practice though, it might help to understand what agency means in the context of artificial intelligence.
So what exactly is agentic AI?
At its core, agentic AI refers to artificial intelligence systems designed to act with a degree of autonomy . That means they don’t just process information; they’re capable of make decisions, pursuing goals and performing tasks independently, using real-time data and context.
Unlike simple automation, which follows rigid rules, or generative AI that responds when prompted, agentic AI combines reasoning, memory and goal-directed actions.
Think of it as moving from a helpful assistant that waits for instructions to a proactive agent that can plan, execute, and adapt… often using tools like Microsoft’s Power Automate or Copilot to act within your existing workflows.
Agentic AI has been a goal of AI researchers for years, but it’s become practical now thanks to advances in large language models (LLMs), richer APIs and platforms like Microsoft’s AI-powered tools.
Taken together, these enable systems to connect information, reason through scenarios and take meaningful actions across business processes.
For organisations of any size, the draw is clear… cost efficiency, productivity, and scalability.
Automating routine tasks is just the start, the real value is in freeing your people to focus on strategy, creativity and complex work that drives growth and impact.
It’s easy to confuse agentic AI with generative AI. Afterall they often work hand in hand… but they do different things.
Generative AI creates text, images or code when asked, which makes it reactive.
Agentic AI on the other hand, combines that generative capability with agency… the power to decide when and how to act to achieve a goal.
Think of generative AI as the creative brain and agentic AI as the doer: an agent might generate donor emails, then personalise, send and optimise them without needing constant human input.
Agentic AI isn’t some far-off sci-fi concept, it’s already delivering value for the organisations that know where to apply it.
Agentic AI unlocks new possibilities for organisations to automate entire workflows end to end, not just single, repetitive tasks.
These systems coordinate multiple actions, adapt in real time as new information comes in and deliver consistent results with minimal day-to-day oversight.
By connecting data, decisions and actions, agentic AI turns routine processes into self-managing operations that free up people to focus on higher-value work.
Many NonProfits are scaling their mission impact by deploying agentic AI to:
Similar to NonProfits, Membership bodies could/already are using agentic AI to:
Some more examples from those involved in Professional service include:
However, like any new and powerful technology, agentic AI comes with both huge potential and important considerations.
Understanding both the benefits and the possible pitfalls will help you make smarter decisions about where, and how, to use it in your organisation, but if you’re worried, I know FormusPro are always happy to advise – Just get in touch here >>
Used wisely, agentic AI will help organisations do much more with much less, streamlining tasks, speeding up service delivery and freeing up their teams to focus on work that truly needs human expertise.
By embedding autonomous AI within familiar tools like Microsoft 365, Copilot and the Power Platform, you’ll amplify the systems you already rely on without starting from scratch.
For a NonProfits, that might mean scaling outreach and managing donor relationships with far less manual effort. Or for a membership organisation, it could be about delivering a more personalised, responsive member experience without adding headcount.
Beyond just efficiency savings though, agentic AI can also open doors to new services, faster innovation and go-to-market and better data-driven decision-making, giving any organisation a competitive edge in an environment where speed and adaptability matter more than ever.
Now the note of caution… with greater autonomy comes greater responsibility, and new risks that can’t be ignored.
While agentic AI will deliver real value for you, it also introduces complexities that organisations need to manage carefully and opportunities to get it wrong and completely new ways!
One key risk to manage is the loss of control.
If an AI agent’s actions aren’t clearly defined and bounded, it could make decisions that go either a little, or even way, beyond what you intended. It could even act on outdated or incomplete data.
That’s why clear rules, oversight and well-designed workflows are so important.
Another risk is in bias and misalignment.
Even the best AI can reflect biases in the data it learns from or misinterpret goals if they’re not precisely set. This can then lead to actions that don’t align with your organisation’s values or could even harm trust with donors, members, or clients.
Regular monitoring, testing, and human review are essential to spot and correct issues early.
Finally, your data security and compliance have to be watertight.
Agentic AI often needs access to sensitive or personal information to be effective. Organisations must ensure that data privacy, storage and handling meet regulatory requirements, and that systems are robust against misuse or unauthorised access.
The good news? These risks can be managed, but only with thoughtful planning, clear governance and a commitment to keeping humans in the loop (or with an awesome partner like FormusPro).
Getting the most out of agentic AI will mean finding the right balance between innovation and oversight. It’s not about letting your systems run unchecked, it’s about setting clear rules for what your AI agents can do, where human approval is needed and how the outcomes are monitored over time.
I’ve always found that a smart first step is to start small: choose a specific, low-risk process where agentic AI can save time but where mistakes won’t have serious consequences.
You can use these early pilots to test how well the AI performs, how it handles unexpected scenarios and how easily your team can review or adjust its decisions.
Building trust in agentic AI is also about giving people the tools and skills they need to work alongside it confidently. That might mean upskilling staff to interpret AI outputs, refining goals and guardrails or even creating whole new roles to oversee AI performance and ethical use.
Above all though, organisations that treat agentic AI as a partner, not just as another tool, will be the ones best placed to innovate responsibly.
By combining clear governance with a culture of continuous learning and improvement, you can unlock the full potential of agentic AI without losing sight of the human judgment that keeps it aligned with your goals and values.
Agentic AI doesn’t need to be a distant, all-or-nothing leap, but getting real value from it does mean putting the right foundations in place.
Organisations that plan ahead, ask the right questions and build understanding step by step are far more likely to see benefits without unexpected headaches.
Before you deploy your first agentic AI pilot, or expand your existing automation, take time to sense-check your readiness with a few practical questions:
Taking time to answer these honestly will help you spot any gaps early, avoid rushed rollouts, and build trust in how agentic AI works inside your organisation.
The aim isn’t to plan for perfection. It should be to start small, learn fast and get your teams comfortable working alongside AI as a trusted partner.
Once you’ve answered those key questions, the next step will be to turn ideas into action, as I say, by starting small, testing carefully and scaling what works.
Here are a few simple ways I’d build early momentum with agentic AI:
Taking practical, low-risk steps now will build your team’s confidence and help you unlock agentic AI’s potential in ways that match your culture, mission, and capacity to manage change.
In my eyes, a realistic first step would be to start small and specific.
Map out all the repetitive, time-consuming tasks that drain your team’s time but don’t need high-level judgment, think standard email follow-ups, routine scheduling or pulling together simple reports.
Pick one well-defined use case, run a controlled pilot with clear boundaries and test how the AI handles it. Keep people in the loop to review results, flag issues and suggest improvements. Then measure the time saved, the quality of the output and how well it fits your workflows.
Once you know what works (and what didn’t), you’ll have a clear path to refining the process and scaling it step by step, without having to jump in too deep, too soon.
Agentic AI isn’t just the future anymore. It’s here now, shaping how organisations operate and grow.
With the right mix of tools, skills, oversight and a great partner, anyone can harness this technology to work smarter, not harder.
Curious how agentic AI could fit into your Microsoft stack? Get in touch to explore practical pilots and see what’s possible.
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