Most organizations know they need a disaster recovery plan. Far fewer have one that’s current, tested and actually complete.
In our experience, that’s rarely a motivation problem. It’s a blank page problem.
People are much better at fixing something than starting something. Hand a team a rough draft and within ten minutes they’ll tell you what’s wrong with it, what’s unrealistic and who got left off the call list. Hand them an empty document and a calendar already full of other priorities, and that document stays empty until roughly the second Tuesday of never.
That’s the honest case for AI in preparedness planning. Not as a replacement for strategy, judgment or accountability, but as a way to get past the blank page and into the part where your people actually think.
One ground rule before we start
Whatever tool you use should be a business grade one operating inside your own environment. Microsoft 365 Copilot working within your tenant is a very different animal from a free consumer chatbot somebody signed up for with a personal email.
Recovery documentation contains exactly the sort of thing you don’t want floating around loose. Admin procedures, vendor account details, system dependencies and staff contact information. If you’re a Texas organization, TRAIGA has been in effect since January and governmental entities in particular have disclosure obligations worth understanding. Shadow AI is already happening inside most organizations we meet with. The question is whether leadership knows about it.
With that settled, here are five places AI earns its spot in the process.
1. Get processes out of people’s heads
The single biggest obstacle to preparedness planning is that most of what your organization knows lives inside a handful of employees, and none of it is written down in a form somebody else could follow at 2am.
AI is good at turning rough material into a clean first draft. Record a fifteen minute conversation with the person who knows how the ERP restore works, feed in the transcript and you’ll get back a structured procedure. Same with scattered notes about who gets called during an outage or what the team does when a critical application is unavailable.
It still has to be reviewed by people who know the business. But a working draft is enormously easier to correct than a blinking cursor.
2. Draft the checklists and response playbooks
Plans get followed when they’re broken into steps somebody under pressure can actually execute. AI can produce solid first drafts of response playbooks for a ransomware event, an extended outage, a severe weather closure or a vendor failure.
The same goes for an outage communications checklist, a continuity checklist or a simple decision tree for who has authority to do what when leadership is unreachable.
Understand what you’re getting though. AI doesn’t know your customers, your insurance requirements, your regulatory obligations or which of your systems quietly depends on that one server in the closet. It produces a starting point. Your leadership team owns the final version.
3. Surface the gaps nobody thought to ask about
The hardest part of continuity planning isn’t answering the questions. It’s knowing which questions exist. This is where AI is legitimately useful, because it has read a great deal more continuity planning material than any of us have time for.
Try prompts along these lines:
- What happens to our operation if internet service is down for eight hours during month end close?
- What should a manufacturer plan for if a winter storm shuts the plant for three days?
- What’s typically missing from a small business continuity plan?
- If we handle criminal justice data, what does CJIS expect around incident response?
It won’t know which of these risks actually matter to you, and anything touching compliance needs a human to verify it. What it will do is put questions on the table that your team can then argue about productively, which is the whole point.
4. Translate technical information into decisions
Most technical documentation was not written with a business leader in mind. Backup reports, vulnerability findings and system notes can be completely accurate and still leave an executive with no idea what to do about them.
AI does a very good job of translating that material into plain English. What does this report say, what does it mean for daily operations and what are the two or three things leadership should raise with their IT provider.
The goal was never for every leader to understand every technical detail. It’s for the right people to understand enough to decide what needs attention now, what can wait until the next budget cycle and what turns into a real problem if it keeps getting ignored.
5. Keep documentation from quietly going stale
Documentation rots faster than most people expect. Roles change, tools get replaced, vendors update their processes and the person listed as the emergency contact retired eighteen months ago.
AI makes the maintenance work less miserable. Compare last year’s procedure against this year’s notes and flag what changed. Standardize formatting across documents written by four different people over six years. Turn a list of recent changes into an updated draft your team can review in a meeting instead of writing from scratch.
Human ownership still matters. AI can move the work along, but only a person can decide what is accurate, what is approved and what your team is expected to follow when it counts.
Where AI stops
Everything above depends on treating AI as a draft engine and a thinking aid. The closer you get to actual business impact, the more that distinction matters. Regardless of how good your prompt is, AI cannot:
- Test your backups or confirm your recovery systems perform under real conditions
- Verify that your recovery time expectations are realistic for how your operation actually runs
- Understand the nuances of your business, your people or your industry
- Coordinate staff during an active incident at 11pm on a Saturday
- Carry the judgment and accountability that come from experience
That part takes leadership, tested processes and someone who can confirm the plan holds up when it’s real.
Where we fit
A recovery plan can look complete on paper and still come apart the first time it’s needed. Usually the difference is whether anyone verified the assumptions underneath it.
That’s the work we do. We know how your systems depend on one another, where the quiet single points of failure are hiding and which parts of the plan have never been exercised. We test restores rather than trusting a green checkmark on a dashboard. And we’ve watched enough organizations across DFW go through real disruptions to know which plans hold and which ones read well.
AI can help you build the first draft. We help make sure the plan survives contact with a bad Tuesday.
The next step is yours
AI can organize the work, structure your thinking and raise questions your team hasn’t considered. Knowing where your organization actually stands takes a different sort of conversation, and that one involves people.
If you’re curious how AI and practical recovery planning fit together, let’s spend ten minutes on it. We’ll look at where your preparedness efforts stand today and what it would take to strengthen them before you need them.
Call The Fulcrum Group at 817-337-0300 or visit www.fulcrumgroup.net to set it up. We’re in Keller, and we’ve helped North Texas organizations through enough real disruptions to have opinions about what works.
