When Clay Gaspar talks about AI, he frames it as three distinct “waves” — not experimentation, but a progression from efficiency → integration → reinvention.
Wave 1: Data accessibility (already deployed company-wide)
- Devon’s engineers used to spend ~75% of their time finding data and 25% analyzing it
- AI flipped that ratio to 25% finding data / 75% analyzing
- Result: engineers are effectively 3× more productive
- This wave is already ubiquitous across the company
What this really means:
AI is already improving subsurface, drilling, and operational decisions simply by removing friction — faster insights, fewer delays, better use of expert time.
Wave 2: AI embedded directly into workflows (live in select teams)
- AI is no longer just a tool — it’s “part of the team”
- Certain groups are already operating with AI inside decision workflows, not bolted on afterward
- This phase is not yet company-wide, but Devon highlighted active breakout teams
What this really means:
This is where AI starts shaping how decisions are made — not just speeding them up. Think real-time optimization, scenario testing, and continuous feedback loops inside operations.
Wave 3: Full process redesign with AI at the center (coming next)
- Clay described this as starting from a blank whiteboard
- Instead of asking “how do we improve this process,” Devon asks:
“If we rebuilt this from scratch today, with technology at the center, what would it look like?” - Devon expects fully rebuilt, AI-first processes live by year-end
What this really means:
This is not efficiency — it’s structural transformation. Entire workflows (planning, execution, optimization) are redesigned around AI, not humans manually stitching systems together.
Why this matters (the unstated signal)
- Devon tied every one of its 80 active value workstreams directly to AI enablement
- AI is framed as a free-cash-flow engine, not an innovation story
- This is how Devon plans to offset shale maturity and productivity plateauing without relying on longer laterals or better rock
What Devon’s AI Strategy Means for OFS Companies
1. “Execution advantage” is replacing “tool advantage”
Devon is no longer looking for vendors with better tools — they’re looking for partners that plug into AI-driven workflows.
Impact on OFS:
- Standalone services (better frac design, faster drilling days, nicer dashboards) matter less
- OFS value is now judged by how well you integrate into the operator’s decision system
- If your offering doesn’t improve Devon’s system-wide outcome, it risks being sidelined
2. Fewer humans, fewer handoffs, fewer service touchpoints
Wave 1 and 2 AI eliminate friction:
- Less manual data prep
- Faster decisions
- More automation across planning → execution → optimization
Impact on OFS:
- Fewer meetings
- Shorter sales cycles only if you’re embedded early
- Fewer chances to “save the job” mid-operation
OFS companies that relied on human intervention and on-the-fly problem solving lose leverage.
3. Procurement shifts from “best-in-class” to “best-fit”
Devon’s Wave 3 mindset starts from a whiteboard:
“If we rebuilt this today with AI at the center…”
Impact on OFS:
- Vendors will be evaluated on API access, data cleanliness, interoperability
- Closed systems and black-box tech get penalized
- OFS firms become modules, not heroes
If your tech can’t be consumed by an AI-first workflow, it’s friction — not value.
4. Performance expectations tighten — fast
AI compresses learning cycles.
Impact on OFS:
- Faster benchmarking across basins
- Less tolerance for “it worked last time”
- Underperformance is spotted earlier and cut faster
- Pilot programs shorten — or disappear entirely
OFS vendors lose the luxury of long learning curves.
5. Pricing power shifts away from service intensity
As operators extract more value internally through AI:
- They need less external optimization
- They expect more outcome-based pricing
Impact on OFS:
- Pressure on day rates and per-job pricing
- More risk-sharing, performance-linked contracts
- Margin compression for execution-only providers
6. The winners look different
The OFS companies that benefit most will:
- Integrate seamlessly into operator AI stacks
- Provide clean, structured, machine-readable data
- Enable faster decisions — not just better ones
- Help operators remove cost, not add capability
In short: OFS firms become infrastructure, not artisans.
The blunt takeaway for OFS leadership
Devon’s AI curve signals this shift:
Operators are internalizing intelligence and externalizing execution.
If an OFS company:
- Sells “expertise” but can’t plug into AI
- Protects data instead of enabling it
- Depends on human workflows to create value
…it will steadily lose relevance, pricing power, and seat-at-the-table access.
