We drafted much of the content you see below (including tables) before we managed to put the workbook Delegating to an AI Assistant together. This free resource includes a space to note what research tasks you already do that could serve as a safe sandbox for experimentation and time-reclamation with Generative AI.

The post below is in stronger dialogue with the TechRadar piece about university research, but the workbook brings that thinking down to earth for professionals, especially those working in social impact who want to try responsible AI, not just read about it.

AI is incapable of curiosity and empathy that comes from experiencing the world, so it can’t help with the core work of social missions or scientific research. Wanting to learn about and make the world better comes from our experience, but the work to do those things doesn’t always need a direct connection to that primary motivation, as we know all too well. Some of that work can be delegated to those who don’t share the vision, and this is where AI can shine. Christian Cawley calls out in a TechRadar piece, AI appropriately helps with dozens of mini-tasks and in the process, amplifies the core impact of research work. As the article says:

“Study and research are being supercharged by AI, which has heralded a new age of industrial-scale theoretical exploration.”

Mission-amplification comes because “the heavy lifting of manual research is now being done by AI.” Meanwhile, “the researcher remains in control, defining tasks and rules.” In a publish-or-perish world, skillfully drawing lines between what computers can do and what humans must do is necessary to be ready for tomorrow. “Monitoring processes and overseeing AI is vital to ensuring the end results meet the demands of the research,” and doing that well involves evolving knowledge and skills. This supports that it’s unsafe to automate the most mission-critical work as “AI results must be balanced with human judgment, insight, and creativity.”

But for tasks that don’t require human empathy and curiosity, what are the tasks AI can and can’t do in research? Can they be applied more generally to “supercharge” the core mission work of any nonprofit or B Corp?

Tasks to consider delegating

First, the tasks that the article says AI already helps with successfully. Here we’ve added similar tasks for any mission. These example analog tasks, although valuable and sometimes rewarding, can consume time and hinder the team’s unique contribution. They’re tasks a team might like to delegate to a competent assistant, here grouped by the research process:

Discover

Action	Research task (mentioned by TechRadar)	Analog for nonprofits / B Corps
Automate	literature reviews	scans for best practices and funder priorities
Identify	related content	adjacent case studies, model programs, or potential funders & partners
Simplify	texts with NLP	policies, grant drafts, and forms to plain language versions
Decipher	complex docs/datasets	and extract requirements and deadlines from RFPs & regulations
Bridge	disparate academic disciplines	popular methods from one sector to another

Hypothesize

Action	Research task (article)	Analog for nonprofits / B Corps
Generate	hypotheses at an accelerated rate	proposals for program tweaks to lift enrollment and engagement
Reveal	gaps & overlooked hypotheses	underserved segments & opportunities
Triage	viability of ideas, allowing for “shotgun” multi-hypothesis testing	pilot ideas before field tests
Model	theoretical approaches	scenario cost/impact trade-offs

Analyze

Action	Research task (article)	Analog for nonprofits / B Corps
Collect	data	and ingest public data, CRM info, and exported logs
Mine data	for correlations	to surface patterns in donations, attendance, or intake forms
Analyze	& process rapidly	& generate descriptive stats for outcomes by site or segment
Detect	new patterns	early-warning signals for risk of dropout or high service demand
Interpret	beyond human capacity	multiple joined data sources to see who benefits and who is left out
Visualize	data	executive “bird’s-eye” overviews & equity breakouts for boards
Synthesize data to test new	methods	dashboards

Write

Action	Research task (article)	Analog for nonprofits / B Corps
Provide feedback	on writing clarity	to tighten grant narratives & check readability
Prep	citations & bibliography	complete references in evaluation reports
Preprocess	to accelerate peer-review step	as a compliance sweep for required disclosures or citations & for a pre-mortem simulation of a tough reviewer
Detect	citation issues	dubious or dates sources before publication

Govern & Operate

Action	Research task (article)	Analog for nonprofits / B Corps
Summarize	papers & reports	80-page research or RFPs into action lists or briefs
Find	potential reviewers	outside advisors or ethics reviewers
Handle	previously tedious steps	de-dupe, reformat, batch tagging, redaction, transcription, OCR, form prefill, email templating, codebook generation, data-entry validation

The analogs demonstrate a haphazardness in the tasks that AI tools are capable of handling. In fact, their capabilities are sufficient for some organizations and workflows, but not for others. Finding opportunities available today benefits from marrying reported successful use case ideas to your workflows and testing whether the AI tool can, in fact, help free up capacity for the human aspects of your work.

This is an act of applied creativity that benefits from experimentation and experience. But acquiring that experience involves safe experimentation, which involves understanding when working with these tools, where people need to keep oversight.

Tasks the AI assistant can’t do

TechRadar’s piece is clear that while today’s new crop of advanced tools can augment work for a mission, humans must set purpose, verify, and decide. It identifies the required human oversight steps throughout the research process that are relevant to any effort to enlist AI’s assistance in creating social value.

Discover

Human oversight step	Impact/what it changes	Risk if skipped
Define tasks & rules for AI	Keeps scope & data aligned to mission & within policy	Irrelevant outputs; privacy mistakes
Maintain healthy skepticism	Prevents plausible nonsense from spreading	Acting on wrong summaries & translations

Hypothesize

Human oversight step	Impact/what it changes	Risk if skipped
Select and own hypotheses	Focuses tests on real, achievable impact questions that merit testing	Chasing shiny but low-impact ideas
Design ethical pilots	Protects people; yields reproducible results	Biased samples; consent gaps

Analyze

Human oversight step	Impact/what it changes	Risk if skipped
Higher-level analysis, strategy, & creativity	Allows for contextualizing outputs & seeing implications	Dashboard theatre; misallocated effort
Monitor & oversee AI	Catches bias, performance decay, and out-of-scope use	Quiet errors at scale
Guard against bias & inaccuracies	Protects equity and research integrity; keeps analyses fair and reliable	Skewed conclusions; inequitable resource allocation; credibility loss

Write

Human oversight step	Impact/what it changes	Risk if skipped
Verify & challenge results	Preserves accuracy and trust	Hallucinated facts; misfit bland tone
Authorship & originality guardrails	Reliability, trustworthiness, and originality of output	Plagiarism risk; credibility loss

Govern & Operate

Human oversight step	Impact/what it changes	Risk if skipped
Set guidelines & governance (Responsible-use policies)	Shared rules, literacy, and enforceable practice as a foundation	Inconsistent practices; reputational risk; lost opportunities for responsible users
Protect privacy & security	Protects people and orgs	Data leaks; contractural breaches
Ongoing stakeholder dialogue	Ensure AI remains an advanced tool	Becomes a replacement for original thought and decision-making
Beware over-dependence	Preserves human agency and critical thinking; prevents deskilling and false certainty	Passive use of AI; weaker problem-solving; misplaced trust in plausible but wrong outputs
Balance with human judgment	Aligns outputs with context, values, and stakeholder standards; sustains legitimacy	Mechanistic decisions; tone/fit problems; loss of trust with funders and communities
Make final decisions	Accountability and fairness	Optimizing metrics over mission

While these seem like common sense, they require uncommon intentionality in practice. As the piece argues, “responsible use must be governed, and that is where guidelines are required.” That tends to be led by human experts with an eye toward changing the world.

Conclusion

In the same way AI is supercharging research, it can supercharge any mission. In the way that science has a hypothesis and tests it, socially beneficial work has a hypothesis and tests it. Yet for social missions, there’s no hope of today’s AI experiencing genuine empathy, so the human drive to learn and improve the world remains irreplaceable. However, not all of the work of an organization with a social mission requires this level of human sensitivity.

To identify the parts of workflows that can be delegated to AI, experimentation is required. Experimentation benefits from understanding the limitations of these systems and where human oversight is needed. That benefits from clear rules and guardrails, and a necessary step to that is AI governance.

More on humans’ necessity to be the true creators.

More on humans’ necessity to be humane creators.

More on our founding human’s drive to build AlignIQ.

[In this document, we used AI for polish, not purpose.]