In the age of AI, the hardest part of buy versus build is no longer building.

E&P companies used to have two options. Build a spreadsheet if the problem was small, or buy a COTS package if it was not. If your workflow was too specific for off the shelf software, you paid a consultant to customise it and hoped the result still worked after the next upgrade.

That world is gone.

Today, a subject matter expert with the right tools can prototype something that would have looked like enterprise software not long ago. The barrier to build has dropped sharply, and the quality floor has risen with it. That has changed the conversation.

The real question is no longer whether you can build. It is whether you should.

Total Cost of Ownership: The Number That Misleads You

Most buy versus build discussions begin with licence cost versus development cost. That framing is comfortable, but it is also misleading. The upfront numbers are usually the easiest to estimate, so they dominate the discussion. In practice, they rarely determine the outcome.

The real comparison is total cost of ownership: what it costs to start, what it costs to run, what it costs in internal resources during build or onboarding or digital change management, and what it costs you in time.

Time to value is the cost most organisations undercount most severely. If the working solution is worth ten million dollars a year, then every year between decision and full operationalisation is ten million dollars you did not capture. A two-year build versus a one-year buy is not a question of development cost. Speed is not a soft benefit, but a hard cost.

Last, but not least, exit costs. It can be the fine prints of a bought solution. But internal solutions are not clean either. A decade from now, someone somewhere will still be exporting data for a report. An unsupported solution is quietly becoming load bearing infrastructure. What is the true cost to your business when that happens?

Strategic Fit: Who Are You, Really?

Strategic fit conversations are around what kind of company do you actually want to be?

If you built your own version with your “secret sauce” and succeeded, would it give you unfair advantage over other operators? Or would you simply have arrived at the same destination everyone else reached years ago, just by a more expensive route?

Organisational capability fit is likely unknown for most E&P companies, who are used to Plug & Play. Building requires data engineering maturity, software development discipline, and a cultural tolerance for iteration and ambiguity. Buying requires vendor management capability, change management muscle, and an integration team that can actually execute a clean onboarding rather than rolling it out before the project deadline.

Technical Capability: Score the Gap

Most assessments treat technical capability as binary. Either the solution can do the thing or it cannot. For many AI or ML solutions heavily dependent on data and instutional intelligence, it’s likely that COTS falls under the latter. But the conversation should never stop there.

The framing should now move to: how difficult is it to close the gap between what current capability of COTS or your team and what is actually required?

A bought solution built for conventional oil and gas might have a genuine gap when applied to some unconventional fields with different operational context. The question is whether the vendor has both the domain understanding and the development capacity to close that gap in a reasonable timeframe.

A build option will typically have the opposite gap. Internal teams tend to have strong surveillance capability built over years of operational necessity, but struggle to build the automated modelling layer and the decision support interface that turns raw data into a ranked action list. That gap is hard to close not because the technology is difficult, but because combining petroleum engineering depth with software engineering discipline in a single team is genuinely rare.

Risks: Everyone Has a Say

Risk scoring in buy versus build decisions tends to be subjective.

Implementation risk is higher for the build path almost universally. Scope creep is endemic. Key person dependency is severe when the team is small. The buy path has its own risks, but they are more bounded.

Adoption risk is where the buy option typically suffers most, and this is counterintuitive. Bought solutions often have better user interfaces, but adoption is not a UX problem. It is a change management problem. People resist workflows that feel imposed from outside the organisation. A solution that engineers helped shape, even imperfectly, tends to get used. A solution that arrived from a vendor tends to get worked around.

If your organisation did not have strong change management capability and genuine executive sponsorship, adoption risk would be your biggest exposure regardless of which path you chose.

The Matrix Is Not the Decision

A weighted decision matrix is not a decision engine. It is a structured way to make your assumptions visible and your reasoning auditable. When buy and build scores end up close, as they often will, the matrix has done its job. It has surfaced the knowns and the unknowns. It has forced people to be explicit about what they believe and why.

Emerging Partnership

What the AI era has changed is the speed at which the build option can move from plausible to real. Prototype fidelity has improved dramatically. The gap between proof of concept and production ready is narrower than it was three years ago.

But maybe the real shift is not buy versus build at all. Maybe a new kind of partnership is emerging. One side owns the physical assets, the institutional intelligence, and the data. The other brings the capability to turn all three into operational value.

The best decisions might not land neatly on either side of the matrix. They might land somewhere in between.