AI can do some remarkable things.

That doesn’t mean your organization needs to use it everywhere.

I help associations and nonprofits identify where AI can create meaningful value, choose the right approach, and turn that opportunity into a workflow that actually works.

[Take the AI Opportunity Assessment]
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Don’t start with AI.

“We need to start using AI.”

Okay.

For what?

Before choosing tools, building agents, automating workflows, or buying another platform with AI bolted onto it, we need to know what we’re trying to improve.

What are your organization’s most important goals?
What’s getting in the way?
Where are you losing time, money, capacity, or opportunities?
Which constraint, if removed, would make the biggest difference?

That’s where AI strategy starts.

Find the opportunity first.

There are probably dozens of places your organization could use AI.

That doesn’t mean they’re all worth doing.

I look for opportunities where solving the problem would create meaningful value for the organization.

That might mean reducing the time staff spend on repetitive administrative work. It could mean making years of organizational knowledge easier to search. It could mean helping staff research, analyze, write, classify, summarize, or respond faster.

It might improve member support, reporting, education, communications, fundraising, sales, or internal operations.

The point isn’t to find the coolest use of AI.

It’s to find the one worth doing.

Then figure out whether AI is actually the best answer.

Finding a good problem doesn’t automatically make it a good AI problem.

There may be several ways to attack the same constraint.

Maybe:

we can eliminate the work entirely.
a conventional automation handles it.
your existing software already solves it.
AI should assist a person rather than replace the process.
an AI system can safely handle most of the workflow with human review.

Or maybe this really is a good place for a more autonomous system.

I compare the options based on business impact, feasibility, cost, time to value, reliability, and risk.

Sometimes AI wins.

Sometimes it doesn’t.

What can AI actually help with?

The answer changes quickly, but today’s systems are already useful for much more than generating marketing copy.

Depending on the organization and workflow, AI can help with things like:

searching and working with large collections of internal knowledge
summarizing documents, meetings, reports, and communications
classifying and routing incoming requests
extracting useful information from documents and forms
assisting staff with research and analysis
drafting responses using organizational information
turning unstructured information into structured data
helping staff create and update documentation
identifying patterns across large amounts of text or data
assisting with member and customer support
supporting internal decision-making
connecting natural-language interfaces to existing systems
handling parts of larger automated workflows

But capability alone isn’t enough.

A useful AI system needs the right information, the right workflow, appropriate safeguards, and a clear reason to exist.

AI should fit into the workflow.

One of the easiest ways to get disappointing results from AI is to treat it as a separate thing employees are supposed to remember to use.

Open ChatGPT => Copy something over => Write a prompt => Copy the answer back => Repeat tomorrow.

Sometimes that’s perfectly useful.

But the larger opportunities often appear when AI becomes one part of a better workflow.

A request arrives => Information is collected => AI analyzes or transforms it => A person reviews the result when necessary => The appropriate action happens => The result is recorded => Performance is measured.

That’s much more interesting than giving everyone a list of prompts.

Keep humans where humans matter.

Not every AI workflow should be autonomous.

Sometimes the right design is:

AI does the tedious part. A person makes the decision.

AI might research, summarize, classify, compare, draft, or prepare something so a staff member can make a better decision faster.

For higher-risk work, human review may be essential.

For lower-risk, repetitive work, more of the process may be safely automated.

The goal isn’t maximum automation.

It’s the right balance between speed, cost, reliability, risk, and human judgment.

Your data matters.

AI strategy isn’t only about choosing a model.

What information does your organization have?
Where is it stored?
Who can access it?
Is it accurate?
Is it sensitive?
Can your systems communicate with each other?
Can AI retrieve the information it needs without exposing information it shouldn’t?

A great AI idea with bad data or the wrong access controls is not a great AI system.

Part of the strategy is understanding what the technology needs in order to work safely and reliably inside your organization.

You don’t need an AI tool for every problem.

AI is being added to practically everything.

That doesn’t mean you need to buy all of it.

Your organization may already have access to useful AI capabilities through software you’re paying for. An inexpensive API might solve a problem better than another enterprise subscription. An open-source model may make sense in situations where control or privacy matters.

Or the right solution might not involve AI at all.

I don’t represent an AI platform, and I’m not trying to sell you seats.

That means the recommendation can start with the problem instead of the product.

What an AI strategy should give you

At the end of the process, you should be able to answer:

What should we use AI for?
Why is that worth doing?
Why is AI the right approach?
What should the workflow look like?
Where do people remain involved?
What systems and information does it need?
What could go wrong?
What should we implement first?
How will we know whether it worked?

If an AI strategy can’t answer those questions, it’s probably not much of a strategy.

Start with an AI Opportunity Assessment.

If you’re wondering what AI could actually do for your organization, you don’t need to figure that out before contacting me.

That’s the point.

The AI Opportunity Assessment starts with your organization’s priorities, constraints, workflows, and opportunities.

Not prompts.

Not models.

Not agents.

Not whatever AI product everyone is talking about this week.

[Take the AI Opportunity Assessment]

What if we don’t want to use AI?

That’s completely fine.

I also help organizations improve workflows, automate repetitive work, evaluate technology, reduce software costs, integrate systems, and explore open-source alternatives without using AI.

AI is part of my technology strategy work.

It isn’t a requirement.

[Explore Technology Strategy]

Built from inside an association.

I’ve spent roughly a decade as Technical Director for the American Institute of Building Design, where technology decisions have to work in the real world: with limited resources, existing systems, staff workloads, member needs, boards, vendors, and everything else involved in running an association.

I’m approaching AI the same way I approach other technology.

Not:

“How can we use this?”

But:

“What can we make better?”

Then we figure out the right tool.

[More About Me]

Find the AI opportunity that’s actually worth pursuing.

Your organization doesn’t need twenty AI experiments.

It needs to know where this technology can make a meaningful difference.

Let’s find that first.