AI can read the deed. What happens next?

For oil and gas land teams, document intelligence is only the beginning. The bigger opportunity is orchestrating what happens next across AI agents, automation, people, and existing land systems.

AI can read a deed. That's impressive. The harder part is connecting what it finds to the work that follows.

Who checks the ownership history? Who identifies the gap in the chain? Who routes the curative work? Who tracks the lease obligation? Who updates the land system?

In too many land departments, the answer is still the same: a person does.

That's why I think the conversation about AI in land needs to move beyond whether the technology can understand complex documents. It can. The more important question is whether we can connect that understanding to the work that follows.

Because that's where the real opportunity begins.

Acreage is outpacing headcount

Land organizations aren't getting simpler. At the end of fiscal year 2024, about 22 million United States (U.S.) federal acres were under lease to oil and gas developers. More than 23,500 producing federal oil and gas leases supported over 91,000 wells, according to the U.S. Bureau of Land Management.

Behind every acre is a land lease lifecycle—from prospect identification and title research through ownership, negotiation, execution, administration, and division orders. And across that lifecycle are countless documents, exceptions, approvals, obligations, and system updates that keep the business moving.

And the specialized expertise required to do that work isn't getting easier to scale. The American Association of Professional Landmen reports that 41.87% of landmen are over age 50. Fewer than 5% are under 30, and fewer than 6% have five or fewer years of experience.

That's a problem you can't solve simply by adding more people.

The productivity opportunity is in how the work moves

The question is how we give the land professionals we already have more capacity to do the work that actually requires their expertise.

Most land departments don't have a technology shortage They have land systems. Document repositories. Spreadsheets. Email. Courthouse records. Title tools. And experienced professionals who know how to make all of it work together.

The problem is that the people are often what makes it work together.

Think about a typical land workflow: a document arrives. Someone reviews it. Someone retrieves supporting records. Someone traces ownership. Someone identifies what's missing. Someone builds or updates the chain. Someone routes an exception. Someone tracks an obligation. Someone enters information into another system. Then another document arrives.

Technology may support every individual step, but people are still connecting the steps. That's an expensive way to use specialized expertise.

And there is a much larger productivity opportunity hiding inside that operating model. Research from McKinsey across more than 50 upstream oil and gas business units found substantial differences between average and top-performing organizations, reinforcing just how much operating models can influence the amount of work a workforce can support.

For land leaders, I don't think the takeaway should be "how do I need fewer people?".

It should be: "how much more could my existing team accomplish if they didn't have to manually connect every part of the process?".

This is where AI changes the equation.

Reading the deed is only the first step

We can increasingly use AI to understand complex deeds, leases, title documents, correspondence, and other unstructured information. But extracting a name, date, acreage figure, ownership interest, or obligation doesn't complete a land process. Something still has to happen with that information:

• An exception may need to be routed

• Another record may need to be retrieved

• A chain of title may need to be assembled for review

• An obligation may need to be tracked

• A system of record may need to be updated

• A land professional may need to make a decision

That's why I see the next evolution happening across three actions: Read. Act. Orchestrate.

Read: use AI to understand complex documents and identify the information that matters.

Act: turn that information into work—retrieve records, prepare outputs, route exceptions, track obligations, and execute repeatable downstream actions.

Orchestrate: coordinate AI agents, automation, land professionals, and enterprise systems across the process. The difference sounds subtle, but operationally it's enormous.

Instead of using AI to make one manual task slightly faster, we can start redesigning how work moves across the land lease lifecycle.

Don't automate the judgment

There is an important line to draw here. Title and ownership work can be nuanced. Land professionals bring context, experience, and judgment that shouldn't simply be handed over to an AI model.

The goal isn't autonomous legal decision making. It's a better division of labor.

AI agents can prepare. Agents can interpret documents, assemble information, identify exceptions, and prepare work for review.

Automation can execute. It can retrieve records, move information between systems, perform repeatable actions, and track deadlines and obligations.

Land professionals can decide. They can validate title, resolve curative issues, negotiate, approve consequential actions, and exercise the judgment the business depends on.

That's also why governance and human oversight matter. The U.S. Department of Commerce's National Institute of Standards and Technology (NIST) provides an AI Risk Management Framework that emphasizes characteristics such as validity and reliability, accountability and transparency, and explainability and interpretability when organizations design and use AI systems.

For land, that translates into a practical principle: use AI to accelerate the work while keeping people in control of the judgment.

Keep your land systems; change the work around them

This isn't a rip-and-replace story. Quorum, Enverus, PakEnergy, Peloton, and other land platforms can continue doing what they're designed to do as systems of record.

The opportunity is to change how work gets to, from, and between those systems.

UiPath IXP (Intelligent Xtraction and Processing) can help understand complex documents. AI agents can reason across information and prepare work. Robots and automation can execute repeatable actions. UiPath Maestro™ Orchestrate can orchestrate agents, robots, people, and systems across an end-to-end process. And human checkpoints can keep consequential decisions with the land professionals who have the expertise to make them. That's a very different proposition from simply applying AI to document extraction. It's about connecting understanding to execution.

We're already seeing this operating model in upstream oil and gas; Continental Resources offers a useful example.

Continental Resources

Owner relations is another complex, document- and data-intensive upstream function. Division orders, owner changes, suspense management, joint interest billing (JIB) reconciliation, and tax reporting can involve spreadsheets, manual review, and employees moving information between disconnected systems.

Continental Resources built and deployed a UiPath agent to automate key royalty workflows while keeping people in control. The solution works alongside royalty analysts and existing systems rather than replacing them. Their implementation combines agents, orchestration, automation, actions, and human-in-the-loop approvals.

What's particularly interesting for land leaders is where the pattern can go next. Continental and UiPath discussed opportunities to extend the same approach into land administration and lease management, as well as other upstream workflows.

That's the bigger idea.

We don't need to automate an entire land department overnight. Start with high-friction work. Prove the model. Keep people in control. Then expand across the lifecycle.

Because the goal isn't AI for the sake of AI. It's giving land professionals more time to do the work we actually need land professionals to do.

The question has changed

For years, one of the obvious questions around AI and land was: can AI really understand a deed?

Increasingly, the answer is yes.

Now the harder question is what happens to the information it finds. When document understanding can trigger action, when automation can execute repeatable work, and when orchestration can coordinate all of it with experienced land professionals in control, we can begin to rethink the land operating model itself.

And that's where things get interesting.

See agentic automation working in upstream oil and gas

Hear directly from Continental Resources about how they built and deployed a UiPath agent for owner relations, kept humans in control, and created an automation pattern that can extend into land administration and lease management.

Watch the on-demand webinar "From Mineral Owner to Payment: How Continental Resources Built a UiPath Agent to Automate Owner Relations."

Sources:

• American Association of Professional Landmen (AAPL), The Emerging Energy Crisis: Securing the Future of Critical Services, April 2026.

• Bureau of Land Management, About the BLM Oil and Gas Program, n.d.

• McKinsey & Company, The Productivity Prize in Oil and Gas: Lessons from Top Performers, February 2024.

• National Institute of Standards and Technology (NIST), AI Risk Management Framework 1.0, January 2023.