Prof. Adegboyega Ojo

Adegboyega Ojo is a Professor and the Canada Research Chair in Governance and Artificial Intelligence in the School of Public Policy and Administration, at 杏吧原创 University. His specialties include digital government and data-intensive public-sector innovation. He co-edited a volume in 2024 called “.” He spoke to PANL Perspectives about AI tools and technology in the public and nonprofit sectors.

Question: How is AI being used within the public and nonprofit sectors?

Adegboyega Ojo: Despite all the media frenzy about AI, and despite all we hear from government about AI, we have very little information on how public-sector and nonprofit-sector organizations are using AI technology and tools. They might be using generative AI tools like ChatGPT, but is this use driven largely by individual use, or is it part of the broader organization鈥檚 strategies and plans? As a researcher, one way to find out is to look at reports on the Canadian Open Data Portal. Unfortunately, there are very few AIAs there, and we have to submit an request — and that’s not usually very easy. Also, unfortunately, the adoption rate of AI is extremely low in the nonprofit sector. My team and I are looking at these issues.

In general, the adoption of AI in Canada seems to be slower than in other countries, and that鈥檚 not necessarily a bad thing, as it鈥檚 good to follow a thoughtful and cautious approach to AI implementation. Last year, a found that 46% of Canadians use generative AI in their workplaces. Virtual assistants, chatbots, AI translation, and other generative AI tools are already embedded in customer relationship management systems, in marketing applications, on websites, and everywhere.

Nonprofit organizations are already using these indirectly 鈥� for instance, when using Copilot within Microsoft Office. However, in terms of direct use, the pace in the nonprofit sector is much slower at an organizational level. Individual workers might be using tools like Perplexity for searches, or Gemini or ChatGPT and other tools for brainstorming, research or drafting documents, but not much is happening in terms of organizational AI use for marketing, fundraising campaigns, and so on.

Question: How can generative AI technology help our sector in any way?

AI tools are becoming more sophisticated, particularly in the area of curated research, deep analysis and reporting, and I think adoption will ramp up soon. But users still have to check everything, because of AI hallucinations and other mistakes. For example, during queries, you should always ask for linked references for searches. A website address isn鈥檛 enough, and you have to click and check. The AI tools, like ChatGPT, Perplexity and Grok3, still make a lot of mistakes but are really good at providing linked references.

And AI tools are good at critiquing and analyzing documents. For example, you can compare a document containing specifications with another document intended to meet those specs, and request a detailed critique. AI excels at this type of analysis 鈥� at evaluating patterns, consistency, and alignment for instance 鈥� and excels less as a repository of factual knowledge.

Training employees is key. Without proper guidance, users might try AI, see the mistakes, get discouraged and think, 鈥淥h my goodness, imagine if we sent this to a client. We鈥檇 be in serious trouble.鈥� As a result, they may abandon the AI tool entirely. People need training on when and how to use AI effectively, understanding different contexts, modes, tasks and subtle nuances. Training and capacity building are key, even for tasks that appear straightforward.

Question: What potential harms or negative issues should we be aware of with generative AI tools?

Adegboyega Ojo: AI is changing decision-making, policy-making, service delivery and work in general. For example, and found that it may be limiting their critical thinking skills. Will a similar dependence happen in the public sector? We need to understand the impact and effects of AI, both positive and negative.

Even when AI has a positive impact, we must ask, 鈥淧ositive for whom?鈥� For instance, if an organization provides a generative-AI-based chatbot, a tool that鈥檚 gaining popularity, some users might find it convenient and efficient. But others might find the same chatbot service difficult to use, unfriendly and even harmful. A notable example is the case against Air Canada, where a chatbot misled a customer to believe they were eligible for a refund for their ticket. The case has since become a poster child for highlighting the risks of AI-driven services.

While AI does a lot of good, it also has the potential to cause harm and safety issues. There are cross-border collaborative efforts on shared understanding of things like taxonomies of AI harms based on reported incidences. This includes privacy issues, copyright issues, and different forms of bias and potential governance and legal issues. We鈥檒l likely see a more coordinated approach to addressing these issues with time.

Also, one of the things I’ve heard repeatedly in the nonprofit space is the need for clear and practical use cases of AI in action. People want to see tangible benefits and compelling business cases for generative AI in their organizations, with potential positives and negatives clearly explained.

Prof. Adegboyega Ojo is on . Photo of apps is courtesy of Saradasish Pradhan.

Question: What is GivingTuesday doing in terms of data related to the nonprofit sector?

Samir Khan: GivingTuesday is creating better data, better tools and better insights about what’s going on in our sector — from individuals’ and institutional behaviours to trends in the nonprofit sector to how the social sector, writ large, is being resourced. This includes working with networks that aren鈥檛 government and aren鈥檛 nonprofits, such as Kickstarter and social enterprises.

Our research initiatives are held together by a research agenda that covers three topics:

1. understanding how resources are mobilized in the social sector;
2. understanding how people feel and what they do in terms of support work in their communities; and
3. understanding GivingTuesday as a lynchpin for diverse actors who work together on community participation and resource mobilization.

For instance, provides infrastructure and research technology that enables the collection, exploration and sharing of data wherever it might exist. So, we鈥檙e partly a think tank, partly a technology service, and partly a complex series of relationships with the public and private sectors. We鈥檝e created platforms such as the 鈥溾�� and the 鈥� and when you go to , you’ll see a bunch of other free survey data and insights that are readable and usable for research.

Q: Which data is Canadian and which, global?

Samir Khan: We have annual surveys about giving behaviour in Canada — something called the , which has about three years of one-year snapshots of data. These were highlighted most recently in 鈥淭he Giving Bridge: A Lookback at 2023 Trends in Global Generosity,鈥� which investigates the nature of generosity globally using datasets. In Canada, as in the US, we see the same trend: a large group of people give in multiple ways.

We also house and curate datasets about giving from around the world 鈥� through . In addition to the , the supports research about the 鈥渆veryday giving market,鈥� which looks at what Indians do to support causes and issues they care about. Also, we’re forging partnerships in the , with research institutes there, to do similar types of projects.

Q: What work are you most proud of at GivingTuesday 鈥� or what stands out for you?

Samir Khan: In what feels like an increasingly fragmented and polarized time, what’s impressive to me is the data showing large numbers of people participating, donating and supporting charitable work in their communities 鈥� across many demographics. A lot of people worry about the slow decline in numbers of people participating and donating, compared to 20 years ago, but I still think there’s a lot more strength in community-purpose and social-purpose organizations than people think there is.

Q: Do you track or collect data related to Kickstarter, GoFundMe or other crowdfunding platforms?

Samir Khan: In datasets, we can see how many people are giving to individuals and can understand a bit about whether that giving is being mediated by one of those platforms. One thing that we鈥檝e found in our datasets is that, in aggregate, people who give one way tend to give in a whole bunch of different ways to both individuals and formal nonprofits 鈥� and they tend to volunteer time as well.

Some people might say, 鈥淪upporting causes through crowdfunders is wrong, because of verification problems,鈥� but it鈥檚 worth asking about those donors鈥� motivations and connections, especially because crowdfunders have evolved with new technologies. There’s probably a lot more to learn from that giving behaviour than to be fearful of.

Q: Is GivingTuesday using generative AI tools 鈥� or researching the topic of new AI tools?

Samir Khan: We published the global results of an AI readiness survey, 鈥溾�� It wasn’t a representative sample, but we learned that people are using generative AI to be more productive, such as creating a good first draft of something that they can edit — and that saves them time. But I think, you know, in 10 years, we’ll look up and ask ourselves if all that investment in AI was needed, whether the benefits were worth it. It’s an exciting time, assuming people know the limitations of the technology, such as 鈥渉allucinations.鈥�

Our data science team has embraced AI in creative ways, using large language models (LLMs) to help them parse certain types of datasets 鈥� like qualitative datasets or plain-text datasets. The goal is to get AI to do more grunt work more quickly. Many folks are also using it to help them to generate first ideas for certain types of code that they know that they have to write, code that’s relatively predictable.

In terms of statistical modeling, though, I haven鈥檛 seen examples where AI has been able to do rigorous regression analysis. Maybe some of your readers or viewers will correct me. This is in the realm of social science research. I won鈥檛 speak about its applications for medical research, which I know a lot of people are excited about. Essentially, AI hasn鈥檛 been able to show evidence of reasoning that a human researcher would apply in a statistical or in a regression-analysis context. That’s my understanding of it. I might be wrong by the time this goes up!

Liban Abokor, CEO of Reimagine LABS.

According to a , only a quarter of registered charities agree or somewhat agree that they understand well the potential applications of Artificial Intelligence (AI) in the sector, and the Canadian Survey on Business Conditions, from Statistics Canada, shows that only 7% of nonprofits say they鈥檙e already using AI. Liban Abokor hopes to change all that with , an AI-powered tool for organizations to design and implement evidence-based services and programs. Abokor is CEO of , a technology company building AI solutions for the social impact sector. He spoke with PANL Perspectives about Navigator, their first product.

Question: What is Navigator, and how did you start it?

Liban Abokor:鈥� Navigator is a productivity tool built by specifically for the social impact sector. Think of it as your smart assistant that helps social impact organizations research, plan and launch better programs and services. What makes it special is that it works with real data and research. Let me give you an example. Say you want to start a food bank in downtown Ottawa. Navigator would pull together actual data about existing food banks in the region and help you design your own, based on your specific needs. Navigator looks at what’s worked before, what hasn’t, and helps you spot potential problems. This means you can launch faster and with fewer mistakes.

Q: What problem did you hope to solve with Reimagine LABS?

Abokor:鈥� Navigator solves a fundamental challenge in the nonprofit sector: the time and expertise gap in program design. Right now, when organizations want to launch a new social impact program, they typically spend months doing research, planning and program design, often reinventing the wheel and working with limited information about what’s actually worked before.

What we’ve built is essentially a shortcut to success. Navigator transforms what could be a six-month research and planning process into a matter of hours. It’s like having instant access to the collective wisdom of thousands of programs and research studies. Instead of starting from scratch, organizations can learn from what’s already working, avoid common pitfalls, and design evidence-based programs from day one.

Let me give you a concrete example. If you’re planning to launch a youth mentorship program, Navigator will quickly show you successful models from across North America, provide detailed budgets based on real programs, and identify potential funding sources. This means you can focus your time and resources, rather than getting stuck in the planning phase.

What makes this especially powerful is that we’re helping nonprofits make data-driven decisions without requiring them to be data scientists. Whether you’re a small grassroots organization or an established charity, Navigator helps you to design and launch programs with the same level of rigor and amount of evidence that larger, well-resourced organizations have access to.

Q: Are you a tech expert?

Abokor: Actually, I don’t come from a tech background at all, which makes me somewhat of an outlier on our team, who bring significant experience in AI, machine learning and database management. These are the folks behind our proprietary database. Imagine the equivalent of about 15,000 books worth of interconnected social impact knowledge — that’s the database. We’ve built a smart system that links together research studies, program evaluations and real-world examples from across North America. Think of it as a massive web of information where everything is connected. When you look up food security programs, for instance, you’re not just seeing isolated examples, but rather, a network of related programs, their outcomes and lessons learned. This gives our users the ability to learn from and potentially connect with organizations running similar initiatives.

Q: How exactly does Navigator work?

Abokor: The process is really straightforward. You start by telling Navigator what social impact area you want to tackle — let’s say education. Then you get more specific about your vision: creating an entrepreneurship program for newcomers and refugees in Toronto, with the goal of helping participants develop business plans over 12 months. Navigator guides you through inputting key details about your resources, like available staff, funding, and whether you’ll run the program virtually or in-person.

What happens next is where the magic happens. Navigator doesn’t just do a simple search. It creates a comprehensive blueprint for your program. You get a detailed research summary that shows what’s worked (and what hasn’t) in similar programs. We provide a practical and customized implementation plan with everything you need: logic models, fully costed budgets, evaluation frameworks, potential funding sources, and partnership opportunities. The system is intelligent; the more specific you are about your needs, the more you input into the tool, the more tailored your blueprint becomes. Think of it as having a team of experts who design your perfect program, but at a fraction of the time and cost.

Q: Does Navigator have phone tech support?

Abokor: We’ve made Navigator really user-friendly, so most people won’t need technical support. But we’re always here to help — you can email us anytime and our team will respond right away. Anyone interested can visit our to schedule a demo.

Liban Abokor is CEO of and a Fellow at the School of Public Policy and Administration, at 杏吧原创 University, and he鈥檚 co-founder and co-chair of the Foundation for Black Communities. He can be found on .