The Three Levels of AI: A Practical Guide for Social Impact Professionals

Akshay V
Aug 8
6 min read

Updated: Aug 8

I’m starting this article with one goal in mind — to help folks in the social impact space cut through the noise around AI. There’s a lot of jargon out there, and honestly, it can get overwhelming if you’re not living and breathing tech every day. My aim here is to simplify things so it’s easier for you to actually understand and use AI in your work.

Most of the tools I’ll mention come from my own personal experience. I’m not endorsing any of them, nor am I getting paid to talk about them. This is just me sharing what’s worked (and what hasn’t) so you can skip the trial-and-error.

In this first post of what I hope will be many, I want to start with the basics: what AI are we even talking about here? The way I see it, there are three levels of AI use cases, and mapping them out can help you figure out what’s right for you.

Level 1: Ready-to-Use AI Tools

Think of Level 1 AI like having an unpaid assistant — or that fresh intern who just joined your team. They’re not here to reinvent your work; they’re here to make the things you already do faster, easier, and more efficient.

This is AI at its most accessible: tools you can start using right away without a steep learning curve. They work best when you map them directly to the tasks you do daily. For me, that means writing endless emails, attending back-to-back calls, doing research, and brainstorming new ideas.

Here’s what my personal Level 1 toolkit looks like:

Writing & Communication

ChatGPT – My go-to for drafting emails, writing proposals, and even voice-typing messages that I later turn into polished text.
Grammarly – Keeps my writing clear, concise, and error-free.

Research & Analysis

Perplexity – Great for quick, source-backed research.
Claude – I use it for writing, coding, and nuanced problem-solving.
Fathom AI – My AI meeting note-taker, so I can focus on conversations knowing I’ll have accurate notes to revisit later.
NotebookLM (by Google) – Lets you upload documents, explore them with AI, and even generate podcast-style conversations between AI hosts explaining your topic — perfect for deep dives and making complex ideas easier to grasp.

Creative & Visual

Canva AI – From social posts to pitch decks, it’s my shortcut to professional-looking visuals.
Gamma – Turns rough outlines into full presentations.

How this plays out in real life:

Emails I used to type manually in 15 minutes now take 3–4 minutes using voice-to-text with ChatGPT.
Research that once took an hour now takes 5–10 minutes — I just give a deep prompt like “Explain this concept in the context of EdZola” or “Summarize the market opportunity for X”.
Every meeting I attend is automatically transcribed and summarized, so I never lose important points.

Pro Tips for Getting the Most Out of Level 1 AI

Go paid if you can. The difference between free and paid models is massive. For example, I’ve used both GPT‑3.5 (free) and GPT‑4 (paid) extensively, and the jump in quality, reasoning, and efficiency is huge.
Watch the data you share. If you’re working with confidential or sensitive information — like student or beneficiary data — avoid putting it into AI tools unless you have explicit permission and understand the risks.
Check your privacy settings. Many AI tools (like ChatGPT) let you opt out of having your data used for training. The option is often buried in settings, but it’s worth finding.
Use context-rich prompts. I often say things like “Explain this in the context of EdZola” or “Teach me like I’m new to this field” to get results that are both relevant and easy to understand.

Quick Decision Framework:

Need it to work right now? ✅ Level 1
Solving everyday problems? ✅ Level 1
Want to try AI with zero setup? ✅ Level 1

Level 2: No‑Code AI Builders

If Level 1 is about using AI tools, Level 2 is about creating with them.

This is where things get exciting — you’re no longer just consuming AI outputs; you’re building your own AI-powered solutions without having to write code. Think of it like upgrading from eating in a restaurant (Level 1) to running your own kitchen, where you can create recipes tailored exactly to your needs.

These platforms give you drag‑and‑drop or visual interfaces to build chatbots, workflows, mockups, and more. The time to value here is usually days to weeks, and it’s perfect for solving very specific workflow or organizational needs.

Examples from my own work:

At EdZola, we spend a lot of time understanding the digital needs of non-profits — often asking the same set of questions over and over. So we thought: What if this was a chatbot on our website? One that could talk to potential clients, categorize their needs, and even book a call with our team. We built WinAI using Zoho SalesIQ’s chatbot builder, integrated it with our website, and linked it to AI. From idea to launch took less than a month.
For UI design workshops, we use lovable.dev to quickly turn ideas into UI mockups. This helps clients visualize concepts during requirement gathering and speeds up decision‑making.
I’ve also experimented with AI video platforms to create AI‑generated avatars of myself. Watch this fun video of my AI Avatar giving some gyan! ;) I had shared this with my team a few weeks back!

Top No‑Code AI Platforms for Social Impact

Microsoft Copilot Studio – Create AI-powered chatbots that give personalized answers, pull live data from your systems, and scale beneficiary support without extra staff.
Agent.ai – Build multi-step, autonomous workflows or video agents that can handle queries, update databases, and follow up with users.
Lovable.dev – Instantly turn text descriptions into working web/app prototypes, perfect for rapid testing with your stakeholders.
Glide with AI – Build mobile/web apps that use AI to summarize, predict, or recommend content directly from your datasets.

Potential Applications for Level 2

Program Management

AI‑assisted beneficiary intake that categorizes and routes cases automatically.
AI-powered multi‑language survey processing.
Automated donor reports generated from project data.

Community Engagement

Chatbots that give accurate program info in local languages.
AI-driven follow-up sequences based on participant behavior.
Recommendation engines that match people with resources.

Monitoring & Evaluation

AI that pulls and cleans data from multiple sources.
Automated extraction of impact stories from beneficiary feedback.
Dashboards that update in real time with AI summarization.

Decision Framework:

Need something specific to your work? ✅ Level 2
Comfortable with visual interfaces? ✅ Level 2
Have a few days to learn? ✅ Level 2

Level 3: Custom AI Development

If Level 1 is about using AI and Level 2 is about building without code, Level 3 is where you roll up your sleeves and create AI from the ground up — with code, custom integrations, and full control over your data, logic, and outputs.

Think of it like going from running your own kitchen (Level 2) to designing the kitchen, inventing the recipes, and even making the ingredients yourself. This is the domain of developers, data scientists, or teams who can work with APIs, machine learning frameworks, and custom infrastructure.

The trade-off?

Time to value: Weeks to months.
Learning curve: Steeper.
Reward: AI that’s perfectly tuned to your organization’s mission and context.

What Level 3 Looks Like in Action

In my work with social enterprises, Level 3 AI usually comes into play when:

Off-the-shelf tools don’t capture the complexity of the problem.
Data sensitivity requires self-hosting or complete control over AI processing.
Integration is needed with legacy or highly customized systems.

Examples from the field:

Zolabs.ai – We built this at EdZola to tackle a very specific challenge: enabling NGOs to collect field data through voice in multiple local languages, even in low-connectivity areas. Researchers can create a form and schedule a call to a local number, collect data naturally from field workers, and the system transcribes, translates, and classifies the responses. We used Twilio for telephony, open-source speech-to-text models for transcription, custom translation pipelines for regional languages, and an AI layer to clean and categorize the data before pushing it into a central dashboard. The result? Faster, more accurate data collection and reduced reporting time for NGOs by weeks.

Potential Applications for Level 3 in Social Impact